Educational gradient of health in rural China

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Research Note

Educational gradient of health in rural China Anning Hu a , Xiujin Guo b,∗ , Yihong Wang c a b c

Fudan University, 1118, Liberal Arts Building, 220 Handan Road, Shanghai, China Shanghai University of Sport, 399, Changhai Road, Yangpu District, Shanghai, China Fudan University Press, 579, Guoquan Road, Shanghai, China

a r t i c l e

i n f o

Article history: Received 29 November 2017 Received in revised form 17 September 2018 Accepted 17 September 2018 Available online xxx Keywords: Education Health Rural China Learned effectiveness

a b s t r a c t The aim of this paper is to examine whether and how rural residents’ educational attainment is associated with their self-rated health in China. Taking advantage of the National Exercise Facility Survey that was collected between December 2015 and March 2016, we find that educational attainment has a significant and positive correlation with self-rated health, net of the effects of age, gender, and geographical region. This correlation is mediated by factors such as perceived importance of exercise and healthy lifestyle. Relatively, people’s cognitive knowledge about health-related information and material resource access fail to play a significant mediating role. The findings highlight the non-material and non-cognitive attributes of educational attainment in shaping the gradient of health in the rural setting of China. Interventions that expand formal education to the mass and optimize teaching contents may offer an effective means to balance the health gradient. © 2018 Western Social Science Association. Published by Elsevier Inc. All rights reserved.

1. Introduction 1.1. Theoretical background and research questions There is a vast literature on the nexus between education and health in the field of health economics (for a comprehensive review, see Böckerman & Maczulskij, 2016; Cutler & Lleras-Muney, 2006; Eide & Showalter, 2011; Zimmerman, Woolf, & Haley, 2015). One research focus in the literature is how to use sophisticated designs (e.g., regression discontinuity design or instrumental variable) to identify causality (Eide & Showalter, 2012). In this vein, the causal relationship from educational attainment to health has been well ascertained (Eide & Showalter, 2011). Another strand of health economics research is to specify the multiple indicators of health. Although self-reported health is on the research agenda (e.g., Silles, 2009), the

∗ Corresponding author. E-mail address: [email protected] (X. Guo).

majority of current studies focus on the objective variables, such as health behavior (Jürges, Reinhold, & Salm, 2011; Li & Powdthavee, 2015), smoking (Jensen & Lleras-Muney, 2012; Maralani, 2013), mortality (Gathmann, Jürges, & Reinhold, 2015), hospitalization (Arendt, 2008), and so forth. What is also of interests for health economists is how parental education is linked to their children’s health conditions (Chou et al., 2010; Weitzman, 2017). Withstanding the huge literatures in health economics, one understudied research question, as pointed out by Eide and Rees (2011), is the potential mediators between education and health. Previous studies frequently examine the mediating role of socio-economic status, but this can only account for a very limited portion, if any, of the overall education–health connection (Cutler & Lleras-Muney, 2006). More comprehensive investigations of material and non-material mediators are in order. What is also understudied is the education–health connection, as well as its formative mechanisms, among specific subpopulations. For the sake of generalization, most economic research utilizes at least nation-scale survey data. Although comparisons

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between socio-demographic groups can be performed, analyses based on national datasets may not well accommodate the specialties of local societies, e.g., the rural areas. It is thus no wonder that many more recent studies have started to pay attention to the rural population that bears significances in its own right (Liu et al., 2017; He et al., 2017). It is necessary to mention that the urban-versus-rural distinction in terms of health outcomes is rather common in almost all societies, but the one in China stands out with its extraordinary severity due to institutional arrangement of the household registration system (Whyte 2010). Everyone born in China is assigned a household registration status, either a rural or an urban one. Compared with urban household registration status holders, rural ones are extraordinarily disadvantaged in terms of all aspects of public services, including but not limited to education, health insurance, and pension (Treiman, 2012). With regard to the educational opportunities in particular, it has been widely documented that rural residents have more limited access, partly due to the ill-funded rural educational institutes, and partly due to the socio-economic hardship of students’ families. Also, the quality of rural education is much worse than the urban counterpart, in light of the difficulty in recruiting qualified teachers and maintaining teaching facilities (Brown & Park, 2002; Hannum, 1999). These disadvantages, according to the resource substitution theory proposed by Ross and Mirowsky (2006), could strengthen the education–health link among rural residents because their other non-educational resources are too limited to maintain and promote their health status (Hu, 2014a, 2014b; Qi, 2006; Ye & Shi, 2014). Perhaps a proper way to place readers into perspective of the urban-versus-rural cleavage in China is to present some numbers. According to Zhao (2011), the urban–rural ratio is 3.4 for the total health expenditure, and 4.1 for the per capita expenditure. Relatively, Health Policy Institute (2003) reports that the ratio of the total health expenditure in the US is 0.96. Moreover, Fan and Guo (2012) note that the urban–rural gap in health expenditure almost does not exist in Australia and Japan because of the strong government-sponsored support programs. Likewise, there is no significant difference in the satisfaction with primary care services between urban and rural residents in Ghana (Yaya et al., 2017). Hence, the health-related gap between urban and rural population in China stands out globally, which necessitates the research into the education–health connection particularly in the rural setting. In this study, we draw on recent survey data collected from rural China, and attempt to answer the following two questions: (1) is one’s educational attainment correlated with self-reported health in rural areas? (2) If the answer to the first question is positive, what factors mediate the education–health nexus? 1.2. Mediators between educational attainment and self-reported health According to the sociological study of Ross and Mirowsky (2010), there are two theoretical perspectives regarding the mechanisms mediating the nexus between

education and health. One perspective is the theory of commodity. Specifically, this theory emphasizes the effect of formal education on the cultivation of health-promoting human capital (Grossman, 1972). Like a type of commodity, human capital generated from formal education serves to help an individual manage disease risks by improving cognitive skills in problem-solving and resource mobilization. In addition, human capital can be converted to health-facilitating material resources, such as income and job security, which afford one to get access to more and better-quality health facilities or to purchase better nutrition and health care (Herd, Goesling, & House, 2007; Link & Phelan, 1995). The other perspective is the theory of learned effectiveness. By effectiveness, we mean the basic state of belief that people can effectively control over their own success or failure, initiatively improve the condition of their life, and actively direct themselves toward any and all values sought after (Lindsay, 2007). For health issues, effectiveness learnt from the educational process can be embodied by both psychosocial and behavioral characteristics (Tones, Robinson, & Tilford, 2013). The psychosocial aspect of effectiveness concerns whether or not one has a positive attitude toward health-promoting activities, while the behavioral aspect of effectiveness refers to whether or not one has the initiative to maintain health-related activities, i.e., a healthy lifestyle. In summary, corresponding to the two theoretical perspectives, we identify four potential pathways between education and health: the material pathway, the cognitive pathway, the psychosocial pathway, and the behavioral pathway (Xu & Xie, 2017). They are illustrated in Fig. 1. 2. Methods 2.1. Procedure The present study utilizes data from the National Exercise Facility Survey that were collected between December 2015 and March 2016 (NEFS 2016 henceforth). The NEFS 2016 adopts a multi-level sampling strategy. The primary sampling unit is province, which is selected to guarantee both geographic and economic representativeness. Geographic representativeness is supported by sampling provinces from eastern, middle, western, and northeastern regions of China. The classification of geographical regions is based on the Classification Scheme of Region released by the National Bureau of Statistics. Economic representativeness is ensured by selecting provinces of different levels of rural economic development from each region. In total, ten provinces are sampled, which are Shandong, Zhejiang, Guangdong, He’nan, Anhui, Hu’nan, Gansu, Chongqing, Guangxi, and Liaoning. From each province, two prefecture-level cities with different economic development levels are selected, which serve as the secondary sampling unit. Subsequently, two towns with different economic development levels are selected from each prefecture-level city. Two villages are then randomly selected from each town, and lastly 20 rural residents are randomly sampled from each village. The selection of survey units above the town level uses the intentional sampling to make sure that the selected units

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Educational attainment

Theory of commodity Material pathway Cognitive pathway

Theory of learned effectiveness psychosocial pathway Behavioral pathway

Self-rated health Fig. 1. Mediators between education and health.

differ from each other as much as possible. Although no weighting schemes are reported, this procedure can be justified to maintain representativeness because it is assisted by the officials of local governments who are familiar with the sampling units. The sample size of the NEFS 2016 is 2,956. Although this sample size is not as large as other large-scale survey data in China, it is sufficient to guarantee the statistical power (Kraemer & Blasey, 2015). Survey items of the NEFS 2016 are designed with reference of multiple previous surveys. In addition, the research group of the NEFS 2016 has invited experts from higher educational institutions and professional associations to evaluate and improve the survey instruments. The study gains ethics approval from Shanghai University of Sport. 2.2. Measures Self-rated health is measured by the question “what is your health status?” with options 1 = with severe health conditions; 2 = with minor health conditions; 3 = neutral; 4 = healthy; and 5 = very healthy. Note that this item, although reported by the respondents themselves, has been widely used in survey settings where professional diagnosis cannot be deployed. This item has been shown to have great validity and reliability (e.g., Hu, 2013; Hu & Hibel, 2013). For instance, it has a strong correlation with symptom-based scales (Bazargan, Bazargan-Hejazi, & Baker, 2005). The predictor of interest is educational attainment. This variable is measured by the question “what is your highest level of education?” The options are 1 = elementary school and below; 2 = junior middle school; 3 = senior middle school; and 4 = college and above. In addition to these two variables, several control variables are considered, including gender (1 = female; 0 = male), age (1 = ≤19; 2 = 20–29; 3 = 30–39; 4 = 40–49; 5 = 50–59; 6 = 60–69; 7 = ≥70), and geographic region (1 = eastern; 2 = middle; 3 = western; and 4 = northeastern). Corresponding to each pathway between education and health laid out above are several variables from the NEFS 2016. The material pathway is gauged by two variables. One

is the respondent’s annual income (RMB), which is coded as 1 = less than 5,000; 2 = 5,000–9,999; 3 = 10,000–14,999; 4 = 15,000–19,999; 5 = 20,000–29,999; 6 = 30,000–49,999; 7 = 50,000–99,999; and 8 = equal to or over 1,00,000. The other variable is the closeness to the nearest exercise facility, as measured by the question “what is the walking distance to the nearest exercise facility?” with options 1 = less than or equal to 10 min; 2 = 11–30 min; 3 = 31–60 min; and 4 = over 60 min. The cognitive pathway is measured by one’s cognitive knowledge that pertains to health. Five variables are adopted: (1), “do you know if there are exercise instructors in your village?” (2), “do you know if there is a bulletin board of exercise in your village?” (3), “have you heard about the Sports Law of People’s Republic of China?” (4), “have you heard about the National Fitness Program?” and (5), “have you heard about the Peasant Physical Exercise Project?” The options to these questions are 1 = yes and 0 = no. The psychosocial pathway refers to the propensity of being active and effective in health-related issues. One indicator for this pathway is one’s recognition of the importance of health-related activities, as measured by the question “do you think exercise important in your life?” Options are 1 = not important at all; 2 = not important; 3 = neutral; 4 = important; and 5 = very important. Lastly, the behavioral pathway mainly refers to the healthy lifestyle, which is measured using three questions: (1), “how often do you participate in sport competition?” with options 1 = never; 2 = 1–2 times a year; and 3 = 3 times or more a year. (2), “how often do you exercise?” with options 1 = never; 2 = irregularly; 3 = 1-3 times a week; and 4 = more than 3 times a week. (3), “how long did your exercise last time?” with options 1 = zero min; 2 = equal to or less than 30 min; 3 = 31–60 min; 4 = 61–90 min; and 5 = over 90 min. One caveat is worth mentioning. The potential mediators are configured to center around one’s physical activities such as exercise or sports training because these are the most prevalent health-promoting activities for rural citizens (Guo et al., 2018). With this said, we do acknowledge that in addition to them, there exists a wide set of other potential mediators. For example, one’s cognitive

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Table 1 Ordered logistic regression result for the educational gradient of health.

Educational attainment (reference = elementary school and below) Junior middle school Senior middle school College and above Gender (female) Region (reference = eastern) Middle Western Northeastern Age Age2

Coef.

Odds ratio

Std. err.

P>z

95% conf. interval

0.38 0.32 0.77 −0.27

1.46 1.38 2.16 0.76

0.12 0.12 0.13 0.07

<0.001 <0.01 <0.001 <0.001

0.16 0.08 0.51 −0.40

0.61 0.56 1.02 −0.13

−0.03 0.05 0.06 −0.02 −0.04

0.97 1.05 1.06 0.98 0.96

0.09 0.09 0.13 0.10 0.01

0.72 0.59 0.67 0.80 <0.001

−0.21 −0.13 −0.20 −0.21 −0.07

0.14 0.22 0.31 0.16 −0.02

−6.11 −3.84 −1.33 0.60

−4.97 −3.02 −0.56 1.37

Cutpoint 1 Cutpoint 2 Cutpoint 3 Cutpoint 4

−5.54 −3.43 −0.95 0.99

LR chi2 (9) N

351.22 2,956

0.29 0.21 0.20 0.20 <0.001

Data source: NEFS 2016.

capacity could include knowledge about mental health and subjective wellbeing. Also, the behavioral pathway could cover risky activities, such as smoking and drinking. However, it is difficult to exhaustively list all of them, and, unfortunately, the NEFS 2016 does not provide relevant measures. Therefore, we have to adopt a practical approach by investigating the items pertaining to physical activities that are of great prevalence in rural China. More nuanced studies can be conducted in the future had more survey items been available. Descriptive statistics of all variables can be found in Table A1 in the Appendix A. 2.3. Limitations The NEFS 2016 serves our research objective by providing items for the multiple mediators, which is the merit that is not equipped by the other large-scale surveys in China. However, there is still room for further improvement. For instance, no objective measures of health are available. Also, due to the individual-focused survey design, no family-based covariates, such as family structure or family socio-economic status, are collected. These limitations should be kept in mind when interpreting the analytical results. Another concern for the NEFS 2016 is the sample selection problem of respondents in the surveying process, where better educated respondents are over sampled because they are able to better understand the meaning of the research questions so as to be more cooperative. In a sense, this is a common problem in questionnaire surveys (Thomas & Heck, 2001). To check if this sample selection biases our analytical results, we take advantage of the post-stratification method that has been frequently used in survey design (Little, 1993). Specifically, we draw on the Chinese General Social Survey 2015 (CGSS) to compute the educational distribution of rural areas among the provinces surveyed by the NEFS 2016 (Guangdong province is omitted because it is not surveyed in the CGSS). Then, the observed educational

distribution computed based on the NEFS 2016 is adjusted according to that based on the CGSS. For example, if the percentage of junior-middle-school respondents is pCGSS in the rural areas of province A in the CGSS and pNEFS in the NEFS 2016, a weight is then computed as pCGSS /pNEFS . This weight is applied to the NEFS 2016, down-weighting the oversampled better educated individuals while up-weighting the under-sampled less educated individuals (the detailed weights can be found in the Appendix A Table A2). As a result, the educational distribution becomes consistent with that of the CGSS. The weighted data are subsequently analyzed and their results serve for the sake of robustness check. If the weighted and unweighted results do not differ substantively from each other, we have the evidence to say that the sample selection problem is not severe.

2.4. Statistical methods Self-rated health has discrete and ordered options, so we use the ordered logistic regression model to examine the correlation between educational attainment and selfrated health, net of the control variables. The mediation analysis is conducted using the method proposed by Karlson, Holm, and Breen (2012). On one hand, like the routine mediation analytical procedure, this method estimates the mediation effect by seeing how the coefficient of educational attainment varies before and after taking into account the mediators. On the other hand, unlike the routine mediation analysis, the Karlson–Holm–Breen mediation analysis has the merit of being immune to the concern of unobserved variance heterogeneity in generalized linear modeling, which is desirable for the research situation of this article. More detailed methodological discussions can be found in Brzoska, Sauzet, and Breckenkamp (2017). All analyses are conducted in STATA 13.0, using the procedures ologit and khb.

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A. Hu et al. / The Social Science Journal xxx (2017) xxx–xxx Table 2 Results of the Karlson–Holm–Breen mediation analysis. Coef.a Theory of commodity <0.001 Material pathway <0.001 Cognitive pathway Theory of learned effectiveness Psychosocial pathway 0.08 Behavioral pathway 0.08

5

Std. err.

P>z

95% conf. interval

<0.01 <0.01

0.86 0.57

−0.01 −0.02

0.01 0.01

can significantly mediate between educational attainment and self-rated health in rural China. Taken together, the Karlson–Holm–Breen mediation analysis suggests that in rural China, the factor that helps to shape the educational gradient of health is mainly learned effectiveness, rather than human capital.

0.01 0.01

<0.001 <0.001

0.06 0.06

0.10 0.10

3.3. Robustness check

Data source: NEFS 2016. Note: control variables include gender, age, age square, and region. a The estimated coefficient represents the change in the coefficient of the predictor, that is, educational attainment for the outcome of selfreported health status before and after taking into account the mediators. Standard errors are computed using the delta method.

3. Results 3.1. Multivariate analysis The result of the ordered logistic regression model is presented in Table 1. Consistent with previous studies, one’s educational attainment has a positive and significant correlation with self-rated health, net of the effects of age, gender, and geographical region. Specifically, relative to those who received elementary school and below (the reference group), people having junior-middle-school education, on average, are 1.46 times (exp0.38 ) the odds of reporting a better health status. For those who have a senior-high-school credential, the odds of reporting a better health status are 1.38 times (exp0.32 ) of those receiving education of elementary school and below. Lastly, collegeeducated individuals’ odds of reporting a better health condition are 2.16 times (exp0.77 ) of the reference group. Altogether, the ordered logistic regression affirms the educational gradient of health in rural China. The next question is how this gradient is formed. 3.2. Mediation analysis The result of the Karlson–Holm–Breen mediation analysis is presented in Table 2. Variables pertaining to the material and cognitive pathways fail to show a significant mediating effect. In contrast, the psychosocial pathway as measured by perceived importance of exercise, and the behavioral pathway of healthy lifestyle,

We check the robustness of our analytical result using the weighted NEFS 2016. The analytical results can be found in Table 3. Clearly, no substantive changes in the conclusions are detected, which suggests that the potential sample selection problem is not a severe concern. 4. Discussion Educational attainment, according to the fundamental cause theory, stands for a type of achieved status that qualifies an individual to get access to an array of resources, which further protect one from health risks (Link & Phelan, 1995). However, the extent of conversion of educational attainment to desirable health status varies across different groups of people. In this regard, one question that is of interests not only for health economists but also for policymakers is whether and how educational attainment can be health-promoting for the disadvantaged individuals. This study sheds light on the answer to this question by examining an institutionally disadvantaged group: rural residents in China. Neither material not cognitive variables exert a significant mediating effect between education and self-rated health. This might be attributed to the fact that the societal condition for the conversion of educational attainment to economic resources or cognitive advantages is under-developed in rural China. However, this does not mean that education is independent from health. On the contrary, by way of learned effectiveness, such as recognition of the importance of exercise and healthy lifestyle, a pattern of educational gradient of health is established. In light of the detected health-promoting effect of educational attainment in rural China, the direct practical implication of this study is to guarantee the educational opportunities for rural residents. This calls for sustained financial support for local schools and faculties, since

Table 3 Results of the robustness analyses.

Results of the ordered logistic regression model Educational attainment (reference = elementary school and below) Junior middle school Senior middle school College and above Results of the Karlson–Holm–Breen mediation analysis Theory of commodity Material pathway Cognitive pathway Theory of learned effectiveness Psychosocial pathway Behavioral pathway

Coef.

Std. err.

P>z

95% conf. interval

0.31 0.32 0.63

0.09 0.14 0.20

0.00 0.02 0.00

0.13 0.06 0.24

0.48 0.59 1.03

−0.02 −0.02

0.03 0.03

0.58 0.64

−0.07 −0.08

0.04 0.05

0.16 0.22

0.05 0.05

0.00 0.00

0.07 0.12

0.25 0.31

Data source: NEFS 2016 weighted based on the educational distribution of rural China from the Chinese General Social Survey 2015. Note: control variables include gender, age, age square, and region.

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their financial resources mostly rely on local governments (Sargent & Hannum, 2005). Needy students should also be subsidized so as to maintain their schooling (Brown & Park, 2002). What also matters is dissemination, where endeavors should be exercised to counteract the emerging ideology of the futility of schooling (Xie, 2017). In addition to schooling opportunities, the curriculum contents should be configured to effectively cultivate students’ non-material and non-cognitive capabilities that are helpful for their health. However, this is not an easy task. For one thing, the curriculum design in China is heavily test-oriented (Byun, Schofer, & Kim, 2012; Hu, 2017; Hu & Hibel, 2015). The contents of teaching are strongly related to the subjects of formal tests where cognitive skills are highly emphasized. In this case, schools are not well motivated to gear courses for non-cognitive capabilities. For another thing, cultivation of non-cognitive capabilities requires considerable investment in extracurricular facilities and faculties, so the increasing financial pressure can be a real concern. These two challenges deserve more attention from the local governments. 4.1. Study limitations Like other observational studies based on crosssectional data, this article faces the challenge of ascertaining the time order between variables of interest. Moreover, due to the limitation of survey items, we cannot examine how and where people earn their educational credentials. In light of this, we are inclined in this study to view rural areas as a specific social setting where education and health interact with each other. Another limitation lies in the outcome variable of self-rated health. Although it is a widely used indicator for personal health status, the potential reporting heterogeneity can be a concern, which calls for more refined survey design, e.g., the anchoring vignette (Xu & Xie, 2017). 4.2. Conclusions A pattern of educational gradient of self-rated health is detected in rural China, where people’s educational attainment has a significant and positive correlation with self-rated health. This correlation is mediated by factors of healthy lifestyle and perceived importance of exercise. Relatively, people’s cognitive skills and access to material resources fail to play a significant mediator role. Acknowledgments This study was supported by the Zhuoxue plan, the Chuangxin group development funding (IDH3458007), Social Attitude Research Think Tank Cultivation Plan, Studies on the Determinants of Subjective Wellbeing, Shanghai Leading Scholar Plan, the Junior Research Team Project from the School of Social Development and Public Policy at Fudan University, the School of Social Development and Public Policy at Fudan University, and the project “social transition and sociological theory” of the “double first class” plan of social science development of Fudan University.

Appendix A. Table A1 Descriptive statistics (N = 2956). Variable Self-rated health With severe health conditions With minor health conditions Neutral Healthy Very healthy Gender (female) Age (years) ≤19 20–29 30–39 40–49 50–59 60–69 ≥70 Educational attainment Elementary school and below Junior middle school Senior middle school College and above Region Eastern Middle Western Northeastern Annual income (RMB) <5,000 5,000–9,999 10,000–14,999 15,000–19,999 20,000–29,999 30,000–49,999 50,000–99,999 ≥1,00,000 Walking distance to the nearest exercise facility ≤10 min 11–30 min 31–60 min >60 min Exercise instructor Yes No Bulletin board of exercise Yes No Sports law of People’s Republic of China Yes No National fitness program Yes No Peasant physical exercise project Yes No Importance of exercise Not important at all Not important Neutral Important Very important Frequency of sport competition Never 1–2 times a year ≥3 times a year Frequency of exercise Never Irregularly 1–3 times a week >3 times a week Duration of exercise (minutes) 0 <30 31–60 61–90 >90

n

%

21 132 876 1,225 702 1,410

0.71 4.47 29.63 41.44 23.75 47.70

445 979 584 394 273 198 83

15.05 33.12 19.76 13.33 9.24 6.70 2.81

439 965 856 696

14.85 32.65 28.96 23.55

872 860 918 306

29.50 29.09 31.06 10.35

294 413 454 285 298 526 469 217

9.95 13.97 15.36 9.64 10.08 17.79 15.87 7.34

854 1,386 478 238

28.89 46.89 16.17 8.05

2,275 681

76.96 23.04

597 2,359

20.20 79.80

753 2,203

25.47 74.53

1,842 1,114

62.31 37.69

479 2,477

16.20 83.80

10 126 1,013 1,001 806

0.34 4.26 34.27 33.86 27.27

1,510 1,155 291

51.08 39.07 9.84

692 1,169 708 387

23.41 39.55 23.95 13.09

692 656 1,277 234 97

23.41 22.19 43.20 7.92 3.28

Data source: NEFS 2016.

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Table A2 Weights for the adjustment based on the Chinese General Social Survey 2015. Province

Elementary school and below

Junior middle school

Senior middle school

College and above

Shandong Zhejiang Guangdonga He’nan Anhui Hu’nan Gansu Chongqing Guangxi Liaoning

2.21 6.98 0.00 3.22 2.69 6.56 4.57 7.08 3.15 3.41

0.88 0.61 0.00 0.85 0.96 1.21 0.45 0.50 1.02 0.85

0.51 0.25 0.00 0.47 0.35 0.36 0.33 0.20 0.26 0.13

0.54 0.34 0.00 0.20 0.10 0.05 0.26 0.12 0.06 0.20

a

Guangdong province is not surveyed in the Chinese General Social Survey 2015, so the weight is automatically zero.

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