Who Chooses to Marry? A Bayesian Analysis of Marital Status and Sociodemographic Outcomes in Japan

Abstract

Delayed marriage and non-marriage have become increasingly important issues in Japan, where marriage remains closely related to household formation and well-being. This study examines which sociodemographic characteristics are associated with being married and how marital status correlates with economic conditions, health behaviors, subjective well-being, and COVID-19-related measures. Using annual panel data from 2014 to 2022, we first conducted descriptive comparisons between married and non-married individuals and then estimated a Bayesian panel logit model with respondent-specific effects to account for unobserved heterogeneity. The analysis was designed to identify associations rather than causal effects. The results showed the strongest positive associations with being married for individuals aged 30–49 years, consistent with delayed marriage. Employment attributes such as holding side work and managerial positions were positively associated with marriage, whereas nonprofit employment and self-employment were negatively or imprecisely associated. Financial assets and total debt were positively correlated with marriage, consistent with joint household formation. Higher happiness and life hope were positively associated with being married; regular exercise and longer weekend sleep were negatively associated, whereas longer weekday sleep was positively associated. In addition, respondent-specific effects revealed substantial heterogeneity beyond observed covariates. These findings identify key socioeconomic and behavioral domains associated with marriage in Japan, highlight the importance of unobserved heterogeneity, and provide evidence that may help identify groups prone to delayed marriage under changing social and economic conditions.

1. Introduction

Marriage is a fundamental institution that links individual life trajectories with broader social structures. The literature indicates that marital status is related to subjective well-being [1,2], psychological health [3,4], and economic outcomes [5,6]. However, the magnitude and direction of these relationships vary across cultural contexts, social institutions, and gender-role norms.

Motivated by delayed marriage and non-marriage in Japan [7], where marriage continues to serve as an aspirational social norm [8], this study focuses on identifying the sociodemographic and socioeconomic characteristics associated with being married. In particular, we examine the association between marital status and socioeconomic attributes and measures of well-being. Throughout, we interpret empirical patterns as associations rather than causal effects.

Moreover, marital status is associated with educational attainment, high labor market positions, and perceived financial satisfaction [9,10]. It also influences quality of life related to household health [11], life satisfaction [12], overall quality of life [13], job satisfaction [14], and personal growth [15].

1.1. Prior Research on Marriage, Health, and Well-Being

Scholars have examined the impact of marriage on physical health from various perspectives. Married individuals exhibit lower mortality from cardiovascular disease and cancer [16,17,18] and decreased risk of depression [19,20]. Conversely, marriage has been linked to high risks of obesity, other lifestyle-related diseases [21,22], suicide [23], hypertension [24], and cognitive differences [25]. The marital status of parents also influences the health-related quality of life of their children [26].

Furthermore, positive links between marriage and happiness are widely reported [27,28]. For example, marital status correlates with better self-rated health [29,30], self-esteem [31] and life satisfaction [12,32], whereas divorce and widowhood exert negative consequences on mental health [33,34]. In general, married individuals report high levels of life satisfaction and positive emotions [4,35], while non-married ones and divorcees exhibit negative effects [36]. In addition, marital quality is important: low levels of satisfaction with marriage yield fewer benefits in terms of well-being [37,38].

Notably, the coronavirus disease 2019 (COVID-19) pandemic highlighted the buffering role of marriage in mental and physical well-being [39,40]. A key caveat in the literature is self-selection: individuals with high baseline levels of well-being or better health may be more likely to marry. Accordingly, differences between married and non-married individuals reflect selection into marriage and potential consequences of marriage. Studies explicitly examining whether marriage increases happiness or whether happier people are more likely to marry have emphasized this concern [41]. Therefore, the current study addresses health and well-being measures as correlates and interprets results in an associational framework.

1.2. Marital Status and Socioeconomic Attributes: Who Opts to Marry?

The study poses a central question: “which individuals decide to marry?” High levels of subjective well-being [41], adverse childhood experiences [42], and legal access to abortion [6] are associated with the decision to marry, while expanding career opportunities for women increases the risk of divorce [43]. Self-esteem, intimacy orientation, and regional or cultural norms influence individuals’ marriage intention [44,45]. Moreover, marriage is a socially embedded institution: reference-group norms, social networks, and prevalent gender-role expectations shape preferences for marriage and perceived payoffs from marrying [8,44].

Moreover, marital status is associated with socioeconomic disparities. Married individuals typically enjoy high levels of income, wealth, and financial satisfaction [46,47]; however, marriage can widen the gender gap in employment [48]. Meanwhile, other studies have found no link between marriage and financial satisfaction [49]. Smoking [50], physical activity [51], and happiness inequality [52] vary with marital status, including late-life social participation, which prevents frailty [53].

Further, gender roles, labor market structures, and family norms in Japan differ from those of other countries. Thus, empirically examining these factors is essential for elucidating local inequality and the social role of marriage in Japan.

Using nationally representative Japanese panel data, this study examines how marital status is associated with socioeconomic attributes and well-being-related measures in Japan. Guided by prior research, we expect marriage to be most strongly associated with life-course timing, employment attributes linked to household formation, assets and liabilities, and selected well-being and time-allocation measures while also allowing for substantial heterogeneity beyond observed covariates. The analysis is associational rather than causal and aims to identify the main correlates of being married in Japan.

The remainder of the paper is structured as follows. Section 2 describes the data and variables and introduces the analytical method. Section 3 presents a preliminary analysis comparing the averages of key variables between married and unmarried individuals and shows the empirical results. Section 4 discusses the findings from the perspective of marriage culture in Japan and conducts comparisons with other countries. Section 5 concludes and presents policy implications, limitations of the study, and directions for further research.

2. Materials and Methods

2.1. Participants and Setting

This study employed annual panel data from a self-administered survey on Japanese adults conducted between 2014 and 2022 by the Panel Data Research Center of the Institute for Economic Studies at Keio University. Across nine waves, 5723 individuals provided 39,250 valid responses. Paper questionnaires were disseminated and retrieved during home visits, and from 2021 onward, an online questionnaire was also available.

To identify respondent-specific effects in the panel logit model, we restricted the estimation sample to respondents who exhibited within-person variation in marital status during the observation period. We excluded individuals who completed fewer than two survey waves, never changed marital status (always married or always not married), or lived outside Japan. The final estimation sample comprised 2427 observations from 309 respondents.

2.2. Measures

The dependent variable was marital status, coded as 1 if the respondent was married and 0 otherwise in each wave. Accordingly, the category “not married” may include never-married, divorced, and widowed respondents as recorded in the survey.

The explanatory variables covered regional and urban-size indicators, demographic variables, employment status and workplace sector, psychological indicators, drinking and smoking habits, health check-up indicators, exercise, sleep duration, satisfaction measures, and economic variables. Regional indicators included Hokkaido, Tohoku, Chubu, Kinki, Chugoku, Shikoku, and Kyushu, with Kanto as the reference category. Urban-size indicators distinguished other cities and towns/villages from metropolitan areas. Demographic variables included sex, age-group dummies, and sex-by-age interactions. Employment-related variables captured side work, leave status, workplace sector, managerial status, and side-job restrictions. The psychological indicators were happiness, life hope, and self-rated health. Health-related behavior variables included drinking frequency, smoking status, health check-up status, regular exercise, and weekday and weekend sleep duration. Satisfaction measures captured income, job, housing, leisure time, leisure style, health, and overall life satisfaction. Economic variables included the logarithms of bank deposits, securities, debt, loan payments, and charitable donations.

Table 1. Descriptive Statistics for Dummy Variables (All Respondents and Entire Period).

Variable	Description	1	0	Mean (1)	Mean (0)
Reg_Hok	Lives in Hokkaido	134	2293	0.552	0.492
Reg_Toh	Lives in Tohoku	144	2283	0.438	0.499
Reg_Chu	Lives in Chubu	449	1978	0.490	0.497
Reg_Kin	Lives in Kinki	370	2057	0.514	0.492
Reg_Chg	Lives in Chugoku	158	2269	0.519	0.494
Reg_Shi	Lives in Shikoku	52	2375	0.385	0.498
Reg_Kyu	Lives in Kyushu	313	2114	0.489	0.497
OtherCity	Lives in other city (vs. metro)	1560	867	0.496	0.496
TownVillage	Lives in town/village	160	2267	0.556	0.491
Year_2014	Survey year 2014	306	2121	0.601	0.480
Year_2015	Survey year 2015	302	2125	0.576	0.484
Year_2016	Survey year 2016	301	2126	0.548	0.488
Year_2017	Survey year 2017	286	2141	0.500	0.495
Year_2018	Survey year 2018	275	2152	0.509	0.494
Year_2019	Survey year 2019	261	2166	0.467	0.499
Year_2020	Survey year 2020	248	2179	0.440	0.502
Year_2021	Survey year 2021	227	2200	0.388	0.507
Year_2022	Survey year 2022	221	2206	0.353	0.510
Male	Male	908	1519	0.520	0.481
Age30_39	Age 30–39	674	1753	0.599	0.456
Age40_49	Age 40–49	415	2012	0.559	0.483
Age50_59	Age 50–59	261	2166	0.414	0.506
Age60_69	Age 60–69	329	2098	0.498	0.495
Age70plus	Age 70+	588	1839	0.457	0.508
Male_Age30_39	Male × Age30_39	329	2098	0.614	0.477
Male_Age40_49	Male × Age40_49	179	2248	0.637	0.484
Male_Age50_59	Male × Age50_59	128	2299	0.422	0.500
Male_Age60_69	Male × Age60_69	99	2328	0.404	0.500
Male_Age70plus	Male × Age70plus	121	2306	0.463	0.497
SideWork	Working alongside home/school	254	2173	0.665	0.476
OnLeave	Full leave (no work)	690	1737	0.536	0.480
SelfEmp	Self-employed	462	1965	0.468	0.502
ForProfit	Work for profit firm	1014	1413	0.510	0.485
NonProfit	Work for non-profit org.	226	2201	0.394	0.506
NoTitle	No managerial title	614	1813	0.458	0.509
Manager	Managerial position	217	2210	0.622	0.483
SideJob	SideJob	276	2151	0.399	0.508
SideJobBanned	Side job banned	834	1593	0.508	0.489
Drink_Month	Drinks few times/month	450	1977	0.440	0.508
Drink_W1_2	Drinks weekly 1–2 days	194	2233	0.515	0.494
Drink_W3plus	Drinks weekly 3+ days	585	1842	0.503	0.493
ExSmoker	Former smoker	511	1916	0.517	0.490
Smoker	Current smoker	571	1856	0.476	0.502
CheckupClear	Annual health check clear	1423	1004	0.485	0.511
CheckupFinding	Finding at check-up	1653	774	0.515	0.453
Exercise	Exercises regularly	1047	1380	0.457	0.525
Vaccinated	COVID vaccination	193	2234	0.332	0.510

Note: Columns “1” and “0” report the number of observations where the dummy equals 1 and 0, respectively. “Mean (1)” and “Mean (0)” report the proportion of married observations conditional on the dummy being 1 and 0, respectively.

and

Table 2. Descriptive Statistics for Quantitative Variables (All Respondents and Entire Period).

Variable	Description	Mean	Median	SD	Min	Max
MainIncome_log	Log(main-job income)	10.54	14.33	6.61	0.00	17.22
Happiness	Happiness score	6.01	6.00	2.29	0.00	10.00
LifeHope	Life hope	3.48	3.00	1.05	1.00	5.00
HealthSelf	Self-rated health	2.57	3.00	0.95	1.00	5.00
SleepWeekday	Weekday sleep hours	6.51	6.50	1.14	2.00	12.00
SleepWeekend	Weekend sleep hours	7.29	7.00	1.23	2.00	12.00
Sat_Income	Satisfaction: income	4.53	5.00	2.55	0.00	10.00
Sat_Job	Satisfaction: job	5.19	5.00	2.57	0.00	10.00
Sat_Housing	Satisfaction: housing	5.99	6.00	2.49	0.00	10.00
Sat_LeisureTime	Satisfaction: leisure time	5.44	5.00	2.50	0.00	10.00
Sat_LeisureStyle	Satisfaction: leisure style	5.63	5.00	2.38	0.00	10.00
Sat_Health	Satisfaction: health	5.75	5.00	2.32	0.00	10.00
Sat_Life	Satisfaction: life	5.91	6.00	2.17	0.00	10.00
COVID_JobLoss	COVID worry: job loss	0.52	0.00	1.24	0.00	5.00
COVID_IncomeDrop	COVID worry: income drop	0.48	0.00	1.15	0.00	5.00
COVID_NoCare	COVID worry: no care	0.40	0.00	0.98	0.00	5.00
COVID_Infection	COVID worry: infection	0.37	0.00	0.90	0.00	5.00
COVID_Collapse	COVID worry: collapse	0.36	0.00	0.89	0.00	5.00
COVID_Vague	COVID worry: vague	0.45	0.00	1.07	0.00	5.00
Deposits_log	Log(bank deposits)	10.76	14.73	7.10	0.00	19.11
Securities_log	Log(securities)	2.77	0.00	5.92	0.00	18.42
Debt_log	Log(debt)	5.32	0.00	7.35	0.00	20.03
Loan_log	Log(loan payment)	4.64	0.00	5.47	0.00	14.91
Donation_log	Log(donations)	1.63	0.00	3.46	0.00	13.82

report overall descriptive statistics for the main variables used in the analysis. Detailed year-specific descriptive statistics and subgroup mean-comparison tables are reported in the Supplementary Materials.

2.3. Design and Procedure

Missing values due to item non-response or entry errors were imputed using a one-nearest-neighbor algorithm. To capture the early impacts of the COVID-19 pandemic, overline versions of selected variables were created using only the 2021 and 2022 survey waves. Because each wave was fielded in February, the 2021 and 2022 responses reflect conditions in 2020 and 2021, respectively.

We first conducted descriptive comparisons between married and non-married respondents for the full sample and separately for the male and female subsamples to examine potential gender-specific patterns. Independent-sample t-tests were used to assess whether observed differences in means were statistically significant. Detailed subgroup comparison results are provided in the Supplementary Materials.

2.4. Statistical Analysis

We estimated a Bayesian panel logit model with respondent-specific effects to examine the association between marital status and individual-level characteristics. Let $y_{it}$ denote marital status for respondent i in year t, where $y_{it}=1$ if married and $y_{it}=0$ otherwise. The probability of being married was modeled as

$$P(y_{it}=1)={logit}^{-1}({\alpha }_i+{\mathit{x}}_{it}^{\prime }\mathit{\beta }),$$

where ${\alpha }_i$ captures respondent-specific time-invariant heterogeneity. Estimation was conducted using Gibbs sampling with Pólya–Gamma data augmentation. Full prior settings, notation, and technical derivations are provided in Appendix A.

3. Results

3.1. Preliminary Analysis of Marital Status and Sociodemographic Characteristics

To explore the relationship between marital status and demographic characteristics, we first conducted a descriptive analysis comparing the mean values of key demographic attributes between married and unmarried individuals. This comparison served as a preliminary step for elucidating underlying sociodemographic differences associated with marital status. We conducted this comparison for the full sample and separately for the male and female subsamples to examine potential gender-specific patterns. Independent-sample t-tests were performed to assess whether observed differences were statistically significant between married and unmarried individuals. Detailed year-specific descriptive statistics and mean-comparison tables between married and unmarried respondents including sex-specific comparisons by year are reported in the Supplementary Materials. The findings provide foundational insights into sociodemographic differences between married and unmarried individuals, which were subsequently explored via logit analysis.

3.1.1. Economic Factors and Asset Status

For the full sample, married individuals tended to exhibit higher savings and securities assets compared with unmarried individuals. This result can be attributed to the fact that savings and asset accumulation tend to progress more efficiently due to shared income after marriage. In contrast, unmarried individuals primarily manage savings individually, which results in relatively lower securities assets and savings. Alternatively, debt and loan balances are significantly higher among married individuals, which may be likely due to joint loans and shared financial burdens such as mortgages.

3.1.2. Health Behaviors

For men, married individuals exhibited lower exercise frequency and higher self-reported health ratings. In the context of married life, household responsibilities (e.g., housework and child rearing) may limit the time available for exercise, while emotional and social support within households may contribute to better self-reported health. For women, married individuals also tend to have less frequent exercise habits, and household responsibilities seemingly play a significant role in the formation of health behaviors.

3.1.3. Happiness and Life Hope

For the full sample, married individuals report higher levels of happiness and life hope. Marriage promotes stability in life, while support within the family likely enhances happiness and optimism about the future. Men and women experience increases in life satisfaction and mental support after marriage, which indicates positive impacts on overall well-being.

3.1.4. Impact of COVID-19

For men, married individuals are less likely to experience job loss or income reduction due to COVID-19. A potential reason is that married individuals, particularly those with multiple sources of income, can distribute economic risks and mitigate the effects of COVID-19 more effectively. Conversely, unmarried individuals are more vulnerable to the economic impacts of the pandemic, because they typically rely on one source of income.

Overall, these descriptive patterns provide a preliminary picture of differences between married and non-married respondents and motivate the multivariate panel logit analysis reported next.

3.2. Results of Panel Data Model

The Bayesian panel logit model was estimated under weakly informative prior settings. The full prior specification, notation, and technical details of the estimation procedure are provided in Appendix A.

Table 3. Posterior Means (SDs) and 95 % Credible Intervals of the Hierarchical Marriage Model. Asterisk * indicates that the 95 % credible interval excludes zero.

${\mathit{\beta }}_{\mathbf{Variable}}$	Description	Mean (SD) *	95% CI
${\beta }_{\mathrm{Reg\_Hok}}$	Lives in Hokkaido	0.29 (0.49)	[−0.71, 1.18]
${\beta }_{\mathrm{Reg\_Toh}}$	Lives in Tohoku	−0.73 (0.48)	[−1.73, 0.16]
${\beta }_{\mathrm{Reg\_Chu}}$	Lives in Chubu	−0.25 (0.31)	[−0.84, 0.38]
${\beta }_{\mathrm{Reg\_Kin}}$	Lives in Kinki	0.25 (0.32)	[−0.40, 0.86]
${\beta }_{\mathrm{Reg\_Chg}}$	Lives in Chugoku	0.88 (0.47)	[−0.05, 1.80]
${\beta }_{\mathrm{Reg\_Shi}}$	Lives in Shikoku	−0.63 (0.72)	[−1.98, 0.81]
${\beta }_{\mathrm{Reg\_Kyu}}$	Lives in Kyushu	−0.15 (0.36)	[−0.84, 0.56]
${\beta }_{\mathrm{OtherCity}}$	Lives in other city (vs metro)	−0.22 (0.22)	[−0.64, 0.24]
${\beta }_{\mathrm{TownVillage}}$	Lives in town/village	0.81 (0.44)	[−0.06, 1.62]
${\beta }_{\mathrm{Year2015}}$	Survey year 2015	−0.42 (0.22)	[−0.85, 0.02]
${\beta }_{\mathrm{Year2016}}$	Survey year 2016	−0.51 (0.23) *	[−0.93, −0.04]
${\beta }_{\mathrm{Year2017}}$	Survey year 2017	−1.21 (0.24) *	[−1.67, −0.75]
${\beta }_{\mathrm{Year2018}}$	Survey year 2018	−1.21 (0.24) *	[−1.69, −0.73]
${\beta }_{\mathrm{Year2019}}$	Survey year 2019	−1.45 (0.25) *	[−1.92, −0.96]
${\beta }_{\mathrm{Year2020}}$	Survey year 2020	−1.71 (0.26) *	[−2.25, −1.21]
${\beta }_{\mathrm{Year2021}}$	Survey year 2021	−2.93 (0.47) *	[−3.86, −2.01]
${\beta }_{\mathrm{Year2022}}$	Survey year 2022	−2.62 (0.69) *	[−3.90, −1.22]
${\beta }_{\mathrm{Male}}$	Male	−0.40 (0.76)	[−1.91, 1.07]
${\beta }_{\mathrm{Age30\_39}}$	Age 30–39	3.58 (0.43) *	[2.79, 4.47]
${\beta }_{\mathrm{Age40\_49}}$	Age 40–49	3.77 (0.53) *	[2.76, 4.82]
${\beta }_{\mathrm{Age50\_59}}$	Age 50–59	2.89 (0.57) *	[1.77, 3.99]
${\beta }_{\mathrm{Age60\_69}}$	Age 60–69	3.33 (0.53) *	[2.30, 4.38]
${\beta }_{\mathrm{Age70plus}}$	Age 70+	2.08 (0.52) *	[1.06, 3.08]
${\beta }_{\mathrm{Male\_Age30\_39}}$	Male × Age30_39	1.24 (0.75)	[−0.25, 2.69]
${\beta }_{\mathrm{Male\_Age40\_49}}$	Male × Age40_49	1.57 (0.84)	[−0.01, 3.29]
${\beta }_{\mathrm{Male\_Age50\_59}}$	Male × Age50_59	0.11 (0.92)	[−1.72, 1.90]
${\beta }_{\mathrm{Male\_Age60\_69}}$	Male × Age60_69	0.20 (0.93)	[−1.59, 2.01]
${\beta }_{\mathrm{Male\_Age70plus}}$	Male × Age70plus	0.93 (0.92)	[−0.91, 2.72]
${\beta }_{\mathrm{SideWork}}$	Working alongside home/school	2.11 (0.26) *	[1.60, 2.62]
${\beta }_{\mathrm{OnLeave}}$	Full leave (no work)	1.25 (0.35) *	[0.57, 1.95]
${\beta }_{\mathrm{SelfEmp}}$	Self-employed	−0.59 (0.38)	[−1.36, 0.15]
${\beta }_{\mathrm{ForProfit}}$	Work for profit firm	−0.50 (0.36)	[−1.16, 0.24]
${\beta }_{\mathrm{NonProfit}}$	Work for non-profit org.	−1.16 (0.43) *	[−2.01, −0.33]
${\beta }_{\mathrm{NoTitle}}$	No managerial title	−0.36 (0.26)	[−0.87, 0.14]
${\beta }_{\mathrm{Manager}}$	Managerial position	0.81 (0.33) *	[0.16, 1.45]
${\beta }_{\mathrm{MainIncome\_log}}$	Log(main-job income)	0.01 (0.03)	[−0.05, 0.07]
${\beta }_{\mathrm{SideJob}}$	SideJob	−0.88 (0.23) *	[−1.34, −0.45]
${\beta }_{\mathrm{SideJobBanned}}$	Side job banned	0.23 (0.18)	[−0.12, 0.59]
${\beta }_{\mathrm{Happiness}}$	Happiness score	0.20 (0.04) *	[0.13, 0.28]
${\beta }_{\mathrm{LifeHope}}$	Life hope	0.16 (0.08) *	[0.01, 0.33]
${\beta }_{\mathrm{HealthSelf}}$	Self-rated health	0.06 (0.09)	[−0.12, 0.24]
${\beta }_{\mathrm{Drink\_Month}}$	Drinks few times/month	−0.50 (0.21) *	[−0.91, −0.11]
${\beta }_{\mathrm{Drink\_W1\_2}}$	Drinks weekly 1–2 days	−0.38 (0.27)	[−0.90, 0.16]
${\beta }_{\mathrm{Drink\_W3plus}}$	Drinks weekly 3+ days	−0.28 (0.23)	[−0.71, 0.17]
${\beta }_{\mathrm{ExSmoker}}$	Former smoker	−0.15 (0.24)	[−0.63, 0.32]
${\beta }_{\mathrm{Smoker}}$	Current smoker	−0.46 (0.26)	[−0.98, 0.04]
${\beta }_{\mathrm{CheckupClear}}$	Annual health check clear	−0.01 (0.19)	[−0.39, 0.36]
${\beta }_{\mathrm{CheckupFinding}}$	Finding at check-up	0.37 (0.19) *	[0.02, 0.75]
${\beta }_{\mathrm{Exercise}}$	Exercises regularly	−0.56 (0.15) *	[−0.86, −0.28]
${\beta }_{\mathrm{SleepWeekday}}$	Weekday sleep hours	0.20 (0.08) *	[0.04, 0.36]
${\beta }_{\mathrm{SleepWeekend}}$	Weekend sleep hours	−0.18 (0.07) *	[−0.32, −0.04]
${\beta }_{\mathrm{Sat\_Income}}$	Satisfaction: income	0.07 (0.04) *	[0.00, 0.14]
${\beta }_{\mathrm{Sat\_Job}}$	Satisfaction: job	0.02 (0.03)	[−0.04, 0.08]
${\beta }_{\mathrm{Sat\_Housing}}$	Satisfaction: housing	−0.13 (0.04) *	[−0.20, −0.06]
${\beta }_{\mathrm{Sat\_LeisureTime}}$	Satisfaction: leisure time	−0.08 (0.04)	[−0.16, 0.00]
${\beta }_{\mathrm{Sat\_LeisureStyle}}$	Satisfaction: leisure style	−0.09 (0.04) *	[−0.18, −0.00]
${\beta }_{\mathrm{Sat\_Health}}$	Satisfaction: health	−0.06 (0.05)	[−0.15, 0.03]
${\beta }_{\mathrm{Sat\_Life}}$	Satisfaction: life	0.04 (0.06)	[−0.06, 0.15]
${\beta }_{\mathrm{COVID\_JobLoss}}$	COVID worry: job loss	0.03 (0.18)	[−0.34, 0.37]
${\beta }_{\mathrm{COVID\_IncomeDrop}}$	COVID worry: income drop	0.00 (0.19)	[−0.37, 0.37]
${\beta }_{\mathrm{COVID\_NoCare}}$	COVID worry: no care	0.18 (0.21)	[−0.24, 0.61]
${\beta }_{\mathrm{COVID\_Infection}}$	COVID worry: infection	−0.16 (0.23)	[−0.63, 0.27]
${\beta }_{\mathrm{COVID\_Collapse}}$	COVID worry: collapse	−0.08 (0.23)	[−0.54, 0.36]
${\beta }_{\mathrm{COVID\_Vague}}$	COVID worry: vague	0.29 (0.18)	[−0.06, 0.65]
${\beta }_{\mathrm{Vaccinated}}$	COVID vaccination	−0.63 (0.61)	[−1.83, 0.53]
${\beta }_{\mathrm{Deposits\_log}}$	Log(bank deposits)	−0.01 (0.01)	[−0.03, 0.02]
${\beta }_{\mathrm{Securities\_log}}$	Log(securities)	0.04 (0.01) *	[0.02, 0.07]
${\beta }_{\mathrm{Debt\_log}}$	Log(debt)	0.07 (0.01) *	[0.05, 0.09]
${\beta }_{\mathrm{Loan\_log}}$	Log(loan payment)	0.00 (0.01)	[−0.02, 0.03]
${\beta }_{\mathrm{Donation\_log}}$	Log(donations)	−0.01 (0.02)	[−0.05, 0.03]
${\mu }_{\alpha }$	${\mu }_{\alpha }$	−2.77 (0.96) *	[−4.64, −0.88]
${\sigma }_{\alpha }^2$	${\sigma }_{\alpha }^2$	2.28 (0.34) *	[1.64, 2.97]

presents the posterior coefficients. The estimation sample is limited to respondents whose marital status changed at least once between 2014 and 2022; therefore, the coefficients should be interpreted as associations rather than causal effects. In the subsequent text, we highlight significant estimates.

3.3. Age and Time Trends: Evidence of Delayed Marriage

Using ages less than 30 years as the reference, the study observed the strongest positive associations with being married for ages 30–39 and 40–49 years, where CI denotes the 95% credible interval. The coefficients remain positive for ages 50–59, 60–69, and 70+ years, which confirms the well-documented postponement of first marriage in Japan. Year dummies became negative after 2015, with the sharpest declines observed in 2020 and 2021.

Given that age dummies are included, the year indicators can be interpreted as net period-level changes in marriage propensity. At the same time, under the standard age–period identification constraint, these effects also reflect a combination of period and cohort shifts. Therefore, the study cautiously interprets the increasingly negative pattern as consistent with broader macro and normative changes over time, with an additional sharp decline observed during the COVID-19 period.

3.4. Employment Attributes and Occupational Status

Relative to public-sector employees, holding a side job and occupying a managerial position are strongly positively associated with being married. Employment in a nonprofit organization displays a significant negative association, whereas self-employment is negative but imprecise.

3.5. Assets and Liabilities

Financial assets and total debt balance are positively associated with being married. The logarithm of monthly repayment flow indicates virtually no association.

3.6. Happiness and Health Behaviors

The Happiness score, as well as optimism about the future, is positively associated with being married. Regular exercise and long weekend sleep are negatively associated, whereas longer weekday sleep is positively associated.

3.7. Large but Statistically Uncertain Associations

Living in a town or village and the male × age 40–49 interaction exhibit large posterior means but 95% credible intervals that cross zero, indicating the need for further investigation using larger samples.

3.8. Individual Effects

Figure 1 displays box plots of the posterior distributions of individual effects for the 309 respondents. The posterior means of individual effects range from $-5.163$ to $1.009$ on the logit scale, yielding a span of $6.172$. Ceteris paribus, this corresponds to an odds ratio of $e^{6.172}\approx 4.8\times {10}^2$, indicating that the most marriage-prone individual is approximately 480 times more likely to marry than the least. In probability terms, the estimated likelihood of marriage increases from approximately 0.6 % to 73 %. This substantial heterogeneity, unexplained by observed socioeconomic covariates, indicates that important determinants of marital behavior are not captured by the observed covariates alone.

4. Discussion

This section interprets the abovementioned empirical results from the perspective of Japan’s institutional and cultural contexts, relates them to previous findings, and summarizes policy relevance and study limitations.

Before turning to interpretation, the main findings can be summarized briefly. Marriage was most strongly associated with ages 30–49, with selected employment attributes such as side work and managerial status, and with higher happiness and life hope. Financial assets and debt were positively associated with being married, while exercise and longer weekend sleep were negatively associated. The estimated respondent-specific effects also revealed substantial heterogeneity beyond observed covariates.

While the analysis is intentionally associational rather than causal, it contributes to the literature by providing Bayesian panel logit estimates with respondent-specific effects using Japanese longitudinal survey data. This framework enables the quantification of covariate associations and substantial unobserved heterogeneity in marriage propensity, which is difficult to assess using purely cross-sectional comparisons.

4.1. Interpretation of Estimated Parameters

4.1.1. Age Profile and LifeCourse Transitions

The age coefficients follow a non-linear pattern: they peak at ages 30–49 years, slightly decrease in the 50s, increase again in the 60s, and decline after the age of 70 years. The scarcity of married individuals aged less than 30 years confirms the well-documented trend of postponed first marriages in Japan [7]. Rebound in the 60s is potentially associated with retirement re-planning and remarriage in later life. Qualitative studies report that older adults regard remarriage as a positive restart [54,55]. Contraction after the age of 70 + years is consistent with widowhood reclassification and the adverse mental health effects of spousal loss [56]. The year dummies sharply decreased in 2020–2021, indicating that the COVID-19 pandemic temporarily deterred marriage formation.

4.1.2. Employment Structure and Institutional Incentives

Side work and managerial status exhibited strong positive associations with being married, which supports household utility models in which higher and more stable income facilitates marriage [57]. Employment in the non-profit sector was negatively related, suggesting that lower pay or slower promotion paths delay family formation. The coefficient for self-employment is negative but imprecise, reflecting heterogeneous entrepreneurial trajectories.

4.1.3. Financial Assets, Debt, and Joint Decision-Making

Net financial assets and total debt are positively correlated with being married, emphasizing that marriage frequently coincides with asset building and liability taking. Similar links between wealth and marriage have been documented in studies outside of Japan [58,59]. If most debt is related to mortgages, then the findings imply that home purchase and marriage are jointly decided; previous studies have also reported this pattern in Japan [60,61,62].

4.1.4. Health Behaviors and Time Allocation

Regular exercise and long weekend sleep hours are negatively associated with marriage, whereas weekday sleep hours yield a positive link. Similarly, prior studies have observed less physical activity after marriage or childbirth [63,64]. Married individuals tend to maintain stable weekday sleep, whereas single individuals compensate on weekends [65,66]. Occasional drinking is negatively related to marriage—evidence that social drinking is more common among single adults [67,68].

4.1.5. Unobserved Heterogeneity

The estimated individual effects exhibit a wide dispersion, thus indicating that baseline marriage propensity varies substantially across respondents. The average covariate effects alone can only partially explain marital behavior. Such heterogeneity may reflect not only stable individual dispositions but also differences in values, normative orientations toward marriage, and marriage-market or social-network environments [8,44,45].

4.2. Policy Relevance

Marriage is closely associated with health and well-being; thus, quantifying individuals who opt to and do not opt to marry provides an evidence base for targeting family, labor, housing, and health initiatives. The estimates enable data-driven prioritization, impact measurement, and early warning along three dimensions: (i) identifying groups who are most prone to postpone or forgo marriage (e.g., young low-income workers); (ii) identifying relevant policy channels (e.g., income, housing finance, and time allocation); and (iii) monitoring vulnerability to macro shocks such as COVID-19.

4.3. Limitations and Future Research

This study has several limitations, which open avenues for future research. The analysis is restricted to the 309 respondents whose marital status changed at least once between 2014 and 2022 to ensure the identification of respondent-specific effects in the panel logit framework. Thus, the estimates are not directly generalizable to individuals who remained continuously married or continuously not married during the observation period. Additional panel waves and refreshment samples would be valuable for comparing these groups within a unified framework.

In addition, the results should be interpreted as statistical associations rather than causal effects. Although the panel structure and respondent-specific effects help control for time-invariant unobserved heterogeneity, causal identification would require exogenous variation such as policy changes or plausibly exogenous regional shocks.

Precision is also limited for covariates with small cell counts (e.g., certain regions or rare occupational categories), leading to wider posterior intervals. Future work could improve precision using a larger effective sample size such as through data-fusion approaches that link panel data to external administrative or government statistics.

Regarding COVID-19 measures, related questions are available only in the 2021 wave, which prevents a full assessment of temporal dynamics during the pandemic period. Future survey waves would allow for a more comprehensive examination of medium-term behavioral changes and long-term adjustments.

Several variables related to health behavior (e.g., exercise, sleep, and alcohol use) rely on self-reports; therefore, differences in respondents’ interpretations and measurement errors may influence estimates. A comprehensive questionnaire design, improved wording, and supplementary validation items would help mitigate this concern.

Furthermore, the current specification excludes explicit controls for dependent children or detailed household composition. Child-related responsibilities may be closely interconnected with time allocation (e.g., sleep and exercise) and marriage decisions. Therefore, incorporating rich measures of household structure is a useful direction for future research.

Moreover, age is modeled indirectly at first marriage or marriage duration. Given that the dependent variable is annual marital status and the estimation sample is restricted to respondents who underwent a transition in marital status, evaluating heterogeneity by marriage timing would require a different design and richer information on marriage histories. We regard this aspect as an important avenue for future research.

Addressing these limitations by expanding the data and refining the analytical framework would enhance the current understanding of the determinants of marriage in Japan.

5. Conclusions

This study examined the relationship between marital status and various outcomes (i.e., economic stability, health, happiness, and age) using panel data from Japan. Results indicated that married individuals exhibited higher savings, assets, and life satisfaction compared with unmarried individuals. This finding suggests that marriage is associated with greater economic and emotional stability. However, married individuals also tended to accrue more debt, which may be attributable to joint financial responsibilities. In terms of health behaviors, married individuals reported less frequent exercise, potentially due to additional household responsibilities. Meanwhile, with respect to the impact of COVID-19, married individuals were less affected by job loss and income reduction, possibly due to economic risk-sharing within households. The study also observed gender differences, with married women in particular having less frequent exercise habits. The study found an evident relationship with age, as individuals who married at older ages tended to have higher economic stability and life satisfaction. These results implied that marriage is associated with increased economic and emotional stability, but household responsibilities can hinder health maintenance. Additionally, the results reflected the trend of delayed marriage in Japan, particularly among individuals aged in their late 30s and 40s, among whom marriage is more strongly associated with increased economic stability and happiness. This finding implies that shifting social and economic factors are influencing the timing of marriage in Japan.