The mediating effect of neighbor relationship between community social support and elderly’s subjective well-being in China

Abstract

This study aimed to investigate the relationship between community social support, neighbor relationship, and the subjective well-being of the elderly in China. Structural equation modeling was used to test the hypothesized model based on a sample of 2732 senior adults from 2010 Chinese General Social Survey. Results showed that community social support could indirectly affect the subjective well-being of the elderly through the complete mediating effect of neighbor relationship, although there was no significant correlation between community social support and the elderly’s subjective well-being. Implications for theory, social work, and social policy were discussed.

Keywords

China community social support elderly neighbor relationship subjective well-being

Introduction

Population aging is a global trend that likely occurs throughout the 21st century (Lutz et al., 2008). The number of elderly people aged 60 years and above is predicted to increase from 901 million to 1.4 billion or by 56 percent between 2015 and 2030 (United Nations, 2015). In China, the largest number of elderly people and the fastest growth rate of the elderly population worldwide have been documented (Zhong, 2014). With the continuous acceleration of global aging, the well-being of the elderly has been extensively investigated (Batterham et al., 2012; Vanhoutte, 2014). Subjective well-being (SWB) is an important indicator of the psychological conditions and life satisfaction of the elderly, and this parameter reflects the personal evaluation of affection and cognition on their life quality (Nguyen et al., 2016; Peterson et al., 2014). However, the declining SWB with aging has emerged as a problem (Gilles et al., 2015). As such, the SWB of the elderly is possibly lower than that of other social groups. Therefore, the SWB of the elderly should be investigated to enhance the quality of life of the elderly and attain active aging.

Social support is an important predictor of life satisfaction throughout an individual’s life (Siedlecki et al., 2014). The relationship between social support and SWB has been examined by a number of researches (Mason, 2016; Shelton et al., 2016; Vaznonienė and Vaznonis, 2014). Main effect model (Cohen, 1988; Fried and Tiegs, 1993) and stress-buffering effect model (Cohen and Hoberman, 1983; Cohen and McKay, 1984) can be used to explain the effect of social support on SWB. The main effect model suggests that the enhanced social support in any case can generally increase the level of one’s mental health, whereas the stress-buffering model states that social support can mitigate the influence of stress events on an individual’s mental health when people face stress events. These theoretical models indicate that social support can help individuals produce favorable psychological outcomes and thus enhance one’s SWB. Empirical research has also confirmed that social support can effectively improve the SWB of the elderly (Chen and Feeley, 2014; Wang, 2016). For example, Pardal et al. (2013) examined 46 samples of institutionalized elderly individuals aged from 65 to 95 years and demonstrated that the perception of social support is related to the SWB of the elderly.

Social support is a comprehensive concept that can be divided into various types based on source: family, peers, and community social support; each type plays different roles on one’s SWB (House, 1981). Community social support, as an important aspect of social support, has direct effects on the SWB of the elderly (Chen and Feeley, 2014; Pardal et al., 2013). Some studies have also investigated the indirect effects of social support on the SWB of the elderly through other mediating effects (Lee et al., 2016; Tu and Yang, 2016).

Social relationship greatly affects SWB (Gleibs et al., 2013; Lincoln and Chae, 2012; Sirgy, 2012). With aging, the physiological functions of the elderly likely degenerate, and the range of social activities can be reduced. Therefore, nearby communities are one of the main social life areas for the elderly, and neighbor relationship constitutes one of the most important parts of social relationships for the elderly and essential predictor of SWB (Lau and Machizawa, 2012; Taniguchi and Potter, 2016).

The effect of neighborhood relationship on the SWB of the elderly has been confirmed by recent studies (Greenfield et al., 2012; Thomas and Blanchard, 2009). Supportive relationships with neighbors can facilitate communication and interaction among community neighbors; as a result, these relationships can possibly and significantly improve the well-being of the elderly (Cramm et al., 2013; Howley et al., 2015; Reyes, 2014). In particular, high-quality relationships with neighbors positively affect different SWB indicators (Dassopoulos et al., 2012; Robinette et al., 2013). On one hand, supportive neighbor relationship can mitigate negative psychological outcomes such as mental illness (Kloos and Townley, 2011). For example, an American study determined that social relationships with neighbors reduce the incidence of depression and mental discomfort (Kim, 2010). On the other hand, high-quality neighbor relationship can improve the positive aspects of SWB, which is confirmed by Taniguchi and Potter (2016) who illustrated that high-quality relationships with neighbors significantly improve people’s life satisfaction and happiness after other factors are controlled. Thus, better neighbor relationship can help enhance people’s SWB (Greenfield, 2014; Jang et al., 2016; Morita et al., 2010).

In sum, although many studies have suggested that community social support significantly affects the well-being of elderly people, to the best of our knowledge, studies have yet to investigate whether neighbor relationships can play a mediating role in regulating the relationship between community social support and SWB. Based on convoy model, which illustrated the relationship among social relations, social support, and SWB, this study aimed to establish an integrated theoretical framework that explained the association among community social support, neighbor relationship, and SWB of the elderly so as to bridge the knowledge gaps in existing literature. In particular, this study attempted to examine (1) whether community social support can directly affect the SWB of the elderly in the context of Mainland China and (2) whether community social support can indirectly influence the SWB of the elderly through the intermediary role of neighborhood relationships. The conceptual framework of this study is shown in Figure 1, and two research hypotheses were presented as follows:

Figure 1.

Conceptual framework.

H1: More community social support would predict increased level of SWB of elderly people.

H2: More community social support would predict better neighbor relationships, which, in turn, would predict higher level of SWB of elderly people.

Method

Data

The data used in this study were from 2010 Chinese General Social Survey (CGSS), which is a national, comprehensive, continuous, large-scale social survey project carried out by Renmin University of China. CGSS used a multi-stage stratified random sampling method to obtain a nationally representative sample all over Mainland China, involving residents in 31 provinces, autonomous regions, and direct municipality (Hong Kong SAR, Macao SAR, and Taiwan were excluded). In the first stage, it drew the samples from 100 counties and 40 streets from five big cities including Beijing, Shanghai, Tianjin, Guangzhou, and Shenzhen to serve as the primary sampling units (PSUs = 140). In the second stage, four neighborhood committees or village committees from each selected county in the PSU were randomly selected; meanwhile, two neighborhood committees were selected from the 40 selected streets in Beijing, Shanghai, Tianjin, Guangzhou, and Shenzhen, so 480 were selected as the second sampling units (SSUs = 480). Finally, in each sampled SSU, it selected 25 households, and then one person was randomly selected from each family to be the survey respondent. Therefore, the total sample collected in the 2010 CGSS included 12,000 people all over Mainland China. In this study, we selected the elderly people who are aged 60 years and older, ultimately giving a final sample of 2732 used for data analysis. The average age of the elderly in the sample is 71 years (standard deviation (SD) = 64.0), 51.2 percent were men, and about 71.1 percent of them were educated. Full descriptive statistics of the sample are presented in Table 1.

Table 1.

Socio-demographic characteristics (N = 2732).

	Frequency (N)	%
Gender
Male	1398	51.20
Female	1334	48.80
Age (years)	Mean = 71	SD = 64.00
Education level
Uneducated	789	28.90
Private school	81	3.00
Primary school	850	31.10
Junior high school	506	18.50
Senior high school/vocational high school	142	5.10
Technical secondary/technical high school	182	6.70
College/university and above	173	6.31
Physical health
Very unhealthy	213	7.80
Relatively unhealthy	729	26.70
General	799	29.20
Relatively healthy	745	27.30
Very healthy	242	8.90
Household register
Agriculture account	1308	47.90
Non-agricultural account	1288	47.10
Others	135	4.90
Self-perceived social class (from 1 to 10)	Mean = 4.03	SD = 1.88

SD: standard deviation.

Measures

Dependent variable

In 2010 CGSS, SWB was measured by the item “In general, how happy do you think you are?” with possible responses of “1 = very unhappy, 2 = unhappy, 3 = between happy and unhappy, 4 = happy, 5 = very happy.” The measure of SWB in this study is reasonable, which had been certified by a previous study (Veenhoven, 1996). Higher scores indicate higher levels of SWB.

Mediating variables

Neighbor relationship was measured by asking the question in the 2010 CGSS: “How much do you agree with the following two statements within the range of a kilometer (about 15 minutes’ walk) around your home?” The situations are “The neighborhoods around me care about each other” and “My neighbors are willing to help me when I am in need.” The options are divided into five dimensions, including 1 = completely disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree, with higher scores associated with higher quality of neighbor relationship.

Independent variable

Since most older people had retired and were rarely able to receive support from colleagues, the help from colleagues in this study can be negligible. Thus, in this study, community social support was measured by the frequency of receiving support from neighborhoods and friends in 2010 CGSS by asking the question “In the past year, how often do your friends, colleagues and neighbors do the following things for you when you are in need?” with three items including (1) Listen to your problems and the things that you are concerned about, (2) Provide financial support, and (3) Do some family chores (e.g. housework, take care of children). Each of the items is accompanied with four options, and the value of each option is from 1 to 5 (1 = never, 2 = rarely, 3 = sometimes, 4 = often, and 5 = always). Higher scores indicate higher community social support.

Control variables

Several control variables are taken into account, including gender (0 = male, 1 = female), age, educational level (from 0 = uneducated to 6 = college/university and above), physical health (0 = very unhealthy, 1 = relatively unhealthy, 2 = general, 3 = relatively healthy, 4 = very healthy), household register (0 = agriculture account, 1 = non-agricultural account), and self-perceived social class (from 0 = lowest to 9 = highest).

Data analysis

Structural equation modeling (SEM) was adopted with Amos 21.0 in aim of testing the hypothesized model. The analyses consisted of two stages (Anderson and Gerbing, 1988). First, we tested the measurement model through confirmatory factor analysis (CFA). Then in the second stage, structural model needs to be verified to examine whether the research hypotheses could be empirically supported. Diverse indices were applied to evaluate the reliability of fit in the structural equation model, including chi-square (χ²), comparative fit index (CFI), and root mean square error of approximation (RMSEA). (1) χ², where an associated non-significant probability value stands for a closer fit of the hypothesized model to the perfection (Bollen, 1989). However, because of the sensitivity to sample size, it is not uncommon for a well-fit hypothetic model to produce a significant χ² if the sample size is large (Byrne, 2001); (2) the CFI, where values above 0.90 indicate a good model fit (Bentler, 1990); and (3) the RMSEA, where values less than 0.05 equal a “close fit,” values between 0.05 and 0.08 suggest a “reasonable fit,” and values above 0.1 represent a “poor fit” (Kline, 2005).

Result

Test of measurement model

The measurement model validation must be carried out first before the structural model validation. The data analysis results demonstrated that the measurement model has a good fit index (χ² = 27.410, degree of freedom (df) = 7, p < 0.001, CFI = 0.990, RMSEA = 0.033). Although the χ² value is significant, the other two indicators (CFI > 0.9, RMSEA < 0.08) indicate that the measurement model is satisfying. Therefore, all the observed variables in this model have significant loadings on the latent variable. The standard factor loading of all the observed variables that make up the latent variable is between 0.595 and 0.938 (details are presented in Table 2), and the accepted factor loading is above 0.3 (Agnew, 1991). Thus, the selected indicators effectively represent the intrinsic structure of the latent variable in a statistically reliable manner.

Table 2.

Standardized factor loadings of observed variables on latent construct.

Latent construct	Observed variable	Factor loading
CSS	Listen to your problems and the things that you are concerned about	0.595
	Provide financial support	0.873
	Do some family chores	0.861
NR	The neighborhoods around me care about each other	0.938
NR	My neighbors are willing to help me when I am in need	0.859

CSS: community social support; NR: neighbor relationship.

Test of structural model

In the case of good fitting of the measurement model, we gather all the control variables, latent variables, and their observed variables into the structural equation model to verify the structural model and test the conceptual framework proposed in the research hypothesis. The test of the structural model yielded that it provided a good fit to the data. Although χ² was significant (χ² = 110.958, df = 31, p < 0.001), considering the sample size (N = 2732) in this study is large, other goodness-of-fit indices demonstrated that the hypothetical structural model has a good adaptability with CFI (0.977) higher than 0.90 and RMSEA (0.031) lower than 0.05. A total of 17 percent of the elderly’s SWB can be interpreted by this model.

The analysis results showed that there was no significant correlation between community social support and SWB of the elderly (p > 0.05), suggesting that community social support cannot directly affect the SWB of the elderly after controlling for other factors. Nevertheless, the increase in community social support would lead to the improvement of the neighbor relationship (β = 0.162, p < 0.001), which, in turn, would further affect the SWB of the elderly (β = 0.111, p < 0.01). This suggests that neighbor relationships would have a complete mediating effect in the relationship between community social support and SWB of the elderly. In other words, community social support can only indirectly affect the elderly’s SWB through the intermediary role of neighbor relationship after controlling for other variables. Standardized solutions for the structural model of neighbor relationship, community social support, and elderly’s SWB are shown in Figure 2 where we only present the associated paths for independent variable, dependent variables, and mediating variables, with control variables omitted for simplicity.

Figure 2.

Standardized solutions for the structural model of community social support, neighbor relationships, and elderly’s subjective well-being.

Among all the control variables involved in this study, gender (β = 0.045, p < 0.05), age (β = −0.052, p < 0.01), educational level (β = 0.044, p < 0.05), physical health (β = 0.162, p < 0.001), and self-perceived social class (β = 0.317, p < 0.001) would significantly affect the SWB of the elderly. The analysis results show that the female’s SWB is higher than the male’s. The younger the age, the higher the level of SWB. In addition, higher levels of education, better physical health, and higher self-perceived social class would predict higher SWB. In contrast, only the household registration did not show its impact on the older adults’ SWB.

Discussion and conclusion

In this study, data from the 2010 CGSS are used to investigate whether and how community social support affects the SWB of the elderly in the Chinese context and to determine whether neighbor relationship plays an intermediary role in the correlation between community social support and elderly’s SWB. Our results reveal that neighbor relationship plays a complete mediating effect in the influence mechanism on community social support and the SWB of the elderly. Our research findings are elaborated in the succeeding sections. The implications for theory and practice are also discussed.

One of the main findings of this study is that community social support does not directly affect the SWB of the elderly. This observation fails to confirm our first hypothesis. This result can be explained by the convoy model of social relationship. Arguably, the convoy model of social relationship (Antonucci and Akiyama, 1995; Kahn and Antonucci, 1980) demonstrates that an individual’s social relationship and social support can influence people’s SWB. Kahn and Antonucci (1980) indicated that individuals’ social relationships can be represented by three sets of concentric circles. The people in the innermost circle are called close social partners, who provide the most social support for a person, whereas people in the middle and outermost circles are called peripheral society partners, who maintain a certain degree of interaction with individuals (Antonucci and Akiyama, 1987). Each level of social relationship possibly affects people’s SWB, but the effects of social relationship and social support on the SWB of the elderly vary in different circles. Convoy model suggests that older adults’ inner circle largely comprise family members, while the outer circle consists of friends and distant relatives (Antonucci and Akiyama, 1987). With aging, the main family members elicit the strongest influence on the elderly, whereas the influence of outer social networks on the elderly decreases (Ajrouch et al., 2001; English and Carstensen, 2014; Fung et al., 2008). According to convoy model, community social support is observed in an individual’s outer circle, and the support provided by peripheral society partners is relatively inadequate. This observation may be the main factor accounted for the non-significant relationship of the community social support and elderly’s SWB in this study.

However, controversial results still exist in empirical studies. On one hand, previous studies showed that family social support appears to have the most significant effect on the well-being of the elderly among all types of social support resources (Liu, 2014; Mohamad et al., 2016; Nguyen et al., 2016). Community-based support may not significantly influence the SWB of the elderly (Peng et al., 2015). On the other hand, some empirical studies demonstrated that the social support from external circle is even more important than that in the inner circle to some extent. For example, individuals who receive more neighbors and community support more likely experience better mental health outcomes than individuals who receive more family support (Litwin and Shiovitz-Ezra, 2006). Li and Zhang (2015) also examined the importance of the social relationship in the outer circle by determining that a friend-focused social network contributes more to one’s health condition than a family-focused social network does. Therefore, future research may focus on this debate and further investigate the different effects of social relationship represented by each circle on the SWB of the elderly.

Another finding in this study is that community social support can indirectly influence the mental health of the elderly through the mediating effect of neighbor relationship. This result validates our second hypothesis, which is consistent with many existing studies (Dassopoulos et al., 2012; Jang et al., 2016). However, other studies have confirmed that the quality of neighborhood relationship is unrelated to the SWB of the elderly (Greenfield and Reyes, 2014; Reyes, 2014). This controversy may be due to the social and cultural differences between China and Western countries (Ram, 2010; Steele and Lynch, 2013; Yu et al., 2016). In Western countries, individual factors, such as personal achievement, self-concept, and self-evaluation, are considered relevant aspects of SWB (Kitayama and Markus, 2000; Lu and Gilmour, 2004). On the contrary, collectivism, such as harmonious social and interpersonal relationships, is considered the basis of happiness in Eastern countries (Uchida et al., 2004; Yan et al., 2016). Thus, neighborhood relationship significantly affects the SWB of the elderly in the cultural background of collectivism in China. However, whether this influence exists in different cultural contexts of Western countries should be examined in cross-cultural studies.

Our results can largely contribute to theoretical development, policy formulation, and social work practice. This research mostly explores the effect of overall social support on SWB, whereas studies on different social support dimensions are insufficient. Nevertheless, our results reveal that community social support cannot directly affect the SWB of elderly people, but it can indirectly influence the SWB of the elderly through the mediating effect of neighbor relationship. This study not only provides empirical evidence supporting main effect model in the context of Mainland China but also enriches the influence mechanism of social support on SWB by validating an integrated theoretical model, which is conducive to the development of social support theory. Moreover, this study validates the rationality of the convoy model by demonstrating that the community social support, as an outer circle support, does not significantly affect the SWB of the elderly in the context of Mainland China.

Several implications of this study on community policy are observed. The function of social support in the SWB of the elderly is unavailable if no intermediary effect of the neighbor relationships exists. Hence, neighbor relationship is essential for elderly’s SWB. Policy makers should consider the importance of neighbor relationship so as to improve the SWB of the elderly. In particular, social policy development should deal with neighbor relationship-related factors, such as community environment, community policing, living arrangements, and other aspects which influence the SWB of the elderly (Secondi et al., 2013; Zhang and Zhang, 2017) and thus encourage the elderly to form healthy relationships in their neighborhoods and a sense of community.

Our results could also provide a basis for gerontological social work and community social work practice. Community social support cannot be ignored in social work practice because it positively affects the SWB of the elderly, even if this mechanism is indirect. Social workers can then serve as a bridge between the elderly and social resources to help the elderly obtain community social support. Social workers can contribute to the improvement of the SWB of the elderly by organizing community activities to enhance communication and interaction among community residents, which consequently helps the elderly support one another and form healthy neighborhood relationships.

Overall, this study establishes an integrated conceptual framework to analyze the effect of community social support on the SWB of the elderly through the intermediary effect of neighborhood relations in the context of Mainland China. The innovative findings of this study have significant contribution to current knowledge. However, this study is also limited by several aspects that deserve mention. First, our data were collected with a cross-sectional method, but this method could not examine the causality among community social support, neighbor relationship, and elderly’s SWB. Therefore, our results should be further validated by longitudinal studies. Moreover, we used a second-hand data to conduct analysis, which was associated with the inevitable drawback that the items selected to measure the variables cannot really reflect the meaning of the concepts. Furthermore, this study only verified the influence of the single dimension of social support (i.e. community social support) on the SWB of the elderly and the mediating effect of neighbor relationship. Whether the SWB of the elderly can be influenced by other dimensions of social support, such as family social support and peer social support, and other mediating roles should be further examined.

Footnotes

Acknowledgements

Data used in this study were from 2010 Chinese General Social Survey (CGSS), originally collected by Renmin University of China. The authors appreciate the assistance in providing data of 2010 CGSS.

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.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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