Sage Journals: Discover world-class research

Abstract

Marriage plays a key role in the exchange of resources, including education, which can affect health outcomes between partners. While the individual cognitive benefits of education are well-established, its potential spillover effects within marital relationships remain insufficiently explored, particularly in non-Western and developing contexts. Using nationally representative data from the 2014 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), this study investigates the associations between educational assortative marriage, spousal education, and late-life cognitive health among Chinese adults aged 65 and older. Results indicate that men in educationally homogamous marriages are significantly less likely to experience cognitive impairment than men in hypogamous unions and women in all marital arrangements. In addition, spousal education is positively associated with cognitive outcomes for men but not for women. These findings suggest that educational resources may be differentially linked to cognitive health within marriage, reflecting both personal and relational dimensions shaped by gender. The study calls for culturally and gender-sensitive public health and educational strategies in late life to promote cognitive well-being among aging populations.

Plain language summary

Can marrying someone with a similar or different education shape your cognitive health later in life?

This study examines how a spouse’s level of education influences cognitive health in later life, focusing on adults in China aged 65 and older. The findings show that older men who marry partners with similar education levels have a lower risk of cognitive decline compared to men with less-educated spouses and to women in all types of marriages. Furthermore, men appear to benefit more from their spouse’s education than women do. These gendered differences highlight the shared, relational impact of education within marriage and underscore the need for culturally and gender-sensitive public health strategies to support cognitive well-being in aging populations.

Keywords

educational homogamy spousal education cognition assortative marriage gender differences China

Introduction

Marriage, as a long-term intimate relationship, profoundly influences various facets of an individual’s life, including physical, emotional, and intellectual well-being. As individuals age, their social networks tend to diminish, leading to an increased reliance on marriage for support and companionship (Curran et al., 2003). The enduring commitment inherent in marriage fosters the sharing of resources and life experiences, one of which is education.

In many societies, men and women are frequently sorted into marriages based on similar demographic and socioeconomic characteristics, a phenomenon known as marital homogamy (Qian & Lichter, 2018). Within this framework, educational assortative marriage can be defined as the tendency for individuals to marry partners with comparable levels of educational attainment. Previous research has linked educational assortative marriage to individuals’ health and well-being across various contexts (Gonzaga et al., 2010; M. Zhao & Zhang, 2025). However, there is a lack of evidence on how educational assortative marriage affects cognitive health among older adults, particularly in developing countries such as China.

In China, historical gender disparities in educational attainment have contributed to the traditional dominance of educational hypergamy, wherein women tend to marry men with higher levels of education. This pattern has been more prevalent than educational homogamy, where partners possess the same level of education, or educational hypogamy, in which women have higher educational attainment than men. Although the relationship between education and cognitive health is well established, the potential spillover effects of one partner’s education on the other partner’s cognition remain underexplored.

This article examines the association between educational assortative marriage and cognitive health among older adults in China. It also considers the influence of a spouse’s educational attainment on an individual’s cognitive outcomes. The analyses are based on the assumption that marriage provides a critical context in which spouses’ resources spill over to influence each other’s health (Bolin et al., 2002). Specifically, this study addresses the following interrelated questions. First, compared to older adults in educational homogamy, do those in educational hypergamy or educational hypogamy experience better or worse cognitive health? Second, is a spouse’s educational attainment associated with an individual’s cognitive health, independent of the individual’s own education? Finally, are there any gender differences in these relationships? In particular, is the cognitive benefit of spousal education (if any) greater for women or men?

Conceptual and Theoretical Framework

Status exchange theory and Becker’s economic model of marriage offer foundational perspectives for understanding how marital dynamics influence cognitive outcomes (Becker, 1991; Merton, 1941). Becker (1991) posits that marriage functions as a rational arrangement to maximize household utility, often by aligning with traditional gender roles—men as economic providers and women as caregivers. Within this framework, educational hypergamy may optimize household gains and be associated with more favorable cognitive outcomes. In contrast, educational hypogamy may introduce relational stress, potentially compromising the cognitive health of both partners more than either hypergamy or homogamy (Fu, 2016, 2021; Pearlin et al., 2005).

Complementing these economic and exchange-based perspectives, social cognitive theory emphasizes that educational similarity between spouses enhances cognitive coordination and shared meaning-making, which reflects the principle of “likes attract likes” (Burney, 2008). From this viewpoint, educational homogamy may promote more effective communication and mutual engagement in cognitively stimulating activities, thereby offering long-term cognitive benefits.

Among the many social relationships adults maintain, marriage is widely recognized as the most influential. The household serves as the primary context through which social factors shape health outcomes (Bartley et al., 2004). The marital resource model suggests that marriage offers advantages by facilitating access to financial resources, emotional support, and behavioral regulation (Carr & Springer, 2010; Hughes & Waite, 2009; Williams & Umberson, 2004). Empirical research further shows that married individuals report greater financial stability, reduced isolation (Albertini & Garriga, 2011; Wilmoth & Koso, 2002), and are more likely to utilize preventive health services (Deb, 2001).

Within the context of marital unions, education may function as a shared resource that shapes the cognitive trajectories of both partners over the life course. A spouse’s educational level can impact an individual’s cognitive functioning through various socioeconomic, psychological, and behavioral mechanisms. For instance, individuals married to more educated spouses typically experience higher household incomes and less financial strain (Rojas, 2011), which are associated with better cognitive outcomes (Aguila & Casanova, 2019). Furthermore, spousal education may influence health behaviors and social engagement. Older adults with more educated spouses are more likely to participate in social activities (Gruneau, 2018), which have been linked to improved cognitive function (Fu, 2015).

Taken together, these conceptual and theoretical perspectives suggest that both the level of spousal education and the educational configuration of marital unions (i.e., homogamy, hypergamy, or hypogamy) may have significant implications for cognitive functioning in later life.

Education, Spousal Education, and Cognition

Cognitive function plays a pivotal role in determining one’s quality of life and the ability to maintain independence, particularly in later stages of life (Medalia & Erlich, 2017). Education has the potential to enhance various cognitive abilities, including information processing, memory acquisition, reasoning skills, and critical thinking (Baker et al., 2011). Research indicates that individuals with higher levels of education generally maintain better cognitive function as they age (Lövdén et al., 2020). As educational attainment is typically achieved early in life, it profoundly shapes various dimensions of socioeconomic status, such as occupation and income (Mirowsky & Ross, 2003). Occupations that are cognitively stimulating may, in turn, have a positive impact on cognitive function (Andel et al., 2007). Furthermore, education can influence cognitive outcomes indirectly by affecting health behaviors, such as dietary habits and smoking (Pampel et al., 2010).

Although studies on the influence of spousal education on cognitive outcomes in older adults are limited, the existing evidence highlights its possible significance. Saenz et al. (2020) found a positive relationship between higher spousal education and cognitive function among older adults in Mexico, with no significant differences by gender. Similarly, Xu (2019) reported that higher spousal education was associated with improved cognition in older adults in the United States, with no gender differences. However, studies focusing on older Chinese populations indicate that gender may play a moderating role in how spousal education influences health outcomes and health-related behaviors. For instance, using a sample of married couples in Shanghai, China, Y. Li et al. (2013) found that spousal education appears to be more important for women’s self-rated health than for men’s. Drawing on a nationally representative sample of Chinese adults, Guo et al. (2020) further reported that wives’ education significantly reduced their husbands’ unhealthy behaviors and health risks, whereas the influence of husbands’ education on their wives was minimal. Despite these insights, the role of spousal education in shaping cognitive outcomes among Chinese older adults, particularly with respect to gender differences, remains underexplored and necessitates further investigation.

Educational Assortative Marriage, Gender, and Health

Several studies conducted in Western contexts have found that educational hypogamy—where men possess lower educational attainment than their wives—is associated with heightened risks of physical health problems and mortality among men, but not among women (Eaker et al., 1983; Suarez & Barrett-Connor, 1984). This phenomenon has been attributed to role incongruity, wherein men may struggle to reconcile their subordinate educational status with traditional expectations of being the primary breadwinner (Suarez & Barrett-Connor, 1984). However, more recent research suggests that men generally experience greater health benefits from marriage than women (Kiecolt-Glaser & Wilson, 2017; Williams & Umberson, 2004). These contrasting perspectives highlight persistent gendered patterns in the relationship between a partner’s educational attainment and individual health. However, the effects of such educational spillovers are not uniform; they vary according to broader socio-structural factors and demographic diversity.

Research on the health implications of mating in China has predominantly concentrated on mental health and psychosocial well-being, with relatively less attention given to physical health outcomes. Within this body of work, Lei et al. (2014) found that hypergamy reduces depressive symptoms and enhances life satisfaction in women but has no significant effect on men. Further research by Qin and Guo (2016) suggests that educational hypergamy positively influences life satisfaction for both genders. Additionally, educational homogamy has been associated with improved self-rated health among internal migrants in China, whereas educational hypogamy has been linked to poorer health outcomes, particularly among women (Zhang & Tan, 2021). Overall, educational assortative marriage appears to have a greater impact on women than on men in the Chinese context. Since mental health is closely tied to cognitive function over the life course (Bunce et al., 2008), it is plausible that educational assortative marriage may influence cognitive function among older Chinese adults, with potential gender differences in these effects.

The Present Study

China offers a distinctive context for investigating gender differences in the cognitive spillover effects of education. Rooted in a long-standing patriarchal tradition, Chinese society has historically exhibited pronounced inequalities in access to education and employment between men and women (Bian, 2002; Y. Liu et al., 2019). Women have historically faced systemic barriers to formal education in their youth and limited access to high-skilled occupations in adulthood. Although gender norms have evolved, contemporary Chinese society continues to impose differentiated expectations on men and women (Zuo & Bian, 2001). Notably, research on cognitive function among older adults reveals divergent gender patterns across cultural contexts: while Western studies typically report cognitive advantages among women (Ahrenfeldt et al., 2019), Chinese studies often find superior cognitive performance among men, particularly in older cohorts (Lei et al., 2012). This contrast underscores the importance of examining gender as a moderating factor in the relationship between spousal education and cognitive health within the Chinese context.

Informed by theoretical frameworks and empirical evidence concerning educational spillover effects and cognitive outcomes, this study proposes the following hypotheses:

Hypothesis 1: Compared to individuals in educational homogamy, those in educational hypergamy will have a lower risk of cognitive impairment, whereas those in educational hypogamy will have a higher risk.

Hypothesis 2: The effect of educational assortative marriage on cognitive function will be more pronounced among women than men.

Hypothesis 3: A higher level of spousal education is associated with a lower risk of cognitive impairment, independent of an individual’s own educational attainment.

Hypothesis 4: The beneficial effects of spousal education on cognitive function will be greater for men than for women.

Methods

Data and Sample

The data for this study were derived from the 2014 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a nationally representative survey of older adults residing in mainland China. The CLHLS investigated information on respondents’ demographic characteristics, socioeconomic conditions (including education), social relationships, health conditions, etc. All information was obtained by in-home, face-to-face interviews, and all participants or their surrogate respondents provided a written informed consent. Notably, participants in this study were born between 1897 and 1955. Approximately 37% reported being illiterate, and 81% had received no more than 6 years of formal education. To ensure a representative sample, the CLHLS attempted to interview all centenarians in the selected counties and cities. For each centenarian, one octogenarian and one nonagenarian living nearby (in the same village or street) were randomly selected for interview, with pre-designated age and sex criteria. This sampling approach was strategically designed to ensure an equal representation of randomly selected male and female octogenarians, nonagenarians, and centenarians. For the purposes of this study, only participants who were married during the survey period were included in the analytical sample. Consequently, the final sample comprises 1,272 married men and 621 married women, all aged 65 years and older.

Dependent Variable

Cognitive function in this study was assessed using the Chinese version of the Mini-Mental State Examination (MMSE). The MMSE index used in this study consisted of 24 items (refer to Appendix A). Among these, 23 items had only two possible responses: either correct (1 point) or otherwise (0 point). As for Item #6, respondents were awarded 1 point for each food they named within 1 min, with the potential to earn up to 7 points in total. The MMSE score ranged from 0 to 30, demonstrating good internal consistency with a Cronbach’s α of .90 in the study sample. Participants were classified as cognitively impaired if their MMSE score fell below 25, consistent with established criteria in prior studies (Riedel et al., 2008; Schaller et al., 2012). This threshold has also been employed in research using the same dataset and population of Chinese older adults (An & Liu, 2016). To facilitate logistic regression analysis, a dichotomous variable for cognitive impairment was used in the analyses, with 1 denoting “cognitive impairment” (MMSE score < 25 points) and 0 representing “no impairment” (MMSE score ≥ 25 points).

Independent Variables

The primary independent variable in this study is educational assortative marriage, operationalized as the alignment between an individual’s educational attainment and that of their spouse. Drawing on the classification proposed by M. Zhao and Zhang (2025), this construct typically includes three categories: educational homogamy (spouses have equal years of schooling), hypogamy (wives have more education than their husbands), and hypergamy (wives have less education than their husbands). While this framework necessitates gender-specific modeling, the present study adopts a gender-neutral operationalization to facilitate analysis in which male and female respondents are pooled. Accordingly, we define educational assortative marriage using three mutually exclusive categories: (1) educational homogamy, where both partners have the same level of schooling; (2) lower education than the spouse, representing hypergamy for women and hypogamy for men; and (3) higher education than the spouse, corresponding to hypogamy for women and hypergamy for men. In addition, spousal education is included as another independent variable, indicated by the spouse’s total years of formal education.

Covariates

The current study took into account respondents’ demographic and socioeconomic characteristics, including age (measured in years), own education (measured in years), occupation (non-professional job vs. professional job), and category of residence (city vs. town vs. rural). Moreover, the educational background of the respondents’ parents was considered as potential covariates, focusing on whether the father and mother had received some education or had no education. Given the robust association between cognition and physical health identified in previous research (Fu, 2019), this study used activities of daily living (not/somewhat/strongly limited) as additional covariates.

Statistical Analysis

Statistical analyses were performed to compare study variable characteristics between married men and women, using t-tests for continuous variables and chi-square tests for categorical variables. The impact of educational homogamy on the risk of cognitive impairment was assessed using logistic regression models. A two-way interaction term between educational homogamy and gender was included in the model to evaluate potential moderating effects. The logistic regression model used to estimate the main and interaction effects was structured as follows:

\begin{matrix} logit (Cognitive Impairment) = β_{0} \\ + β_{1} * (Educational Assortative Marriage) \\ + β_{2} * (Gender) + β_{3} * (Own Education) \\ + β_{4} * (Father' s Education) + β_{5} * (Mother' s Education) \\ + β_{6} * (Occupation) + β_{7} * (Category of Residence) \\ + β_{8} * (Age) + β_{9} * (Activities of Daily Living) \\ + β_{10} * (Educational Homogamy * Gender) \end{matrix}

Additionally, the effect of spousal education on cognitive impairment was analyzed in the total sample (results not shown) and separately by gender. The full logistic regression model, estimated separately for men and women, was specified as follows:

\begin{matrix} logit (Cognitive Impairment) = β_{0} + β_{1} * (Own Education) \\ + β_{2} * (Spousal Education) + β_{3} * (Father' s Education) \\ + β_{4} * (Mother' s Education) + β_{5} * (Occupation) \\ + β_{6} * (Category of Residence) \\ + β_{7} * (Age) + β_{8} * (Activities of Daily Living) \end{matrix}

All analyses were conducted using STATA version 17.0, and results are presented as odds ratios (ORs) with corresponding 95% confidence intervals (CIs) and p-values.

Results

Descriptive Statistics and Univariate Analysis

As shown in Table 1, the descriptive and univariate analyses revealed several gender-based differences among respondents. The mean age for men (N = 1,272) was 80.46 years, whereas women (N = 621) had a mean age of 78.15 years. Educational attainment differed significantly by gender: men completed an average of 4.42 years of schooling, while women completed 2 years (p < .001). Regarding spousal education, men’s spouses averaged 2.05 years, compared to 3.69 years for women’s spouses (p < .001). Women were significantly more likely than men to marry a spouse with higher educational attainment (48.15% vs. 9.36%), whereas men were more likely to marry a spouse with lower educational attainment (59.20% vs. 12.40%). Therefore, the majority of participants in this study exhibited educational hypergamy (husband having more education than wife) rather than educational homogamy or hypogamy. Cognitive impairment was more prevalent among women (24.48%) than men (17.45%). No significant differences were found between men and women in parental education or residential category. However, a higher proportion of men reported better physical functioning than women (p = .001).

Table 1.

Descriptive Characteristics of Study Variables and Univariate Comparisons by Gender, CLHLS 2014.

Variables	Total (N = 1,893)	Men (N = 1,272)	Women (N = 621)	p value
Quantitative variables	Mean (SD)	Mean (SD)	Mean (SD)
Age (years)	79.70 (7.69)	80.46 (7.79)	78.15 (7.25)	<.001***
Education (years)	3.63 (3.85)	4.42 (3.92)	2.00 (3.14)	<.001***
Spousal education (years)	2.59 (3.52)	2.05 (3.26)	3.69 (3.77)	<.001***
Categorical variables	n (%)	n (%)	n (%)
Education compared to spouse
Same education as spouse	645 (34.07%)	400 (31.45%)	245 (39.45%)	<.001***
Less education than spouse	418 (22.08%)	119 (9.36%)	299 (48.15%)
More education than spouse	830 (43.85%)	753 (59.20%)	77 (12.40%)
Cognitive impairment
Cognitive impairment	374 (19.76%)	222 (17.45%)	152 (24.48%)	<.001***
No impairment	1,519 (80.24%)	1,050 (82.55%)	469 (75.52%)
Father’s education
Some education	416 (21.98%)	296 (23.27%)	120 (19.32%)	.052
No education	1,477 (78.02%)	976 (76.73%)	501 (80.68%)
Mother’s education
Some education	62 (3.28%)	43 (3.38%)	19 (3.06%)	.713
No education	1,831 (96.72%)	1,229 (96.62%)	602 (96.94%)
Occupation
Professional job	516 (27.26%)	413 (32.47%)	103 (16.59%)	<.001***
Non-professional job	1,377 (72.74%)	859 (67.53%)	518 (83.41%)
Category of residence
City	335 (17.70%)	221 (17.37%)	114 (18.36%)	.486
Town	661 (34.92%)	436 (34.28%)	225 (36.23%)
Rural	897 (47.39%)	615 (48.35%)	282 (45.41%)
Activities of daily living
Not limited	1,344 (71.00%)	929 (73.03%)	415 (66.83%)	.001**
Somewhat limited	356 (18.81%)	209 (16.43%)	147 (23.67%)
Strongly limited	193 (10.20%)	134 (10.53%)	59 (9.50%)

Note. SD = standard deviation.

p < .05; **p < .01; ***p < .001.

Educational Assortative Marriage and Cognitive Impairment

Table 2 presents the main and interaction effects of educational assortative marriage and gender on the odds of cognitive impairment. The results reveal notable gender differences in the association between educational homogamy and cognitive outcomes. To facilitate interpretation, Table 3 presents the predicted odds of cognitive impairment, stratified by gender and the three types of educational assortative marriage. The six odds ratios in Table 3 are derived from Table 2 and are adjusted for age, own education, father’s and mother’s education, occupation, residential category, and activities of daily living.

Table 2.

Main and Interaction Effects of Educational Homogamy and Gender on the Odds of Cognitive Impairment Among Older Adults (N = 1,893), CLHLS 2014.

Variables	OR (95% CI)
Main effects
°Educational homogamy
°°Homogamy (ref.)	1
°°Less education than spouse	1.52 (1.01, 2.31)*
°°More education than spouse	0.75 (0.38, 1.47)
°Women (vs. men)	2.17 (1.43, 3.27)***
°Education (years)	0.86 (0.81, 0.92)***
°Father had no education (vs. some education)	1.13 (0.80, 1.61)
°Mother had no education (vs. some education)	0.71 (0.31, 1.62)
°Non-professional job (vs. professional job)	1.04 (0.70, 1.54)
°Category of residence
°°City (ref.)	1
°°Town	0.95 (0.61, 1.50)
°°Rural	1.11 (0.71, 1.75)
°Age (years)	1.07 (1.05, 1.09)***
°Activities of daily living
°°Not limited (ref.)	1
°°Somewhat limited	2.22 (1.64, 2.99)***
°°Strongly limited	8.83 (6.18, 12.60)***
Interaction effects
°Less education than spouse*Women	0.42 (0.17, 0.99)*
°More education than spouse*Women	0.96 (0.43, 2.11)
Constant	0.001 (0, 0.004)***
Log pseudo-likelihood	−760.79

Note. Cognitive impairment: MMSE < 25; OR = odds ratio; CI = confidence interval.

p < .05; **p < .01; ***p < .001.

Table 3.

Interaction Effects of Educational Homogamy and Gender on the Odds of Cognitive Impairment (N = 1,893), CLHLS 2014.

	Men (N = 1,272)	Women (N = 621)
Variables	OR	OR
Educational assortative marriage
°Homogamy	1.00 (ref.)	2.17***
°Hypergamy	0.75	1.39*
°Hypogamy	1.52*	1.56*

Note. Cognitive Impairment: MMSE < 25. The table omits control variables including age, own education, father’s education, mother’s education, occupation, category of residence, and activities of daily living.

p < .05; **p < .01; ***p < .001.

The predicted odds of cognitive impairment for women in hypergamous unions were 1.39, based on the product of 1.52, 2.17, and 0.42. For women in hypogamous unions, the predicted odds rose to 1.56, derived from multiplying 0.75, 2.17, and 0.96. These estimates suggest that, regardless of whether women are married to more or less educated spouses, they experience a consistently higher risk of cognitive impairment compared to men in homogamous unions. Among men, those in hypogamous unions had 1.52 times higher odds of cognitive impairment compared to men in homogamous unions, controlling for all covariates (p < .05). Among women, no statistically significant differences in cognitive impairment were observed across the different types of educational assortative marriage.

A gender comparison reveals that women across all marriage types had higher odds of cognitive impairment than men in homogamous unions, after adjusting for all covariates. Notably, women in homogamous unions were 2.17 times more likely to experience cognitive impairment than men in homogamous unions (p < .001). Similarly, women in hypergamous and hypogamous unions exhibited 1.39 (p < .05) and 1.56 (p < .05) times higher odds of cognitive impairment, respectively, compared to men in homogamous unions. Overall, men in homogamous unions demonstrated a lower risk of cognitive impairment compared to both men in hypogamous unions and women across all marital types.

Spousal Education and Cognitive Impairment

Table 4 presents the odds ratios of cognitive impairment in relation to spousal education for men and women, respectively. Model 1 included individuals’ own education and all control variables, while Model 2 additionally included spousal education. A comparison between the two models illustrates the effect of spousal education on cognitive health, independent of one’s own education and other covariates. Among men, both their own and their spouse’s education levels were significantly associated with cognitive impairment. Specifically, each additional year of spousal education was associated with a 9% reduction in the odds of cognitive impairment among men (p < .05), independent of their own education, parental education, age, current socioeconomic status (SES), and activities of daily living (ADL). Notably, the addition of spousal education to the model diminished the influence of men’s own education on cognitive impairment by 2%, lowering the risk reduction from 9% to 7%. In contrast, spousal education did not show a significant association with cognitive impairment among women. However, each additional year of a woman’s own education corresponded to a 15% reduction in the odds of cognitive impairment (p < .01).

Table 4.

Effects of Individual and Spousal Education on the Odds of Cognitive Impairment Among Older Men (N = 1,272) and Women (N = 621), CLHLS 2014.

	Men (N = 1,272)		Women (N = 621)
Variable	Model 1, OR (95% CI)	Model 2, OR (95% CI)	Model 1, OR (95% CI)	Model 2, OR (95% CI)
Education (years)	0.91 (0.86, 0.96)**	0.93 (0.88, 0.99)*	0.84 (0.76, 0.93)**	0.85 (0.76, 0.94)**
Spousal education (years)	-	0.91 (0.83, 0.98)*	-	0.96 (0.90, 1.03)
Father had no education (vs. some education)	1.10 (0.70, 1.71)	1.09 (0.70, 1.70)	1.25 (0.71, 2.19)	1.19 (0.68, 2.11)
Mother had no education (vs. some education)	0.72 (0.23, 2.24)	0.67 (0.21, 2.13)	0.62 (0.18, 2.08)	0.59 (0.17, 2.01)
Non-professional job (vs. professional job)	1.13 (0.72, 1.78)	1.10 (0.70, 1.74)	0.91 (0.39, 2.11)	0.85 (0.36, 2.00)
Category of residence
°City (ref.)	1	1	1	1
°Town	0.95 (0.54, 1.67)	0.82 (0.46, 1.47)	1.04 (0.48, 2.25)	1.04 (0.48, 2.26)
°Rural	1.02 (0.58, 1.80)	0.88 (0.49, 1.57)	1.37 (0.63, 2.98)	1.34 (0.62, 2.92)
Age (years)	1.08 (1.05, 1.10)***	1.07 (1.05, 1.09)***	1.07 (1.04, 1.10)***	1.07 (1.04, 1.10)***
Activities of daily living
°Not limited (ref.)	1	1	1	1
°Somewhat limited	2.65 (1.78, 3.96)***	2.66 (1.78, 3.97)***	1.69 (1.07, 2.67)*	1.68 (1.06, 2.65)*
°Strongly limited	12.63 (8.15, 19.58)***	12.78 (8.22, 19.89)***	4.21 (2.29, 7.76)***	4.17 (2.26, 7.70)***
Constant	0 (0, 0.004)***	0.001 (0, 0.008)***	0.002 (0, 0.024)***	0.003 (0, 0.034)***
Log pseudo-likelihood	−457.21	−454.32	−301.75	−301.03

Note. Cognitive impairment: MMSE < 25; OR = odds ratio; CI = confidence interval.

p < .05; **p < .01; ***p < .001.

Discussion

As people age and social networks diminish, marriage becomes an increasingly vital source of support (Curran et al., 2003). Within the marital context, partners often share resources that may shape each other’s health and well-being (Bolin et al., 2002). Education is one such shared resource, as it can shape an individual’s economic opportunities and further affect health through various pathways (Aguila & Casanova, 2019; Rojas, 2011; M. Zhao & Zhang, 2025). This study examines the role of educational assortative marriage and spousal education on cognitive functioning among older adults in China. Participants in this study were born between 1897 and 1955. Over one-third were illiterate, likely due to the widespread disruption of China’s educational system during the early to mid-20th century. The findings suggest that spousal education and educational homogamy are more strongly associated with cognitive health among men than women. Notably, men appear to derive significant cognitive benefits from having more educated spouses, whereas women do not exhibit similar advantages. This gender disparity has important implications, as it underscores the need to consider structural and cultural factors that may differentially shape how men and women benefit from shared marital resources later in life.

These gendered patterns can be further illuminated through the lens of several theoretical frameworks. From a life course theory perspective, the cumulative nature of advantages and disadvantages across the lifespan helps explain how spousal education—often acquired earlier in adulthood—can shape one’s cognitive outcomes in later life (Cohen et al., 2010). Educationally advantaged marriages may provide sustained access to economic resources, knowledge, and health-promoting behaviors over time, particularly benefiting men whose life trajectories are more strongly influenced by their spouse’s social capital (Strazdins & Broom, 2004; Taylor, 2006). Cognitive reserve theory adds another layer, suggesting that intellectually stimulating environments help buffer against cognitive decline with age (Stern, 2012). A highly educated spouse may help maintain such an environment, thereby enhancing an individual’s cognitive reserve over time. However, structural and cultural gender norms in China may mediate these mechanisms unequally. While men may more readily benefit from their wives’ educational resources, women’s caregiving roles and emotional labor may constrain their ability to convert similar resources into cognitive gains (Chen et al., 2015; Y. Liu et al., 2019).

The results offer partial support for Hypothesis 1, confirming its second component but contradicting the first. Both men and women in hypogamous unions demonstrated increased odds of cognitive impairment relative to men in educationally homogamous unions. Specifically, men in hypogamous unions were 1.52 times more likely, and women 1.56 times more likely, to experience cognitive impairment than their homogamous male counterparts. However, no significant difference in cognitive impairment risk is observed between individuals in educationally homogamous versus hypergamous unions. The cognitive advantage associated with educational homogamy among men aligns with social cognitive theory, which posits that homogamous marriages may yield greater health benefits in later life than heterogeneous ones (Burney, 2008). Interestingly, whereas existing research on Chinese populations has primarily associated educational hypogamy with poorer health outcomes in women (Zhang & Tan, 2021), this study identifies a detrimental effect of educational hypogamy on cognitive outcomes in both men and women, relative to men in homogamous unions. Moreover, in contrast to earlier studies that report health advantages associated with educational hypergamy (Lei et al., 2014; Qin & Guo, 2016), the present findings indicate no such cognitive benefits for either gender.

Hypothesis 2 is not supported, as the effect of educational assortative marriage on cognitive impairment was more pronounced among men than women. In particular, men in homogamous marriages exhibited better cognitive functioning compared to men in hypogamous unions and women across all marital types. However, there was little variation in the odds of cognitive impairment among women across different marital arrangements. This lack of variation could point to structural or cultural constraints that diminish the potential cognitive benefits women might otherwise gain from spousal education, such as disproportionate caregiving burdens or less reciprocal intellectual engagement in marriage, as documented in prior research (Strazdins & Broom, 2004; Taylor, 2006). The observed gender-specific pattern diverges from previous research that found no significant gender differences in the association between spousal education and cognitive outcomes (Saenz et al., 2020; Xu, 2019). Nevertheless, the observed pattern and odds ratios align with recent research, which suggests that men derive greater cognitive benefits than women from upward marital mobility (Fu et al., 2023; Fu & Liu, 2022). Notably, women in homogamous unions were 2.17 times more likely to experience cognitive impairment than their male counterparts. This stark gender gap highlights potential structural and relational dynamics that limit women’s ability to derive equal cognitive benefits from educational similarity or spousal advantage within marriage. The persistence of gender gaps—despite comparable or even higher educational attainment among women—may reflect underlying inequalities in marital relationship quality, access to health resources, or opportunities for cognitive reserve development over the life course (Cong, 2008; Umberson & Williams, 2005).

Hypothesis 3 is supported in the case of men, as the findings reveal that men with a spouse who had a higher level of education were significantly less likely to experience cognitive impairment, regardless of their own educational attainment. This suggests that spousal education may function as a critical social and cognitive resource, particularly for Chinese men in later life. Several pathways may account for this significant relationship between spousal education and cognitive outcomes. Prior studies have shown that highly educated spouses can enhance cognitive function by facilitating knowledge exchange and encouraging cognitively stimulating activities (Andel et al., 2007; Saenz et al., 2020). Furthermore, spousal education may shape cognitive outcomes by influencing health-related behaviors, such as diet, physical activity, and adherence to medical recommendations (H. Liu & Umberson, 2008). Additionally, education plays a role in structuring advantages such as access to better employment opportunities and higher household income (Mirowsky & Ross, 2003), which accumulate over time to influence cognitive trajectories (Aguila & Casanova, 2019; Andel et al., 2007).

Hypothesis 4 is supported by the findings, as the beneficial effects of spousal education on cognitive functioning were greater for men than for women. Among men, both individual and spousal education were significantly and inversely associated with cognitive impairment. Each additional year of a spouse’s education corresponded to a 9% reduction in the odds of cognitive impairment, independent of the man’s own education. This finding lends support to the concept of educational spillover within marital relationships, particularly by showing that men’s cognitive benefits are partly attributable to their spouses’ educational attainment. In contrast, spousal education was not significantly associated with women’s cognitive impairment. Notably, women’s own education emerged as a robust protective factor, with each additional year of schooling linked to a 15% reduction in the odds of cognitive impairment. This gendered pattern underscores the importance of analyzing the association between spousal education and cognitive health through a gendered lens.

Previous studies suggest that, in China, wives are more likely than husbands to assume caregiving responsibilities, offer emotional support, and manage their partner’s health behaviors (Chen et al., 2015; Y. Liu et al., 2019). Cross-cultural research further finds that women often bear a disproportionate emotional burden in intimate relationships (Strazdins & Broom, 2004; Taylor, 2006), which may limit their capacity to access the same cognitive benefits that men obtain from a spouse’s education. Additionally, the stress associated with upward mobility and shifts in social roles—often more burdensome for women than men—may reduce the cognitive advantages otherwise gained (Janicki-Deverts et al., 2011). These dynamics underscore that while spousal education may serve as a cognitive resource for men, it may generate competing demands or even psychological strain for women, thereby complicating the benefits.

Limitations and Future Directions

Several limitations should be acknowledged in interpreting the findings of this study. First, the majority of participants had relatively low levels of formal education, which may limit the generalizability of the results to populations with higher educational attainment. Second, given the cross-sectional nature of the study and the possibility of unmeasured confounding variables (e.g., self-selection into marriage types, health-related selection), the findings should be interpreted as associative rather than causal. Third, some key study variables—such as parental education—were based on retrospective self-reports or accounts from family members. This reliance on memory may have introduced misclassification bias, potentially leading to an underestimation of observed associations (Batty et al., 2005). Nonetheless, prior research has demonstrated that retrospective reports can still offer valuable insights into long-term patterns (Hardt & Rutter, 2004). In addition, the study may be subject to measurement biases that affect the accuracy of its findings. For instance, cognitive function was assessed using the MMSE, which may disadvantage participants with limited education, language proficiency, or sensory and physical impairments (Riedel et al., 2008). These issues could contribute to the overestimation of cognitive impairment in certain subgroups within the study population. Furthermore, due to data limitations, the study did not account for confounding factors such as traumatic life events, parental health conditions, and environmental exposures, which are recognized as potential predictors of individual psychosocial outcomes across the life course (M. Li et al., 2019; Richards et al., 2017). Lastly, the analysis focused solely on formal education, omitting the possible contributions of informal cognitive engagement and mentally stimulating activities to cognitive function.

Given the limitations of the present study, future research should aim to incorporate more comprehensive measures of psychosocial and environmental factors that may influence cognitive health. With the continued global rise in educational attainment among women and shifting marriage patterns based on education (Lin et al., 2020), future studies could explore how various forms of educational assortative marriage contribute to disparities in health outcomes across cultural and gendered contexts. Investigating how the educational composition of couples influences health within various sociocultural settings could yield deeper insights into both direct and spillover effects of education on individual health. Notably, in the current study, intermediate variables such as financial stability and health behaviors exhibited limited variability across participants or introduced instability into the statistical models. As a result, they were excluded from the final analyses. Nevertheless, given the well-established associations between these factors and cognitive function (Aguila & Casanova, 2019; H. Liu & Umberson, 2008), future research involving more diverse populations should consider exploring these potential explanatory pathways. Moreover, to better understand the mechanisms underlying cognitive impairment, future research could combine neuroimaging techniques—such as brain magnetic resonance imaging (MRI)—with behavioral and social measures to investigate brain function. Additionally, future studies would benefit from evaluating informal cognitive training, lifestyle choices, financial and emotional support from spouse, and participation in intellectually enriching activities within marriage, all of which may significantly impact cognitive aging beyond formal education (Fu & Xu, 2022; Gruneau, 2018). Finally, future investigations should explore the effects of educational assortative marriage on a wider spectrum of physical health outcomes to better delineate the full extent of the educational spillover effects between spouses.

Implications for Policy and Practice

The findings of this study carry significant policy and practical implications. The observed cognitive benefits associated with both educational homogamy and individual educational attainment underscore the importance of investing in education and promoting lifelong learning as strategies for supporting cognitive health in aging populations. Due to historical constraints on formal education for some older Chinese adults, implementing adult learning initiatives may serve as an effective strategy to foster cognitive resilience and reduce disparities in cognitive functioning later in life (Fu & Xu, 2022). Existing research identifies a range of lifelong learning strategies for older adults with limited formal education, including engaging in informal online learning through social media platforms, participating in community-based educational activities, and enrolling in adult education programs such as senior citizen colleges (S. Zhao et al., 2021). Such initiatives have the potential to enhance cognitive resilience and promote sustained mental engagement and social participation among older adults. From a clinical perspective, healthcare professionals and practitioners should provide tailored educational interventions and cognitive support, paying close attention to gender-specific needs in later life (Abrahamson et al., 2018; Fu et al., 2023). Although global trends point to rising educational levels, disparities remain, particularly in less developed regions. Addressing these disparities requires deliberate policy efforts aimed at expanding both educational and cognitive health resources. Expanding the scope of lifelong learning to include diverse, accessible, and culturally meaningful pathways may prove critical for fostering cognitive resilience and promoting equitable aging.

Conclusion

This study investigated the impact of educational assortative marriage and spousal education on the risk of cognitive impairment among older adults in China. The findings indicate that men in educationally homogamous unions experience a significantly lower risk of cognitive impairment compared to men in hypogamous unions and to women across all marital arrangements. Furthermore, spousal education was positively associated with cognitive outcomes for men, whereas no such effect was observed in women. These results underscore the dual nature of education—as both a personal achievement and a shared marital resource—that differentially shapes cognitive outcomes for men and women in later life. Taken together, the findings underscore notable gender-based differences in the cognitive spillover effects of education and emphasize the importance of developing culturally and gender-sensitive public health interventions to support cognitive well-being in aging populations.

Footnotes

Appendix

Appendix A.

Mini-Mental State Exam (MMSE) Questions, CLHLS 2014.

Item	MMSE question	Score (total = 30)
Orientation	1. What time of day is it right now (morning, afternoon, evening)?	1
	2. What is the month (Western or Chinese calendar) right now?	1
	3. What is the date (Chinese calendar day and month) of the mid-autumn festival?	1
	4. What is the season right now, spring, summer, fall, winter?	1
	5. What is the name of this county or district?	1
Naming foods	6. Please name as many kinds of food as possible in 1 min (1 point for each food and 7 points for those who name 7 or more foods)	7
Registration	7. Repeat the name of “table” at the first attempt	1
	8. Repeat the name of “apple” at the first attempt	1
	9. Repeat the name of “clothes” at the first attempt	1
Calculation	10. $20 − $3 = ?	1
	11. $20 − $3 − $3 = ?	1
	12. $20 − $3 − $3 − $3 = ?	1
	13. $20 − $3 − $3 − $3 − $3 = ?	1
	14. $20 − $3 − $3 − $3 − $3 − $3 = ?	1
Copy a figure recall	15. Draw a figure following a given example.16. Repeat the name of “table” a while later	11
	17. Repeat the name of “apple” a while later	1
	18. Repeat the name of “clothes” a while later	1
Language	19. Name “pen”	1
	20. Name “watch”	1
	21. Repeat the following sentence: “What you plant, what you will get.”	1
	22. Follow the interviewer’s instruction and take a piece of paper using the right hand.	1
	23. Follow the interviewer’s instruction and fold the paper in the middle using both hands.	1
	24. Follow the interviewer’s instruction and place the paper on the floor.	1

Acknowledgements

The author is grateful to the participants of the CLHLS study and the members of the CLHLS study team. The CLHLS study was managed by the Center for Healthy Aging and Development Studies, Peking University. The CLHLS was supported by funds from the US National Institute on Aging (NIA), the China Natural Science Foundation, the China Social Science Foundation and the United Nations Fund for Population Activities (UNFPA).

ORCID iD

Rong Fu

Ethical Considerations

The CLHLS was approved by the research ethics committee of Peking University (IRB0000105213074).

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Siena University.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

For the submitted paper, data were derived from the 2014 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), which is available through ICPSR at the following link: .

References

Abrahamson

Pressler

Fulton

Morgan

Anderson

(2018). Organizational role and perception of organizational safety culture: A survey of registered nurses, physicians and managers. Online Journal of Issues in Nursing, 23(3), 7. https://doi.org/10.3912/ojin.vol23no03ppt25

Aguila

Casanova

(2019). Short-term impact of income on cognitive function: Evidence from a sample of Mexican older adults. Journal of Aging and Health, 32(7-8), 591–603. https://doi.org/10.1177/0898264319841155

Ahrenfeldt

L. J.

Scheel-Hincke

L. L.

Kjærgaard

Möller

Christensen

Lindahl-Jacobsen

(2019). Gender differences in cognitive function and grip strength: A cross-national comparison of four European regions. European Journal of Public Health, 29(4), 667–674. https://doi.org/10.1093/eurpub/cky266

Albertini

Garriga

(2011). The effect of divorce on parent-child contact: Evidence on two declining effect hypotheses. European Societies, 13(2), 257–278. https://doi.org/10.1080/14616696.2010.483002

Andel

Kåreholt

Parker

M. G.

Thorslund

Gatz

(2007). Complexity of primary lifetime occupation and cognition in advanced old age. Journal of Aging and Health, 19(3), 397–415. https://doi.org/10.1177/0898264307300171

Liu

G. G.

(2016). Cognitive impairment and mortality among the oldest-old Chinese. International Journal of Geriatric Psychiatry, 31(12), 1345–1353. https://doi.org/10.1002/gps.4442

Baker

D. P.

Leon

Smith Greenaway

E. G.

Collins

Movit

(2011). The education effect on population health: A reassessment. Population and Development Review, 37(2), 307–332. https://doi.org/10.1111/j.1728-4457.2011.00412.x

Bartley

Martikainen

Shipley

Marmot

(2004). Gender differences in the relationship of partner’s social class to behavioural risk factors and social support in the Whitehall II study. Social Science and Medicine, 59(9), 1925–1936.

Batty

G. D.

Lawlor

D. A.

Macintyre

Clark

Leon

D. A.

(2005). Accuracy of adults’ recall of childhood social class: Findings from the Aberdeen children of the 1950s study. Journal of Epidemiology & Community Health, 59(10), 898–903. https://doi.org/10.1136/jech.2004.030932

10.

Becker

G. S.

(Ed.). (1991). A treatise on the family (enlarged ed.). Harvard University Press.

11.

Bian

(2002). Chinese social stratification and social mobility. Annual Review of Sociology, 28, 91–116. https://www.jstor.org/stable/3069236

12.

Bolin

Jacobson

Lindgren

(2002). The family as the health producer—When spouses act strategically. Journal of Health Economics, 21, 475–495. https://doi.org/10.1016/s0167-6296(01)00135-7

13.

Bunce

Tzur

Ramchurn

Gain

Bond

F. W.

(2008). Mental health and cognitive function in adults aged 18 to 92 years. Journals of Gerontology Series B, 63(2), 67–74. https://doi.org/10.1093/geronb/63.2.p67

14.

Burney

V. H.

(2008). Applications of social cognitive theory to gifted education. Roeper Review, 30(2), 130–139. https://doi.org/10.1080/02783190801955335

15.

Carr

Springer

K. W.

(2010). Advances in families and health research in the 21st Century. Journal of Marriage and Family, 72(3), 743–761. https://doi.org/10.1111/j.1741-3737.2010.00728.x

16.

Chen

Liu

Vikram

Guo

(2015). For better or worse: The health implications of marriage separation due to migration in rural China. Demography, 52(4), 1321–1343. https://doi.org/10.1007/s13524-015-0399-9

17.

Cohen

Janicki-Deverts

Chen

Matthews

K. A.

(2010). Childhood socioeconomic status and adult health. Annals of the New York Academy of Sciences, 1186(1), 37–55. https://doi.org/10.1111/j.1749-6632.2009.05334.x

18.

Cong

(2008). From “Cainü” to “Nü Jiaoxi”: Female normal schools and the transformation of women’s education in the late Qing period, 1895-1911. In Qian

Fong

G. S.

Smith

R. J.

(Eds.), Different worlds of discourse: Transformations of gender and genre in late Qing and early Republican China (pp. 115–144). Brill.

19.

Curran

S. R.

McLanahan

Knab

(2003). Does remarriage expand perceptions of kinship support among the elderly? Social Science Research, 32(2), 171–190. https://doi.org/10.1016/s0049-089x(02)00046-7

20.

Deb

(2001). A discrete random effects probit model with application to the demand for preventive care. Health Economics, 10(5), 371–383. https://doi.org/10.1002/hec.627

21.

Eaker

E. D.

Haynes

S. G.

Feinleib

(1983). Spouse behavior and coronary heart disease in men: Prospective results from the Framingham heart study. II. Modification of risk in type A husbands according to the social and psychological status of their wives. American Journal of Epidemiology, 118(1), 23–41. https://doi.org/10.1093/oxfordjournals.aje.a113614

22.

(2015). Early parental loss and cognitive impairment in old age: Is participating in leisure activities a pathway? The Gerontologist, 55(S2), 418. https://doi.org/10.1093/geront/gnv184.19

23.

(2016). Social support and cognitive impairment in the oldest old: Structure, function, and mutual effect. The Gerontologist, 56(S3), 428. https://doi.org/10.1093/geront/gnw162.1706

24.

(2019). Early parental death and cognitive impairment in late life: A cohort study. Sage Open, 9(3), 1–11. https://doi.org/10.1177/2158244019879135

25.

(2021). Educational mobility through marriage and risk of cognitive impairment in late life. Innovation in Aging, 5(S1), 706–707. https://doi.org/10.1093/geroni/igab046.2645

26.

Abrahamson

Campbell

(2023). Occupational mobility and chronic health conditions in middle and later life: A systematic review. International Journal of Behavioral Medicine, 30(5), 605–615. https://doi.org/10.1007/s12529-022-10135-0

27.

Liu

(2022). Intergenerational socioeconomic mobility and cognitive impairment among Chinese older adults: Gender differences. Journal of Applied Gerontology, 41(7), 1733–1743. https://doi.org/10.1177/07334648221084996

28.

(2022). Motivational factors affecting the intention to volunteer in elder care agencies: A pilot study. Innovation in Aging, 6(S1), 571. https://doi.org/10.1093/geroni/igac059.2150

29.

Gonzaga

G. C.

Carter

Buckwalter

J. G.

(2010). Assortative mating, convergence, and satisfaction in married couples. Personal Relationships, 17, 634–644. https://doi.org/10.1111/j.1475-6811.2010.01309.x

30.

Gruneau

M. F.

(2018). Reconsidering the partner effect on voting. Electoral Studies, 53, 48–56. https://doi.org/10.1016/j.electstud.2018.03.003

31.

Guo

Lin

Zhang

(2020). The cross-spousal effect of education on health. Journal of Development Economics, 146, 102493. https://doi.org/10.1016/j.jdeveco.2020.102493

32.

Hardt

Rutter

(2004). Validity of adult retrospective reports of adverse childhood experiences: Review of the evidence. Journal of Child Psychology and Psychiatry, 45(2), 260–273. https://doi.org/10.1111/j.1469-7610.2004.00218.x

33.

Hughes

M. E.

Waite

L. J.

(2009). Marital biography and health at mid-life. Journal of Health and Social Behavior, 50(3), 344–358. https://doi.org/10.1177/002214650905000307

34.

Janicki-Deverts

Cohen

Matthews

K. A.

Jacobs

D. R.

Adler

N. E.

(2011). Occupational mobility and carotid artery intimamedia thickness: Findings from the coronary artery risk development in young adults study. Psychosomatic Medicine, 73(9), 795–802. https://doi.org/10.1097/psy.0b013e3182365539

35.

Kiecolt-Glaser

J. K.

Wilson

S. J.

(2017). Lovesick: How couples’ relationships influence health. Annual Review of Clinical Psychology, 13(1), 421–443. https://doi.org/10.1146/annurev-clinpsy-032816-045111

36.

Lei

McArdle

J. J.

Smith

J. P.

Zhao

(2012). Gender differences in cognition among older adults in China. Journal of Human Resources, 47(4), 951–971. https://doi.org/10.3368/jhr.47.4.951

37.

Lei

Zhao

(2014). Does marrying up make your life more satisfied? Marriage pattern and its long-term effects. China Economic Quarterly, 14(1), 31–50.

38.

Xue

Wang

(2019). Intergenerational association of maternal obesity and child peer victimization in the United States. Journal of Health and Social Behavior, 60(1), 69–83. https://doi.org/10.1177/0022146518824566

39.

Lin

Desai

Chen

(2020). The emergence of educational hypogamy in India. Demography, 57(4), 1215–1240. https://doi.org/10.1007/s13524-020-00888-2

40.

Liu

Umberson

D. J.

(2008). The times they are a Changin’: Marital status and health differentials from 1972 to 2003. Journal of Health and Social Behavior, 49(3), 239–253. https://doi.org/10.1177/002214650804900301

41.

Liu

Roberto

K. A.

Savla

(2019). Depressive symptoms among adult children caregivers in China: Moderating effects of working status and gender. Aging & Mental Health, 23(10), 1391–1399. https://doi.org/10.1080/13607863.2018.1488943

42.

Zhao

Luo

Kawachi

(2013). Influence of spousal education on partner's self-rated health: Cross-sectional study among 1382 married couples in Shanghai, China. Asia-Pacific Journal of Public Health, 25(5), 398–408. https://doi.org/10.1177/1010539511420417

43.

Lövdén

Fratiglioni

Glymour

M. M.

Lindenberger

Tucker-Drob

E. M.

(2020). Education and cognitive functioning across the life span. Psychological Science in the Public Interest, 21(1), 6–41. https://doi.org/10.1177/1529100620920576

44.

Medalia

Erlich

(2017). Why cognitive health matters. American Journal of Public Health, 107(1), 45–47. https://doi.org/10.2105/ajph.2016.303544

45.

Merton

R. K.

(1941). Intermarriage and the social structure. Psychiatry: Interpersonal and Biological Processes, 4, 361–374.

46.

Mirowsky

Ross

C. E.

(2003). Education, social status, and health. Aldine de Gruyter.

47.

Pampel

F. C.

Krueger

P. M.

Denney

J. T.

(2010). Socioeconomic disparities in health behaviors. Annual Review of Sociology, 36(1), 349–370. https://doi.org/10.1146/annurev.soc.012809.102529

48.

Pearlin

L. I.

Schieman

Fazio

E. M.

Meersman

S. C.

(2005). Stress, health, and the life course: Some conceptual perspectives. Journal of Health and Social Behavior, 46(2), 205–219. https://doi.org/10.1177/002214650504600206

49.

Qian

Lichter

D. T.

(2018). Marriage markets and intermarriage: Exchange in first marriages and remarriages. Demography, 55(3), 849–875. https://doi.org/10.1007/s13524-018-0671-x

50.

Qin

Guo

(2016). Marriage matching, life satisfaction and divorce risk in first marriage: A research based on China Family Panel Studies. Studies in Labor Economics, 4(1), 43–69.

51.

Richards

E. A.

McDonough

(2017). Longitudinal examination of social and environmental influences on motivation for physical activity. Applied Nursing Research, 37, 36–43. https://doi.org/10.1016/j.apnr.2017.07.007

52.

Riedel

Klotsche

Spottke

Deuschl

Förstl

Henn

Heuser

Oertel

Reichmann

Riederer

Trenkwalder

Dodel

Wittchen

H.-U.

(2008). Cognitive impairment in 873 patients with idiopathic Parkinson’s disease. Results from the German Study on Epidemiology of Parkinson’s Disease with Dementia (GEPAD). Journal of Neurology, 255(2), 255–264. https://doi.org/10.1007/s00415-008-0720-2

53.

Rojas

(2011). Intra-household arrangements and adult health satisfaction: Evidence from Mexico. In McGillivray

Dutta

Lawson

(Eds.), Health inequality and development (pp. 132–153). Palgrave Macmillan.

54.

Saenz

J. L.

Beam

C. R.

Zelinski

E. M.

(2020). The association between spousal education and cognitive ability among older Mexican adults. The Journals of Gerontology: Series B, 75(7), e129–e140. https://doi.org/10.1093/geronb/gbaa002

55.

Schaller

Sidelnikov

Theiler

Egli

Staehelin

H. B.

Dick

Dawson-Hughes

Grob

Platz

Can

Bischoff-Ferrari

H. A.

(2012). Mild to moderate cognitive impairment is a major risk factor for mortality and nursing home admission in the first year after hip fracture. Bone, 51(3), 347–352. https://doi.org/10.1016/j.bone.2012.06.004

56.

Stern

(2012). Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurology, 11(11), 1006–1012. https://doi.org/10.1016/S1474-4422(12)70191-6

57.

Strazdins

Broom

D. H.

(2004). Acts of love (and work): Gender imbalance in emotional work and women’s psychological distress. Journal of Family Issues, 25(3), 356–378. https://doi.org/10.1177/0192513x03257413

58.

Suarez

Barrett-Connor

(1984). Is an educated wife hazardous to your health? American Journal of Epidemiology, 119, 244–249.

59.

Taylor

S. E.

(2006). Tend and befriend: Biobehavioral bases of affiliation under stress. Current Directions in Psychological Science, 15(6), 273–277. https://doi.org/10.1111/j.1467-8721.2006.00451.x

60.

Umberson

Williams

(2005). Marital quality, health, and aging: Gender equity? The Journals of Gerontology: Series B, 60(Special Issue 2), S109–S113. https://doi.org/10.1093/geronb/60.Special_Issue_2.S109

61.

Williams

Umberson

(2004). Marital status, marital transitions, and health: A gendered life course perspective. Journal of Health and Social Behavior, 45(1), 81–98. https://doi.org/10.1177/002214650404500106

62.

Wilmoth

Koso

(2002). Does marital history matter? Marital status and wealth outcomes among preretirement adults. Journal of Marriage and Family, 64(1), 254–268. https://www.jstor.org/stable/3599792

63.

(2019). Spousal education and cognitive functioning in later life. The Journals of Gerontology: Series B, 75(7), e141–e150. https://doi.org/10.1093/geronb/gbz014

64.

Zhang

Tan

(2021). Educational assortative mating and health: A study in Chinese internal migrants. International Journal of Environmental Research and Public Health, 18(4), 1375. https://doi.org/10.3390/ijerph18041375

65.

Zhao

Zhang

(2025). Gendered outcomes? Educational assortative marriages, gender ideology, and depressive symptoms in China. Journal of Affective Disorders, 372, 463–469. https://doi.org/10.1016/j.jad.2024.12.033

66.

Zhao

Kinshuk

Yao

(2021). Adoption of mobile social media for learning among Chinese older adults in senior citizen colleges. Educational Technology Research and Development, 69(6), 3413–3435. https://doi.org/10.1007/s11423-021-10048-x

67.

Zuo

Bian

(2001). Gendered resources, division of housework, and perceived fairness—A case in urban China. Journal of Marriage and Family, 63(4), 1122–1133. https://www.jstor.org/stable/3599818

Cognitive Impairment in Late Life: The Role of Educational Assortative Marriage and Spousal Education

Abstract

Plain language summary

Keywords

Introduction

Conceptual and Theoretical Framework

Education, Spousal Education, and Cognition

Educational Assortative Marriage, Gender, and Health

The Present Study

Methods

Data and Sample

Dependent Variable

Independent Variables

Covariates

Statistical Analysis

Results

Descriptive Statistics and Univariate Analysis

Educational Assortative Marriage and Cognitive Impairment

Spousal Education and Cognitive Impairment

Discussion

Limitations and Future Directions

Implications for Policy and Practice

Conclusion

Footnotes

Appendix

Acknowledgements

ORCID iD

Ethical Considerations

Funding

Declaration of Conflicting Interests

Data Availability Statement

References