Health & Place 27 (2014) 38–44

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Health & Place journal homepage: www.elsevier.com/locate/healthplace

A multilevel analysis of social capital and self-rated health: Evidence from China Tianguang Meng a, He Chen b,n a b

Department of Political Science, School of Social Sciences, Tsinghua University, Beijing 100084, China School of Public Health, Peking University, Beijing 100191, China

art ic l e i nf o

a b s t r a c t

Article history: Received 6 September 2013 Received in revised form 12 January 2014 Accepted 22 January 2014

We investigate relationship between social capital and self-rated health (SRH) in urban and rural China. Using a nationally representative data collected in 2005, we performed multilevel analyses. The social capital indicators include bonding trust, bridging trust, social participation and Chinese Communist Party membership. Results showed that only trust was beneficial for SRH in China. Bonding trust mainly promoted SRH at individual level and bridging trust mainly at county level. Moreover, the individuallevel bridging trust was only positively associated with SRH of urban residents, which mirrored the urban–rural dual structure in China. We also found a cross-level interaction effect of bonding trust in urban area. In a county with high level of bonding trust, high-bonding-trust individuals obtained more health benefit than others; in a county with low level of bonding trust, the situation was the opposite. & 2014 Elsevier Ltd. All rights reserved.

Keywords: Social capital Self-rated health Urban–rural difference Multilevel analysis China

1. Introduction During the last two decades, the relationship between social capital and health has been widely discussed in western countries (Kawachi and Berkman, 2000; Murayama et al., 2012a; Szreter and Woolcock, 2004). There is considerable evidence of an association between social capital and a series of health indicators, such as self-rated health (SRH) and mortality (Ferlander, 2007; Kawachi et al., 2004; Murayama et al., 2012a). However, most of these studies are conducted in western countries. We lack the knowledge about the impact of social capital on health in China, one of the representatives of Eastern culture (Norstrand and Xu, 2012; Yip et al., 2007). There is an ongoing debate about whether social capital is an individual attribute or a collective property. Following Bourdieu (1986), Portes (1998) argues that the greatest theoretical value of social capital lies at the individual level in terms of social network and social support. However, from the viewpoints of Coleman (1988, 1990) and Putnam (1993, 2000), social capital works at the contextual level through mechanisms such as informal social control and collective efficacy. There are rich empirical literature to support these two arguments and both sides have their points (Szreter and Woolcock, 2004). n Correspondence to: School of Public Health, Peking University, No.38 Xueyuan Road, Haidian District, Beijing 100191, China, Tel.: þ86 10 18311063835. E-mail addresses: [email protected] (T. Meng), [email protected] (H. Chen).

1353-8292/$ - see front matter & 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.healthplace.2014.01.009

As figured out by Lin (2001) and Kawachi et al. (2004), the individual and collective perspectives are not mutually exclusive and they may work at the same time. In recent years, the number of studies, which employ a multilevel analytical framework to investigate the relationship between social capital and SRH, has been rising (Engstrom et al., 2008; Murayama et al., 2012b; Snelgrove et al., 2009). Most studies indicate that social capital or some of the social capital indicators are positively correlated with SRH at both individual and contextual levels (Murayama et al., 2012b; Poortinga, 2006a, 2006c; Snelgrove et al., 2009; Sundquist and Yang, 2007). However, a handful of papers also find that the beneficial effect of social capital on SRH only functions at the individual level. Moreover, the results are very mixed for different dimensions of social capital. Take, for example, structural and cognitive social capital. Structural social capital refers to externally observable aspects of social organization and is characterized by behavioral manifestations of network connections or civic engagement, and the cognitive social capital refers to subjective attitudes such as trust and norms of reciprocity (Putnam et al., 1993; Murayama et al., 2012b). Some studies reported cognitive social capital (e.g. trust) to be protective for SRH at both individual and contextual levels (Poortinga, 2006c; Snelgrove et al., 2009; Subramanian et al., 2002) and others conclude the opposite (Murayama et al., 2012b; Poortinga, 2006b). Similar situation happens to structural social capital (Murayama et al., 2012b; Poortinga, 2006b, 2006c). Many factors might contribute to these inconsistent conclusions, for example, lacking uniform measurement scale of social capital, the variety in the levels of

T. Meng, H. Chen / Health & Place 27 (2014) 38–44

contextual social capital (Kawachi et al., 2004), and the extent to which the confounders are controlled (Engstrom et al., 2008). Furthermore, there is evidence of cross-level interaction effect between contextual- and individual-level social capital (Han et al., 2012; Poortinga, 2006b; Subramanian et al., 2002). The direction and strength of the association between individual-level social capital and SRH depend on the contextual social capital (Subramanian et al., 2002). Even though researchers such as Kawachi et al. (2004) and Subramanian et al. (2002) emphasize the importance of examining the cross-level interaction and their influence for health, only a handful of papers conducted such analyses during the last decade (Carpiano, 2007; Han et al., 2012; Kim and Kawachi, 2006; Mansyur et al., 2008; Poortinga, 2006b; Subramanian et al., 2002). The cross-level interaction effect of trust and social participation seem to be different. Living in a area with high level of trust, health-promoting effect of contextual social trust is significantly greater for high-trust individuals (Kim and Kawachi, 2006; Mansyur et al., 2008; Poortinga, 2006b; Subramanian et al., 2002). However, living in a area with high level of social participation, low-participation individual benefit more from the contextual social participation (Carpiano, 2007; Han et al., 2012; Kim and Kawachi, 2006; Mansyur et al., 2008). Empirical evidence regarding the relationship between social capital and self-rated health in China is very limited. With a comprehensive literature review, we only obtained 3 papers aimed to discuss such relationship among Chinese population (Sun et al., 2009; Wang et al., 2009; Yip et al., 2007). Using data collected in three rural counties of Shandong province, China, Yip et al. (2007) found that cognitive social capital was positively associated with SRH at the individual level, but not at the village level and structural social capital had little statistical relationship with SRH. Wang et al. (2009) also indicated a positive association between cognitive social capital and SRH among rural residents in China. In a study conducted in two Chinese cities, high level of neighborhood cohesion, reciprocity and social support tended to promote SRH and social participation and interpersonal relationship network had no such effect (Sun et al., 2009). Without a nationwide data, all above studies examined the association between social capital and SRH in either urban or rural area. Considering the urban–rural difference in the pattern of social capital (Xu et al., 2010), an urban–rural comparison may help us better understand the impact of social capital on SRH in China. Using a nationally representative data, we employed the multilevel analytical framework to study the relationship between social capital and self-rated health while adequately controlling for demographic, socioeconomic confounders at individual and contextual levels. We also take account of the cross-level interaction effect between contextual- and individual-level social capital in the analysis. This paper aims to answer two questions: (1) Does social capital enhance SRH in China? (2) Is there any difference in social capital's relationship with SRH between urban and rural areas in China?

2. Methods 2.1. Data source and study population We used two data sources in the analysis. First, the Chinese General Social Survey 2005 (CGSS 2005) data was collected in 2005 and it is one of the few nationally representative data sources covering both social capital and health information in China. Its target population was persons aged 18 years old and above. CGSS 2005 employed a four-stage stratified sampling scheme with unequal probabilities. Specifically, the survey sampled 125 countylevel administrative units from five major strata to serve as primary sampling units (PSUs). In each sampled PSU, 4 districts or townships

39

were selected as secondary sampling units (SSUs). In each sampled SSU, 2 neighborhood or village committees were selected as third sampling units (TSUs). And, in each sampled TSU, 10 households were selected (Survey Research Center at the Hong Kong University of Science & Technology, 2013). Finally, CGSS 2005 collected information from 10372 adults by face-to-face interview. And, 59% of respondents were from urban area, which is defined as the area under the jurisdiction of neighborhood committee, and 41% from rural area, which is defined as the area under the jurisdiction of village committee. During the whole survey, strict measures were carried out to guarantee data quality. For example, all the completed questionnaires were examined for 3 times and 30% of them were chosen randomly to check the answers by telephone interview. All the survey details have been described by Chinese General Social Survey Project at National Survey Research Center of Renmin University of China (NSRH-CGSS) (2009). CGSS 2005 provided all the variables in current models except gross domestic product (GDP) per capita for each county. Second, we obtained the information on GDP per capita from Department of Comprehensive Statistics and Department of Rural Survey of National Bureau of Statistics in China (2006). There are totally 10,372 adults nested in 125 county-level units and the number of adults in each unit is between 80 and 90. 2.2. Dependent variables Self-rated health was used to measure health outcome. SRH is a powerful and independent predictor of disability and mortality (Fayers and Sprangers, 2002; Idler and Benyamini, 1997). SRH is also one of the most frequently used health indicators in the studies on the relationship between social capital and health (Kawachi et al., 2004). During the survey, respondents were asked to assess their health for the last month using a six-point Likert scale (1 ¼extremely good, 2 ¼very good, 3 ¼good, 4¼ fair, 5 ¼ poor, 6 ¼very poor). Consistent with previous studies (Murayama et al., 2012b; Oksanen et al., 2008), we dichotomized SRH into “good” (response of 1, 2 or 3) and “poor” (response of 4, 5 or 6). 2.3. Independent variables 2.3.1. Social capital Although no consensus has been reached on how to define social capital, social networks, norms of reciprocity and trust are mentioned in most definitions (Ferlander, 2007). In this paper, we used trust, social participation and Chinese Communist Party (CCP) membership as measures of social capital. Trust was measured with the question “In daily social contacts not directly involving money, how many persons listed below can be trusted?” The response options were “the majority cannot be trusted, most cannot be trusted, half–half, most can be trusted, the majority can be trusted and not applicable (see details in Table 1). The option of not applicable was treated as missing values. Considering the long-standing urban–rural dual structure in China, we set different lists for urban and rural residents. Principal component analysis was then performed for factor extraction. Results indicated the existence of bonding trust and bridging trust. Bonding trust refers to the trust among people who are similar in terms of their social identity (e.g. family members and colleagues); bridging trust refers to the trust among people who are unlike in social identity (e.g. persons participating leisure or voluntary activities together). Finally, trust at individual level was measured with factor score; trust at county level was measured with average factor scores within each county unit. Higher trust score indicated higher trust in others. In line with previous Chinese studies (Norstrand and Xu, 2012; Yip et al., 2007), participation in social networks was measured

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T. Meng, H. Chen / Health & Place 27 (2014) 38–44

Table 1 Factor loadings on bonding and bridging trust. Urban (n¼ 6098)

Close neighbors Neighbors Relatives Colleagues Classmates Ordinary friends Fellow townsmen met outside hometown Persons participating leisure activities together Persons participating voluntary activities together Strangers

Rural (n¼4274) Close neighbors People living in the same village but not neighbors People living in the same village and having the same family name People living in the same village and having the different family name Relatives Ordinary friends Fellow villagers met outside hometown Strangers

Table 2 Factor loadings on social participation. Bonding trust a 0.752 0.684 0.750 0.703 0.548 0.390 0.241 0.143 0.105 0.015

Bonding trust a 0.815 0.792 0.838

Urban (n ¼6098)

Bridging trust a 0.068 0.242  0.002 0.191 0.285 0.523 0.676 0.725 0.667 0.696

Bridging trust a  0.026 0.205 0.116

0.780

0.268

0.611 0.257 0.190  0.009

 0.060 0.712 0.666 0.746

Sports/exercising groups Recreation groups Alumni/fellow villagers (townsmen)/ professional associations Educational groups for children Educational groups for yourselves Public service organizations

a

Rural (n¼ 4274)

0.668 0.719 0.718

0.661 0.699 0.696

0.584 0.756 0.714

0.556 0.773 0.670

a

a The factor loadings were obtained from principal component analysis. The social participation factor accounted for 46.13% and 48.36% of variance among urban and rural adults, respectively. All the figures were higher than 0.5 and were used to construct social participation factor.

categories were combined. Employment referred to having a job during the last 3 months. The total household income comprised wage, operating revenue, award, allowance, bonus, and gift. In analysis, the natural logarithm of household income per capita was used. Furthermore, at county level, we incorporated the natural logarithm of GDP per capita and poverty rate.

2.4. Statistical analysis

a

The factor loadings were obtained from principal component analysis. Among urban adults, the bonding and bridging trust together accounted for 50.00% of variance; among rural adults, the two factors together accounted for 58.95% of variance. The figures in bold in column “Bonding trust” and column “Bridging trust” were higher than 0.5 and were used to construct each of the two factors.

with two indicators: social participation and CCP membership. Respondents were asked about their frequency of participation in six types of organizations including sports/exercising groups, recreation groups, alumni/fellow villagers (townsmen)/professional associations, educational groups for children, educational groups for yourselves and public service organizations. Principal component analysis was performed to extract social participation factor (Table 2). Social participation at individual level was measured with factor score and at county level was measured with average factor scores within each county unit. The higher social participation score indicated more social participation. In addition, considering the influence of CCP membership on income and obtaining public position (Norstrand and Xu, 2012; Zhou, 2000), which may bring people resources relevant to health, we used CCP membership (1 ¼yes, 0 ¼no) as an indicator of social capital. Within each county unit, the percentage of CCP was computed.

2.3.2. Demographic and socioeconomic variables Demographic and socioeconomic variables were included as confounders. At individual level, demographic variables included gender, age, marital status, household registration (Hukou) type and migrant status. Socioeconomic status variables included subjective and objective aspects. Subjective socioeconomic status was measured with a single item “how would you describe your family's socioeconomic status compared to others?” Respondents answered on a 6-point Likert scale (1 ¼top, 2 ¼upper middle, 3 ¼middle, 4¼ lower middle, 5 ¼lower middle, 6 ¼bottom). As the response of “top” only accounted for about 1%, the first 2 options were combined. Objective socioeconomic status consisted of education attainment, employment status and household income per capita. Education attainment had 5 categories: lower than elementary school, elementary school, junior high school, high school, and higher than high school. For rural adults, the last two

Descriptive statistics were used to describe the characteristics of study population. T-test and Chi-square test were performed to examine the difference between urban and rural residents. Principal components analysis was used to extract factors of social capital elements. To model the effects of compositional (individual level) and contextual (county level) social capital on self-rated health, we fitted the data using a two-level Logistic regression with self-rated poor health as health outcome (Raudenbush and Bryk, 2002). We performed a series of six models: Model 1 was a null model containing no explanatory variables. Intra-class correlation coefficient (ICC) was computed to examine the necessity of fitting multilevel models. Model 2 included all the demographic or socioeconomic confounders at individual and county level (Kim et al., 2006; Snelgrove et al., 2009). Model 3 and Model 4 added individual and county level social capital into Model 2, respectively. Model 5 added both individual and county level social capital into Model 2. Comparing Models 3–5, we are able to assess the impact of compositional and contextual social capital on SRH and their changes after controlling for each other. Thus, in Model 6, we further added cross-level interaction effect between county and individual level social capital. All the analyses were performed for urban and rural adults separately. Moreover, we also performed a test on proportional-odds/parallel-lines assumption using Model 6 (Williams, 2006) to examine whether the original ordinal measurement of SRH can be used as the dependent variable for multilevel logit model. In both urban and rural areas, models violated the proportional-odds/parallel-lines assumption (urban: Likelihood-ratio Chi2(69)¼122.87, po0.001; rural: Likelihood-ratio Chi2(72)¼217.43, po0.001). Weights were applied to increase the representative ability of the data. The weights were created using two steps: first, the probability weights were calculated for each respondent according to the sampling design of CGSS 2005; second, post-stratification adjustment was made to the probability weights to match the data of 1% National Population Sample Survey in 2005 in China (Chinese General Social Survey Project at National Survey Research Center of Renmin University of China (NSRH-CGSS), 2006). To better compare social capital's association with SRH between urban and rural population, the weights were separately

T. Meng, H. Chen / Health & Place 27 (2014) 38–44

computed for urban and rural areas. Moreover, statistical analyses were conducted using SPSS 16.0 and HLM 6.08.

3. Results Tables 3 and 4 present the residence-stratified descriptive statistics of independent variables at individual and county level, respectively. Table 3 shows that the proportion reporting poor health was very close between urban and rural areas (38.5% vs. 39.1%, p o0.10). 14% of urban adults achieved CCP membership, which nearly doubled that of rural adults (7%). To analyze the urban–rural difference in social capital, we reported the mean value, rather than factor score, of social participation, bonding trust and bridging trust indicators. Compared with rural residents, urban residents were more active in participating social organizations, had lower bonding trust and had higher bridging trust. Table 4 shows that urban counties were generally richer than rural counties in terms of GDP per capita and poverty rate. The distribution of social capital among urban and rural counties was similar with the results obtained at individual level. Table 5 displays the results from the series of multilevel models for urban adults. Without including any explanatory variables, 10.3% of the variance in individual poor SRH came from the county level and there was significant difference among counties (Model 1). After adding confounders, the county-level variance decreased, but still significant (Model 2). In Models 3, among the social capital measures at individual level, only bonding (OR ¼0.85, po 0.001) and bridging (OR¼ 0.91, p o0.05) trust exhibited healthpromoting effect and the results were quite stable even after including county-level social capital (Model 5). In Model 4, Table 3 Individual-level descriptive statistics by residence location. Variable

Poor SRH health þ Malen Age Marriednnn Urban Hukounnn Migrantnnn

Urban (n¼ 6098)

Rural (n¼ 4274)

Mean/ proportion

S.D.

Mean/ proportion

0.39 0.47 44.36 0.81 0.89 0.10

0.49 0.39 0.50 0.48 15.43 44.83 0.39 0.90 0.31 0.06 0.30 0.02

Subjective socioeconomic statusnnn Upper middle class 0.06 Middle 0.37 Lower middle 0.33 Bottom class 0.24 Educationnnn Lower than elementary school Elementary school Junior high school High school Higher than high school Employednnn Ln (household income per capita)nnn Social capital indicators Social participationnnn CCP membershipnnn Bonding trustnnn Bridging trust1; þ nn

po 0.01. þ

po 0.10. n po 0.05. nnn p o 0.001.

S.D.

0.49 0.50 13.72 0.30 0.24 0.13

0.29 0.48 0.47 0.43

0.09 0.45 0.25 0.21

0.39 0.50 0.43 0.41

0.06

0.25

0.18

0.38

0.15 0.31 0.31 0.17 0.48 3.75

0.36 0.46 0.46 0.37 0.50 0.39

0.38 0.33 0.10 0.01 0.08 3.24

0.49 0.47 0.30 0.10 0.27 0.36

1.57 0.14 3.85 2.79

0.69 0.34 0.63 0.61

1.10 0.07 4.10 2.77

0.28 0.25 0.65 0.70

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bridging trust at county level reduced the probability of reporting poor health. Opposite to our hypothesis, living in a county with higher proportion of CCP members was detrimental to SRH. After including individual-level social capital, the influence of contextual social capital was slightly reduced (Model 5). Model 6 indicated that only bonding trust had cross-level interaction (OR¼ 0.85, p o0.10). In a county with a high level of bonding trust, high-bonding-trust individuals were more likely to report health than low-bonding-trust individuals. However, in a county with a low level of bonding trust, the situation was the opposite. Moreover, from Models 2 to 6, the county-level variance decreased by 20%, which implied social capital had good explanatory power for the variance of individual poor SRH, even after adequately controlling for confounders. The regression results for rural adults are presented in Table 6. Model 1 showed that about 9% of the variance of individual poor SRH resided at county level and significant difference existed among counties. After including confounders, the value of ICC and county-level variance went up (Model 2). At individual level, only bonding trust exhibited positive impact on SRH (Model 3), which was almost the same after controlling for social capital at county level (Model 5). At county level, people living in counties with higher bonding or bridging trust were less likely to report poor health (Model 4). And, after including individual-level social capital in the analysis, the significant benefit of county-level bonding trust on health disappeared (Model 5). Unlike the results for urban residents (Table 5), there was no cross-level interaction between individual and contextual social capital in rural area.

4. Discussion This paper examined the relationship between social capital and self-rated health in the context of eastern culture. We extended previous Chinese studies by comparing such relationship between urban and rural areas. We also provided a more accurate assessment of the impact of social capital on SRH in China through adequately controlling for confounders, employing the multilevel analytical framework and including the cross-level interaction effect between individual- and county-level social capital. The results indicated that bonding and bridging trust had positive but differential associations with SRH in urban and rural China. The existing evidence regarding the relationship between trust and SRH is inconsistent (Murayama et al., 2012b; Poortinga, 2006b; Snelgrove et al., 2009). Yip et al. (2007) found, in rural China, trust exhibited positive influence on SRH at individual level, not at village level; however, Wang et al. (2009) concluded the opposite. By distinguishing between bonding and bridging trust, Table 4 County-level descriptive statistics by residence location. Variable

Urban (n¼ 6098)

Rural (n¼ 4274)

Mean

S.D.

Mean

S.D.

Ln (GDP per capita)nnn Poverty ratennn

4.15 0.17

0.36 0.16

3.90 0.29

0.29 0.18

Social capital indicators Bonding trustnnn Bridging trust Social participationnnn CCP membershipnnn

3.88 2.80 1.52 0.14

0.27 0.25 0.34 0.08

4.09 2.75 1.12 0.07

0.26 0.32 0.15 0.06

þ

p o 0.10. p o0.05. p o 0.001.

n

nn

nnn

po 0.001.

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T. Meng, H. Chen / Health & Place 27 (2014) 38–44

Table 5 Multilevel logistic regression estimates in urban China (odds ratios and 95% confidence intervals) and variance components of poor self-rated health (SRH), N ¼ 6098 individuals nested within N ¼ 104 counties. Variable

Model 1

Model 2

a

Model 3

Individual-level social capital Bonding trust_i Bridging trust_i Social participation_i CCP member_i

a

Model 4

a

Model 5

0.85(0.79–0.91)nnn 0.91(0.85–0.98)n 1.01(0.92–1.10) 1.05(0.88–1.25)

County-level social capital Bonding trust_c Bridging trust_c Social participation_c CCP member_c

0.80(0.56–1.15) 0.59(0.39–0.87)nn 0.86(0.64–1.16) 3.86(0.94–15.80) þ

a

a

0.85(0.79–0.92)nnn 0.92(0.86–1.00)n 1.02(0.93–1.12) 1.04(0.87–1.24)

0.84(0.78–0.91)nn 0.92(0.86–0.99)n 1.04(0.95–1.15) 1.19(0.77–1.84)

0.94(0.66–1.35) 0.63(0.42–0.94) n 0.85(0.63–1.15) 3.62(0.89–14.79) þ

0.98(0.68–1.40) 0.64(0.43–0.96) n 0.86(0.64–1.15) 3.86(0.85–17.61) þ

Individual/county interaction Bonding trust_in Bonding trust_c Bridging trust_in Bridging trust_c Social participation_in Social participation_c CCP member_in CCP member_c Variance components County-level variance Intra-class correlation

Model 6

0.85(0.72–1.01) þ 0.99(0.82–1.21) 0.91(0.80–1.05) 0.43(0.04–4.36) 0.378nnn 0.103

0.259nnn 0.073

0.237nnn 0.067

0.208nnn 0.059

0.212nnn 0.061

0.208nnn 0.059

a At individual level, adjusted by gender, age, marriage, hukou, migrant status, subjective socioeconomic status, education, employment, natural logarithm of household income per capita, and at county level, adjusted by poverty rate and natural logarithm of GDP per capita. þ p o 0.10. n po 0.05. nn p o0.01. nnn p o0.001.

Table 6 Multilevel logistic regression estimates in Rural China (odds ratios and 95% confidence intervals) and variance components of poor self-rated health (SRH), N ¼ 4274 individuals nested within N ¼ 76 counties. Variable

Model 1

Model 2

a

Individual-level social capital Bonding trust_i Bridging trust_i Social participation_i CCP member_i County-level social capital Bonding trust_c Bridging trust_c Social participation_c CCP member_c

Model 3

a

Model 4

a

0.90(0.84–0.96)nn 0.95(0.86–1.05) 1.07(0.97–1.05) 0.88(0.66–1.16) 0.70(0.50–0.97)n 0.67(0.48–0.96)n 1.08(0.80–1.46) 0.18(0.01–2.71)

Model 5

Model 6

a

0.91(0.84–0.97)nn 0.97(0.87–1.08) 1.06(0.96–1.17) 0.89(0.66–1.19)

0.90(0.84–0.97)nn 0.97(0.87–1.08) 1.08(0.95–1.22) 0.87(0.50–1.53)

0.77(0.55–1.07) 0.70(0.48–1.01) þ 1.03(0.76–1.41) 0.20(0.01–3.27)

0.77(0.55–1.07) 0.70(0.48–1.03) þ 1.07(0.74–1.52) 0.23(0.01–4.77)

Individual/county interaction Bonding trust_in Bonding trust_c Bridging trust_in Bridging trust_c Social participation_in Social participation_c CCP member_in CCP member_c Variance components County-level variance Intra-class correlation

a

0.89(0.74–1.07) 0.91(0.75–1.11) 0.98(0.85–1.12) 1.05(0.01–109.06) 0.319nnn 0.088

0.337nnn 0.093

0.315nnn 0.087

0.297nnn 0.083

0.299nnn 0.083

0.298nnn 0.083

a At individual level, adjusted by gender, age, marriage, hukou, migrant status, subjective socioeconomic status, education, employment, natural logarithm of household income per capita, and at county level, adjusted by poverty rate and natural logarithm of GDP per capita. þ po 0.10. n p o0.05. nn p o0.01. nnn p o 0.001.

this paper reveals that the way trust affects SRH depends on the forms of trust. First, individual-level bonding trust promoted SRH for both urban and rural residents, and county-level bonding trust had no such impact, which is generally consistent with Kim and Kawachi (2007). Bonding trust may function through many mechanisms to influence SRH, including enhancing the flow of information and knowledge, maintaining healthy behaviour, and developing social support and mutual support (Beaudoin, 2009; Kawachi and Berkman, 2000). Furthermore, in China, bonding

trust is probably an important channel for socioeconomic attainments. For example, Bian (1997) found Chinese usually utilized strong social networks (Guanxi) composed of relatives, friends and acquaintances to get jobs. Second, we found that county-level bridging trust was beneficial for SRH in both urban and rural China. Only a handful of literatures have discussed how contextual bridging trust affects health (Elgar et al., 2011; Putnam et al., 1993). A possible explanation is that, at county level, bridging trust helps to build a social environment beneficial for social and economic

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development (Elgar et al., 2011; Putnam, 2000; Putnam et al., 1993), which turns to promote SRH, for example, by delivering better health service (Office of the World Health Organization Representative in China and Social Development Department of China State Council Development Research Center, 2005; Tang and Parish, 2000). One interesting finding is that individual-level bridging trust was only beneficial for the SRH of urban residents. Unlike the mechanism between county-level bridging trust and SRH, individual-level bridging trust promotes SRH through day-to-day interaction among persons with different social identities, which would bring health-related information and other resources (Engstrom et al., 2008; Kawachi and Berkman, 2000). In urban China, where market economy mode and social system is more similar with that of developed countries, residents have a great chance of knowing people from different backgrounds (Tang and Parish, 2000; Whyte, 2010). On the other hand, the features of agricultural society largely exist in rural China, where people tend to think and act along the lines of kinship solidarity and usually interact with acquainted persons (Steinhardt, 2012). There are not many opportunities for residents to socialize with people with different social identities in everyday life (Kipnis, 1997), which probably blocks the path between bridging trust and SRH at individual level. In this paper, no matter in urban or rural area, social participation had no association with SRH. Among the research conducted in developed countries, there is no agreement on the strength and direction of the impact of social participation on SRH (Murayama et al., 2012b; Poortinga, 2006b, 2006c; Snelgrove et al., 2009). And, all the previous Chinese studies on the relationship between social capital and SRH arrived at the same conclusion as we did (i.e. no association exists) (Sun et al., 2009; Yip et al., 2007). Although civil society in China gradually emerges in the past decades, Chinese population still seldom participate in social groups or activities (Wang, 2011). Our results showed that the mean value of social participation was 1.57 among urban residents and 1.10 among rural residents, both of which were quite below the full score of 5 (Table 3). According to Yip et al. (2007), the limited social participation in China is usually used as a channel to increase socioeconomic status, but not the health status, which further reduces the health-promoting effect of social participation. In China, CCP membership is regarded as an important way to obtain resource, especially from public sector (Norstrand and Xu, 2012; Zhou, 2000). In a study carried out in rural China, CCP membership was reported to positively associate with SRH (Yip et al., 2007). Our results showed, in rural area, CCP membership tended to increase the possibility of reporting health, but the relationship was not significant. Opposite to our hypothesis, in urban China, people living in an area with higher percentage of CCP members were more likely to report poor health. One possible explanation is that, in our urban sample, the areas with higher percentage of CCP members tended to be older (r ¼0.129, p ¼0.191). And, age is generally negatively associated with SRH (Poortinga, 2006c; Snelgrove et al., 2009). As this is the first paper to analyze the relationship between CCP membership and SRH in urban China under the multilevel analytical framework, further investigations are needed. We found a cross-level interaction effect between county- and individual-level bonding trust in urban area. Consistent with previous findings in developed countries (Kim and Kawachi, 2006; Poortinga, 2006b; Subramanian et al., 2002), the direction and strength of the relationship between individual-level bonding trust and SRH depend on the contextual social capital. When a person has the similar level of bonding trust as most residents in the same area do, he/she is expected to get more health benefit. Such interaction effect also appeared in rural area, but not

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significantly. In addition, unlike other studies (Carpiano, 2007; Han et al., 2012; Mansyur et al., 2008), this paper did not indicate the existence of cross-level interaction effect for structural social capital indicators for both urban and rural residents. It may be partly attributed to the results that, in urban and rural areas, social participation and CCP membership barely had impact on SRH at both individual and county level, which have been discussed in previous paragraphs. Our study mainly has two limitations. First, we used a crosssectional data and thus cannot conclude any causal relationship between social capital and SRH. The use of longitudinal data in this area is very limited. In a literature review, Murayama et al. (2012a) found 13 prospective multilevel studies on the association between social capital and health and only 2 with SRH as health outcome. More prospective surveys are needed in China, which will help us examine the health impact of social capital more accurately and provide more solid evidence for further health promotion policies. Second, we did not include the norm of reciprocity in the models. The survey asked respondents whether they provide help to or get help from other members in the same social organization. However, this question is based on the items concerning social participation. There is a very strong association between social participation and the reciprocity (urban: r ¼0.844, po 0.001; rural: r ¼0.867, p o0.001), which causes serious multicollinearity. Thus, we deleted the reciprocity indicator from the models. Although reciprocity is seldom used in studies (Engstrom et al., 2008; Ferlander, 2007), it is reported to positively associate with SRH by some researchers (Browning and Cagney, 2002; Wen et al., 2003). Lacking measures of reciprocity may affect the estimates of other social capital indicators. Despite the limitations, we still add to the empirical evidence on the relationship between social capital and self-rated health in China. Trust is generally beneficial for self-rated health in both urban and rural areas. Bonding trust mainly promotes SRH at the individual level and bridging trust mainly at the county level. There is almost no relationship between structural social capital (i.e. social participation and CCP membership) and SRH. Based on the urban–rural comparison, we found the health-promoting effect of individual-level bridging trust and the cross-level interaction effect of bonding trust only existed in urban China. Our findings suggest that social capital is a potential way to enhance health in China. Policies aimed to promote health through social capital need to be tailored according to the specific contextual social capital. For example, the success of home-based elderly care policy in China partly depends on high level of contextual social capital. Moreover, promoting bridging trust may produce more health benefit in urban China.

Acknowledgement Data analyzed in this paper were collected by the research project “China General Social Survey (CGSS)” sponsored by the China Social Science Foundation. This research project was carried out by Department of Sociology, Renmin University of China & Social Science Division, Hong Kong Science and Technology University, and directed by Dr. Li Lulu & Dr. Bian Yanjie. The authors appreciate the assistance in providing data by the institutes and individuals aforementioned. The views expressed herein are authors' own. References Beaudoin, C.E., 2009. Bonding and bridging neighborliness: an individual-level study in the context of health. Soc. Sci. Med. 68, 2129–2136. Bian, Y., 1997. Bringing strong ties back in: indirect ties, network bridges, and job searches in China. Am. Sociol. Rev. 62, 366–385.

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