Trajectories of Intergenerational Emotional Closeness in Multi-Child Aging Families in China

Abstract

Intergenerational emotional closeness (IEC) in multi-child families manifests not only in the overall parent-child relationship but also by the extent of its within-family differentiation. This study aimed to identify distinct trajectories of collective IEC in multi-child families and to examine its potential associated factors. The study used four waves of the China Family Panel Studies (2010–2018). Based on a sample of 3474 older adults (age >60), growth mixture modelling and logistic regression were conducted. Two latent trajectory patterns of IEC were identified: increasingly tight-knit (93%) and persistently collectively ambivalent (7%). Compared to the latter trajectory, older adults who had a married/cohabiting partner, had better health and received support from all children, were more likely to have increasingly tight-knit trajectories of IEC. The study tells a story more about emotional cohesion between Chinese older adults and multiple offspring over time. Aging families experiencing persistently collectively ambivalent IEC deserve more attention.

Keywords

intergenerational relationship multi-child family emotional closeness within-family difference collective ambivalence

Intergenerational relationships between older adults and their children are an important issue for both generations (Chen & Jordan, 2019; Merz et al., 2009; Polenick et al., 2016). Among the multiple dimensions used to evaluate such parent-child bonds, intergenerational emotional closeness (IEC) is a crucial component as it manifests affectual solidarity across generations (Hank et al., 2017). Studies in both Western countries and China have shown parent-adult child emotional closeness to be positively consequential for the mental health and well-being of older parents (Chen & Zhou, 2021; Lai et al., 2019; Li & Zhang, 2023). However, owing to the complexities associated with the conceptualization and measurement of intergenerational relationships, there are still research gaps in the investigation on the characteristics and patterns of intergenerational emotional closeness.

One of the research gaps is related to different approaches of measuring intergenerational relationships. Among the abundant relevant literature over the past two decades, most previous studies were based on the reports of intergenerational relationships with a specific/focal child (Guo et al., 2016; Schenk & Dykstra, 2012; Ward et al., 2009). This evaluation method tends to overlook the characteristics of other parent-adult child dyads within the same family. Particularly, in multi-child families, IEC is much more complicated in terms of collective ambivalence, as older parents may experience positive emotions with some children and negative with others. Older parents may have substantially differential treatment across multiple offspring even within the same family (Kim et al., 2016; Pillemer & Gilligan, 2018; Suitor et al., 2008, 2017). So far, there is a lack of typology studies on IEC in multi-child families that could consider collective affections between older parents and all the offspring rather than focus on one specific parent-adult child dyad.

The second research gap is that the affectual aspect of parent-child relationship has been mostly discussed with cross-sectional data in previous literature (Chai et al., 2020; Guo et al., 2016; Liu et al., 2022). Families actually undergo challenges in different life stages, especially those associated with difficult life events and deteriorating physical functioning among older parents. A life course framework is necessary to capture the dynamic nature of parent-adult child emotional closeness across time. Much remains to be known about how collective IEC would change and under what contingencies.

To fill the aforementioned research gaps, this study aimed to address two research questions in the Chinese context: (1) As older parents’ ages advance, how IEC changes at family level in Chinese multi-child families? (2) What potential factors are associated with the change trajectory patterns?

Multi-Child Aging Families in the Context of China

Extended families with multiple offspring were prevalent in China before the implementation of the one-child policy in the 1980s that mainly affected urban families. Families in rural China could still have multiple children. Most parents giving birth to multiple offspring before the one-child policy have stepped into late life today. The study report of the 2014 China Longitudinal Aging Social Survey found that the current cohort of older adults (age ≥60 years) in general still had three children on average. Specifically, urban older adults have 2.5 children on average compared to 3.4 children had by their rural counterparts. Intergenerational relationships in multi-child aging families deserve more attention.

Chinese families particularly emphasize intergenerational relationships. Family relationships between older parents and adult children have a strong basis in the Confucian norm of filial piety, according to which adult children are expected to take care of older parents and ensure their well-being in late life (Sung, 1995). In addition, Chinese collectivist family culture highly values collective cohesion and solidarity (Chuang, 2005; Lin & Chen, 2018). Chinese social and cultural contexts provide legitimacy and necessity of investigating collective IEC in contemporary Chinese multi-child families.

Intergenerational Relationships in Multi-Child Families

The perspective of “collective ambivalence” describes the mixed quality of intergenerational relationships across multiple children within the same family. It suggests that older parents can have close and intimate relationships with some children whereas discordant links with others (Ward, 2008; Ward et al., 2009). Stemming from this collective ambivalence perspective, within-family differences (WFD) approaches are proposed to render instrumental merits of analyzing family-level data collected from or about multiple children in the past decade (Carr & Utz, 2020; Kim et al., 2016; Pillemer & Gilligan, 2018; Suitor et al., 2017, 2018). Empirical studies have confirmed that within-family differentiation is very common in multi-child families in terms of parental differential treatment, maternal favoritism, IEC, and support exchanges (Chen et al., 2021; Chen & Zhou, 2021; Kim et al., 2016; Suitor et al., 2013, 2016).

Kim et al. (2016) identified four within-family patterns of parental support to multiple adult children (i.e., high support-low differentiation, medium support-high differentiation, low support-low differentiation, and low-support-very high differentiation) in America. Chen and Zhou (2021) classified parent-child emotional closeness in Chinese multi-child families into two within-family patterns, namely, tight-knit and highly ambivalent. They argued that a majority of Chinese families tended to be equally emotional close to all children (i.e., tight-knit). However, the within-family patterns get more complicated when involving more dimensions of intergenerational relationships (e.g., functional exchanges) into the typology study. Li and Zhang (2023) echoed in a Chinese study that although the socioeconomically privileged older parents prioritize all-round close relationships, over 41.64% of older parents had different types of intergenerational relationships with their different children. In some families, a parent may have equally good relationships with all children, whereas in others a parent may noticeably favor one child over another. Despite existing relevant studies exploring within-family patterns of intergenerational relationships in both Western and Chinese context, most of them are cross-sectional and fail to portray the potential changes and dynamics of parents’ relationship patterns with multiple adult children over time.

Intergenerational Emotional Closeness from a Life Course Perspective

Some empirical studies have examined changes in the affectual aspect of intergenerational relationships over time but mainly based on a focal parent-child dyad, whereas most of them were conducted in the Western context. Although a small proportion of change did exist, prior literature showed more continuity in intergenerational relationships, regardless of the length of observational period (Boll et al., 2010; Hogerbrugge & Silverstein, 2015; Schenk & Dykstra, 2012; Suitor et al., 2013). Nevertheless, the study findings were inconsistent in describing the characteristics of the most-likely stable intergenerational relationships. Some concluded that harmonious and affective intergenerational relationships were least likely to change over time (Hogerbrugge & Silverstein, 2015), while others argued that estranged parent-child relationships tended to continue for years (Gilligan et al., 2022). Research has not reached consensus when taking into account different features of parent-child emotional relationships. In contrast, Lin and Chen (2018) found that IEC was subject to change rather than remaining stable over time in Chinese families. Their study investigated the evolution of IEC at family level, what they called as collective emotional closeness, in multi-child families in rural China. It is the only study exploring collective IEC in the Chinese context among literature thus far reviewed. The findings showed that two-thirds of older parents in rural China experienced either improvement or deterioration in their IEC with multiple children over three years.

In addition to the empirical evidences, some theoretical perspectives also support a dynamic and longitudinal understanding of changes in collective IEC over time. The life course perspective highlights the importance of uncovering changes in individual and family development (Elder, 1994; Moen & Hernandez, 2009). It emphasizes inextricable interrelation between family members’ lives (Bengtson et al., 2005; Moen & Hernandez, 2009). Life events and transitions experienced by either parents or children could induce alterations in intergenerational relationships (Suitor et al., 2013). In addition, as our study focused on IEC between older adults and their multiple offspring, we also draw on socioemotional selectivity theory (Carstensen, 2019; Charles & Carstensen, 2010) as a supplementary theoretical perspective to expect the potential trajectories of IEC over time. Socioemotional selectivity theory was originally developed to explain changes in individuals’ goals as they grow old in the life course. When individuals perceive that they have limited time to achieve life goals, they tend to enhance their focus on socioemotional goals compared to the others such as instrumental goals. Socioemotional selectivity theory recognizes that older adults increasingly emphasize personal relationships meaningful to their lives and avoid relationship stress as they age (Carstensen, 1992). As the parent-child relationship is one of the most important personal relationships, older parents are thus expected to obtain better IEC within family as old age advances.

Factors Related to Intergenerational Emotional Closeness

With respects to life transitions that older parents are likely to encounter in later life from a life course perspective, we specifically considered parents’ marital status, health condition and support receipt. These factors have been shown to alter parent-child relationships within the family referring to previous studies (e.g., Chen et al., 2021; Chen & Zhou, 2021; Kim et al., 2016; Lendon et al., 2014). First, change in marital status can reflect one major life transition of older parents in later life, as they may experience separation, divorce or widowhood. For example, widowhood could increase positive feelings and closeness between older parents and adult children as they tend to cope with their loss together and children are likely to pay particular attention to the surviving parent (Carr & Boerner, 2013; Hammersmith, 2019; Suitor et al., 2014). While divorced mothers tend to have an increase in weekly contact with an adult child, they are also more likely to get estranged from at least one adult child compared with married mothers (Shapiro, 2003). Second, older parents’ health condition is another factor giving rise to their needs for children’s assistance. Parents’ health decline often increases tension and ambivalence between parents and adult children (Fingerman et al., 2006; Wilson et al., 2006). Experiencing serious health events may not necessarily reconcile parents’ relationships with estranged children. However, it may reinforce parents’ connections with children from whom they received sustained and reliable support in the past (Gilligan et al., 2022). Third, whether and how older adults receive support from children matter for parent-child relationships in later life, especially for Chinese older parents. Traditional Chinese values emphasize filial piety and adult children’s responsibility of taking care of parents in old age (Lin et al., 2011). Studies showed that older parents experienced less ambivalence and rated their relationships with children highly when they received instrumental, emotional, or financial support from children due to a sense of fulfillment of the parents’ normative expectations (Guo et al., 2016; Lendon et al., 2014). If an older parent has more than one child, and all his/her children provide support and fulfill filial commitment, he/she may experience a higher level of cohesive IEC within the family, compared to those who only received support from some children.

The Present Study

Guided by the life course perspective and socioemotional selectivity theory, we sought to examine trajectories of IEC at family level based on an 8-year observation period. This study contributes to the literature in the three aspects. First, rather than focusing on one specific parent-child dyad, we investigated the IEC between older parents and all of their children. We took into account the sense of family collectiveness in multi-child families. Second, we adopted the WFD approach not only to examine the within-family average but also to unveil the within-family differentiation of IEC across multiple offspring. Third and most importantly, rather than using cross-sectional data, we used the longitudinal data to demonstrate how IEC in Chinese multi-child families evolved over time. We also emphasized culture sensitivity when studying intergenerational relationships in different cultural contexts. In Chinese multi-child families, family cohesion and solidarity are strongly expected over time as they are also emphasized by traditional Chinese family culture (Chuang, 2005). Therefore, in the present study, we hypothesized that the dominant type of IEC trajectory in Chinese multi-child families would be characterized by increasingly close and cohesive relationships between older parents and all children. Moreover, we explored factors related to the different patterns of collective IEC trajectories, particularly paying attentions to parents’ marital status, health condition and support receipt.

Method

Data

We used data from the China Family Panel Studies (CFPS), which is a nationally representative longitudinal household survey of Chinese communities, families, and individuals launched in 2010. All members over age 9 in a sampled household were interviewed and followed up every two years from the first data collection (i.e., in 2012, 2014, 2016, and 2018). CFPS used a multi-stage probability sampling strategy and covered 25 provinces or their administrative equivalents. More details of the sampling method and the study design can be found at https://www.isss.pku.edu.cn/cfps/. As the question on IEC was not asked in 2014, this study made use of CFPS 2010, 2012, 2016, and 2018.

In the baseline survey, there were 5515 older adults (age >60) who had at least two children. We did not include those aged 60 in the baseline for the reason that the IEC question in 2010 was only asked among those aged above 60. In the following three waves, 4,240, 3640 and 2338 respondents from the original sample continued to participate. We provided a supplementary analysis on the sample attrition in the robustness check. One important reason for sample attrition was mortality. Those who dropped out were older, in poorer health, with lower education, and more likely to have no marriage partners. There were two additional restrictions on the sample selection: (1) only individuals who reported their IEC with at least two children in at least two waves over the study period were included (1278 cases dropped), because at least two time points of observation of IEC were needed to estimate the trajectory; (2) only individuals who had same number of children over the study period were included so that the situation where older adults lost children or became stepparents could be avoided (607 cased dropped). As the number of cases with missing information on independent variables was minor (156 cases), they were dropped. For more details of sample selection, please refer to the supplementary Figure S1 that demonstrates the selection process in a flow chart. Thus, the final analytical sample is 3474. For cases that did not have full information on the IEC over the four waves, Full-Information Maximum Likelihood (FIML) procedures were applied (Bowen, 2015). FIML allows us to include individuals even if they have incomplete observation at some time point. This method is often used to deal with attrition in longitudinal data analysis (Li et al., 2013; O’Connor et al., 2012; Wickrama et al., 2013). Especially, FIML in structural equation models could also correct for selective attrition (Asendorpf et al., 2014). The whole analysis was conducted in Mplus 8.

Measures

Intergenerational Emotional Closeness

In the CFPS, older adults aged over 60 years and with at least one living child were asked to answer the question for each child “in the past 6 months, how was the relationship between you and ## (the child’s name here)” on a 5-point scale from 1 (not close at all) to 5 (very close). Inspired by previous studies (Boyle et al., 2004; Chen & Zhou, 2021; Kim et al., 2016), two family-level indicators of IEC were constructed for each family: (a) the mean score of IEC between the older parent and his/her multiple children, reflecting the family average level; and (b) the coefficient of variation (COV)—that is, the standard deviation (SD) of the IEC score divided by the mean score, reflecting the within-family difference.

Focal Independent Variables

In this study, we focused on the roles of older adults’ marital status, health status, and the receipt of intergenerational support. Marital status was measured by binary variables (i.e. “having a married/cohabiting partner”, “divorced/separate/widowed”). Health status was self-rated and classified into three categories (i.e., “unhealthy”, “fair”, and “healthy”). Regarding the receipt of intergenerational support, older adults aged over 60 were asked whether they received economic support, housework help, and daily-care support from children, and from which children they received such help. Based on such information, two variables were generated, that is, receiving economic support and receiving instrumental (including housework and care) support. Both variables were categorical (i.e., “not receiving”, “receiving from all children”, and “receiving from some children”).

Besides, we included other demographic and socioeconomic characteristic of older adults as control variables. They were older adults’ age (treated as a categorical variable, i.e. “60–69”, “70–79”, and “80 and above”), sex (i.e., “female”, “male”), residential area (i.e., “rural” and “urban”), education levels (i.e., “primary or below” — primary school, semi-illiteracy or illiteracy, “secondary” — junior and senior middle school”, and “college or above”— junior college, college, master’s degree, and doctoral degree), as well as personal relative income. Personal relative income referred to the self-perceived relative income, measured by asking the question “how do you rate your personal income relative to the local level of income” (ranging from 1 = very low to 5 = very high, 0 = no income). It was treated a continuous variable. We did not use the absolute income for two reasons: first, the absolute income (e.g. whether including allowance, subsidies etc.) was measured very differently across waves; second, the average level of income varies greatly across provinces in China, therefore we cannot precisely infer respondents’ socioeconomic status in the local place based on the absolute income.

We also controlled for characteristics of children, including the number of children, sex composition, age gap between the youngest and oldest child, and whether at least one child lived with the older parents. We created a variable describing the total number of living children. Sex composition was categorized as “mixed sex”, “only daughters”, and “only sons”. We also tested alternative classification for sex composition in the robustness checks. The presence of a co-resident child was represented by a binary variable (i.e., “having a co-resident child” and “no co-resident child”).

Analytical Strategy

Growth mixture modelling (GMM) was used to examine the growth trajectories of IEC in two dimensions––the family mean level and the within-family difference over the study period of 2010–2018. GMM not only allows us to investigate unknown population heterogeneity in growth trajectories but also to classify individuals into different latent classes (Muthen et al., 2002). Figure 1 shows the whole analytical procedures. Specifically, in this study, GMM was represented as a structural equation model. There are four latent factors: two for the intercept and slope of the family mean level (i.e., mean score of IEC within family) and the other two for the intercept and slope of the within-family difference (i.e., COV of IEC within family). The two intercepts reflect the average level of within-family mean and COV at baseline, respectively. The two slopes reflect the change rates for within-family mean and COV. There is also one categorical latent variable representing class membership that loads on the intercept and slope factors. Individuals were classified into different latent classes according to their highest posterior probabilities of membership. Time was centered at baseline. To determine the most appropriate number of latent class of growth trajectories, we built and compared four models (from two to five classes), based on model fit indicators: Akaike’s information criterion (AIC), Bayesian information criterion (BIC), Entropy, and the Lo–Mendell–Rubin likelihood ratio test (LMR LRT). Lower values for AIC and BIC generally indicate a better model fit, and higher Entropy values indicate a clearer distinction of latent classes. The LMR LRT test is to identify whether a model solution with more classes performs better than a simpler model. The class size identified should also not comprise less than 5% of the sample, which is one of the widely-used rules (Infurna & Grimm, 2018; Weller et al., 2020). In addition, we tested the linear model and quadratic model. The linear shape of trajectories provided the better model fit with the data than did a quadratic shape.

Figure 1.

Growth mixture model of IEC. Note: IEC = intergenerational emotional closeness; COV = coefficient of variation.

After determining the optimal latent class, we further investigated factors associated different IEC growth trajectories. The latent class identified from GMM was the dependent variable. A logistic regression model was carried out to examine the association between the four variables of interests and IEC trajectories (i.e. marital status, health status, recipient of financial support, recipient of instrumental support). As shown in Figure 1, both time-varying factors at the baseline and the time-invariant factors were considered in the logistic regression. Considering that the time-varying factors may also have short-term effects on mean and COV of IEC, we also estimate their associations with the mean score and COV in a structural equation model. Given that there were no strong reasons to suggest the association of time-varying variables with the IEC means and COVs would change over time, these coefficients were constrained to be equal over the four waves.

Results

Descriptive Results

Table 1 provides the descriptive statistics of the sample at the baseline. About 63% of the respondents were aged 60–69 and about 32% were aged 70–79. Only about 5% of respondents were older than age 80 in the baseline survey. About 51% of the respondents were male, and 81% had a married/cohabitating partner at baseline. Most of the respondents had a low level of education. About 43% of them were living in urban areas. The respondents had 3.4 children on average. Regarding the sex composition of children, 18% of respondents had sons only, and 7% had daughters only. About 75% of respondents had both sons and daughters. About 58% of respondents did not live with their children in 2010. About 65% of older adults did not receive economic support, 17% received it from all children and 19% received it from some children in 2010. About 61% did not receive instrumental support, 15% received it from all children, and 24% received it from some children. At baseline, the mean score of IEC was around 4.07 and COV was about 0.06, giving a general picture of rather close and even intergenerational relationship in multi-child families.

Table 1.

Descriptive Statistics of Sample at the Baseline.

	n (%)	Mean (SD)
Characteristics of older parents
Age group
Age 61–69	2200 (63.33)
Age 70–79	1108 (31.89)
Age 80+	166 (4.78)
Personal relative income		1.92 (1.14)
Sex
Male	1767 (50.86)
Female	1707 (49.14)
Marital status
Having a married/cohabitating partner	2803 (80.69)
Divorced/separate/widowed	671 (19.31)
Physical health
Unhealthy	1004 (28.90)
Fair	1393 (40.10)
Healthy	1077 (31.00)
Education
Primary or below	2737 (78.79)
Secondary	646 (18.60)
College or above	91 (2.62)
Residential area
Urban	1482 (42.66)
Rural	1992 (57.34)
Characteristics of multiple children
No. of children		3.43 (1.29)
Sex composition
Sons only	631 (18.16)
Daughters only	236 (6.79)
Mixed sex	2607 (75.04)
Having a co-resident child
Yes	1469 (42.29)
No	2005 (57.71)
Receiving support
Receiving economic support
No	2246 (64.65)
From all children	575 (16.55)
From some children	653 (18.80)
Receiving instrumental support
No	2134 (61.43)
From all children	512 (14.74)
From some children	828 (23.83)
Intergenerational emotional closeness
Mean score		4.07 (0.73)
Coefficient of variations		0.06 (0.12)

Note. Personal relative income is a scale, ranging from 0 to 5; regarding education, primary or below = primary school, semi-illiteracy, or illiteracy, secondary = junior or senior middle school, college or above = junior college, college, master’s degree, and doctoral degree.

Trajectory Patterns of Intergenerational Emotional Closeness

Growth mixture modelling classified older adults into different groups based on the growth trajectories of the within-family mean score and COV of IEC. Table 2 provides detailed information comparing different models with two to five classes. From a two-class solution to a five-class solution, AIC and BIC gradually decreased, whereas the entropy was highest under the two-class solution (entropy = 0.95). Especially, the entropy of a three-class solution dropped to 0.69, making it an undesirable solution. The LMR-LRT test showed that the two-class solution was significantly better than the one class (p < .001), while the test of three-class, four-class or five-class solution did not reach statistical significance. In addition, models with four or five classes had identified classes with less than 5% of the sample. We also further examined the theoretical meanings of each class in a four-class and five-class solutions. The trajectory patterns of a four-class solution and five-class solution, particularly patterns of small classes, are difficult to interpret theoretically, while the patterns of larger classes share much similarity with those of a two-class solution. From both statistical and theoretical perspective, a two-class solution was selected.

Table 2.

Model Fit for the Optimal Number of Classes in the GMM.

Class No.	AIC	BIC	Entropy	LMR-LRT test (p value)	Class distribution (%)
2	3153.01	3276.07	0.95	2 versus1: 2215.86 (p < .001)	7%; 93
3	2977.26	3131.09	0.69	3 versus 2: 185.74 (p = .283)	6%; 75%; 19
4	1975.88	2160.48	0.89	4 versus 3: 457.67 (p = .121)	2%; 5%; 8%; 85
5	1713.23	1928.58	0.90	5 versus 4: 272.66 (p = .595)	0.5%; 7%; 6%; 2%; 84

We labeled the two classes “increasingly tight-knit” and “persistently collectively ambivalent”. Figure 2 shows the trajectories of the family mean score and COV of IEC for the two classes. The first class, “increasingly tight-knit”, had much higher within-family mean scores for IEC and very low within-family COV at baseline. Over the 8 years’ observation, the mean score of this class increased at a rate of 0.045 (p < .001), whereas COV also increased but at a very low rate of 0.002 (p = .011). Older adults in this class maintained increasingly close relationships among all children equally over the observation period. The second class, “persistently collectively ambivalent”, had much lower within-family mean scores for emotional closeness and higher within-family COV at baseline. Over time, the mean score decreased (slope = −0.050, p = .053) and the COV increased (slope = 0.021, p = .041). In this class, the parent-child relationship was, on average, not very close and highly differentiated at baseline. Over time, the average relationship quality with all children got worse while became more differentiated within the family throughout the study period. Older parents were probably emotionally close to only one child or a few, whereas distant from all of the others. About 93% of respondents belonged to the “increasingly tight-knit” class, and 7% were in the “persistently collectively ambivalent” class.

Figure 2.

The trajectories of the two-class model of IEC. Note: IEC = intergenerational emotional closeness; COV = coefficient of variation.

Factors Associated with the Trajectory Patterns of Intergenerational Emotional Closeness

A logistic regression was conducted to examine the factors associated with the latent class membership. Results are presented in Table 3. Older parents’ marital status, health status, and receipt of economic and instrumental support were all significant factors. Those who had a married/cohabiting partner were less likely to belong to the “persistently collectively ambivalent” class (OR = 0.584, p = .005). In other words, respondents who had no partner were more likely to experience the “persistently collectively ambivalent” trajectory. Compared to older adults who were in fair health condition, those who reported “unhealthy” at baseline were also more likely to belong to the “persistently collectively ambivalent” class as well (unhealthy, OR = 1.458, p = .046). Regardless of the types of support, those who received support from some children were more likely to belong to the “persistently collectively ambivalent” class (economic support from some children, OR = 1.740, p = .002; instrumental support from some children, OR = 1.485, p = .030). However, older adults who received support from all children were more likely to have the “increasingly tight-knit” trajectories.

Table 3.

Analyses on the Factors Associated With the Class Membership (Ref = Class1, “Increasingly Tight-Knit”), Mean of IEC and COV of IEC.

Variable	Class membership		Mean of IEC		COV of IEC
Variable	OR^a	SE	Coef.^b	SE	Coef.^b	SE
Gender (Ref. = Female)
Male	1.26	0.20
Education (Ref. = Primary or below)
Secondary	1.13	0.27
College or above	0.62	0.44
Residential area (Ref. = Rural)
Urban	0.88	0.16
No. of children	0.96	0.09
Age gap of children	1.01	0.03
Sex composition (Ref. = Mixed sex)
Sons only	1.24	0.28
Daughters only	0.56	0.24
Age group (Ref. = age 60–69)
Age 70–79	1.37	0.27	−0.04*	0.02	0.01**	0.003
Age 80+	2.59**	0.77	−0.07*	0.03	0.02***	0.01
Personal relative income	0.80**	0.06	0.002	0.001	−0.001	0.001
Co-residence with children (Ref. = No)
Yes	0.94	0.17	0.01	0.02	0.001	0.003
Marital status (Ref.=Divorced/separate/widowed)
Having a married/cohabiting partner	0.58**	0.11	0.08***	0.02	−0.02***	0.003
Health status (Ref. = Fair)
Unhealthy	1.46*	0.28	−0.05**	0.02	0.01***	0.003
Healthy	1.05	0.21	0.08***	0.02	0.001	0.002
Receiving economic support (Ref. = No)
All children	0.41**	0.13	0.17***	0.02	−0.01***	0.002
Some children	1.74**	0.32	0.03***	0.01	0.02***	0.003
Receiving instrumental support (Ref. = No)
All children	0.49^†	0.18	0.17***	0.02	−0.02***	0.002
Some children	1.49*	0.27	0.01***	0.003	0.01**	0.004

^aPresented in the column are odds ratio of the time-invariant variables and baseline time-varying variables associated with the class memberships.

^bPresented in the column are unstandardized regression coefficients of the time-varying variables associated with the IEC mean score and COV of the corresponding time points.

^†p < .1, *p < .05, **p < .01, ***p < .001.

Table 3 also shows the associations of different time-varying independent variables with the mean score and COV of IEC. It was found that these variables were also significantly associated with the mean score and COV of IEC. Having a married/cohabiting partner, receiving economic or instrumental support from all children were associated with a higher mean score and lower COV of IEC. Having a good health status was associated with a higher mean without related to the COV of IEC, while having a bad health status was associated with a lower mean and a higher COV of IEC. Receiving economic or instrumental support from some children had a positive association with the mean and COV.

As for other control variables, it was found that older adults who were aged 80 or older, and had lower personal relative income were likely to have the “persistently collectively ambivalent” trajectories. However, sex, education level, and the residential area of the respondents were not significantly associated with the class membership. Other factors reflecting characteristics of multiple children (including number of children, age gap of children, sex composition of children, and whether co-residing with children) were also non-significant in their associations with the class membership.

Robustness Analysis

We first examined attrition of the sample through a logistic regression on who remained in the final analytic sample. It was found that older adults who were aged 60–69, had fair or good health, a married/cohabiting partner, and higher education were more likely to remain the final analytic sample. Consequently, our results may slightly overestimate the proportion of the “increasingly tight-knit” group and underestimate the size of the “persistently collectively ambivalent” group. We further tested that whether older adults in rural and urban areas would demonstrate different patterns of IEC trajectories by conducting GMM on the rural and urban samples separately. Both rural and urban samples exhibited a best solution of two classes, mirroring patterns identified in the combined sample. Additionally, we also examined the characteristic of children with more focus on the sex composition. Two alternative measurements—five categories (all sons, all daughters, more sons than daughters, more daughters than sons, same number of sons and daughters) and three categories (more sons than daughters, more daughters than sons, same number of sons and daughters)—were tested but yielded non-significant results.

Discussion

In this study, we identified trajectories of IEC in Chinese multi-child families over an 8 year observation period. We took into account multiple parent-child dyads in a family as well as their collectiveness and within-family differentiation. Specifically, we looked at two dimensions of IEC: within-family average and within-family differentiation. In addition, we also investigated potential factors associated with trajectory memberships. Our analyses showed two trajectory patterns of IEC—increasingly tight-knit (93%) and persistently collectively ambivalent (7%). Among Chinese multi-child families, the majority of older parents experienced the former trajectory. We also found older parents’ marital and health status, as well as receiving economic/instrumental support from all children, are salient for differentiating and locating trajectory memberships between the two IEC trajectory patterns.

As hypothesized, most Chinese older parents experienced increasingly close relationships with modest within-family differentiation among multiple children over time. This trajectory of IEC, named as “increasingly tight-knit”, accounted for more than 90% of sampled Chinese multi-child families. Different from what most Western studies concluded as stability of parent-child relationships in late life (Gilligan et al., 2022; Hogerbrugge & Silverstein, 2015; Schenk & Dykstra, 2012; Suitor et al., 2013), we observed an overall upward trend of collective IEC in the increasingly tight-knit families over the 8-year period in the Chinese context. The prevalence of such families aligns with the deeply rooted Chinese cultural value expressed in the proverb “Jia He Wan Shi Xing (家和万事兴),” meaning that a harmonious family cultivates prosperity. Chinese families cannot emphasize family solidarity and cohesion more, especially harmonious relationships across generations (Chen & Zhou, 2021; Chuang, 2005). This is consistent with Li and Zhang’s (2023) study showing Chinese families’ strong preference for all-round harmonious relationships across multiple children. To some extent, our study also echoed with Lin and Chen’s study (2018) which identified that approximately 30% of Chinese older parents experienced improvement of emotional cohesion with multiple children over time. As suggested by the socioemotional selectivity theory (Carstensen, 1992), older adults tend to increasingly prioritize meaningful personal relationships such as relationships with their children as well as avoid relationship stress and tension in late life. In China, high level of emotional closeness with all children at the beginning is likely to create a virtuous circle of family solidarity across generations to the end of older parents’ life.

In contrast, what warrants more attention is the minority group of “persistently collectively ambivalent” families. Congruent with the perspective of collective ambivalence, in these families, older parents tended to have close affections with some specific children but becoming very estranged from the others. Similar to what Gilligan et al. (2022) found in the Western families, the estranged and poor intergenerational relationships tended to continue for years. Such differentiated situation of IEC at the family level worsened gradually, although marginally, over time in Chinese families. Compared to their counterparts experiencing increasingly tight-knit trajectory of IEC, the persistently collectively ambivalent families seemed to deviate from the mainstream of Chinese family dynamics in contemporary multi-child aging families. For one thing, the emergence of these families implies the potential coexistence of pluralistic family values in Chinese society (Chen & Zhou, 2021; Ji, 2017), where individualistic and collectivistic family cultures intertwine with each other. Some adults may place more emphases on self-interest and independence hence estranged from older parents. For another, having persistently collectively ambivalent IEC with children, parents in these families are likely to be more vulnerable to life transitions and risks in old age. It necessitates more academic and practical attentions to these older adults in terms of elderly care and support.

As suggested by the life course perspective, we did find that older parents’ life events and experiences mattered for the trajectory of their relationships with children over time. Specifically, we found older parents’ widowhood/separation/divorce would give rise to the undesirable IEC trajectory (i.e., persistently collectively ambivalent) over time in multi-child families. Marital dissolution may not only have a negative impact on the well-being of older adults themselves, but also would influence IEC with their children. As many western societies have witnessed the “gray divorce revolution”, China has also seen the rising divorce rate among older couples (China Daily, 2015). Given such a demographic trend, it is possible that the proportion of older adults who have “persistently collectively ambivalent” IEC trajectories would increase.

Older parents with better health and receiving support from all children were more likely to experience increasingly tight-knit IEC over time. Consistent with the previous literature, parents’ poor health was strongly associated with parents’ ambivalence towards children and deterioration in parent-child relationships (Kaufman & Uhlenberg, 1998; Kiecolt et al., 2011). Due to the linked lives between older parents and children, disadvantages in health may enhance older parents’ dependence on their children, triggering tension and conflict in parent-child relationships (Ha & Ingersoll-Dayton, 2008; Silverstein et al., 2010). Besides, regarding the receipt of support, we also found that when older adults received support from some children, they were more likely to have “persistently collectively ambivalent” IEC trajectories. It was not sure whether it was the collectively ambivalent IEC brought about the variations of support provided by children or the variations of support from children led to the collectively ambivalent IEC within family. Nonetheless, the finding, to some extent, can still suggest that when all children can provide support to older parents, more harmonious intergenerational relationships can be expected.

The present study is not without limitations. First, due to data availability, IEC was derived from a single question —how was the relationship between you and your child on a 5-point scale. A 5-point scale may not be able to thoroughly capture the variation of IEC, and a single question may not reflect different aspects of emotional closeness (Duflos et al., 2022). Although this measurement has been applied and validated in many studies (Chen & Zhou, 2021; Lin & Chen, 2018; Zhang & Silverstein, 2022), more solid measurement and scales should be explored and adopted in the future studies. Second, the two indicators of IEC (i.e., within-family mean level and differentiation) were measured from only the older parents’ reports of emotional closeness with each child. The intergenerational stake phenomenon postulates that older parents’ reports tend to be positively biased compared with children’s reports regarding emotional closeness (Giarrusso et al., 2004; Lendon et al., 2014). This could also be a reason why we found an overwhelming majority of older parents following increasingly tight-knit trajectory of IEC in Chinese multi-child families. Future studies should compare the discrepancy in the reports from two generations when identifying IEC trajectories. Third, the measurement of receipt of economic/instrumental support did not provide any insights on the amount or frequency of support from children. Future studies can further explore this issue. Fourth, given that the CFPS is a household-based survey with attrition over time, the findings may not fully represent all older adults in China. Finally, the results on the factors associated with the IEC trajectories should be interpreted with caution, as they are not indicative of causal relationships.

Despite this limitation, our study contributes to the existing literature by investigating longitudinal trajectories of IEC and the associated factors in Chinese multi-child families in late life. It captured a comprehensive yet parsimonious representation of parent-child interactions over time taking a family as a whole. It theoretically demonstrated the salience of applying WFD approaches and the life course perspective to understand multiple parent-child relationships within family and substantively highlighted the importance of cultural sensitiveness when studying IEC. In conclusion, our findings told a story of emotional cohesion between older parents and multiple children over the 8-year period. The distinct trajectories were associated with older parents’ health and marital status, and their receipt of economic and instrumental support from the offspring.

Since 2021, couples in China will be allowed to have up to three children. The latest relaxation of family planning by the Chinese government puts multi-child families under the spotlight. Understanding the complexities and dynamics of IEC between older parents and their children in the context of multi-child families in contemporary China could contribute to policy making in relation to elder care and family education in the future. Specifically, family and community services aimed at promoting family solidarity and elderly care should pay particular attention to families experiencing persistently collectively ambivalent trajectory of IEC, where older parents could be most vulnerable to detrimental life events in old age. Older adults who encounter negative changes in marital and health status as well as receive support from none or only some of their children should be provided with supportive services for their psychological well-being. Family education should target the importance of balancing affectual intimacy with and reducing parents’ differential treatment toward multiple children in the long run.

Supplemental Material

Supplemental Material - Trajectories of Intergenerational Emotional Closeness in Multi-Child Aging Families in China

Supplemental Material for Trajectories of Intergenerational Emotional Closeness in Multi-Child Aging Families in China by Jia Chen and Mengni Chen in Research on Aging

Footnotes

Acknowledgments

The authors thank the public dataset – China Family Panel Studies (CFPS), designed by Peking University (PU) research team, supported by PU 985 funds and implemented by the Institute of Social Science Survey (ISSS) PU.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Social Science Fund of China (grant number 19CSH034); and “Chenguang Program” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission (grant number 19CG47).

ORCID iD

Jia Chen

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Dr Jia Chen is an associate professor of the Department of Social Work, Shanghai University. Her research interests include intergenerational relationship, aging and family care, etc.

Dr. Mengni Chen is an assistant professor at Copenhagen University. Her interests include marriage and family, population dynamics and economic development, and social policies.

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