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Article

Institutional Differences in Individual Wellbeing in China

by
Youzhi Xiao
1,
Xuemin Liu
2,* and
Ting Ren
1
1
HSBC Business School, Peking University, Shenzhen 518055, China
2
School of Economics and Management, Dongguan University of Technology, Dongguan 523808, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(2), 721; https://doi.org/10.3390/su14020721
Submission received: 17 December 2021 / Revised: 6 January 2022 / Accepted: 6 January 2022 / Published: 10 January 2022
Versions Notes

Abstract

:
An imbalanced distribution of income and welfare characterizes a developing or transitional economy such as China’s. Even after forty years of reform and rapid economic growth, there is still considerable disparity in wellbeing across different institutional settings in China. Major inequalities exist between rural and urban areas, public and for profit sectors, and state-owned and private enterprises. This paper presents the descriptive differences in individual wellbeing across these kinds of institutional settings from objective and subjective perspectives, enabled by the five waves of the Chinese General Social Survey (CGSS; the years of 2010, 2011, 2012, 2013 and 2015). The results show that: (1) people in urban China enjoy more objective wellbeing than people in rural China, but less subjective wellbeing; (2) people who work for the public sector enjoy more objective and subjective wellbeing than those for the for profit sector; (3) people who work for the state owned enterprises enjoy more objective wellbeing than those for the for profit sector, but subjective happiness is not significantly different. Furthermore, people’s perception of subjective wellbeing not only relies upon substantive objective wellbeing, but also an affiliation with a certain type of institution.

1. Introduction

How do people living in different institutional contexts enjoy happiness and wellbeing differently? Especially in a transitional economy such as China, institutional factors exert considerable influence on people’s lives. From a conventional viewpoint, urban Chinese people and rural Chinese people live quite different kinds of lives. A large number of young peasants migrate every year from rural areas to cities, searching for better lives. In cities, people work in different institutional contexts, including governments, nonprofit organizations, and for profit firms that are further divided into state owned enterprises (SOEs), privately owned enterprises (POEs) and foreign invested enterprises (FIEs). Their wellbeing is indeed different. In addition, people with different work backgrounds may enjoy different kinds of wellbeing. The present study is to demonstrate the difference in wellbeing between people in different institutional contexts in China.
Conventional wisdom in China suggests that different socioeconomic and institutional factors affect people’s life quality and the subjective evaluation of life satisfaction, which creates a great opportunity to study wellbeing [1]. For instance, there is a gap between SOEs and POEs in pay and the difference between eastern and western areas of the country are quite considerable. However, whether urban residents enjoy better welfare than rural residents, benefits in the state owned enterprises are better than those in the private enterprises, and working for government agencies as civil servants is better than working for enterprises? In the present study, we investigate these issues in more detail and provide insights on welfare distribution in Chinese society. The comparative framework includes three perspectives: urban versus rural areas, public organizations versus for profit enterprises, and SOEs versus POEs versus FIEs.
The difference between urban and rural areas has been characterized by the dual economic model, mainly referring to the coexistence of industrialized mass production as the main characteristics of an urban economy and small scale production in the rural economy [2], and the coexistence of the agricultural sector and the industrial sector [3]. To urban or rural residents, the differences between urban and rural areas include mainly employment opportunities, social security and medical benefits, tax burdens, and other social benefits for individuals and households. Though the results are mixed, for instance, reporting a slightly higher subjective wellbeing rating from rural households than their urban counterparts using the national household survey for 2002 [4] (the reason may be the contribution of the difference between expectations of satisfaction, the assessment of income changes and the assessment of improvement of living conditions), in general urban residents tend to report higher happiness [5,6]. This can be further substantiated by the exogenous changes in household registration status due to land expropriation: receiving an urban household registration was found to substantially enhance subjective wellbeing within a household [7].
According to the data from the Statistics Communique on National Economy and Social Development of China 2017, the disposable income gap between urban and rural areas is substantial, that is, RMB 36,396 and RMB 13,432 respectively. More than 10% of China’s total inequality can be attributed to the rural–urban gap [8]. The consumption gap between urban and rural areas is an important indicator of the urban–rural dual structure. In 2008, the average annual living consumption expenditure of city residents was approximately RMB 11,243. In contrast, the number for rural residents was only RMB 3661, approximately one third of urban residents’ [9]. Meanwhile, in 2017, the numbers are RMB 24,445 and RMB 10,995, respectively. In addition, in most cities there exists a relatively complete social security net to safeguard citizens. On the contrary, in most rural areas, that security net is largely absent. During the course of urbanization, the urban and rural dual economic structure is inevitable. However, it is of interest to examine to what extent these kinds of differences that stem from institutional differences affect people’s wellbeing across sectors.
The difference between public and for profit sectors in China is quite unique in comparison to the western context. The public sector in China contains functional departments of government. According to the rule of the public servant law in China, civil servants are staff who work for the government, perform their duties, and are paid by the government. According to statistics, in China, public servants’ average annual income is relatively stable and decent. In addition, government jobs offer both employment security and social status. In contrast, in for profit enterprises, employees’ earnings rely upon the profitability of the enterprise.
One representative study was carried out by Borzaga and Tortia [10], who used a unique data set created in 1999 including a sample of 228 public, nonprofit, and for profit organizations operating in the social service sector, and 2066 workers. They tested whether workers’ satisfaction and loyalty are influenced by the different organizational forms. They found that, for satisfaction, intrinsic and relational attitudes toward work had the greatest influence, whereas workers motivated by economic interests are less satisfied. As for loyalty to the organization, satisfaction with economic and process related aspects of the job appears to have the greatest impact. In addition, the conclusion showed that, for nonprofit organizations, the result is consistent with the theories. By contrast, workers in public bodies are the least satisfied, higher monetary incentives notwithstanding [11].
The differences between SOEs and POEs in the for profit sector is also unique, in the sense that in China the state owned sector still dominates a substantial proportion of the economy. SOEs and POEs implement radically distinct structures, including income, training, decision making and working hours. For example, according to the National Statistics Bureau, there is a certain gap between the average annual wage of employees in POEs and SOEs, which is related to China’s long history of deploying centrally planned economic systems. Nowadays, these differences are still evident, although the gap may shrink over time, due to the development of China’s private economy and the reform of SOEs. These are the factors that suggest ownership will influence employees’ happiness.
To summarize, the present study takes the aforementioned three perspectives to demonstrate how institutional setting affects individual wellbeing in China. The main contributions of this study are threefold: (1) To investigate the relationship between institutional differences and individual wellbeing by providing empirical evidence from China. By wellbeing, both subjective and objective aspects were included, in an attempt to depict a holistic picture of the distribution of wellbeing in China’s transitional economy and society, which enriches related literature on institutional impacts on individual wellbeing [12,13,14]. (2) The rural versus urban areas, public versus for-profit sectors, and state owned versus private enterprises were put into one framework, from which we can see how the different institutional contexts affect individual wellbeing. Therefore, the core concept of wellbeing is further explained from three perspectives, overcoming the limitations of using a broad definition of happiness that is roughly synonymous with the combined hedonic/eudaimonic view. (3) By using the five waves of CGSS (the years of 2010, 2011, 2012, 2013 and 2015), the impacts over different periods can be seen, which provide a dynamic insight into the understanding of the relationship between institutional differences and individual wellbeing.

2. Theory and Hypotheses

2.1. Objective Wellbeing and Subjective Wellbeing

Wellbeing is a fundamental human goal—we all have a desire for our life to go well [15]. The experience of life going well involves both feeling good and functioning well. Feeling good all the time would not be conducive to wellbeing, as it would devalue the role of negative or painful emotions, which play an important part in our lives when experienced in the appropriate context, such as sadness following misfortune or distress or even anger following injustice [16]. Some scholars define wellbeing in terms of positive emotions alone, or the balance of positive to negative emotions [17,18]. However, emotional experiences or “hedonic” wellbeing are only one part of wellbeing, since emotions are, by their nature, transient, whereas wellbeing refers to a more sustainable experience [19]. Therefore, wellbeing can be defined from objective and subjective perspectives. In the viewpoint of subjective wellbeing, it is two way traffic: top down and bottom up. The top down mechanism refers to the change in people’s evaluations of their wellbeing that is caused by outside factors, whereas the bottom up mechanism refers to the change in the subjective evaluation of wellbeing caused by people’s internal demands, such as the demand for food and work. Whether the demand can be met leads to the change in happiness and contentment [20].
In terms of the top down mechanism, from a macro viewpoint, improvements in economic environment, such as a declining unemployment rate or the increase in personal income, can promote a sense of wellbeing and happiness. Research has shown that some macro factors, such as CPI, GDP, and urbanization rate, may influence peoples’ wellbeing or happiness [21]. Therefore, we can improve economic situations to improve happiness and wellbeing. However, improving an economic situation alone may not be sufficient, since people also care about other social factors that influence social progress and welfare. Therefore, both economic and social progresses have to be synchronized [22], for instance, social integrity and a reduction in corruption are found to have an important role on individual welfare [23].
From a micro point of view, technological progress in workplaces can influence employees’ personal wellbeing [24]. Working hours and salaries can influence employees’ perception of wellbeing [25]. Although in most developed countries employees’ working hours have seen a decrease, in developing countries employees’ average working hours are rising substantially [26]. This can cause a difference in the perception of wellbeing across countries.
Some researchers have found that job security becomes an important issue in employee wellbeing after corporate restructuring [27]. For example, Worrall and Cooper [28] found that among 5000 executives in the United Kingdom, 60 percent faced layoffs within 12 months after corporate restructuring. Different styles of management practices also have different effects on employees’ performance and wellbeing. For instance, Beehr and Gupta [29] found that in an organization with a large workload, traditional management style and poor communication, the pressure on employees is higher than organizations with a democratic management style. Similar results were obtained by Sparks, Faragher and Cooper [30] by examining the standards of working security, working hours, work flexibility, and management styles.
Therefore, to maximize companies’ interests, it has been widely accepted that increasing employees’ wellbeing is necessary to enhance work motivation and productivity [30]. If an employee has a positive attitude, s/he can be more flexible and productive [31]. Conversely, if an employee doesn’t like their work, s/he will have a lower output and a negative influence on coworkers [32]. Various studies have proven that wellbeing is a critical factor to companies’ success, which can directly influence employees’ effort and turnover, and then affect the cost of business, profits, and survival [33]. Therefore, the top down mechanism suggests that favorable external conditions, no matter from a social perspective or a company’s perspective, are important to promote individual wellbeing.
On the other hand, the bottom up mechanism suggests that the perception of wellbeing stems from people’s internal demands. The key question asked in this context is “what makes people happy?” Scholars have offered various definitions for “happiness”. Different scholars have suggested that happiness includes different aspects, such as economic status [34], activity levels [35,36], adaptation levels [37], life goals [38], and life events [39]. Some studies suggest that happiness is an outcome of the evaluation of material and social aspirations and achievements, including marriage, children, freedom and health, and religious activities, which are beneficial to increase personal happiness [40]. Clark and Oswald [41] studied the relationship between employment and happiness, and found that under normal circumstances, the employment situation and people’s sense of happiness were positively related; however, unemployed people who are in long term unemployment have more happiness than those in short term unemployment. Wulfgramm [42] further confirmed the detrimental effect of an unemployment status on happiness. Myers and Dinner [43] believed that happiness reflects broader trends in social development, including concern for personal value, and the importance of an objective evaluation of life.
In terms of the relationship between objective and subjective wellbeing, Blanchflower and Osward [44] selected a sample of 100,000 people in the United States and Britain, and found that more money can indeed bring greater happiness. Helliwell [45] found that social capital in transportation, health and communication can provide people more happiness, and a better education and income can make people much happier. Stones and Kozma [46] studied happiness from an emotional perspective, and found that happiness is a kind of phenomenon that contains a variety of emotions. Some scholars in China have carried out similar studies. For instance, Zheng et al. [47] conducted a panel study and found that household conditions and income were positively related to life satisfaction.

2.2. Differences in Objective Wellbeing across Types of Institutional Settings in China

Some studies have suggested that institutional settings will influence the happiness of residents. The extant studies have investigated factors including the reform and opening up policy [48], international trade [49], fertility rate [50], and more. Besides that, studies have shown that happiness in countries characterized by collectivism is lower than that in countries that emphasize individualism [51].
In China, the difference across institutional settings is salient. The biggest difference between rural areas and urban areas is the hukou (household) problem [52], meaning an official registration of urban or rural status that prevents people from migrating across sectors. Rural people intend to migrate to urban areas to pursue better jobs and welfare, which further causes various conflicts [53], such as the allocation of limited resources for education, provision of social welfare, and more. In addition, the data has shown that in the year of 2017, the disposable personal income (DPI) for an urban resident was RMB 36,396 and DPI for a rural resident was RMB 13,432. Some studies have shown that income is positively correlated with happiness. What is more, since, in China, a large number of rural areas are still under developed, lacking a basic supply of high quality utilities, food, education and medical care, it is reasonable to assess the overall level of objective wellbeing in urban areas as higher than that in rural areas.
According to the extant studies, the perception of happiness for employees is determined by three main factors: work pressure, social support, and psychological capital. Work pressure is caused by the employee’s interaction with the external environment, and the reaction to such interaction [54]. Social support includes three dimensions: social network resources, helping behavior and social support evaluation. In the job market, social support includes leadership support, family support, and peer support [55]. Psychological capital emphasizes a positive attitude towards life and employment. Some studies have shown that psychological capital is positively related to releasing work stress, increasing life satisfaction and happiness [56]. Since the difference in work pressure, social support and psychological capital exists in the division of the public and the for profit sectors, and among SOEs, POEs and FIEs, we can propose the hypothesis that the happiness of employees in different institutions will be different. Furthermore, in the objective aspects, such as wage, welfare, and unions’ bargaining power, work environments are diverse across different institutions, and all of these will influence the happiness of employees.
As to the difference between the public sector and the for profit sector, Fama and Jensen [57] argued that organizations with different ownership models face different types of principal–agent problems, which bring about different solutions to motivate employees. For instance, for profit employees may be better paid than their nonprofit counterparts because nonprofit employees donate part of their labor [58]. Nonprofit workers may want to work there intrinsically [59]. In addition, employees in for profit organizations may bring more profits for companies; therefore obtaining a better reward for return [60]. Yet, conflicting findings have also been obtained based on different rationales. For instance, employees in nonprofit organizations may be paid better because nonprofit organizations treat their employees more generously [61]. Nonprofit organizations may need to pay a premium to attract more qualified people [62].
It seems that the mechanism that an organization adopts to achieve its unique goals is what dictates the difference in employees’ objective wellbeing. Different organizations may use different motivators to balance pay and benefits [63]. Nonprofit organizations may not use a pay for performance scheme, but may rely more on a democratic management style to promote employee motivation [64]. A pay for performance scheme may work better in for profit organizations, to increase managers’ morale, but be less effective for nonprofit managers, who may require better benefits [65]. There may be less internal inequity in a nonprofit organization [66]. In general, compared to for profit organizations, nonprofit and local government organizations are less likely to provide financial incentives, pay a relatively equal amount of pay and benefit packages, and have less wage inequality [67].
SOEs, with preferential policies and important positions in the national economy, attract many talents in China. Sponsored by the government, SOEs in China also tend to demonstrate similar behaviors, including offering rewards to employees.
In balance, we propose the following based on literature and conventional wisdom:
Hypothesis 1.
People in urban China enjoy an overall higher level of objective wellbeing than counterparts in rural China.
Hypothesis 2.
People who work for the public sector enjoy an overall higher level of objective wellbeing than counterparts who work for the for profit sector.
Hypothesis 3.
People who work for SOEs enjoy an overall higher level of objective wellbeing than counterparts who work for POEs or FIEs.

2.3. Differences in Subjective Wellbeing across Types of Institutional Settings in China

Institutional factors facilitating individual initiative and autonomy substantively enhance people’s perception of wellbeing [12]. In China, though, regional disparities do exist and impact an individual’s assessment of subjective wellbeing, and the influence is relatively minor compared to materially tangible factors such as the quality of health [68], income and other social demographic variables [69]. There is a considerable gap in objective wellbeing across different institutional settings, and better objective wellbeing will bring a better subjective perception of individual wellbeing. In addition, when considering work pressure, social support and psychological capital, according to reports and the related questions in CGSS, compared with the for profit sectors, the public sectors have less work pressure, more social support and more psychological capital. Compared with POEs and FIEs, SOEs also have more social support and psychological capital. Therefore, we proposed the following:
Hypothesis 4.
People in urban China enjoy an overall higher level of subjective wellbeing than counterparts in rural China.
Hypothesis 5.
People who work for the public sector enjoy an overall higher level of subjective wellbeing than counterparts who work for the for profit sector.
Hypothesis 6.
People who work for SOEs enjoy an overall higher level of subjective wellbeing than counterparts who work for POEs and FIEs.

3. Methods

3.1. Sample

We used data from five waves (2010–2015) of the China General Social Survey (CGSS), which was launched in 2003. In the General Social Survey family, CGSS is the youngest (the South Korean General Social Survey started at the same year as the Chinese General Survey, but two months earlier than the Chinese General Social Survey). However, in mainland China, the CGSS is the earliest nationally representative continuous survey project run by an academic institution, which aims to systematically monitor the changing relationship between social structure and quality of life in both urban and rural China. By now, the CGSS has finished 2010, 2011, 2012, 2013 and 2015 fieldwork for Cycle II, which is also the data source of this article. The sample includes observations from 28 provinces and province level cities, covering 125 townships, 500 streets, 1000 communities and 10,000 households in mainland China. Surveys were sent to each household in the sample and answered by an adult member of the household. The total number of complete observations is 10,000. Due to omitted information, the analyses may include reduced subsets of the sample. The stratified sampling strategy provides support for the sample to represent the situation in mainland China.

3.2. Dependent Variables

We constructed measures for wellbeing from questions in the survey. We measured individual wellbeing by objective and subjective aspects. To measure subjective wellbeing, we used two single question measures for happiness, respectively. The happiness measure asked the respondents to evaluate their degree of feeling happy on a five point scale, from 1, very unhappy, to 5, very happy [70].
To measure objective wellbeing, we used ten factors in the whole sample: income, insurance, housing ownership, working hour, work environment, job satisfaction, work stress, work pressure, training, and promotion. For “income”, the information is from the question “How much did you earn in the past year?” We winsorized the sample to alleviate the influence of extreme values. In addition, we took logarithm of the data when necessary. For “insurance”, the information is from a set of questions asking whether the organization provides certain insurance. The responses were summed up to construct the variable, with values ranging from 0 to 4. For “housing ownership”, the information is from a set of questions asking the respondents whether they own the house where they live, or borrow or rent from the organization or others. Therefore the variable is constructed as a dichotomous variable with 1 indicating self-owned and 0 otherwise. For “working hours”, the information is from the question asking the number of hours worked in a typical week. For the 2011 data, “work environment” is from the question “The health problem is caused form the work environment”; the answers range from 1 to 5, representing the worst work environment to the best work environment.
For the 2012 data, “job satisfaction” is from a question “Are you satisfied with your job”; the answers range from 1 to 5, representing very unsatisfied to very satisfied. For the 2013 data, “work stress” is from the question “Do you feel stressed when doing your job”; the answers range from 1 to 4, representing the most stressed and the least stressed. For the data in 2015, for “work pressure”, the variable is constructed as a scale variable with 1 indicating most work pressure and 5 indicating least work pressure. For “training”, the variable is a dichotomous variable with 1 indicating the survey respondent received training before work. For “promotion”, the variable was constructed based on the question asking the survey respondent to evaluate the likelihood of getting a promotion in the next three years on a five point scale, with 1 for certain and 5 for most unlikely.

3.3. Independent Variables

The key independent variables are a set of dichotomous variables denoting different institutional settings. The major purpose of the present study is to show the comparative difference; therefore dichotomous variables are appropriate. To compare between rural and urban, a dichotomous variable was created with 1 if the survey respondent has an urban residency and 0 if rural. To compare between public and for profit sectors, the variable is scored 1 if the survey respondent works for the public sector, including government or state sponsored nonprofit organizations, and 0 if for profit companies. To compare between SOEs, POEs and FIEs, the variable is scored 1 if the survey respondent works for SOE and 2 if POE and 0 if FIEs.
The empirical analysis includes two parts in order to see the trend. At the beginning, we grouped the CGSS data in 2010, 2011, 2012, 2013 and 2015 (whole sample), then we found the same variables which can be used to describe objective happiness and subjective happiness. Next, we used the data of each year and added the different variables to perform similar analyses. The variables we used are presented in Table 1.
We controlled for several variables in the analyses at two levels. The individual level controls are gender, age, education, political status (party or not), marital status and health condition. According to some previous research, we also used the square of the age as the control variables, to specify the U shape of the relationship between the happiness and age. In addition, we controlled for province and year.

3.4. Regression Models

We applied OLS or probit model to the regression analyses, according to the characteristics of the dependent variables. Model specifications are as the follows, with OWB indicating objective wellbeing and SWB subjective wellbeing:
Model 1: OWB = f(institution, gender, age, education, political status, marital status, health, organizational size, region)
Model 2: SWB = f(institution, gender, age, education, political status, marital status, health, organizational size, region)
Model 3: SWB = f(OWB, gender, age, education, political status, marital status, health, organizational size, region)
Model 4: SWB = f(institution, OWB, gender, age, education, political status, marital status, health, organizational size, region)
Model 1 and 2 are the simple comparison of objective and subjective wellbeings, respectively, and between different intuitional settings, controlling for various factors. Model 3 examines the influence of objective wellbeing on people’s perception of subjective wellbeing. Model 4 examines the institutional difference in subjective wellbeing, while controlling for difference in objective wellbeing. Basically, Model 3 and 4 together examine whether people’s subjective wellbeing is completely derived from objective wellbeing, or is attached to a certain institutional setting that itself exerts some influence.

4. Results

4.1. Descriptive Statistics

Table 2 shows the definitions, means, and standard deviation of variables, and the simple comparison between institutional settings. (1) Urban vs. rural. Urban people seem to enjoy a higher level of overall happiness than rural people. Among the aspects of objective wellbeing, urban people also tend to have a better situation than rural people: higher average annual income, more kinds of insurance from organizations. We did not compare the other three aspects of objective wellbeing due to the lack of relevance to a rural situation. (2) Public vs. for-profit. People working for the public sector enjoy a considerably higher level of overall happiness than do people working for the for profit sector. In terms of objective wellbeing, people who work in the public sector enjoy a lower average annual income, more insurance, and are more likely to own house. (3) Firm ownership. In terms of level of overall happiness, FIEs’ employees tend to enjoy a higher quantity of home ownership than do SOE and POE employees. Regarding the objective wellbeing, FIE employees tend to have more insurance. However, POEs’ employees have the most working hours and are under the most pressure.

4.2. Regression Results of Comparison of Objective Wellbeing

Table 3 shows the regression results across different institutional settings in the whole sample, controlling for gender, age, health, political status, education, marriage, time and province (for the interest of space, we did not include the results of control variables in the all the regression tables, but these are available upon request). The regression coefficients of income and insurance are positive and have passed the 1% significance level test, indicating that urban residents have obvious advantages in income and insurance protection. However, compared with living in the countryside, housing (Coef. = −0.369, p < 0.01) in cities is challenging. For the robustness of the results, Table A1, Table A2, Table A3, Table A4 and Table A5 show the regression results between rural and urban in the sample of years 2010, 2011, 2012, 2013 and 2015, respectively (see Appendix A). The results are similar across the whole sample, although the value of the coefficient is different year on year. In the year of 2011, we can know that the work environment in urban areas is better than rural. In the year of 2012, working in an urban area was more satisfying than in rural. Other factors are not significant among rural and urban.
Table 4 shows the difference in objective wellbeing between the public and for profit sectors for the whole sample. Specifically, significant regression results for income (Coef. = 0.180, p < 0.01) and insurance (Coef. = −0.149, p < 0.01) are revealed rather than housing or working hours, which illustrates that the impacts of institutional difference caused by public versus for profit organizations on objective wellbeing is mainly concentrated in income and insurance. Similarly, for the reliability of the results, Table A6, Table A7, Table A8, Table A9 and Table A10 list the regression results between the public and for profit sectors in the sample of years 2010, 2011, 2012, 2013 and 2015, respectively (see Appendix B). The results are similar with the whole sample but the number of coefficient is different year on year. In the year 2015, we can know that those working in the public sector enjoy less work pressure and are less likely to get promoted. Other factors are not significant among public sector and for profit sector.
Table 5 shows the difference in objective wellbeing between SOEs, POEs and FIEs of the whole sample. Regarding SOEs vs. FIEs, the regression coefficient of insurance is 0.274 at a significant level of 0.01, while income, housing and working hours have not passed the significance level test, indicating the positive effect of insurance on SOEs. As for POEs vs. FIEs, the negative impacts of income (Coef. = −0.262, p < 0.01) and insurance (Coef. = −0.174, p < 0.01) on POEs are discovered. In addition, Table A11, Table A12, Table A13, Table A14 and Table A15 list the regression results of the subsamples of years 2010, 2011, 2012, 2013 and 2015, respectively (see Appendix C). The results are similar with the whole sample but the number of coefficient is different year on year. From the results of each year, we can know that the income of POEs increased from 2010 to 2015. In the year of 2015, compared with the FIEs, employees working in SOEs and POEs were less likely to get promoted. Other factors are not significant among SOEs, POEs and FIEs.

4.3. Regression Results of Comparison of Subjective Wellbeing

Table 6 shows the differences in people’s subjective happiness between different types of institution in the whole sample. It can be seen that institutional differences have a negative impact on the subjective wellbeing of urban residents (Coef. = −0.047, p < 0.01), as well as positive influences on the public sector (Coef. = 0.189, p < 0.01). Furthermore, for the robustness of the results, Table A16, Table A17, Table A18, Table A19 and Table A20 present the differences in people’s subjective happiness between different types of institution in the sample of years 2010, 2011, 2012, 2013 and 2015, respectively (see Appendix D). The results are similar with the whole sample but the number of coefficient is different year on year. From the results of each year, we can find that the influence of the institutions is fading year on year, and in the year of 2015, the institutional difference did not influence the people’s happiness.

4.4. Regression Results of Influence of Objective Wellbeing on Subject Wellbeing

Table 7 shows the aspects of objective wellbeing influence resident’s perception of happiness. It turns out that aspects of objective wellbeing such as income (Coef. = 0.163, p < 0.01) and insurance (Coef. = 0.022, p < 0.01) have a positive effect on the happiness of subjective wellbeing. Moreover, Table A21 presents empirical results of the subsamples in terms of 2010, 2011, 2012, 2013 and 2015 (see Appendix E), which are similar with the whole sample but the number of coefficient is different year on year.
Furthermore, Table 8 shows the difference in the perception of happiness between institutional settings, controlling for objective wellbeing including income, insurance, housing ownership, and working hours. The results are the same as Table 7. When controlling for objective happiness, we can find that the institutional difference improves. The reason may be that with increasing development, the difference in objective happiness between different institutional settings is becoming smaller and smaller, so the institutional difference is becoming more and more important.

5. Conclusions

An imbalanced distribution of income and welfare characterizes a developing or transitional economy such as China. Even after forty years of reform and rapid economic growth, there is still considerable disparity in wellbeing across different institutional settings in China. The present study compares people’s objective wellbeing, namely, various aspects of material conditions, and the subjective evaluation of wellbeing, namely, the perception of levels of satisfaction and happiness in different institutional settings in China. The comparisons are between urban and rural areas, public organizations and for-profit firms, and SOEs and POEs. We found that people enjoy different levels of objective and subjective wellbeing in different institutional settings. In general, people in urban areas have higher objective wellbeing but lower subjective wellbeing than people in rural areas, people in the public sector have higher subjective and objective wellbeing than people in the for profit sector, but the difference in both aspects of wellbeing between people in SOEs and POEs is not evident. The study enriches related literature on institutional impact on individual wellbeing by providing empirical evidence from China. Although China’s context is unique, the findings are largely consistent with those in western studies.
The institutional difference is evident when we controlled for the actual level of objective wellbeing. In addition, from the results of each year, we can see the institutional difference is becoming more and more pronounced from 2010 to 2015, facilitated by the five waves of Chinese General Social Survey. This suggests that in China, especially during the current transitional stage, people’s subjective evaluation of their life quality relies substantially upon nontangible conditions such as social status and prestige, in addition to tangible conditions that provide material support. Therefore, for policy making, the key to balance people’s subjective and objective wellbeing is to reduce the disparity between material and nonmaterial conditions across different institutional settings, including between rural and urban areas, between the public sector and the for profit sector, and across different firm ownership models. For managerial purposes, companies should recognize the sources that may cause dissatisfaction in their workforce. Since job satisfaction and happiness with one’s life are key to work motivation, it is important to use various means to deliver those desired conditions to help promote people’s wellbeing.
The implications of this study are threefold: (1) in order to balance the individual wellbeing among rural and urban areas, the difference between objective and subjective wellbeing indicates that, for rural areas, some objective conditions of rural areas, including housing conditions, social security, salary and infrastructure construction, need to be improved, while for urban areas some subjective conditions such as pressure, social justice need to be improved; (2) for the public sector and for-profit sector, the difference between objective and subjective wellbeing indicates that, in order to balance individual wellbeing, the working conditions of the for profit sector need to be improved or some hidden benefits of the public sector need to be reduced; (3) as time goes by, we can see subjective wellbeing plays an increasingly significant role in determining individual wellbeing, therefore, for policy making, improving the invisible conditions such as social equality, social justice, social assistance etc. becomes progressively more important.
This article makes theoretical and practical contributions to the current literature. Theoretically, the primary contribution is that factors that predict wellbeing, including both subjective and objective aspects, are documented. Evidence is provided that institutional settings, including the difference between rural and urban areas, public and for-profit sectors, state-owned and private enterprises, affect individual wellbeing. In addition, we show that the institutional factors that affect subjective and objective wellbeing are different, which enriches the extant theory about the factors affecting wellbeing. Furthermore, we extend the theories from a dynamic insight by using data over different periods. Practically, based on these conclusions, the ways to balance overall individual wellbeing are different between people in different institutional contexts. For the rural areas and for profit sector, some objective conditions, such as working conditions, housing conditions, social security etc., should be improved. However, for urban areas, some subjective conditions, such as pressure, justice etc., should be improved.
Finally, the possible limitation of this article lies in the omissions in the measurement of wellbeing, such as labor market status, which is an important determinant of wellbeing. In future research, labor market status, measured by unemployment, can be introduced to explore its impact on wellbeing.

Author Contributions

Conceptualization, Y.X. and X.L.; methodology, Y.X., X.L. and T.R.; software, Y.X.; validation, Y.X., X.L. and T.R.; formal analysis, Y.X., X.L. and T.R.; investigation, Y.X., X.L. and T.R.; resources, Y.X., X.L. and T.R.; data curation, Y.X. and X.L.; writing—original draft preparation, Y.X. and X.L.; writing—review and editing, X.L. and T.R.; visualization, X.L.; supervision, T.R.; project administration, T.R.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

We used data from five waves (2010–2015) of the China General Social Survey (CGSS) which was launched in 2003. In the world General Social Survey family, CGSS is the youngest (the South Korean General Social Survey started at the same year as the Chinese General Survey, but two months earlier than the Chinese General Social Survey). However, in mainland China, the CGSS is the earliest national representative continuous survey project run by an academic institution, which aims to systematically monitor the changing relationship between social structure and quality of life in both urban and rural China. By now, the CGSS has finished 2010, 2011, 2012, 2013 and 2015 fieldwork for Cycle II, which is also the data source of this article. The sample includes observations from 28 provinces and province level cities, covering 125 townships, 500 streets, 1000 communities and 10,000 households in the mainland China. Surveys were sent to each household in the sample and answered by an adult member of the household. The total number of complete observations is 10,000.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Regression results of objective wellbeing: urban versus rural (2010).
Table A1. Regression results of objective wellbeing: urban versus rural (2010).
Income
(OLS)		Insurance
(OLS)		Housing
(Logit)		Working Hours
(OLS)
Urban
(vs. Rural)		0.528 ***
(0.024)		0.079 ***
(0.014)		−0.322 ***
(0.030)		0.083 ***
(0.018)
ProvinceControlControlControlControl
All control variables included
R20.4790.161-0.054
Chi2- 1613.57-
Prob > Chi2- 0.000-
n893011,69111,6646665
Note: *** p < 0.01. Robust standard errors are used.
Table
Table A2. Regression results of objective wellbeing: urban versus rural (2011).
Table A2. Regression results of objective wellbeing: urban versus rural (2011).
Income
(OLS)		Insurance
(OLS)		Housing
(Logit)		Working Hours
(OLS)		Work Environment
(Ordered Logit)
Urban
(vs. Rural)		0.524 ***
(0.035)		0.102 ***
(0.022)		−0.060
(0.050)		0.082 ***
(0.026)		0.193 ***
(0.069)
ProvinceControlControlControlControlControl
All control variables included
R20.4650.241-0.071
Chi2- 758.18-136.97
Prob > Chi2- 0.000-
n45105558448334155448
Note: *** p < 0.01. Robust standard errors are used.
Table
Table A3. Regression results of objective wellbeing: urban versus rural (2012).
Table A3. Regression results of objective wellbeing: urban versus rural (2012).
Income
(OLS)		Insurance
(OLS)		Housing
(Logit)		Working Hours
(OLS)		Job Satisfaction
(Ordered Logit)
Urban
(vs. Rural)		0.510 ***
(0.023)		0.087
(0.017)		0.496 ***
(0.491)		0.009
(0.167)		−0.012
(0.077)
ProvinceControlControlControlControlControl
All control variables included
R20.4820.127-0.019
Chi2- 1277.52-445.18
Prob > Chi2- 0.000-0.000
n9647116591167469454005
Note: *** p < 0.01. Robust standard errors are used.
Table
Table A4. Regression results of objective wellbeing: urban versus rural (2013).
Table A4. Regression results of objective wellbeing: urban versus rural (2013).
Income
(OLS)		Insurance
(OLS)		Housing
(Logit)		Working Hours
(OLS)		Work Stress
(Ordered Logit)
Urban
(vs. Rural)		0.597 ***
(0.024)		−0.056
(0.017)		−0.276 ***
(0.031)		0.078 ***
(0.016)		−0.129
(0.026)
ProvinceControlControlControlControlControl
All control variables included
R20.5070.100-0.112
Chi2- 2049.07-1715.98
Prob > Chi2- 0.000-0.000
n897511,30811,275661811,189
Note: *** p < 0.01. Robust standard errors are used.
Table
Table A5. Regression results of objective wellbeing: urban versus rural (2015).
Table A5. Regression results of objective wellbeing: urban versus rural (2015).
Income
(OLS)		Insurance
(OLS)		Housing
(Probit)		Working Hours
(OLS)
Urban
(vs. Rural)		0.560 *
(0.029)		−0.014
(0.188)		−0.287
(0.058)		0.156 ***
(0.024)
ProvinceControlControlControlControl
All control variables included
R20.4510.130-0.096
Chi2--1275.92-
Prob > Chi2--0.000-
n7132850385034502
Note: * p < 0.10, *** p < 0.01. Robust standard errors are used.

Appendix B

Table
Table A6. Regression results of objective wellbeing: public versus for profit organizations (2010).
Table A6. Regression results of objective wellbeing: public versus for profit organizations (2010).
Income
(OLS)		Insurance
(OLS)		Housing
(Probit)		Working Hours
(OLS)
Public
(vs. For-profit)		0.205 ***
(0.041)		−0.126 ***
(0.032)		0.081
(0.067)		0.070
(0.141)
ProvinceControlControlControlControl
TimeControlControlControlControl
All control variables included
R20.3460.479-0.246
Chi2- 387.72-
Prob > Chi2- 0.000-
n200425592554362
Note: *** p < 0.01, two-sided tests. Robust standard errors are used.
Table
Table A7. Regression results of objective wellbeing: public versus for profit organizations (2011).
Table A7. Regression results of objective wellbeing: public versus for profit organizations (2011).
Income
(OLS)		Insurance
(OLS)		Housing
(Logit)		Working Hours
(OLS)		Work Environment
(Ordered Logit)
Public
(vs. For-profit)		0.074
(0.645)		−0.953 ***
(0.107)		−0.094
(0.083)		0.147
(0.155)		−0.160
(0.161)
ProvinceControlControlControlControlControl
All control variables included
R20.3590.261-0.243
Chi2- 676.34-54.11
Prob > Chi2- 0.000-0.012
n866555844831451038
Note: *** p < 0.01, two-sided tests. Robust standard errors are used.
Table
Table A8. Regression results of objective wellbeing: public versus for profit organizations (2012).
Table A8. Regression results of objective wellbeing: public versus for profit organizations (2012).
Income
(OLS)		Insurance
(OLS)		Housing
(Logit)		Working Hours
(OLS)		Job Satisfaction
(Ordered Logit)
Public
(vs. For-profit)		0.215 ***
(0.037)		−0.042
(0.032)		0.958
(0.108)		0.054 *
(0.031)		0.292
(0.230)
ProvinceControlControlControlControlControl
All control variables included
R20.3570.163-0.038
Chi2- 325.54-118.94
Prob > Chi2- 0.000-0.000
n2106248824841470502
Note: * p < 0.10, *** p < 0.01, two-sided tests. Robust standard errors are used.
Table
Table A9. Regression results of objective wellbeing: public versus for profit organizations (2013).
Table A9. Regression results of objective wellbeing: public versus for profit organizations (2013).
Income
(OLS)		Insurance
(OLS)		Housing
(Logit)		Working Hours
(OLS)		Work Stress
(Ordered Logit)
Public
(vs. For-profit)		0.788 *
(0.046)		−0.056 *
(0.034)		−0.168
(0.116)		0.002
(0.077)		−0.039
(0.059)
ProvinceControlControlControlControlControl
All control variables included
R20.3220.115-0.212
Chi2- 325.54-225.66
Prob > Chi2- 0.000-0.000
n1945237324843182328
Note: * p < 0.10, two-sided tests. Robust standard errors are used.
Table
Table A10. Regression results of objective wellbeing: public versus for profit organizations (2013).
Table A10. Regression results of objective wellbeing: public versus for profit organizations (2013).
Income
(OLS)		Insurance
(OLS)		Housing
(Logit)		Working Hours
(OLS)		Work Pressure
(Ordered Logit)		Training
(Probit)		Promotion
(Ordered Logit)
SOEs
(vs. FIEs)		0.374 ***
(0.058)		0.016
(0.043)		−0.237 *
(0.138)		−0.142
(0.162)		0.149
(0.425)		−0.078
(0.288)		0.859 ***
(0.3123)
POEs
(vs. FIEs)		0.896 *
(0.503)		0.256
(0.301)		−0.116
(0.714)		−0.953 ***
(0.268)		−0.241
(0.422)		−0.216
(0.285)		0.634 ***
(0.296)
ProvinceControlControlControlControlControlControlControl
All control variables included
R20.3030.138-0.261 -
Chi2--200.88-21.2495.6433.05
Prob > Chi2--0.000-0.0200.0000.000
n1598143014311352391650240
Note: * p < 0.10, *** p < 0.01, two-sided tests. Robust standard errors are used.

Appendix C

Table
Table A11. Regression results of objective wellbeing: SOEs, POEs and FIEs (2010).
Table A11. Regression results of objective wellbeing: SOEs, POEs and FIEs (2010).
Income
(OLS)		Insurance
(OLS)		Housing
(Probit)		Working Hours
(OLS)
SOEs
(vs. FIEs)		0.331 **
(0.137)		0.160 *
(0.097)		0.219
(0.187)		−0.100
(0.247)
POEs
(vs. FIEs)		0.082
(0.134)		−0.018
(0.095)		0.282
(0.183)		−0.083
(0.216)
SOEs vs. POEs
(F-test)		0.9281.30 ***0.17−0.04
ProvinceControlControlControlControl
All control variables included
R20.3050.206-0.192
Chi2- 421.18-
Prob > Chi2- 0.000-
n224329242917558
Note: * p < 0.10, ** p < 0.05, *** p < 0.01, two-sided tests. Robust standard errors are used.
Table
Table A12. Regression results of objective wellbeing: SOEs, POEs and FIEs (2011).
Table A12. Regression results of objective wellbeing: SOEs, POEs and FIEs (2011).
Income
(OLS)		Insurance
(OLS)		Housing
(Logit)		Working Hours
(OLS)		Work Environment
(Ordered Logit)
SOEs
(vs. FIEs)		−0.089
(0.064)		0.157
(0.284)		−0.388 **
(0.189)		−0.090
(0.127)		0.150
(0.150)
POEs
(vs. FIEs)		−0.377 ***
(0.087)		−0.149
(0.325)		−0.277
(0.221)		−0.007
(0.100)		0.070
(0.180)
SOEs vs. POEs
(F-test)		1.91 **0.49−1.76 *−0.372.24 **
ProvinceControlControlControlControlControl
All control variables included
R20.3370.274-0.200
Chi2- 187.47-54.99
Prob > Chi2- 0.000-0.013
n1184124311833341419
Note: * p < 0.10, ** p < 0.05, *** p < 0.01, two-sided tests. Robust standard errors are used.
Table
Table A13. Regression results of objective wellbeing: SOEs, POEs and FIEs (2012).
Table A13. Regression results of objective wellbeing: SOEs, POEs and FIEs (2012).
Income
(OLS)		Insurance
(OLS)		Housing
(Probit)		Working Hours
(OLS)		Job Satisfaction
(Ordered Logit)
SOEs
(vs. FIEs)		0.524 **
(0.184)		−0.020
(0.113)		−0.056
(0.345)		0.172
(0.148)		−1.028
(0.674)
POEs
(vs. FIEs)		0.207
(0.185)		−0.161
(0.112)		−0.155
(0.343)		0.134
(0.149)		−0.923
(0.655)
SOEs vs. POEs
(F-test)		4.91 ***1.66 *2.58 **0.58−12.2408 ***
ProvinceControlControlControlControlControl
All control variables included
R20.3240.177-0.031
Chi2- 360.88-110.72
Prob > Chi2- 0.000-
n2379291429101734608
Note: * p < 0.10, ** p < 0.05, *** p < 0.01, two-sided tests. Robust standard errors are used.
Table
Table A14. Regression results of objective wellbeing: SOEs, POEs and FIEs (2013).
Table A14. Regression results of objective wellbeing: SOEs, POEs and FIEs (2013).
Income
(OLS)		Insurance
(OLS)		Housing
(Probit)		Working Hours
(OLS)		Work Stress
(Ordered Logit)
SOEs
(vs. FIEs)		0.519
(0.146)		0.137
(0.121)		0.134
(0.375)		−0.485
(0.167)		0.099
(0.163)
POEs
(vs. FIEs)		−0.337
(0.145)		0.153
(0.118)		0.223
(0.372)		0.169
(0.129)		−0.023
(0.162)
SOEs vs. POEs
(F-test)		25.21 ***1.74 *0.99 **−2.58 **0.02
ProvinceControlControlControlControlControl
All control variables included
R20.3340.116-0.239
Chi2- 363.47-212.15
Prob > Chi2- 0.000-
n1837225922543102219
Note: * p < 0.10, ** p < 0.05, *** p < 0.01, two-sided tests. Robust standard errors are used.
Table
Table A15. Regression results of objective wellbeing: SOEs, POEs and FIEs (2015).
Table A15. Regression results of objective wellbeing: SOEs, POEs and FIEs (2015).
Income
(OLS)		Insurance
(OLS)		Housing
(Probit)		Working Hours
(OLS)		Work Pressure
(Ordered Logit)		Training
(Probit)		Promotion
(Ordered Logit)
SOEs
(vs. FIEs)		0.374 ***
(0.058)		0.016
(0.043)		−0.237 *
(0.138)		−0.142
(0.162)		0.149
(0.425)		−0.078
(0.288)		0.859 ***
(0.312)
POEs
(vs. FIEs)		0.896 *
(0.503)		0.256
(0.301)		−0.116
(0.714)		−0.953 ***
(0.268)		−0.241
(0.422)		−0.216
(0.285)		0.634 ***
(0.296)
SOEs vs. POEs
(F-test)		16.25 ***1.66 *−0.02−2.58 **−3.25 ***−5.17 ***3.51 ***
ProvinceControlControlControlControlControlControlControl
All control variables included
R20.3010.138-0.261 -
Chi2--200.88-21.2495.6433.05
Prob > Chi2--0.000-0.0200.0000.000
n598143014311352391650240
Note: * p < 0.10, ** p < 0.05, *** p < 0.01, two-sided tests. Robust standard errors are used.

Appendix D

Table
Table A16. Regression results of subjective wellbeing (2010).
Table A16. Regression results of subjective wellbeing (2010).
UrbanPublicFIEs
(vs. Rural)(vs. For Profit)
Institution0.042 *
(0.025)		0.116 **
(0.057)
SOEs 0.634
(0.133)
POEs −0.078
(0.127)
SOEs vs. POEs
(F-test)		 4.58 ***
ProvinceControlControlControl
All control variables included
Chi21441.82364.941596.47
Prob > Chi20.0000.0000.000
n11,68025552920
Note: * p < 0.10, ** p < 0.05, *** p < 0.01, two-sided tests. Robust standard errors are used.
Table
Table A17. Regression results of subjective wellbeing (2011).
Table A17. Regression results of subjective wellbeing (2011).
UrbanPublicFIEs
(vs. Rural)(vs. For Profit)
Institution−0.079 **
(0.036)		0.104
(0.085)
SOEs −0.910
(0.081)
POEs −0.162 *
(0.010)
SOEs vs. POEs
(F-test)		 3.59 ***
ProvinceControlControlControl
All control variables included
Chi2467.46158.97221.45
Prob > Chi20.0000.0000.000
n555310591452
Note: * p < 0.10, ** p < 0.05, *** p < 0.01, two-sided tests. Robust standard errors are used.
Table
Table A18. Regression results of subjective wellbeing (2012).
Table A18. Regression results of subjective wellbeing (2012).
UrbanPublicFIEs
(vs. Rural)(vs. For Profit)
Institution−0.031
(0.0258)		0.082 **
(0.0549)
SOEs 0.142
(0.159)
POEs 0.137
(0.157)
SOEs vs. POEs
(F-test)		 6.77 ***
ProvinceControlControlControl
All control variables included
Chi21126.90282.77333.03
Prob > Chi20.0000.0000.000
n11,65424802905
Note: ** p < 0.05, *** p < 0.01, two-sided tests. Robust standard errors are used.
Table
Table A19. Regression results of subjective wellbeing (2013).
Table A19. Regression results of subjective wellbeing (2013).
UrbanPublicFIEs
(vs. Rural)(vs. For Profit)
Institution−0.033
(0.046)		0.115 *
(0.060)
SOEs −0.860
(0.204)
POEs −0.066
(0.203)
SOEs vs. POEs
(F-test)		 8.55 ***
ProvinceControlControlControl
All control variables included
Chi21150.47327.36302.77
Prob > Chi20.0000.0000.000
n11,25123532239
Note: * p < 0.10, *** p < 0.01, two-sided tests. Robust standard errors are used.
Table
Table A20. Regression results of subjective wellbeing (2015).
Table A20. Regression results of subjective wellbeing (2015).
UrbanPublicFIEs
(vs. Rural)(vs. For Profit)
Institution−0.312
(0.146)		0.142
(0.131)
SOEs 0.062
(0.135)
POEs 0.052
(0.481)
SOEs vs. POEs
(F-test)		 9.25 ***
ProvinceControlControlControl
All control variables included
Chi2214.35204.52188.52
Prob > Chi2 0.0000.0000.000
n155715351431
Note: *** p < 0.01, two-sided tests. Robust standard errors are used.

Appendix E

Table
Table A21. Regression results of subjective wellbeing (controlling for objective wellbeing; 2011–2015).
Table A21. Regression results of subjective wellbeing (controlling for objective wellbeing; 2011–2015).
Happiness20102011201220132015
Income0.148 ***
(0.331)		0.115 ***
(0.027)		0.129 **
(0.059)		0.297 ***
(0.035)		0.036
(0.135)
Insurance0.104 ***
(0.026)		−0.012
(0.081)		0.110 *
(0.059)		0.224 ***
(0.037)		0.408 ***
(0.136)
Housing Ownership0.099 ***
(0.033)		−0.087
(0.053)		0.032
(0.973)		0.922
(0.059)		0.165
(0.243)
Working Hours−0.140 ***
(0.028)		−0.030 ***
(0.043)		0.082
(0.077)		−0.304 ***
(0.069)		−0.238
(0.212)
Work Environment −0.058 *
(0.030)
Job Satisfaction 0.728
(0.058)
Work Stress 0.097
(0.035)
Work Pressure −0.085 **
(0.040)
Training 0.349
(0.223)
Promotion 0.260 **
(0.121)
ProvinceControlControlControlControlControl
All control variables included
R2758.09221.16431.55735.13122.96
Chi20.0000.0000.0000.0000.000
Prob > Chi25909248921825968494
n758.09221.16431.55735.13122.96
Note: * p < 0.10, ** p < 0.05, *** p < 0.01, two-sided tests. Robust standard errors are used.

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Table
Table 1. The main variables.
Table 1. The main variables.
YearCommon VariablesAdditional Variables
2010Happiness
Income
Housing Ownership
Insurance
Working Hours
2011Work Environment
2012Job Satisfaction
2013Work Stress
2015Work Pressure, Training, Promotion
Table
Table 2. Description and descriptive statistics of variables: urban vs. rural, public vs. for profit, firm ownership.
Table 2. Description and descriptive statistics of variables: urban vs. rural, public vs. for profit, firm ownership.
VariablesDescriptionsUrban vs. RuralPublic vs. For-ProfitFirm Ownership
UrbanRuralPublicFor-ProfitFIEsSOEsPOEs
MeanS.D.MeanS.D.MeanS.D.MeanS.D.MeanS.D.MeanS.D.MeanS.D.
HappinessScale 1–5: 1 = very unhappy, 5 = very happy3.83 ***0.833.780.883.99 ***0.783.810.853.95 ***0.803.880.843.770.87
Objective Wellbeing
IncomeIncome last year31,434.66 ***123,799.8011,753.1576,858.9227,028.14 ***31,148.8817,531.2721,749.1122,419.58 ***51,836.8122,875.8324,392.5213,124.5418,692.38
InsuranceNumber of Insurance entered (0–4)1.91 ***0.841.630.702.11 ***0.8241.770.922.38 ***0.452.150.831.850.93
Housing Ownership1 = self-owned, 0 = otherwise0.460.500.560.500.62 ***0.490.500.500.550.500.560.500.480.50
Working HourWork hours per week49.3720.2449.1919.9749.0820.0649.4820.2050.6520.3349.7120.3448.9619.83
Control Variables
Female1 = female, 0 = male0.48-0.49-0.56-0.43-0.42-0.48-0.42-
AgeAge47.65 ***25.4550.7824.9165.02 ***38.6255.1027.2854.78 ***17.5462.4516.5946.8625.20
HealthScale 1–5: 1 = very bad, 5 = very good3.741.023.471.123.381.043.451.053.69 ***0.993.331.033.561.07
MarriageScale 1–7: 1 = Not Marriage 7 = Widowed3.17-3.31-3.69-3.47-3.55 ***1.543.65-3.32-
EducationScale 1–14: 1 = no schooling, 13 = graduate schooling5.85 ***3.223.331.875.67 ***2.924.612.335.72 ***2.805.052.194.283.20
Political Status0 = Not Communist Party 1 = Communist Party0.15-0.06-0.35-0.13-0.26-0.21-0.08-
Time2010, 2011, 2012, 2013, 2015
Province1–32
Note: *** attached to mean values denote significance at 0.01 level, for comparisons between urban and rural, public and for profit organizations, and SOEs, POEs and FIEs, respectively.
Table
Table 3. Regression results of objective wellbeing: urban versus rural (whole sample).
Table 3. Regression results of objective wellbeing: urban versus rural (whole sample).
Income
(OLS)		Insurance
(OLS)		Housing
(Logit)		Working Hours
(OLS)
Urban
(vs. Rural)		0.578 ***
(0.025)		0.531 ***
(0.002)		−0.369 ***
(0.052)		0.007
(0.047)
ProvinceControlControlControlControl
TimeControlControlControlControl
All control variables included
R20.4770.485-0.008
Chi2- 5933.64-
Prob > Chi2- 0.000-
n40,69734,55749,95429,103
Note: *** p < 0.01. Robust standard errors are used. All regressions control for respondents’ gender, age, age square, health condition, education level, political status, marital status, province, and year (the same below).
Table
Table 4. Regression results of objective wellbeing: public versus for profit organizations (whole sample).
Table 4. Regression results of objective wellbeing: public versus for profit organizations (whole sample).
Income
(OLS)		Insurance
(OLS)		Housing
(Logit)		Working Hours
(OLS)
Public
(vs. For-profit)		0.180 ***
(0.042)		−0.149 ***
(0.013)		−0.083
(0.112)		−0.109
(0.010)
ProvinceControlControlControlControl
TimeControlControlControlControl
All control variables included
R20.3450.479-0.019
Chi2- 1161.06-
Prob > Chi2- 0.000-
n8873525410,6226175
Note: *** p < 0.01, two-sided tests. Standard errors are in parentheses.
Table
Table 5. Regression results of objective wellbeing: SOEs, POEs and FIEs (whole sample).
Table 5. Regression results of objective wellbeing: SOEs, POEs and FIEs (whole sample).
Income
(OLS)		Insurance
(OLS)		Housing
(Probit)		Working Hours
(OLS)
SOEs
(vs. FIEs)		0.013
(0.050)		0.274 ***
(0.015)		0.008
(0.068)		0.028
(0.800)
POEs
(vs. FIEs)		−0.262 ***
(0.053)		−0.174 ***
(0.010)		−0.001
(0.069)		0.013
(0.036)
SOEs vs. POEs
(F-test)		2.67 ***18.30 ***0.070.05
ProvinceControlControlControlControl
TimeControlControlControlControl
All control variables included
R20.3430.360-0.014
Chi2- 1171.21-
Prob > Chi2- 0.000-
n9488875511,5096766
Note: *** p < 0.01, two-sided tests. Standard errors are in parentheses.
Table
Table 6. Regression results of subjective wellbeing (whole Sample).
Table 6. Regression results of subjective wellbeing (whole Sample).
UrbanPublicFIEs
(vs. Rural)(vs. For Profit)
Institution−0.047 ***
(0.021)		0.189 ***
(0.047)
SOEs −0.065
(0.096)
POEs −0.120
(0.099)
SOEs vs. POEs
(F-test)		 15.22 ***
YearControlControlControl
ProvinceControlControlControl
All control variables included
Chi24876.471155.101798.00
Prob > chi20.0000.0000.000
n50,98410,80111,756
Note: *** p < 0.01, two-sided tests. Standard errors are in parentheses.
Table
Table 7. Regression results of subjective wellbeing (controlling for objective wellbeing; whole sample).
Table 7. Regression results of subjective wellbeing (controlling for objective wellbeing; whole sample).
Happiness (Ordered Logit)
Income0.163 ***
(0.036)
Insurance0.022 ***
(0.002)
Housing Ownership0.054
(0.065)
Working Hour0.0483
(0.043)
TimeControl
ProvinceControl
Control Variables Included
Wald chi22300.33
Prob > chi20.000
n22,582
Note: *** p < 0.01, two-sided tests. Robust standard errors are used.
Table
Table 8. Regression results of subjective wellbeing across institutional settings (controlling for objective wellbeing; whole sample).
Table 8. Regression results of subjective wellbeing across institutional settings (controlling for objective wellbeing; whole sample).
Happiness (Ordered Logit)
Urban (vs. Rural)Public (vs. For Profit)FIEs
Institution−0.173 ***
(0.034)		0.203 **
(0.071)
SOEs −0.019
(0.162)
POEs −0.088
(0.168)
Income0.206 ***
(0.016)		0.176 ***
(0.040)		0.147 ***
(0.037)
Insurance0.046
(0.035)		0.010
(0.100)		−0.004
(0.0468)
Housing Ownership0.007
(0.029)		−0.028
(−0.061)		−0.067
(0.583)
Working Hour0.019
(0.023)		 0.019
(0.023)
SOEs vs. POEs
(F-test)		 15.28 ***
TimeControlControlControl
ProvinceControlControlControl
Control Variables Included
Wald chi22320.19610.20601.13
Prob > chi20.0000.0000.000
n22,58249435298
Note: ** p < 0.05, *** p < 0.01, two-sided tests. Robust standard errors are used.
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Xiao, Y.; Liu, X.; Ren, T. Institutional Differences in Individual Wellbeing in China. Sustainability 2022, 14, 721. https://doi.org/10.3390/su14020721

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Xiao Y, Liu X, Ren T. Institutional Differences in Individual Wellbeing in China. Sustainability. 2022; 14(2):721. https://doi.org/10.3390/su14020721

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Xiao, Youzhi, Xuemin Liu, and Ting Ren. 2022. "Institutional Differences in Individual Wellbeing in China" Sustainability 14, no. 2: 721. https://doi.org/10.3390/su14020721

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