Abstract
Why do communities in different spatial areas display different types of governance performance? Applying the perspective of spatial theory, this paper proposes an analytical concept of “power space”. The concept refers to the distance between a community and the municipal administrative power center. Based on data drawn from the Shanghai Urban Neighborhood Survey, this study examines the variation in governance performance across communities located in different areas of the spatial structure of city power, and analyzes the causes and mechanisms underlying these differences. The study suggests that the spatial distance between the community and the center of governance power is not merely physical and geographical in nature but also social and political. We find that the distance to the center of power has a significant effect on the types of community governance performance that are easily observable, but little effect on those that are less perceptible. This reveals that power space exerts a strong effect on phenomena that can be easily recognized by higher-level officials, but not on phenomena that are less visible but nevertheless appreciated by the residents. Such a pattern can be explained by the current governance performance assessment system and incentive mechanism. Power space exerts an influence through the mechanisms of public resource allocation and governance performance benchmarks, which are equally applicable to other areas of social governance. In sum, this study contributes to the understanding of the underlying logic of grassroots social governance in contemporary China.
Research questions
There are currently two analytical methods in academia for assessing the factors that influence community governance performance: top-down and bottom-up, respectively. The former focuses on the role of state power in community governance and mainly examines the influence of residents’ committees’ capacity for internal resource mobilization and external resource acquisition on the community's governance performance from the perspective of institutional capacity (Sun and Huang, 2012). The latter, meanwhile, focuses on the role of civil power in community governance and mainly examines the impact of factors such as residents’ social networks or social trust on community governance performance from the perspective of social capital (Chen and Lu, 2009; Luo et al., 2014). The above two analytical methods and perspectives both assume that community governance practices take place in homogeneous urban spaces, thereby ignoring the spatially heterogeneous characteristics of community governance performance. The attributes of urban spaces are not merely geographical and physical in nature but also social, economic, political, and cultural. These attributes exert a significant impact on the processes and outcome of community governance.
In the 1970s, Lefebvre (1991[1974]) constructed a social theory framework based on spatial ontology, initiating the spatial turn in contemporary social theory (Jing, 2013; Wen and Huang, 2012; Wu, 2008). The Chicago School was the first to introduce a spatial perspective into community studies, and also proposed various urban spatial models for depicting the spatial distribution and structural characteristics of urban communities. However, these analyses are mainly descriptive in nature and lack an in-depth exploration of spatial attributes and their impact on urban governance. While researchers of the new urban sociology continue to focus on space, they are also paying increasing attention to the multiple attributes and meanings of urban space, regarding it as not only a simple reduction to physical space, but also a product of the interactions between political, economic, and social forces. In short, urban space is “constituted by the relationships between people, institutions and systems” (Cai, 2003; He, 2006; Li, 2006).
Existing research has noted that communities in different urban spaces have different living environments, amenities, community management styles, levels of convenience, etc. (Wang, 2002). For example, some studies have found that during the urbanization process, diverse spatial areas are formed, and this differentiation in the spatial structure shapes different types of urban communities. Under the dual impact of the spatial structure and social relationship structure, these communities develop different governance structures, governance mechanisms, neighborhood relationships, etc. (He, 2019; Zhu, 2019). Although these studies reveal that urban space is not homogeneous and that communities in different locations are endowed with different attributes and governance styles, there remains a lack of in-depth discussion on how spatial factors affect community governance.
Following the concepts of the new urban sociology with regard to space, this paper introduces a spatial perspective into the analysis of community governance performance, and seeks to answer the following questions: Why does the governance performance of communities in different urban locations differ? How is this difference related to the current institutional arrangements for urban community governance in China? Behind this governance difference, what is the action logic of grassroots governments as the leaders of community governance? How does this action logic lead to differential spatial patterns of governance performance?
On the one hand, the answers to these questions provide a better insight into the spatial disparity between community governance performance, while on the other, by exploring the reasons for this disparity, we can reveal the institutional elements that influence community governance performance, as well as grassroots governments’ basic action logic of community governance, thus showcasing the political and social implications behind the location of communities.
Literature review
Community governance performance and its influencing factors
Community governance refers to the joint management of public affairs in urban communities by multiple stakeholders, including government departments, community organizations, and residents. Community governance performance is a comprehensive assessment of the quality and effectiveness of community governance. Community governance involves multiple governance bodies, such as the government, market, and society. Its content includes public management, public services, and public safety, and involves multiple dimensions, such as institutions, organizations, and personnel. Therefore, to assess a community's governance performance comprehensively, it is necessary to take into account various factors related to the community: antecedent and consequent factors, tangible and intangible foundations, objective and subjective evaluations, input costs and output effectiveness, etc. There are currently two basic perspectives on the factors that influence community governance performance.
The first is the institutional capacity perspective. Researchers who have applied this perspective draw lessons from the analytical concept of “institutional capacity” developed from western urban governance theory. “Institutional capacity” refers to a specific local capacity or local culture in a specific area that enables government agencies, business sectors, and civil society to coordinate and cooperate, and integrate resources, and plays a vital role in local governance and development (Healey, 1998). This perspective focuses more on community governance bodies with administrative characteristics, such as residents’ committees. Sun and Huang (2012) classified the institutional capacity of residents’ committees into an internal mobilization capacity and an external resource acquisition capacity, and found a significant positive correlation between the institutional capacity of residents’ committees and community governance performance by analyzing survey data collected from 45 neighborhoods in Shanghai. Lu (2013) used the same analytical tool and discovered that residents’ committees still rely mainly on community activist networks, as well as on favors, face-saving, reciprocity, etc. to carry out their work. However, the traditional internal mobilization model has become increasingly ineffective for conducting administration in relation to social control, so residents’ committees need to cooperate with forces beyond the community, such as government departments, public institutions, business agencies, and civil groups, to solve problems relating to community governance. Such an external resource acquisition capacity, characterized by coordination, cooperation, and resource mobilization, is becoming increasingly important for community governance performance.
The second is the social capital perspective. Researchers who have adopted this perspective have generally reached a consensus on the “causal” relationship between social capital and community governance effectiveness. This perspective focuses more on social bodies in community governance. The impact of social capital on community governance performance lies in community identity and trust. Social capital strengthens community identity, provides a basis for trust in social collaboration, and provides a foundation for social collaboration with its social network. Luo et al. (2014) quantified community interaction and participation, triggered by community identity and trust, as relational social capital, and found that it was positively correlated with residents’ satisfaction. Some scholars have also discovered that different types of social capital have different effects on community governance performance. For example, Chen and Lu (2009) classified social capital into generalized social capital and particularized social capital. The former, embodied in indiscriminative social trust and inclusive social networks, has a positive impact on community governance, while the latter, as manifested in discriminative social trust and exclusive social networks, has a negative one.
The above two perspectives emphasize the impact of different community governance bodies on governance performance. The institutional capacity perspective focuses on the influence of administrative bodies’ mobilization capacity and resource acquisition capacity on community governance. The social capital perspective focuses on the influence of elements such as networks, identity, and trust on social bodies in community governance. The measurement of community governance performance also varies across different perspectives. Under the institutional capacity perspective, researchers measure governance performance by the extent to which the functions of the administrative bodies are realized, which is generally a top-down approach to measuring higher-level officials’ assessment of community governance effectiveness. The social capital perspective, on the other hand, is more of a bottom-up approach, intended to measure governance effectiveness based on the residents’ perceptions, including indicators such as residents’ satisfaction with governance efforts.
Although these two perspectives examine the relevant factors that affect community governance performance from both governance bodies—government and society—neither takes account of the differentiation of community governance in urban spaces. Community governance practices do not take place in homogeneous urban spaces, but are influenced by the location of communities. Communities in different urban locations receive different levels of attention, and thus enjoy different levels of financial input and public resource allocation. In recent years, although governments around the world have been vigorously advocating the equalization of basic urban public services, people in different regions can still perceive the unevenness of urban public management and services. Therefore, this paper attempts to introduce a spatial perspective into the examination of community governance performance, and analyze the specific mechanisms behind the spatial differentiation in community governance performance.
Spatial perspectives in community studies
Lefebvre (1991[1974]) made a groundbreaking contribution to the discussion about space. He introduced space, which used to be the background to social events and social interactions, into the core of analysis, explored the political and social connotations of space, and reflected on and critiqued the social space of capitalism. He pointed out that space does not simply exist physically, but is also a product of people-to-people social relations, “an essential part of productivity and relations of production”. Lefebvre's theory of space directly inspired later in-depth studies of space by Castells (1977), Foucault (1995), Harvey (1992), and Soja (1996), among others. As a new research perspective, space has brought new analytical tools to many fields in the social sciences (Jing, 2013; Wen and Huang, 2012).
In the field of urban sociology, the Chicago School was the first to examine communities from a spatial perspective, and proposed the concentric zone, sector, and multiple nuclei models, among others, to discuss the spatial distribution patterns and structural characteristics of various types of urban communities (Park, 2016[1925]). Based on this theory, McKenzie explored the evolving process of the spatial structure of urban communities in two dimensions, namely, “concentration” and “dispersion”, and dynamically analyzed how various types of communities emerge, develop, and then become extinct (Ye, 2005). The Chicago School's studies on community types and spatial differences inspired later research on community differentiation, which proposed that spatial difference resulted from and deepened community differentiation (Xiao, 2011). In the late 1940s and then the 1950s, scholars represented by Shevky and Williams (1949) and Bell (1955) proposed the concept of a “social area” as a method for explaining urban spatial structure and the main factors influencing the differentiation of “social areas” in cities, and found that socio-economic status, family status, and ethnic status were the three main influencing factors (Bell, 1955). Since the 1980s, western scholars have not only studied the evolution of “social areas” across multiple time periods, but also expanded their research to residential segregation, that is, the spatial distribution of housing, industry, fertility, etc.
Inspired by western urban spatial studies, Chinese scholars also began to study the socio-spatial structure of Chinese cities in the late 1980s and early 1990s. Xu et al. (1989) applied the factorial ecological method to a spatial analysis of Guangzhou city. Compared with foreign countries, the differentiation and evolution of the socio-spatial structure of Chinese cities is more influenced by administrative forces, and the government's administrative planning and intervention exert an important impact on the formation and development of specific social areas (Sun and Jiang, 2018). Urban planning, industrial planning, and the household registration system are important influencing factors regarding the formation and differentiation of urban socio-spatial spaces in China (Feng and Zhou, 2003; Wang and Yang, 2015).
Communities, as the basic units of the urban spatial structure, are also influenced by administrative forces. After reform and opening up, the work unit (danwei) system was gradually replaced by the subdistrict (jieju) system, which became the dominant mechanism of grassroots community governance in cities. With the reform of the urban housing system in the late 1980s, housing became commercialized and the real estate market flourished. Thus, a large number of residential complexes of commercial housing emerged, forming different types of communities. The administrative and market forces began jointly to shape the spatial structure of Chinese cities, which then developed major differentiation. This differentiation between the urban spaces and communities led to the problem of spatial justice in community governance. There was a drastic contrast between the prosperity of the central urban areas and the decline of the peripheral areas, as well as between the living environments of the wealthy communities and the poor ones. The main reason for this contrast is the uneven allocation of spatial resources under the influence of the dominant power. People living in communities of different classes have different degrees of proximity to public resources and enjoy different levels of quality of public services (He et al., 2011). In addition to the uneven spatial allocation of public goods, the factors of production flow unevenly and even come to be arranged in a “non-market” way, as administrative offices give different degrees of priority to the development of different spaces (He and Zhao, 2019). This shows that the spatial differentiation of communities is influenced by the top-down plans and arrangements of administrative forces. As the basic spatial and social units of urban governance, the bodies of administrative power exert an influence on communities that cannot be ignored. The political attributes of community space always exist, reflecting the action logic of these bodies.
Power space and governance performance
Community studies that adopt a spatial perspective reveal the political feature of community space. Lefebvre and others mainly discussed the political features of space theoretically, regarding it as an abstract object that took the form of a political game between government, capital, and civil society. This study attempts to measure in a practical sense the political feature of urban space and its effect on community governance performance in the context of China's hierarchic bureaucracy and the basic logic of urban community governance.
It has been argued that the basic operation of Chinese society can be simplified as a mode centering around a power center (Zhai, 2012). Power refers mainly to political power. The power center dominates the allocation of resources based on a long-established, consistent, and powerful bureaucracy. In the Chinese context, therefore, the distance from the power center is endowed with a social and political significance that transcends physical space. The political feature of space is always closely related to power. The space produced by power must, naturally, be managed, but also serves as a vehicle for the operation of power. He and Zhao (2019) used the term “differential space” to describe the urban space shaped by the government in which urban public resources decrease as one moves from the center to the periphery.
Regarding the political features of space, economists were the first to attempt to measure the political impact of space according to distance. As distance is one of the fundamental features of space, the distance from a dominant power center can be seen as a spatial projection of the dominant power. When a region is relatively close to the power center, it is more likely to receive a favorable allocation of resources from it. For example, researchers examined the relationship between the geographical distance from the provincial capital government and the supply of local government health services, and explained the operational mechanisms using institutional rents and government competition: institutional rents are the result of non-market institutional arrangements, and rent-seeking is an important manifestation of institutional rents. Therefore, the closer one is to the power center, the more rent-seekers there are. This is because it is easier for areas closer to the higher-level governments to have timely contact with the higher-level authorities, which facilitates rent-seeking and makes it easier to obtain more financial support (Kopczewska, 2013). The “government competition theory” explains that areas near provincial borders may enjoy special benefits from the higher-level governments. Therefore, there is a U-shaped correlation between the distance from the provincial capital government and health services (Zhao, 2017).
It has also been found that the government's direct intervention in and control over economic activities will cause power centers to become geographically important in economic development and the allocation of the factors of production (Luo, 2014). The distance from the power center may affect the cost of coordinating relevant economic activities with the authorities as well as the differences in investment and labor demand. Proximity to various power centers brings more job opportunities and greater advantages in terms of wages. Moreover, government redistribution is also correlated with the distance to the power center (Luo, 2014). Research on financial regulation has found that the further away a listed company is from the local branch of the China Securities Regulatory Commission, the greater the “information asymmetry” and the higher the regulatory enforcement costs, but the lower the deterrent effect the regulator will exert on the listed company. Researchers have described this correlation by drawing parallels with the classical Chinese idiom of center–periphery relations “The mountains are high, and the emperor is far away—it is difficult to get justice” (Tian and Wang, 2019).
All of the above studies suggest that the distance from the power center has both social and political significance, beyond its physical nature, in the Chinese context. Administrative power exerts an important influence on the distribution and evolution of spatial patterns in Chinese cities. Therefore, communities, as micro-units in the urban spatial structure, exert different impacts on governance performance according to their distance from the power center. This study proposes the analytical concept of “power space” and defines it in a practical sense as “the distance from the power center”, to provide a tool for examining the impact of the spatial distance between communities and the power center on community governance performance.
A literature review of studies on community governance performance indicates that academia has not reached a consensus on its measurement due to the diversity and complexity of the bodies involved in and the content of community governance. Different researchers tend to choose their own indicators according to their different research purposes. However, there are generally two types of evaluators of community governance performance: one is community residents from the bottom, and the other is government departments from the top. Within power space, the evaluation of community governance performance mainly involves the examination of lower-level departments by higher-level departments. Although the latter sometimes include residents’ comments in the evaluation system, they tend to pay more attention to indicators that are easy to notice and observe, which have a greater influence on the evaluation system. Based on the study of the visibility of public goods, Wu and Zhou (2018) proposed the concept of “visible governance performance” in a further discussion of the performance evaluation of government officials under the political “promotion tournament” system. Visible governance performance refers to performance indicators that constitute the subjective evaluation made by higher-level evaluators and leave a good impression to lift the scores. This concept emphasizes that during performance appraisals, higher-level evaluators form a subjective assessment during site visits and inspections to complement or even partially replace objective performance indicators. The significance of visual governance performance is that it motivates lower-level governments and officials to invest more in visual governance performance indicators, represented by items that are easily observable and directly perceptible, such as landscaping and sanitation.
Research idea and hypothesis
This study proposes the concept of “power space” and introduces the spatial factor of “the distance from the governance power center” into community governance to assess its performance. On the one hand, it reveals the power relations and political features behind space. On the other hand, it explores the basic logic of the grassroots government departments when dealing with superordinate appraisals of community governance performance.
According to the regions of community governance, this study divides the assessment of community governance performance into two levels. The first level is the residential complex, focusing on the governance performance of each complex within the jurisdiction of a residents’ committee. At this level, the power center of community governance is the residents’ committee. Although residents’ committees are defined by relevant laws as autonomous organizations of residents rather than administrative agencies, practically, residents’ committees are strongly “administrative” in nature and perceived to be an extension of grassroots administrative power. In addition, the community management affairs of the subdistrict offices are basically carried out by the residents’ committees (Wang, 2016). Therefore, in a community under the jurisdiction of a residents’ committee, its office becomes the actual power center of the community. This paper thus measures a community's location in power space according to “whether the residents’ committee is in this community”.
The second level is the subdistrict level, at which the community governance performance of each residents’ committee within the jurisdiction of the subdistrict office is examined. In the urban community governance system of “governance at two levels and management at three levels” (liangji zhengfu, sanji guanli), subdistrict offices, as government agencies, play a decisive role in community governance (Wei, 2003). Subdistrict offices are granted “the right to participate in part in urban planning, hierarchical management, comprehensive coordination, regional management, along with other community governance powers, making them the center of administrative power at the subdistrict level” (Zhu and Zhang, 1998). Meanwhile, the subdistrict office's performance assessment of residents’ committees and the fact that it guides their work also make it the leader in terms of resource allocation and the supervisor of public affairs within the community (Wu, 2015). Therefore, at the subdistrict level, this paper takes the distance from the subdistrict office as a variable for measuring the location of the residents’ committee in power space.
In power space, the following two mechanisms may influence community governance performance.
The public resource allocation mechanism
Administrative power can influence community governance performance through the allocation of public resources, such as public facilities and services. Existing research has shown that public resource allocation is significantly correlated with the proximity to the urban center, and the distance from the urban center is the main factor contributing to uneven resource allocation in relation to public services and facilities (McAllister, 1976). Studies of first-tier mega-cities in China have also found a trend of decreasing allocation of public services and facilities when moving from the city center to the urban periphery. For example, in Shanghai, the allocation of medical facilities and community health services resources generally decreases when moving from city center areas to the suburbs and then to the outer suburbs (Liang, 1995; Shi et al., 2010). In Guangzhou, the quality of medical facilities is also reduced when moving from the center to the peripheral areas in a circular pattern (Hu and Chen, 2008).
It is well known that resource allocation in relation to various public services and facilities in Chinese cities is basically the result of top-down planning by various administrative departments, and the center of the city is generally the political, economic, and cultural center, that is, the power center. The above studies show that the closer an area to the power center, the more public resources it will be allocated. Meanwhile, the allocation of public service facilities is one of the criteria for measuring community governance performance. In the existing assessment system, the coverage of public services and allocation of public facility resources are important measurement indicators (Bi and Li, 2020; Ma et al., 2016). Therefore, to a large extent, the number of public services and facilities in the community determines the level of community governance performance. Moreover, due to the same logic, spatial differentiation in the allocation of public services and facilities exists not only at the level of the city as a whole, but also at various levels of administrative area within the city. This means that even in the smallest governance unit, the distance from the power center affects the governance performance by influencing the allocation of public resources. Therefore, the first hypothesis of this study concerns the interrelationship between power space, the allocation of public resources, and community governance performance.
The performance benchmark mechanism
Currently, the Chinese government has adopted a “promotion tournament” model to increase the pressure on and incentives for officials at all levels. Under this model, local government officials must outperform their competitors in performance evaluations and gain recognition from their superiors if they seek promotion (Zhou, 2007, 2009), which provides them with incentives to create “bright spot” projects that make them stand out. “Cost-benefit” and “explicit incentives” are two important factors in creating bright spot projects. Due to the financial limitations, local officials tend to choose projects with a low threshold, low cost, and short return period, rather than so-called “hard bones” projects that are difficult, and require high investment and a long development period. “Explicit incentives” refer to the fact that “visible and tangible” public goods are more attractive for government officials to invest in than those that are difficult to observe. Higher-level governments, when examining the performance of their subordinates, consider both the objective indicators of economic and social development and “impressions” of individuals’ abilities and performance based on subjective evaluations made during on-site visits. In order to highlight their performance, lower-level government officials tend to invest their fiscal expenditure in visible areas under the combined influence of fiscal limits and explicit incentives (He and Zhao, 2019), leading to a “visibility bias” in public goods spending (Mani and Mukand, 2007). Studies have verified the importance of “visible performance”, such as landscaping and sanitation, and its incentives for government officials (Wu and Zhou, 2018). Since places close to the administrative offices of government departments at all levels are those most likely to be chosen for inspection, each level of government has a significant incentive to ensure that this area is sufficiently clean, tidy, and beautiful to leave a good impression on the higher-level authorities. Therefore, the second and third hypotheses of this study concern the relationship between power space and visible community governance performance.
In summary, the core concept and analytical framework of this paper are shown in Figure 1.
Analytical framework.
Data and variables
Data
The data for this paper are mainly drawn from the Shanghai Urban Neighborhood Survey (SUNS), a collaborative project between the Center for Data Science and Urban Studies at Shanghai University and the Center for Applied Socioeconomic Research at the Hong Kong University of Science and Technology. The survey collects multi-level tracking data on communities, households, and individuals. Three sub-projects have been completed to date: the neighborhood survey, the household survey, and community observation.
Among them, the neighborhood survey was completed in 2015, with a random sample of 10% of the 5732 residents’ committees (or village committees) in Shanghai. Meanwhile, 537 residents’ committees were surveyed to collect basic information and further details on community elections, community governance, relationships between the residents’ committees and other institutions, foreign population, community environment, public services, etc. The household survey was completed in early July 2017 and included 5201 household questionnaires, 8631 adult questionnaires (adults aged 15 years or older), and 1892 questionnaires for minors (under 15 years old).
The community observation was completed at the end of 2018. In this, 425 residents’ committees and 1296 residential complexes under their jurisdiction in 16 districts of Shanghai were selected for structured observation, including neighborhood observation and community observation. The survey aimed to collect data at both the residents’ committee and residential complex levels, including information on public facilities in the neighborhood, community environment, social environment, pet ownership, parking, safety, housing types, and convenience of living. Based on these data, we can assess the green space in the neighborhood, public facilities available, and community governance at the neighborhood and community levels. In addition, we obtained the latitude and longitude data of each residents’ committee and subdistrict office in the survey sample through Gaode Maps’ application programming interface (API), in order to calculate the spatial distance from each residents’ committee to the subdistrict office to which it belongs. We obtained the housing price data of the surveyed residential complexes from the real estate listings website Anjuke, and matched these with the community governance data to form the database required for the analysis.
Variables
This study examines the relationship between power space and community governance performance at both the residential complex and subdistrict levels 1 ; therefore, variable measurements and statistical analyses were conducted at both levels.
Dependent variables
The outcome variable in this study is “community governance performance”. We further distinguish between “visible performance” and “invisible performance”. As the name implies, the former refers to governance effectiveness that can be easily observed or perceived directly, while the latter refers to governance effectiveness that cannot.
At the residential complex level, we chose three indicators based on data collected from the community observation survey to measure “visible performance”, “green space coverage”, “presence of garbage bins for sorting waste (i.e., according to paper, plastic, etc.)” 2 , and “presence of volunteer patrols” within the residential neighborhood. “Invisible neighborhood governance performance” was measured by the “presence of piles of objects in the corridors of the building”.
At the subdistrict level, we used data on the “number of recognitions (e.g., awards, medals, and honors) received by residents’ committees” from the village survey and household survey to measure “visible community governance performance”, that is, the type of performance recognized by the higher authorities. Two subjective indicators, “community satisfaction” and “sense of belonging”, were selected to measure “invisible community governance performance”, that is, the type of performance recognized by the residents.
In examining the specific mechanisms by which power space acts on community governance performance, we include “the number of community elderly services” and “the degree of community environmental cleanliness” as proxies for the “public resource allocation mechanism” and “performance benchmark mechanism”, respectively.
Key independent variable
The key independent variable in this study is “power space”, operationalized as “spatial distance from the center of governance power”. At the residential complex level, we use “whether the residents’ committee is located in the residential complex” as a measure of power space. If the residents’ committee's office is located in the residential complex, this means that the residential complex is closer to the center of power, and neighborhood governance is more likely to be influenced by the residents’ committee.
At the residential complex level, we further distinguish between “absolute distance” and “relative distance”. The former is measured directly by the spatial distance between the residents’ committee and its subdistrict office. For the latter, we first calculate the spatial distances of all of the residents’ committees within the jurisdiction of the subdistrict office, then rank these distances, and use the ranking as a measure of relative distance in the subdistrict power space. The relative distance can control the influence of the size of the subdistrict office jurisdiction.
Control variables
In order to control the influence of the community location, type, economy, culture, and history, we use “community type” (i.e., whether it is a commercial housing community) and “average housing price of the community” as the control variables at the community level. At the residential complex level, we use the “proportion of the population with a Shanghai household registration”, “length of time of the community's establishment”, “type of community”, and “location of community” as the control variables.
See Table 1 for the operationalization and measurement of the above dependent variables, key independent variables, and control variables.
Variables and their measurement.
API: application programming interface.
Community power space and governance performance
Analysis at the residential complex level
The impact of residential complex power space on visible governance performance
Currently, in urban residential complexes, there are diversified governance structures and governance bodies, including community committees of the Communist Party of China (CPC), residents’ committees, homeowners’ committees, property service enterprises, homeowners, etc. These bodies interact with each other and jointly affect the effectiveness of residential complex governance. However, the power relations of these governance subjects are unequal. As representatives of the CPC and state, community party and residents’ committees dominate the governance of neighborhoods at its center. This is particularly evident in Shanghai. For example, Article 6 of the newly revised Shanghai Residential Property Management Regulations of 2020 clearly requires that “residents’ committees and village committees shall, in accordance with the law, assist the people's government at the town and township level and subdistrict offices in carrying out work related to property management in community management and community services, strengthen the guidance and supervision of the homeowners’ committees, and guide them to operate in a standardized manner with self-governance” (National Laws and Regulations Database). This shows that in the case of community governance, CPC community committees and residents’ committees possess the core power and, thus, supervise, guide, and direct other governance subjects. Therefore, at the spatial level, they lie at the center of the community power space. Since CPC community committees and residents’ committees usually work in the same location, we take the residents’ committee office as the center of the neighborhood power space and examine whether the governance performance of the residents’ committee's neighborhood is better than that of other neighborhoods.
Table 2 presents a statistical description of the variables analyzed at the residential complex level, with a valid sample of 1156 neighborhoods, of which 325 (about 28%) are residential complexes where the residents’ committees are located and 702 (about 61%) are commercial housing neighborhoods with an average housing price of ¥51,700/m2. Table 3 shows the effect of “whether the residents’ committee is located in the residential complex” on visible and invisible governance performance. Since the first measure of visible governance performance, “green space coverage”, is a fixed-order variable, we use an ordered logistic model to analyze it. The remaining two measures of visible performance, that is, the “presence of volunteer patrols” and “presence of piles of objects in the corridors of the building”, as well as the measure of invisible performance, are dichotomous variables; therefore, they are analyzed using a logit model.
Statistical description of the residential complex-level variables.
The impact of power space on residential complex governance performance.
Note: N = 1156; ***p < 0.01, **p < 0.05, *p < 0.1.
The results from Models 1–3 in Table 3 show that residential complexes in which residents’ committees are located perform better in terms of green space coverage, the placement of garbage bins for sorting waste, and volunteer patrol arrangements in comparison with residential complexes in which residents’ committees are not located. Under the condition of limiting the type of residential complex and average housing price, the level of green space coverage in the neighborhoods in which residents’ committee are located is 78.4% higher (e0.579), the probability of placing garbage bins for sorting waste is 77.9% higher (e0.576), and the probability of having volunteer patrols is 39.2% higher (e0.331) than for residential complexes in which residents’ committees are not located. All three of these indicators of governance performance are relatively easy to observe and perceive and, thus, form part of visible governance performance. The results of the analysis suggest that in terms of residential complex governance, the closer to the center of power an area is, the higher the visible governance performance.
The impact of residential complex power space on invisible governance performance
However, in measures of invisible governance performance, do residential complexes with residents’ committees also perform better? The results of Model 4 shown in Table 3 suggest that this is not the case, as the residents’ committees did not perform significantly better in the case of objects piling up in the corridors of buildings. The main reasons for this result are as follows. On the one hand, phenomena such as piled up objects are not immediately observable when first entering a residential complex, unless there is a special inspection and, in general, the higher-level officials do not enter the hallways when they visit a residential complex to inspect it. They are willing to devote more effort to improving the visible performance of governance, such as green space, garbage classification, and volunteer patrols, which are noticeable as soon as they enter the residential complex. On the other hand, even if the residents’ committees are willing to rectify the piling up of objects, such behavior is ultimately related to the standards upheld by the residents themselves and, thus, is hard to change even over the long term. People's habits do not change overnight, and to do so requires unremitting effort; therefore, the effect cannot be perceived in the short term.
Combining the results of the four models in Table 3, we find that at the residential complex level, power space centered on the CPC community committees and the residents’ committees had a significant impact on visible governance performance, but there is no significant effect on invisible governance performance, which confirms Hypothesis 3.
Analysis at the subdistrict level
The impact of subdistrict-level power space on governance performance as recognized by superiors
After analyzing the residential complex level, we expand the governance scope to the subdistrict level, that is, the level under the jurisdiction of subdistrict offices. Since the subdistrict offices are community government agencies, they are directly responsible for the public services, public management, and public safety within their jurisdiction, and must undertake a variety of protection work that meets people's basic needs within the jurisdiction. They not only provide financial support to the residents’ committees, but also largely influence their staff composition and job responsibilities, and assess their work. Therefore, at the residential complex level, the subdistrict office is located at the center of power space. We will next examine how the spatial distance between residents’ committees and the subdistrict office affects the governance performance of the former. In power space, the evaluation of subordinates by superiors best reflects the essential characteristics of the space and the logic of power operations. Therefore, we choose the number of recognitions received by residents’ committees from superiors as a governance performance indicator measuring “recognition by superiors”, which is a comprehensive indicator that may both evaluate visible performance and imply invisible performance.
Table 4 presents the statistical description of the variables at the subdistrict office level, with a total sample of 355 valid residents’ committees. The mean number of these committees receiving various types of commendations from their superiors is about 24, and the standard deviation is about 27. The mean linear distance of the residents’ committees from the subdistrict office to which they belong is about 1.4 km, with the nearest linear distance being 0.074 km, and the farthest 7.6 km. Regarding the characteristics of the communities under the jurisdiction of the residents’ committees, the average percentage of residents with Shanghai household registration is 78.3%; the smallest percentage is 11.4%, and the largest percentage is 97.8%. The average time period since the residents’ committees were established is 25.6 years, with the shortest being 5 years and the longest 69 years. In terms of community types, 30 communities (8.45%) have lilong housing as the main type, 3 41 (11.55%) have old publicly owned housing as the main type, 63 (17.75%) have privately owned, subsidized housing as the main type, 125 (35.21%) have ordinary commercial housing as the main type, 21 (5.92%) have high-end commercial housing as the main type, and 75 (21.13%) have resettlement housing (i.e., housing into which people are placed after the demolition of the previous housing) as the main type. In terms of geographical location, 118 communities (33.24%) are located in the city center, 120 (33.8%) in the suburbs, and 117 (32.96%) in the outer suburbs.
Statistical description of the subdistrict-level variables.
In Table 5, we analyze the effect of the spatial distance between the residents’ committees and subdistrict offices on the number of recognitions received by the residents’ committees as a way of examining the effect of power space at the subdistrict level on community governance performance. The number of recognitions received by the residents’ committees is a non-negative integer count variable, and the distribution of this variable shows an obvious left-skewed pattern, that is, the vast majority of residents’ committees received fewer than 30 recognitions, concentrated in the interval range of 10–25, which is consistent with the basic characteristics of a Poisson distribution, so we use a Poisson regression for the analysis. Model 1 shows the effect of the absolute distance between the residents’ committees and subdistrict offices on the number of recognitions received by the residents’ committees. The results of the analysis show that when controlling for community demographics, establishment duration, housing type, and geographical location, the number of recognitions received by the residents’ committees decreases by 17.4% for every 1 km increase in the absolute linear distance between the residents’ committee and the subdistrict office [1−exp (−0.191)]. This indicates that the less the spatial distance between the residents’ committee and the subdistrict office, the more the governance performance is recognized by superiors.
The impact of power space on subdistrict community governance performance.
Note: N = 355; ***p < 0.01, **p < 0.05, *p < 0.1.
Although we used the cluster command to eliminate the heteroskedasticity effect between streets in constructing the regression model of distance in power space and community governance performance, the absolute distance between the residents’ committees and subdistrict offices in the city center, even if they are located far away from each other, is still a smaller value relative to that in the suburban and outer suburban areas, since the average area of jurisdiction of the subdistrict offices in the city center is far smaller than that of the subdistrict offices in the suburbs or outer suburbs.
To examine further differences in community governance performance due to differences in the distance between the residents’ committees and the governance power center within the same subdistrict, this study uses relative distances by comparing the distances between the residents’ committees and the governance power centers in the same district in order. The smaller the relative distance, the closer the residents’ committee is to the center of governance power within the subdistrict to which it belongs. The results of the analysis are presented in Model 2 and are largely consistent with those for Model 1. When controlling for basic community characteristic variables, the further the residents’ committee is from the subdistrict office in the subdistrict to which it belongs, the less recognition it will receive. Specifically, the number of higher-level recognitions received by the residents’ committees decreases by 9.2% for each one-degree drop in relative distance from the subdistrict office to which they belong [1−exp (−0.097)]. Thus, it is clear that power space centered on the subdistrict office has a significant effect on the community governance performance of the residents’ committees in terms of receiving superior recognition at the subdistrict level.
The impact of subdistrict-level power space on governance performance as recognized by the residents
If the recognitions received by the residents’ committees constitute community governance performance as recognized by the higher administrative authorities, does the effect of power space still exist for governance performance as recognized by the community residents? Since 2014, Shanghai has further implemented reforms to build innovative social governance and strengthen community construction, promoting a shift in the focus of subdistrict work from economic development to strengthening the promotion of CPC community committees and social governance concerning public services, public management, and public safety, with the aim of improving residents’ life satisfaction, and sense of satisfaction and belonging. Therefore, this study uses “community satisfaction” and “sense of community belonging” to measure governance performance as recognized by the residents. Because these two indicators are difficult to observe directly, they are classified as invisible indicators of governance performance.
We matched individual residents’ data with community data, aggregated the individual-level “community satisfaction” and “sense of community belonging” scores to the community level, calculated the mean value for each community, and obtained a total of 69 valid community samples. Table 6 shows the statistical description of the variables. Shanghai residents’ satisfaction and sense of belonging to the community are 3.105 and 3.213 on a scale of 1 to 5, respectively. The other variables are generally consistent with the mean values or proportions and their distributions of the subdistrict level variables in the previous section based on 355 community samples, indicating that those 69 community samples remain representative, despite their reduced number.
Statistical description of the variable.
The results of the analysis of the effect of subdistrict power space on residents’ community satisfaction and sense of belonging are presented in Table 7. Since the dependent variables are numerical, we use ordinary least squares regression models for the estimation, where Model 1 has 39% explanatory power for the variability of resident satisfaction and Model 2 has 57.7% explanatory power for the variability of residents’ sense of belonging. The overall F-test outcome for both models is at the 0.01 level of statistical significance. The results show that the distance between the community and the subdistrict office does not have a significant effect on the residents’ community satisfaction and sense of belonging; only the age of the community and the predominant type of housing in the community affect the residents’ satisfaction and sense of belonging, the pattern being that the longer the community has existed and the older the housing, the lower the residents’ satisfaction and sense of belonging. This result suggests that at the residential complex level, power space also has little effect on invisible performance that cannot be directly observed and perceived, such as the residents’ community satisfaction and sense of belonging, which again confirms Hypothesis 3. This phenomenon is related to the effect of the mechanisms of power space on community governance performance.
The impact of power space on community satisfaction and sense of community belonging at the subdistrict level.
Note: N=69; *** p < 0.01, ** p < 0.05, * p < 0.1.
The impact of the mechanisms of power space on community governance performance
The above analysis indicates that at both the residential and subdistrict levels, the closer a community is to the power center, the better its governance performance, especially with regard to visible performance. The mechanisms involved will be further explored in this section.
The public resource allocation mechanism
Previous studies show that public resource allocation is significantly correlated with the distance to the city center (McAllister, 1976) and that administrative power can influence social governance performance through public resource allocation, that is, public facilities and public services. Therefore, we will first explore the mechanism of public resource allocation through which power space affects governance performance.
Since Shanghai's aging population problem is becoming increasingly serious, home-based elderly care services occupy an important position in the supply of public services and resource allocation to communities in Shanghai. Thus, we choose the number of home-based elderly care programs provided by the community as a proxy variable for measuring the community's public resource allocation, and further analyze the relationship between power space, elderly care services, and governance performance. The Regulations of Shanghai Municipality on Home-Based Elderly Care Services, introduced in 2010, clearly propose 10 basic services that comprise the community's home-based elderly care, and the SUNS asked about the provisions of these. According to the descriptive statistics in Table 4, the average number of home-based elderly care service programs provided by communities is 3.7, with a standard deviation of about 2, ranging from 1 at the lowest to 10 at the highest.
Table 8 shows the results of the analysis of the relationship between power space, the community's elderly care services, and community governance performance. Since the dependent variables are all countable, a Poisson regression model is applied to all analyses. Model 1 explores the relationship between the spatial distance from the community to the subdistrict office and the provision of elderly care services in that community. The results indicate that the further the absolute distance between the community and the subdistrict office, the fewer home-based elderly care service programs are provided by the community. Specifically, for every 1 km increase in the linear distance from the subdistrict office, the supply of elderly care services decreases by about 7.2% [1−exp (−0.075)]. In addition, compared with communities in which regular commercial housing is the main type, communities in which lilong housing and privately owned, subsidized housing are the main types provide more home-based elderly care services, while communities in which high-end commercial housing is the main type provide fewer. Communities located in the suburbs and outer suburbs also have relatively fewer resources for providing home-based elderly care compared with communities in the city center. Such discrepancies between the supply and allocation of elderly care resources may be because communities in different locations and of various types have different proportions of senior residents and, thus, have different demands regarding elderly care services.
Relationship between power space, community elderly care services, and community governance performance.
Note: N = 345; *** p < 0.01, ** p < 0.05, * p < 0.1.
Model 2 further analyzes the relationship between community governance performance (in terms of number of recognitions from superiors) and the number of home-based elderly care programs that are provided to the community. The results show that when the absolute distance from the community to the subdistrict office and other features of the community remain unchanged, the higher the number of home-based elderly care programs and the better the governance performance. Specifically, for every additional home-based elderly care program, the number of recognitions received by the community increases by 7.5% [exp (0.072)−1]. Models 3 and 4 explore the relationship between the relative distance from the community to the subdistrict office and the number of elderly care programs in the community. When the relative distance and other features of the community remain unchanged, the relationship between the number of elderly care programs and governance performance derived from Models 3 and 4 is consistent with the results for Models 1 and 2. The above results strongly demonstrate that power space can have an impact on a community's governance performance through the public resource allocation mechanism, thereby confirming Hypothesis 1.
Relationship between power space, the performance benchmark mechanism, and community governance performance.
Notes: N=355; *** p<0.01, ** p<0.05, * p<0.1
The performance benchmark mechanism
Previous studies have indicated that government officials tend to invest more resources and make more effort in visible sectors, such as landscaping, the environment, and sanitation, to win them more recognition during superordinate appraisals and, thus, have a higher chance of promotion (He and Zhao, 2019; Wu and Zhou, 2018). Therefore, we seek to measure the performance benchmark mechanism through the cleanliness of the community environment to analyze the relationship between power space, the performance benchmark mechanism, and community governance performance. The SUNS sent inspectors to rate the cleanliness of the community environment, with ratings ranging from 1 to 7. The average rating for the 355 communities was about 5, with a standard deviation of 1.2 (Table 4). In Table 9, Model 1 examines the relationship between the absolute distance from the community to the subdistrict office (proxying distance in power space) and the cleanliness of the community environment (proxying the performance benchmark mechanism). The regression results show that the further the absolute distance from the community to the subdistrict office, the worse the cleanliness of the community environment. Specifically, for every 1 km increase in the absolute distance, the rating of the cleanliness of the community environment decreases by about a 0.16 unit. In addition, the older the community, the worse its environmental cleanliness. The environment of communities in which lilong housing and privately owned, subsidized housing are predominant is worse compared with that of communities in which commercial housing is predominant.
In Table 9, Model 2 examines the relationship between the cleanliness of the community environment and community governance performance. When the absolute distance from the community to the subdistrict office and other features of the community remain unchanged, the cleaner the community's environment and the better its governance performance. Specifically, for every 1 unit increase in the cleanliness of the community environment, the number of recognitions received by the community increases by 10.7% [exp (0.102)−1]. Models 3 and 4 explore the relationship between the relative distance from the community to the subdistrict office and the cleanliness of the community environment. When the relative distance and other features of the community remain unchanged, the relationship between the community's environmental cleanliness and governance performance derived from Models 3 and 4 is largely consistent with the results for Models 1 and 2. The above results suggest that power space can have an impact on community governance performance through the performance benchmark mechanism, thereby confirming Hypothesis 2.
Conclusion and discussion
This paper began with the concept of “power space” and inserted spatial factors into the discussion of community governance performance. By operationalizing power space as “the distance from the power center”, this paper measured the substantial impact of “power”, an abstract concept, on social life and grassroots governance from the perspective of spatial theory. The results show that the spatial distance from the power center has a significant effect on community governance performance. This effect is characterized by a distinctive “center–periphery” model; in other words, the closer a community is to the power center, the better its governance performance. Specifically, at the residential complex level, the residential complex where the residents’ committee, which is the administrative power center, is located, performs better than other complexes in terms of visible governance performance such as landscaping, garbage sorting facilities, and volunteer patrols; at the subdistrict level, the less the spatial distance to the subdistrict office, the more the community governance performance is recognized by higher-level officials.
The differential influence of power space on community governance performance results from both the urban public resource allocation mechanism and the grassroots government performance benchmark mechanism. On the one hand, the spatial distance between a community and the power center affects the allocation of public services and resources, such as home-based elderly care service programs, as well as visible performance, such as the cleanliness of the community environment; on the other hand, these public service resources and visible performances are significantly and positively associated with community governance performance that is recognized by the higher-level officials. Because of the vital role of the mechanism of performance benchmarks, power space strongly affects visible governance performance, but not performance that is less visible but that is appreciated by the residents.
This study shows that the spatial distance between a community and the center of governance power is not merely physical and geographical in nature, but also social, political, economic, and cultural. In addition, spatial location exerts a subtle influence on community governance. Communities are not located in the same homogeneous spatial environments, but in different areas of the structure of power space. Their location not only determines the public services and resources available to the community, but also has an impact on their external environment, thus affecting the residents’ quality of life. However, this study also reveals that power space has a significant effect on community governance performance that is directly observable and perceptible, and can be recognized by higher-level officials, but little effect on that which is less visible but appreciated by the residents. Such a pattern can be explained by the current governance performance assessment system and incentive mechanism. It is these institutional factors that lead to the strong pursuit of visible performance by grassroots governance bodies; thus, they neglect invisible governance performance. Nevertheless, governance performance that cannot be directly observed or perceived, but is appreciated by the residents, tends to exert a longer-lasting and more important influence on the long-term sustainable development of communities. Driven by the mechanism of both public resource allocation and performance benchmarks, the logic of the impact of power space on community governance performance is also applicable to other areas of social governance. Therefore, research on this topic contributes to a deeper understanding of the underlying logic of current grassroots social governance.
Footnotes
Contributorship
Zhiming Sheng framed the research questions, provided basic ideas for the research and drafted the manuscript. Qing Zhou conducted data clearing, matching, and analysis under Zhiming Sheng's guidance. Both authors read and approved the final manuscript.
Declaration of conflicting interests
The authors have no conflicts of interest to declare.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is supported by the Shuguang Project (19SG36) funded by the Shanghai Education Commission and Shanghai Education Development Foundation in 2019.