Voluntary monitoring of households in waste disposal: An application of the institutional analysis and development framework

Resources, Conservation & Recycling 143 (2019) 45–59

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Full length article

Voluntary monitoring of households in waste disposal: An application of the institutional analysis and development framework

T

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Zhijian Zhang , Liange Zhao School of Economics, Zhejiang Gongshang University, 18 Xuezheng Road, Hangzhou 310018, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Waste disposal monitoring Social capital Intention behavior gap Institutional analysis and development framework

While increasing consensus has grown around the important role of monitoring in environmental resource governance, little is known about under what conditions people would rather pay private costs to implement monitoring and punishment. Using field survey data on communities and households from four suburb areas in China, this paper employs Institutional Analysis and Development framework to empirically examine households’ willingness and actual activities on waste disposal monitoring. The empirical results show that population density, community modernization, and being male significantly increase the likelihood of households supervising waste disposal while community size and heterogeneity of wealth and ethnicity seriously impede the involvement of households in waste disposal supervision. More importantly, our estimation results reveal that staffing community with full-time cadres for sanitation management suppress the enthusiasm of households in conducting waste disposal supervision, but stock of social capital and peer monitoring substantially increase the intention to supervise waste disposal and the possibility of households engaging in waste disposal supervision activities. In addition, social norms, household income, and householder age are primary predictors of the intention-behavior gap between hypothetical willingness to monitor and actual monitoring behavior. Therefore, improving community infrastructure and economic condition, reducing external intervention on community affairs, and cultivating social capital stock are important approaches to enhance public participation in environmental governance.

1. Introduction Various institutional approaches and arrangements have been identified to avoid the occurrence of tragic outcomes and promote longterm environmental resources conservation, including governmental intervention, property privatization, and community collective action (Ostrom, 1990; Agrawal, 2001; Coleman, 2009). However, poorly designed or implemented institutions may fail to provide adequate incentives to resource users, and therefore lead to the tragedy of the commons proposed by Hardin (Dietz et al., 2003). Recently, some studies have begun to realize that monitoring plays an important role in implementing these institutional approaches and arrangements. For example, Gibson et al. (2005) declare that regular monitoring and sanctioning of rules is necessary for successful resource management. Pagdee et al. (2006) also find that effective enforcement has a strong correlation with forest management performance, and monitoring is closely associated with successful forest management. Moreover, Coleman (2009) confirms that monitoring appears to be a powerful predictor of changes in forest conditions, even after controlling for a

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series of institutional and ecological variables. While an increasing consensus that strengthening monitoring can effectively avoid the overuse of environmental resources has been reached (Hayes et al., 2017; Frey et al., 2016; Luintel et al., 2018), providing monitoring obviously presents a second-order collective action problem (Panchanathan and Boyd, 2004; Ostrom, 2005). In essence, costly monitoring is a second-order free ride problem (Heckathorn, 1989; Wei and Jiang, 2013). Although recent laboratory and field experiments have shown a widespread phenomenon that participants are general willing to reward those who adhere to rules and sanction those who deviate from them (Fehr and Gächter, 2000; Ostrom, 1998; Henrich et al., 2006; Sefton et al., 2007), little is known about why people would rather pay private costs to implement monitoring and punishment (Boyd and Mathew, 2007). More specifically, it is still unknown about under what conditions local actors can successfully overcome collective-action problems to provide voluntary monitoring within community. To the best of our knowledge, this important problem, especially in developing countries, has not received enough attention as it deserves in the research literature.

Corresponding author. E-mail address: [email protected] (Z. Zhang).

https://doi.org/10.1016/j.resconrec.2018.12.018 Received 3 May 2018; Received in revised form 26 November 2018; Accepted 12 December 2018 0921-3449/ © 2018 Elsevier B.V. All rights reserved.

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existing research, shifting the IAD framework away from common-pool resources governance to waste disposal monitoring. To date, no study has explicitly applied the framework to explore public participation in waste management. Most waste management studies examine the determinants of public participation exclusively from an independent individual perspective, but ignore the effects of higher levels on lower levels and dissever the interactions among individuals. This study therefore expands literature on individual voluntary monitoring and provides unique insights into the determinants of household waste disposal monitoring. Another feature of this study is that we take into account willingness to monitor and actual monitoring behavior in waste disposal simultaneously, and figure out the major factors that lead to the two sides deviating from each other. Furthermore, this study examines the impacts of social capital and its components on household waste disposal monitoring separately, and adopts multiple groups of instrumental variables to alleviate the potential endogenous problem of social capital. These techniques effectively reduce artificial bias caused by single index of social capital in the previous literature, which improve the robustness and reliability of our results.

Guaranteeing the compliance and involvement of stakeholders has been proved as a precondition for sustainable solid waste management (Joseph, 2006; Nguyen et al., 2015). However, illegal or improper waste dumping exist extensively in most developing countries (Agamuthu and Fauziah, 2011; Imam et al., 2008; Šedová, 2016), which imposes an urgent threat to human health, environment, esthetics, and economy. In addition to negatively affect surrounding property values and lead to natural disasters such as fire at illegal dump sites and flood caused by drains and culverts blocked with abandoned waste, improper dumping of waste like toxins or hazardous materials may sometimes causes short term and long term health issues. What’s worse, beyond negative health outcomes due to pollution and toxic waste, accumulations of waste improperly dumped provide a breeding ground for mosquitos and mice, and consequently increase the spread of infectious-diseases. Along with the accelerating process of urbanization, a large number of rural people have moved to suburban areas in China. In most suburb areas of China, the majority of local inhabitants dump their wastes casually and do not deliver wastes at designated places due to diversity reasons, such as lack of the habit of waste source separation, poor awareness of environment and health, and long walking distances of waste collection sites from their houses. Waste dumped along roads, underneath bridges, at corners and in open areas rather than in communal containers has gravely affected the health and wellbeing of local residents, especially in communities without fulltime sanitation workers responsible for cleaning. How to monitor and enforce households correctly deal with their waste and whether local inhabitants are willing to voluntarily supervise others’ waste disposal activities immediately influence the whole waste management performance. Peer monitoring normally prevents improper waste dumping behaviors through two channels. On the one hand, householders can deter others from littering or illegal dumping through persuasion before they happen. On the other hand, for those who ignore dissuasion and continue to improperly dumping waste, householders could punish them through social sanctions like social exclusion, gossip, or felt unwanted, left out. However, introduction of social sanction against the free-rider problem of collective action requires social capital from actions that simply facilitate information pooling (Collier, 2002). Intensive social networks, reciprocity norms and sufficient trust in others can effectively reduce monitoring cost (Ostrom, 2009). For instance, individuals with higher stock of social capital are more likely to monitor and manage their resources to achieve sustainable growth of forest in India (Behera, 2009) and fisheries in 44 countries (Gutiérrez et al., 2011), while the barrenness of social capital has caused the lack of self-organized supervision and the subsequent intensification of coastal erosion in Argentina (Rojas et al., 2014). Social capital might therefore effectively bridge the intention-behavior gap in waste disposal supervision. As individual decision-making is the outcome of the combined effects of external circumstances and individual attributes, the Institutional Analysis and Development (IAD) framework provides a useful analytical paradigm to explore how different individuals interact with each other in one action arena. This paper attempts to shed light on these issues by identifying the potential factors leading to local households spontaneously implementing voluntary monitoring in waste disposal. We seek to answer the following specific questions: What role does external third-party monitoring play in local households’ engagement in voluntary monitoring in waste disposal? Does high stock of social capital stimulate them to participate in waste disposal monitoring voluntarily? Given the fact that some households might be willing to monitor but take no actions in reality just as other intention behavior discrepancy discovered in previous waste management researches (Graham-Rowe et al., 2015; Russell et al., 2017), what are some of salient factors lead to the gap between hypothetical willingness to monitor and actual monitoring behavior. Different from the existing literature, the main contributions of this study are threefold: first, the analysis in this paper builds upon the

2. Theoretical framework and research hypotheses This paper uses IAD framework developed by Ostrom (2005) and her colleagues to analyze household voluntary monitoring decisions in waste disposal. IAD framework has been successfully applied to a number of researches on common pool resource governance across the world (see Andersson, 2006; Imperial and Yandle, 2005; Nigussie et al., 2018; Raheem, 2014; Rahman et al., 2012). IAD framework is devoted to explaining how external variables including biophysical conditions, attributes of community and rules-in-use affect the incentives and the resulting outputs of participants in an action arena, and then the outcomes of interactions from such action arena are often assessed, which in turn gives feedback to the system (McGinnis, 2011). Since household monitoring in waste disposal process is essentially a matter of collective action, employing IAD framework not only helps us explore monitoring decisions of households in waste disposal at both community and individual levels, but also enables us identify key variables that affect willingness to monitor and actual monitoring behavior of households in the same framework. Fig. 1 shows the basic elements of IAD framework. The primary task of IAD framework is to identify an action arena, where participants interact with one another in a specific action situation. An action situation is the core component of an action arena, which determines how the external variables are connected to the results through the participants’ behaviors in the whole framework (Ostrom, 2010). In this study, the action situation is the collective action dilemma of providing waste disposal monitoring. Waste disposal monitoring refers to households’ supervision of whether other households’ waste disposal behaviors in community meet prescribed requirements, such as whether other households in community waste littering, sorting waste according to standards or delivering bags of rubbish at designated places. In this context, participant attributes affect the strategies they choose. External variables, such as biophysical conditions, attributes of community and prevailing rules, form the environment of the action arena and constrain or enhance potential

Fig. 1. Basic components of the IAD framework (adapted from (Ostrom, 2005)). 46

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Table 1 Variable descriptions and the expected impacts on waste disposal monitoring. Category/variable Dependent variables Willingness to monitor Actual monitoring behavior Biophysical conditions Environmental quality Waste pollution Illegal dumping Attributes of the community Population density Community size Average income Heterogeneity Modernization

Rules in use Sanitation cadres Punishment measures Peer monitoring Attributes of participant Gender Age Educational attainment Household income Pollution cognition Social capital

Variable definition and measurement

Anticipated sign

Are you willing to monitor waste disposal activities of other households in community? ( = 0 if no; = 1 if yes) How often you monitor waste disposal activities of other households in community in daily life? ( = 1 if never; = 2 if sometimes; = 3 if frequently)

\ \

How has environmental quality of community changed compared to five years ago? ( = 1 if worsened; = 2 if no change; = 3 if improved) Is waste pollution the worst polluter in community? ( = 0 if no; = 1 if yes) Does anyone dump wastes casually in community? ( = 1 if never; = 2 if sometimes; = 3 if often; = 4 if usually; = 5 if always)

−

Number of residents per hectare (Ten thousand persons) Number of households (Thousand) Average annual household income (Ten thousand Yuan) Composite index based on proportion of migrant population, poor–rich gap, and whether an ancestral hall exists in community Composite index based on distance from the city center, proportion of cement roads, proportion of mobile phone users, proportion of tap water users, proportion of households using commercial energy for cooking, and whether exist sewage discharge facilities in community

+ ? + ? ?

Is the community equipped with full-time sanitation management cadres? ( = 0 if no; = 1 if yes) Does neighborhood committee would take punishment measures when households are found to be illegal dumping? ( = 0 if no; = 1 if yes) The average monitoring frequency of other households in community (the mean value of “ = 1 if never; = 2 if sometimes; = 3 if frequently”)

+ +

You are? ( = 0 if female; = 1 if male) Your age is? (Years) How many schooling years have you received? (Years) Annual household income (Ten thousand Yuan) How many substances such as water, air, soil and food do you think would be negatively affected by waste pollution? (Categories) Composite index based on social networks, social norms, interpersonal trust, and institutional trust

+ ? ? + +

+ +

+

?

Notes: one thousand Yuan equals to 150.55 USD; the detailed measurements of heterogeneity, modernization and social capital can be obtained from the authors.

biophysical conditions, including environmental quality, waste pollution and illegal dumping. The worse the environment quality, the more serious the waste pollution; and the more frequent the phenomenon of illegal dumping, the more willing and likely the households to participate in waste disposal monitoring. Second, attributes of communities. As a basic social unit of inhabitants living together, each community has its own unique natural and social characteristics, which profoundly affect the behaviors of households. Following previous researches, we use five variables to reflect community attributes in this study, and these are population density, community size, average income, degree of heterogeneity, and degree of modernization. Population density. The greater the population density, the more densely the households live together. In densely populated communities, waste disposal behavior of each other is more easily observed, which helps reduce the difficulty and cost of supervision. At the same time, highly inhabited communities are often characterized by the nature of acquaintance society, and there more incentives and constraints based on reputation for households. Hence, the phenomenon of illegal dumping is less and the cost of supervision is lower. We use the number of residents per hectare to capture population density. Community size. The influence of group size on collective action remains a controversial and complex issue (Poteete and Ostrom, 2004). Olson (1965) argues that not only the contribution of one individual to collective result is negligible in larger group, but also the share of one individual gains from the collective action is small. Besides, as group size grows, the difficulty of effective supervision and the cost of coordination among members will also increase. Therefore, the phenomenon of free-riding in collective action will intensify with the expansion of the collective scale, which finally leads to the dilemma of collective action. For instance, the difficulty of organizing collective action is found to increase significantly as member number in some irrigation associations rises (Fujiie et al., 2005). On the contrary, Oliver

strategies. The outcomes of the interactions from the action arena after being evaluated will retroact to current institutional arrangements. In this study, monitoring decision of household on waste disposal includes two dependent variables, which are willingness to monitor and actual monitoring behavior. We assume that the set of variables affecting willingness to monitor are the same as the set of variables influencing actual monitoring behavior while their specific impact intensities vary. We also posit that Pr (willingness to monitor/actual monitoring behavior) = F (biophysical conditions, attributes of the community, rules in use, attributes of participants, θ ), where Pr indicates the probability of an event occurring, F is a bivariate normal link function, and θ is a parameter vector linking independent variables to monitoring decisions. Table 1 describes the variables used in our analysis and their anticipated effects on decisions of local households to engage in waste disposal monitoring. First, biophysical conditions. Natural environmental condition determines the extent to which local inhabitants demand for environmental resources. Each community might face more or less environmental problems, but some communities are more polluted than others. The scarcity of environmental resources is likely to force residents to organize themselves to jointly manage environmental resources (Conroy et al., 2002; Araral, 2009). In communities where waste pollution is extremely serious, it is insufficient that only few people take measures to protect environment while the majority choose to take a free ride. More households have to involve themselves in collective action if they want to satisfy their need for a good environment. In this sense, the serious waste pollution we are talking about actually refers to the scarcity of good environment resources. According to the argument, proposed by Thomson and Perry (2006), and Dinar (2009), that resource scarcity is an important driving force for increasing levels of collaboration, we hypothesize that, ceteris paribus, households living in communities with more serious waste pollution are more willing to supervise and enforce supervision. Three variables are used to reflect

47

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formal rules in household waste management, our study includes sanitation cadres and punishment measures. External intervention policies are often considered as important measures to avoid overexploitation of natural resources and the plight of environmental degradation (Baral and Heinen, 2007). In China, waste governance and enforcement vary widely across communities. Wealthy communities usually have dedicated sanitation cadres and their neighborhood committees are also increasingly concerned about the problem of improper waste dumping, while the situation of poor communities is opposite due to financial constraints. Sanitation cadres are local government officials, who are in charge of sanitary inspection. They will require rectification and recleaning if sanitation condition of the inspected community does not meet the standard. Hence, we expect that, in communities which are staffed with full-time sanitation cadres and where the neighborhood committee takes punishment measures against improper dumping behaviors, local households face less resistance, consume less efforts to monitor waste disposal, and are more willing and likely to participate in waste disposal supervision. With regard to informal rules, average monitoring frequency of other households is adopted to denote it. The behaviors of other households in the community invisibly establish an informal standard for the behavior of the household surveyed. Due to the factors like conformity or herding effect, people’s cooperative behavior depends largely on the behavior of other people around them (Röttgers, 2016; Frey and Meier, 2004). We hypothesize that the higher the level of peer monitoring, more willing and more likely the households to implement waste disposal supervision, ceteris paribus. Finally, attributes of participant. Apart from being influenced by aforesaid external variables, households’ willingness and behavior of waste disposal monitoring are also affected by their own attributes. In this study, attributes of participants cover gender, age, educational attainment, household income, pollution cognition and social capital. Gender. Peer waste disposal monitoring usually occurs outdoors and requires higher levels of social communication ability and influence power. Because of the fact that division-of-labor arrangement of “breadwinning men and homemaking women” is still widespread in China (Liu and Anne, 2015), i.e., men are in charge of the outside affairs while women are responsible for housework. Hence, we expect that males are more willing and likely to implement waste disposal monitoring compared with females even though females dispose of waste at home more frequently. Age. The impact of age on individual participation in collective action remains unanimous. Dolisca et al. (2006) show older farmers are less willing to engage in community forest management in Haiti, while Azizi Khalkheili and Zamani (2009) find that age is positively correlated with farmers’ participation in irrigation management but not significant in Iran. Educational attainment. Educated residents tend to have a sophisticated awareness of environmental issues and are more able to identify the potential benefits of collective management of environmental resources, and are more willing to participate in environmental resource management (Dolisca et al., 2006; Huang et al., 2009). Nevertheless, empirical studies have also shown that there is a negative correlation between the level of education and the collective participation of members (Azizi Khalkheili and Zamani, 2009; Wang et al., 2016). In this study, educational attainment is proxied by length of school year. Household income. Compared to poor families, affluent families are more willing and more capable of participating in collective management of community resources, such as community forest and water management (Dolisca et al., 2006; Huang et al., 2009). This paper uses the annual household income to characterize it. Pollution cognition. Cognitive structure of the actor is the basis for the formation of an action, and cognition of necessity and potential benefits of environmental protection is an important first step for actors to take environmental protection measures (Reimer and Prokopy, 2014; Larson et al., 2011). Therefore, the higher the perception of waste

and Marwell (1988) challenged the aforementioned viewpoint, and argue that larger groups have more resources and are more likely to have a critical mass of highly interested and resourceful members, and thereby enhancing collective cooperation. In fact, successful commonpool resource management may not be confined to smaller systems (Frey et al., 2016), and large group can bear a small amount of betrayers (free riders) (Szolnoki and Perc, 2011). Different from the above views, Ternstrom (2003) finds that group size can’t explain any significant differences in collective action through empirical analysis. The present study employs the number of households to measure community size. Average income. Average income reflects the level of economic development. Coleman and Steed (2009) find that, in areas with higher levels of GDP, forests are more valuable to local population, and local inhabitants are more likely to participate in forest protection monitoring. When households have a higher average income, their demands for environmental quality will also increase, and they are more willing and likely to participate in waste disposal supervision. In our study, average income is measured by average annual household income. Heterogeneity. Abundant evidence in empirical literature shows that heterogeneity affects the likelihood of collective action. For example, Baland and Platteau (1996) analyze the impact of heterogeneity on collective action from the aspects of ethnicity and economic interests, and contend that racial and interest heterogeneity reduce collective cohesion, increase communication costs, and thus imped cooperation. Zhou (2013) conclude that while pond contractors want to farm as much fish as possible, the poor demand adequate water for irrigation. The heterogeneity of interests not only undermines collective action, but also causes inefficient use of waste resources. However, a contending view holds that if a group is heterogeneous enough to contain a critical mass who have a strong interest in and rich resources for collective action, this critical mass would play a leading and exemplary role in promoting the occurrence of cooperation (Oliver et al., 1985). Employing the ethnographic data of pastoralists in East Africa, Ruttan and Mulder (1999) indicate that income inequality lead to an increase in grazing conservation because rich herders can compel poor pastoralists to participate in collective action. Furthermore, compared to low and high levels of wealth heterogeneity, moderate levels of wealth heterogeneity is beneficial for collective action (Naidu, 2009). Thus, the specific impact of heterogeneity on collective action is still controversial. In this paper, we use Principal Components Analysis (PCA) method to construct the heterogeneity variable on the basis of the proportion of migrants, the poor–rich gap, and whether an ancestral hall exists in the community. Modernization. Highly modernized areas often have advanced transport facilities, convenient trade market and developed communication networks, which are conducive for residents to communicate with each other. Communication can effectively improve the possibility of the occurrence of collective action (Ostrom, 1998; Smith, 2010), and thus it favors the implementation of waste disposal monitoring. Whereas, some scholars propose that households in modern community generally have lower social identity and cohesion compared with those in traditional community (Cai and He, 2014), and consequently, they are unlikely to conduct waste disposal monitoring for collective interests. In our study, a composite index of modernization is created through PCA method on the basis of distance from the city center, proportion of cement roads, proportion of mobile phone users, proportion of tap water users, proportion of households using commercial energy for cooking, and whether sewage discharge facilities exist in the community. Third, rules in use. Rules designate share understanding of those involved to who must, must not, or may take which actions affecting others subject to sanctions and may evolve over time as those involved in one action situation interact with others (Ostrom, 2010). They can be formal legal documents, and can also be customs and habits formed by long-tern interaction among group members (McGinnis, 2011). As for 48

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pollution, the more willing and likely the households to supervise waste disposal. Social capital. The positive role of social capital in environmental resource management has gained support from many scholars (Pretty, 2003; Brondizio et al., 2009; Pretty and Ward, 2001; Górriz-Mifsud et al., 2016; Adger, 2003; Calfucura, 2018). However, Bodin and Crona (2008) find that social capital may have a negative impact on monitoring. Due to fear of reporting rule breaking would embarrass the off ;ender and they themselves would risk social rejection, fishers are reluctant to report rule breaking in community with relatively high levels of social capital. From the above analysis, it is discovered that social capital is an important factor affecting individual’s participation in monitoring activities, but its effect is still obscure. It is worth noting that the controversy of the concept of social capital itself is also an important reason leading to its blur effect. Much of existing literature treats its components as social capital. For example, Keene and Deller (2015); Paudel and Schafer (2009) and Behera (2009) equate social capital with social networks, while Ahlerup et al. (2009); Pan et al. (2009) and Baliamoune-Lutz (2011) understand social capital in a narrow sense as social trust. Social capital is actually a multi-dimensional construct consisting of social networks, norms and trust (Putnam et al., 1993; Dekker, 2007; Górriz-Mifsud et al., 2016). In addition, according to different trust objects, social trust can also be classified as interpersonal trust and institutional trust (Rus and Iglič, 2005; He et al., 2015). We obtain the values of social capital and its components through PCA method on measurement items of each dimension.

Fig. 2. Geographic distribution of the sample cities.

questionnaire can be fully understood by respondents, we conducted a small-scale pretest before the formal survey. During the period from February to March 2015, we accomplished 30 questionnaires in the suburbs of Nanchang and Hangzhou through occasional sampling. According to actual feedback of respondents in pretest, we amended and adjusted the questionnaire like option settings, expressions, questionnaire length, and subsequently obtained formal questionnaire. Before implementation of formal investigation, we gave a detailed explanation and training to enumerators to minimize interview bias. Ultimately, we guided the enumerators to our four sample cities and carried out the formal surveys in the period between July 2015 and July 2016. In the suburbs of each city, we randomly selected three communities for surveys both at community and household levels. At the household level, we determined the sample size of each community according to community population. We planned to complete 480 questionnaires after carefully balancing the appropriate sample size for multiple regression analysis and budget constraints. Hence, to maintain representativeness of our sample, the sample size for each community was equal to population of this community divided by total population of all communities surveyed and multiplied by 480. When interviewers had entered into the targeted community, they first collected basic information upon community through local neighborhood committee. Specially, in interviews with community leaders, we obtained detailed data on biophysical conditions of community such as the situation of environmental quality, waste pollution, and illegal dumping, and socioeconomic characteristics of community like population density, community size, average income, heterogeneity, and modernization, as well as rules in use on waste management. Next, under the help of local community cadres, we conducted faceto-face interviews with households to obtain basic information at the household level, including socio-demographic characteristics of householder, their cognition and behaviors on household waste. In order to minimize selection bias and confounding bias, households in each community were selected on the basis of simple random sampling, using the most current household registration lists for each community. Meanwhile, the potential for any interviewer bias was also addressed in several ways. First, interviewers were blinded to the outcome of interest and put the interviewees at ease so that a two-way, open communication climate existed. Second, interviews were ensured to act as a neutral medium through which questions and answers were transmitted, and to avoid asking leading questions and giving overt signals such as smiling and nodding approvingly. Third, after every interview that was undertaken, interviewee feedback was also collected to make sure that they were in no way influenced or biased by the interviewer. The field

3. Materials and methods 3.1. Study area and survey design Rural-urban fringe is selected as our study area, which often has the dual characteristics of urban and rural areas. On the one hand, with rapid economic development, the migrant population of outskirts increases fast, and life style and consumption structure of local inhabitants are undergoing great changes. The lifestyle gradually convergences with the counterpart of urban areas and generates rapidly increasing amount of household waste generated. On the other hand, as the city supporting services are mainly concentrated in the downtown area, waste management services generally lag behind in suburban areas. Although a small number of township governments or neighborhood committees collect and dispose of household garbage in suburbs of developed areas, the phenomenon of waste improperly dumped and indulgently heaped will continue to exist for a long time in most suburban areas. Community is the basic residential unit of urban and rural residents, where waste disposal condition differs with each other. Communities with better economic conditions commonly have sanitation cadres and their neighborhood committee would punish the offenders of illegal dumping when they were detected. In addition, due to long history of evolution, each community has its own distinctive characteristics, and residents living in one community more or less with its mark. The decisions of household’s willingness and behavior on waste disposal monitoring are made in this community circumstance. In order to fully reflect regional conditions in China, we selected Chengde, Yichang, Nanchang and Hangzhou as our sample cities (see Fig. 2). In the process of questionnaire design, we use the scale developed by the World Bank to construct measurement questions for the four dimensions of social capital, namely, social networks, social norms, interpersonal trust and institutional trust (Grootaert et al., 2004). This set of indicators has been widely used in previous researches such as Halkos and Jones (2012) and Daniele and Geys (2015). As for the measurement of heterogeneity and modernization, the questions mainly refer to the researches of Naidu (2009); Poteete and Ostrom (2004), and Krishna and Uphoff (2002). In order to ensure the comprehensiveness and reasonableness of the questionnaire design, and questions in the 49

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survey issued a total of 480 questionnaires, and 404 valid questionnaires were finally kept after dropping those with unanswered questions or obviously contradictory answers.

Table 2 Principal components analysis of heterogeneity, modernization, and social capital. Item

3.2. Estimation model

Heterogeneity Proportion of migrant population Poor–rich gap An ancestral hall exists Eigenvalue:

Since our study aims to examine willingness and behavior of household on waste disposal monitoring simultaneously, we employ bivariable probit model to conduct regression estimation following the method of Greene (2012). It should be noted that although our survey data have a hierarchical structure, a multilevel model is not well suited for application where the sample is routinely recommended with at least 30 groups (Hox, 1998; Clarke, 2008). In order to correct the biased downward standard errors when using single-level models, we apply robust standard errors clustered at the community level to substitute the conventional standard errors (Cameron et al., 2011). Defining outcome variable y1* is the latent variable of respondents’ monitoring willingness and y1 is the observable variable of that, and also defining outcome variable y2* is the latent variable of respondents’ monitoring behavior and y2 is the observable variable of that, the decisions of respondent i on waste disposal monitoring can be expressed as the following equation system:

y1*i = x1i β1 + ε1i y2*i = x2i β2 + ε2i

Modernization Distance from the city center Proportion of cement roads

(1)

Where x1i and x2i are independent variable vectors, β1 and β2 are parameter vectors need to be estimated, ε1i and ε2i are random disturbance terms, and the observable variables y1i and y2i are determined by the following equation

y1i =

⎧ 1ify *2i ≤ r1 ⎪ ⎧1ify1*i > 0 , y2i = 2ifr1 < y *2i ≤ r2 ⎨ ⎨ 0ify1*i ≤ 0 ⎩ ⎪ 3ifr2 < y *2i ⎩

Loading

Activities Factor 2: Social norm

0.758

0.896 0.607 −0.619 1.555

Assistances Reciprocities Thefts

0.822 0.794 −0.737 −0.865 1.850

0.786

Disputes Eigenvalue: Factor 3: Institutional trust Government

0.852 0.903

Committee Environment

0.815 0.888

0.856 4.392

Eigenvalue: Factor 4: Interpersonal trust

2.263

Trust Distrust Trustworthy Eigenvalue:

0.817 −0.818 0.765 1.760

0.743 0.822 0.840

0.900

sampling adequacy and Bartlett’s test of sphericity indicate that there is a strong relationship between the items, and that a PCA is appropriate. The correlations between measurement items and corresponding factors ranging from 0.746 to 0.893 indicate satisfactory construct validity. With respect to social capital with multidimensional nature, we labeled the four factors as ‘social network’, ‘social norm’, ‘institutional trust’, and ‘interpersonal trust’ respectively after comprehensive consideration of the meanings of all measurement items which were included in corresponding factor group. Table 3 reports descriptive statistics on all the variables in our analysis. As shown in Table 3, for biophysical conditions variables, on average, respondents believe that environmental quality in community has not changed dramatically within five years, the phenomenon that inhabitants dump their wastes casually happened occasionally, and 38.1% of respondents believe that waste pollution is the most serious pollution in community. As for community attributes, on average, population density of community is 20.87 thousand people per hectare, and community size is 2.1 thousand households. Regarding rules in use,

(2)

In addition, we assume that disturbances (ε1i, ε2i ) from Eq. (1) follow two-dimensional joint normal distribution with an expectation of 0, a variance of 1 and a correlation coefficient ofρ , and are orthogonal to explanatory variables in x1i and x2i , the correlation matrix of the two can be written as

1 ρ⎤ ⎞ ε ⎡ ε1 ⎤ |x1, x2 ∼ N ⎜⎛ ⎡ 0 ⎤, ⎡ ⎟ ⎣ 2⎦ ⎣ρ 1⎥ ⎦⎠ ⎝ ⎣0⎦ ⎢

Item

−0.898 0.833

Proportion of mobile phone users Proportion of tap water users Share using commercial energy for cooking Sewage discharge facilities exist Eigenvalue: Social capital Factor 1: Social network Acquaintances Friends Helpers

Loading

(3)

Obviously, if ρ = 0 , Eq. (1) is equivalent to two separate probit equations; if ρ > 0 or ρ < 0 , then Eq. (1) is a bivariable probit model. In the former case, Eq. (1) reduces to a set of independent probit models. In the latter case, respondents’ willingness to monitor and actual monitoring behavior are complementary or exclusive with each other. As behavioral willingness tends to be positively associated with actual behavior although sometimes it's weak, there is a need to conduct a bivariable probit model instead of running two regressions individually. In actual regression operation, as willingness to monitor is a binary variable and actual monitoring behavior is an ordered variable, we use probit model to estimate the former and use ordered probit model to estimate the latter.

Table 3 Sample descriptive statistics.

4. Result and discussion 4.1. Descriptive statistical analysis As heterogeneity and modernization variables consist of several distinct but associated elements, and social capital is a multidimensional concept, we employ PCAs to group these elements into a small number of interpretable underlying factors. Table 2 presents the results of PCA. In each PCA analysis, we select the factor axes with an eigenvalue above one. Both the Kaiser–Meyer–Olkin measure of 50

Variable

Mean

Std. Dev.

Min

Max

Environmental quality Waste pollution Illegal dumping Population density Community size Average income Heterogeneity Modernization Sanitation cadres Punishment measures Peer monitoring Gender Age Educational attainment Household income Pollution cognition Social capital Willingness to monitor Actual monitoring behavior

2.042 0.381 3.002 8.268 2.087 9.492 0 0 0.347 0.351 1.700 0.500 41.339 11.083 9.492 3.087 0 0.606 1.700

0.886 0.486 0.923 6.354 1.014 3.366 1 1 0.476 0.478 0.512 0.501 13.965 4.012 8.221 1.042 1 0.489 0.760

1 0 1 1.254 0.482 5.403 −2.475 −2.496 0 0 1 0 16 0 0.200 1 −2.759 0 1

3 1 5 20.589 3.523 16.346 1.619 0.802 1 1 2.591 1 81 20 88.000 5 3.945 1 3

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Table 4 Mean statistics across waste disposal monitoring groups. Willingness to monitor

No(n = 159)

Rules in use Sanitation cadres Punishment measures Peer monitoring

Willing to but did not do(n = 42)

MD (did – wiling to but did not do)

Yes(n = 245)

MD (Yes-No)

Never (n = 195)

Sometimes (n = 135)

Frequently (n = 74)

MD (S-N)

MD (F-N)

MD (F-S)

2.057

0.038

2.021

2.022

2.135

0.001

0.114

0.113

2.021

0.041

0.388 2.959

0.017 −0.110

0.359 3.077

0.452 2.941

0.311 2.919

0.093 −0.136

−0.048 −0.158

−0.141 −0.022

0.331 3.048

0.071 −0.115

8.096

8.379

0.284**

8.102

8.355

8.546

0.254*

0.444***

0.191

8.427

−0.005

2.209 9.190 0.348 −0.080

2.008 9.688 −0.226 0.052

−0.201* 0.498* −0.574*** 0.132**

2.193 9.612 0.399 −0.051

2.010 9.320 −0.228 0.034

1.948 9.490 −0.636 0.072

−0.183** −0.292 −0.627*** 0.085*

−0.245** −0.122 −1.035*** 0.123***

−0.062 0.17 −0.408*** 0.038

2.133 9.652 −0.217 0.045

−0.145 −0.272 −0.155 0.002

0.447 0.321

0.282 0.371

−0.165*** 0.050

0.451 0.318

0.341 0.378

0.081 0.392

−0.110 0.060

−0.370*** 0.074

−0.260*** 0.014

0.395 0.343

−0.146 0.040

1.362

1.920

0.558***

1.376

1.889

2.213

0.513***

0.837***

0.324***

1.742

0.262

0.543 41.992 11.035

0.109** 1.659 −0.122

0.456 37.585 11.282

0.489 41.948 10.989

0.635 50.122 10.730

0.033 4.363*** −0.293

0.179** 12.537*** −0.552

0.146 8.174*** −0.259

0.524 32.571 11.135

0.017 12.271** −0.238

9.702

0.533

9.056

9.878

9.937

0.822**

0.881**

0.059

9.079

0.820***

3.196

0.278

2.908

3.230

3.297

0.322

0.389

0.067

3.192

0.062

0.463

1.177***

−0.677

0.352

1.141

1.029***

1.818***

0.789***

0.272

0.359

Biophysical conditions Environmental 2.019 quality Waste pollution 0.371 Illegal dumping 3.069 Community attributes Population density Community size Average income Heterogeneity Modernization

Actual monitoring behavior

Attributes of participant Gender 0.434 Age 40.333 Educational 11.157 attainment Household 9.169 income Pollution 2.918 cognition Social capital −0.714

Notes: *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively; MD (S-N) denotes the mean difference between sometimes and never, MD (F–N) denotes the mean difference between frequently and never, and MD (F–S) denotes the mean difference between frequently and sometimes; Bonferroni adjustment made for multiple comparisons.

insignificant, suggesting that waste disposal supervision has no obvious correlation with biophysical conditions. For community attributes, respondents in communities with higher population densities tend to more willing and frequently to conduct waste disposal supervision while respondents in larger communities are more likely to be reluctant to supervise and do not implement supervision in reality. The connection between household waste disposal supervision and average income is relatively complex. In communities with higher average incomes, respondents are more willing to engage in waste disposal monitoring but do not put supervisory behavior into practice. Respondents in communities with medium level of average income appear to regularly conduct waste disposal supervision. In addition, respondents are more willing and likely to implement waste disposal supervision as community heterogeneity decreases and community modernization increases. For application rules, respondents in communities without full-time sanitation cadres are more willing to and more often conduct waste disposal monitoring. There is a modest positive correlation between waste disposal monitoring and neighborhood committee taking punishment measures when households are found to be illegal dumping. Respondents are more willing and likely to implement the supervision of household waste disposal when their peers frequently participate in waste disposal monitoring activities. For attributes of participant, males and elders are more willing and likely to conduct household waste disposal supervision while the relationship between educational attainment and waste disposal monitoring of respondents appears a weak negative correlation. In addition, respondents with higher household income, higher pollution cognition or higher stock of social capital are more willing and likely to perform supervision compared to others, but the differences in pollution cognition across willingness to monitor and

on average, 34.7% of respondents express that their communities are staffed with full-time sanitation management cadres, 35.1% of respondents indicate that their neighborhood committees will take punishment measures when households are found to be illegal dumping, and the mean value of peer monitoring is 1.7, i.e., the frequency of peer monitoring is between never monitor and sometimes monitor. Concerning participant attributes, the proportion of male respondents is 50.0%, the average age and average education level of respondents are 41.3 years old and 11.8 years respectively, and average annual household income of respondents is 94.9 thousand Yuan. Besides, respondents believe that improper waste disposal will result in three types of pollution among water pollution, air pollution, soil pollution, food pollution and other pollution on average. As far as dependent variables, 60.6% of respondents express willingness to monitor other households’ waste disposal behavior while only 51.7% of respondents indicate that they have implemented waste supervision behavior in daily life. Among the latter, respondents who sometimes implement monitoring behavior account for 33.4%, and respondents who frequently implement monitoring behavior account for 18.3% (see Table 4). In general, the statistical characteristics of the sample are basically in line with our expectations, indicating reasonable representativeness of the sample. In order to explore the relationship between waste disposal monitoring and independent variables, we report the mean values of variables across waste disposal monitoring groups, and mean differences between them using bonferroni correction for multiple comparisons (the detailed results are shown in Table 4). It is not difficult to find that differences in environmental quality, waste pollution and illegal dumping among the groups of waste disposal monitoring are small and

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Table 5 Determinants of respondents’ waste disposal monitoring. Separate estimation

Environmental quality Waste pollution Illegal dumping Population density Community size Average income Heterogeneity Modernization Sanitation cadres Punishment measures Peer monitoring Gender Age Education Income Pollution cognition Social capital ρ Log-likelihood AIC BIC Observations

Joint estimation

Willingness to monitor

Actual monitoring behavior

Willingness to monitor

Actual monitoring behavior

0.014 0.013 0.053 0.071** −0.323** 0.121** −0.635*** 0.983*** −0.096* 0.186 0.710** 0.292* 0.002 0.021 0.012 0.074 0.915*** 0.000(fixed) −413.230 896.460 1036.509 404

−0.012 0.032 −0.015 0.045** −0.282** 0.036 −0.576*** 0.730*** −0.134** 0.056 0.623** 0.252* 0.013* 0.004 0.021*** 0.067 0.987***

0.075 −0.098 0.102 0.071*** −0.292* 0.140*** −0.543*** 0.946*** −0.120* 0.146 0.685** 0.306** 0.003 0.019 0.010 0.027 0.937*** 0.764*** (0.076) −360.193 790.386 930.436 404

−0.016 −0.005 −0.006 0.049** −0.295** 0.040 −0.565*** 0.758*** −0.116** 0.031 0.644** 0.242* 0.012* −0.005 0.022*** 0.072 0.964***

(0.084) (0.166) (0.093) (0.029) (0.159) (0.054) (0.175) (0.221) (0.053) (0.173) (0.321) (0.157) (0.008) (0.027) (0.012) (0.077) (0.144)

(0.070) (0.133) (0.083) (0.020) (0.143) (0.037) (0.124) (0.155) (0.061) (0.149) (0.302) (0.134) (0.007) (0.023) (0.007) (0.073) (0.117)

(0.079) (0.150) (0.084) (0.023) (0.153) (0.045) (0.142) (0.188) (0.063) (0.156) (0.314) (0.141) (0.007) (0.023) (0.011) (0.068) (0.129)

(0.067) (0.130) (0.084) (0.020) (0.139) (0.035) (0.122) (0.152) (0.057) (0.148) (0.305) (0.130) (0.007) (0.023) (0.007) (0.069) (0.114)

Notes: figures in parentheses are clustered standard errors of estimates; *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively; AIC denotes Akaike's information criterion, and BIC denotes Bayesian information criteria; to save the space, this table does not report the estimates and standard deviations of the cut points in ordered probit model, as well as the estimates and standard deviations of the constants in probit model.

4.2.1. Influence of biophysical conditions on household waste disposal monitoring As reported in Table 5, inconsistent with our expectations, environmental quality, waste pollution and illegal dumping cannot pass the significant test in both equations of willingness to monitor and actual monitoring behavior, indicating that scarcity of good environmental quality does not significantly affect the willingness and possibility of respondents to supervise household waste disposal. According to comparison of the sample characteristics, there are no notable changes in environmental quality of most communities within five years, about 38.1% of respondents in each community holding that waste pollution is the most serious pollution in their community, and the phenomenon of illegal waste dumping occasionally exists in all communities. This manifest environmental conditions of community do not show sizable differences with changes in geographical factors, and environmental conditions are not the fundamental predictors that explain variation in respondents’ waste disposal monitoring. Huang et al. (2009) also found that scarcity of water resources in villages was not directly related to the establishment of water resources collective management and farmers’ water association. Mushtaq et al. (2007) further confirmed that scarcity of water resources in the Zhanghe Basin area, where water scarcity was similar, did not significantly account for the differences in collective pond management performance.

actual monitoring behavior are small and insignificant. Finally, compared to respondents who have carried out waste disposal monitoring behavior, those who willing to but do not conduct waste disposal monitoring behavior are often younger, with lower household income or lower stock of social capital. Although the above descriptive statistics initially support some of our hypotheses, simple mean comparison method can neither control for simultaneous influence of other factors nor reflect the specific impact of our concerned variables. Therefore, more reliable conclusions require further quantitative analysis.

4.2. Baseline regression result and discussion Table 5 shows basic estimation results of the models. In the columns of separate estimation, the correlation coefficient is fixed at zero. Namely, the equations of willingness to monitor and actual monitoring behavior are estimated individually. In the columns of joint estimation, we estimate willingness to monitor equation and actual monitoring behavior equation together, and allow the two equations to be related. It appears that the estimation results of this two approaches are basically the same in terms of coefficient magnitude and significance level of each variable, to a large extent indicating that the stability and reliability of the results are eligible. Nevertheless, the estimation of correlation coefficient shows that the null hypothesis that correlation coefficient can be fixed to zero is rejected at the conventional significance levels, and the correlation coefficient is 0.764. This demonstrates that willingness to monitor and actual monitoring behavior indeed have a complementary relationship, which is in line with our expectation that respondents with higher monitor willingness are more likely to conduct waste disposal monitoring behavior. Meanwhile, likelihood ratio test, Akaike information criterion and Bayesian information criterion show that the results of joint estimation is much better than those of separate estimation. Therefore, we will mainly focus on the regression results of joint estimation.

4.2.2. Influence of community attributes on household waste disposal monitoring Population density effectively increases respondent’s willingness to monitor waste disposal and the possibility of participation in waste disposal monitoring activities at the 5% significance level or better, which is coincide with our expectation. In communities with large population densities, on the one hand, it is easier for respondents to observe and know waste disposal behavior of other households and thus reduce the monitoring cost; on the other hand, the monitoring behavior for the purpose of protecting community environment is more likely to be recognized and appreciated by other households thereby increasing perceived benefits such as intrinsic pleasure and positive social image. Community size substantially suppresses the willingness and 52

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may be more willing to work together to further improve the environment condition, such as community vegetation and sanitation conditions.

behavior of respondents to monitor waste disposal at the 1% level. This is similar to the result of Wang et al. (2013). They found that relatively large group size of users in surface irrigation, compared to the group size of users in groundwater irrigation, led to an increase of monitoring costs. Araral (2009) further concluded that group size was positively associated with monetary free riding of farmers in operation and maintenance of public irrigation systems. It can be seen that as the size of community expands, transaction costs like coordination costs and supervisory costs are gradually increasing, and individuals are no longer willing to participate in waste disposal supervision when transaction costs obviously exceed the benefits that critical mass can bring. The coefficient of average income is significantly positive in the equation of willingness to monitor while it has no significant difference compared with zero value in the actual monitoring behavior equation, indicating that average household income in one community only affects respondents’ intention to monitor waste disposal, and has little statistical correlation with respondents’ supervisory behavior on waste disposal. The possible reason for this discrepancy might be as follows. The average household income in one community represents the economic conditions of the community. Although some researches in psychology find that people in developing countries tend to show more pro-environmental values than those in developed areas, it partly because that the environmental pollution in developing countries is far more serious than the counterpart in developed countries. With all else held equal, the better the economic condition, the stronger the environmental awareness of the respondents, which largely determines a higher willingness to supervise waste disposal. Whereas, actual supervision of respondents is not only related to their willingness to supervise, but also more related to their own conditions and needs, which are determined by household level factors such as household income, and leisure time. Heterogeneity has a negative impact on both the willingness to monitor and actual monitoring behavior at the 1% level. Respondents are more willing and likely to conduct waste disposal supervision when community members are homogeneous. A supporting view is postulated by DöSilva and Pai (2003) who argued that due to a serious ethnic differentiation of local residents and a large gap between the rich and the poor, internal coordination costs and supervision costs were higher, which finally led to low performance of collective forest and river basin management. Using survey data from irrigation communities, Ito (2012) came to a similar conclusion that income disparity and heterogeneity of ethnic groups had significant negative impacts on household labor contribution to irrigation facilities. Nevertheless, note that, there are variety of heterogeneity like wealth heterogeneity, racial heterogeneity, cultural heterogeneity and interest heterogeneity, and different types of heterogeneity may have different effects on collective action, and the effect of one specific heterogeneity will also be different in diverse institutional contexts (Poteete and Ostrom, 2004). For example, while Ruttan and Mulder (1999) discovered that income inequality can promote grazing conservation in East African because rich herders can force poor pastoralists to participate in collective action, Wang et al. (2016) indicated that economic heterogeneity captured by Gini coefficient had a significant adverse eff ;ect on collective irrigation in China. However, inconsistent with above two sides, Naidu (2009) confirmed that moderate wealth disparity was associated with high collective management and moderate levels of social diversity decreased collective management. Modernization significantly promotes willingness to monitor and implementation of waste disposal monitoring at the 1% level. In communities with higher levels of modernization, such as developed transportation and communication networks, convenient water supply and energy using, respondents are easier to communicate with each other, which can more effectively promote the occurrence of cooperation for collective interests (Ostrom, 1998; Smith, 2010). Furthermore, according to Maslow's need hierarchy theory, after the improvements of basic material conditions for survival and social contact, respondents

4.2.3. Influence of application rules on household waste disposal monitoring Equipping community with sanitation cadres reduce respondent’s willingness to supervise waste disposal and possibility of conducting supervisory behavior at the 10% or less level, which seems to be inconsistent with our expectations. A possible explanation for this is that, for a long time, the top-down social control pattern has made the public form a habit to serious depend on government, and the establishment of sanitation cadres has exacerbated the conceptions of local households that maintaining a good sanitation environment is the responsibility of relevant cadres. Therefore, staffing community with full-time cadres for sanitation management suppresses respondents’ initiative to participate in waste disposal monitoring. This confirms the previous work by Coleman and Steed (2009), who found that government monitoring and sanctioning may replace local efforts and be counterproductive since local forest users feel like government officials can or should be responsible for these activities. This in turn may have undesirable consequences for private waste disposal monitoring. However, it should not be construed to mean that external enforcement should be abandoned for voluntary monitoring since government often play a fundamental role in checking abuse of local power and sustain local conservation efforts (Ostrom, 1990; Lejano and Ingram, 2007). The coefficients of punishment measures have no significant difference with zero value at the 10% level in both equations of willingness to monitor and actual monitoring behavior, demonstrating that willingness to monitor and actual monitoring behavior of local inhabitants on waste disposal are not absolutely associated with whether neighborhood committee takes punishment measures to against illegal dumping. One potential reason for this result is that, on the one hand, due to limited budget or lack of enough staff members, neighborhood committees of some communities hardly take any punishment measures for illegal waste dumping even though punishment is one of their work responsibilities; on the other hand, with the transformation of China's economy and society, the dominating position of neighborhood committees in the hearts of residents has gradually declined, and its legitimacy has even been questioned, hence the phenomenon that local residents do not comply with punitive measures is also commonplace (Anderies et al., 2004). Therefore, unlike sanitation cadres significantly reducing the probability of performing monitoring behavior, community punishments have an insignificant effect on individual waste disposal monitoring. Consistent with our expectations, peer monitoring drastically increases the willingness to supervise and the initiative to implement supervisory behavior on waste disposal at the 5% level, indicating that most respondents are conditional cooperator, that is, they will engage in monitoring if others monitor waste disposal. This resonates with the results of Jaramillo et al. (2010) that members invest more time in monitoring their forest in groups with larger conditional cooperator share. We conjecture that the reason why households’ willingness to monitor and actual monitoring behavior may depend largely on monitoring behavior of others is that other people’s behavior establishes a reference benchmark or conduct norms for their behavior. Therefore, when their neighbors carry out supervisory behavior, in order to avoid isolation and access to group identity, households have to choose to follow the normative guidelines to implement supervisory behavior. For example, interviewed household also implements waste recycling behavior when other households implement waste recycling behavior (Abbott et al., 2013). On the other hand, because of incompleteness of information and with limited knowledge or computational capabilities, they often believe that most people’s opinions are correct when they get information from others, especially when it is difficult to determine whether the act is in their favor or not, and thus implement herd behavior in complex situations (Velez et al., 2009; Griskevicius et al., 53

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conforms with the notion that informal monitoring processes among members are crucial for public resource management and the existence of an abundance of social capital is the key factor in the implementation of informal monitoring (Rojas et al., 2014; Calfucura, 2018). Behera (2009) analogously found that presence of social capital was also likely to promote good forest growth. Social capital can powerfully refrain moral hazard and opportunistic behaviors by promoting information flowing on individual conduct and increasing the potential cost of deceiving or violation in any single transaction, and eventually reduce supervision cost (Durlauf and Fafchamps, 2005; Grafton, 2005). In addition, social capital helps to increase the opportunities for communication between stakeholders and accelerate the diffusion of relevant knowledge (Putnam et al., 1993; Adger, 2003). These are conducive to reducing the cognitive conflict of stakeholders on environmental resources, which was found to be a prominent factor in hindering stakeholder participation in collective cooperation (Adams et al., 2003). Finally, social capital may also promote supervision activities through reshaping individual preferences. When any uncooperative or free riding behavior will be exposed in dense social networks and suffer from reputation sanctions, social capital forces people to feel guilty for violating collective interests and to feel satisfied for preserving collective interests through reciprocity norms and mandatory trust (Passy and Monsch, 2014; Passy and Giugni, 2000). Social capital thus unconsciously changes stakeholders’ perceived benefits and costs for involvement in collective action (Jones et al., 2010). In such a case waste disposal supervision has become a habitual preference.

2006). 4.2.4. Influence of participant attributes on household waste disposal monitoring Gender is significantly positively correlated with respondent’s willingness to monitor waste disposal and their actual behavior, indicating that men are more willing and likely to participate in household waste disposal activities than women, which is accordance with the findings of females participating in environmental resource management less in other developing countries (Coulibaly-Lingani et al., 2011). In China, long-term social norms and conventions shape the gender division of labor, resulting that women are more confined to family affairs while men are adept at the public affairs. Although women are frequently responsible for disposing their waste at home, men are more likely to supervise waste disposal of their neighbors since peer monitoring involves neighborhood interaction and governance of public affairs. Inconsistent with previous studies (e.g., Azizi Khalkheili and Zamani, 2009; Dolisca et al., 2006), the coefficient of age does not differ significantly from zero in willingness to monitor equation but is significantly positive in actual monitoring behavior equation. This signifies that people with different ages do not appear statistically different willingness to monitor waste disposal while older respondents are more likely to supervise domestic waste disposal in real life. The explanation is that although respondents with different ages have similar intentions for waste disposal monitoring, older respondents have more leisure time to associate themselves with their neighbors compared with young residents who usually need to go to work early and come back late. Therefore, elders are more likely to conduct waste disposal monitoring behavior. Education attainment does not matter in both equations of willingness to monitor and actual monitoring behavior, which is consistent with the results of Nenadovic and Epstein (2016). They found that participation in surveillance activities of fishers was not straightly related to their education levels. Although highly educated households may have advanced ideas and good environmental awareness, out of rational economic man principle and objective time constraints, households with higher education levels are not necessarily involved in community environmental protection activities. In fact, the effect of education on people participating in public affairs is to a large extent context-dependent (Wang et al., 2016). Household income cannot significantly explain willingness to monitor waste disposal but can significantly explain actual monitoring behavior at conventional significance levels. Potential explanations for this are that, on the one hand, average income at the community level to a certain extent weakens the explanatory power of household income to predict respondents’ willingness to monitor waste disposal. On the other hand, the opportunity cost of monitoring for wealthy households is high in general, and hence their willingnesses to monitor are low. Nevertheless, because of their high household income, their demand for good environmental quality are also high and thus have to implement supervision. The coefficient of pollution cognition is positive in both equations of willingness to monitor and actual monitoring behavior, but it is not significantly different from zero at the conventional significance levels. Although previous studies have emphasized that environmental pollution cognition is a fundamental factor in explaining stakeholders taking relevant response measures (Anderson et al., 2007; Shi and He, 2012), environmental pollution cognition is only a necessary but not sufficient condition for stakeholder to take actions. In some coastal areas of Argentina, even when citizens have recognized the problem of erosion and vulnerability, communities are still absence of corresponding monitoring and sanction rules to protect the coast (Rojas et al., 2014). The coefficient of social capital is significantly positive at the 1% level, implying that respondents with higher stock of social capital are more willing and likely to implement waste disposal monitoring. This

4.3. Dimensionality estimation and endogeneity problem of social capital 4.3.1. Dimensionality estimation of social capital The role of social capital in household waste disposal monitoring is one of main focus of this paper. As mentioned in the preceding section, most of the existing literature regarded one component of social capital as itself and the research that examined the impact of social capital from multiple dimension was very limited. Therefore, we replace social capital variable with its four components and re-estimate the original model. The detailed estimation results are reported in Table 6. It is apparent from this table that the results of joint estimation are better than those of separate estimation while the estimation results of these two approaches are basically the same in terms of coefficient signs and significances. Social network significantly boosts respondent’s willingness to monitor waste disposal and actual behavior at the 5% level or less. In a broad and intensive social network, it’s easier for supervisors to observe and know waste disposal behaviors of other households and thereby reduce the cost of their supervisory behavior; on the other hand, illegal waste dumping and other uncivilized behaviors can easily be detected, and the information will be quickly spread in social networks once offenders are detected. For reputation reasons, households have to reduce or even eliminate the undesirable behaviors in disposal of domestic waste, which in turn further cut down the costs of supervision. Likewise, Nenadovic and Epstein (2016) documented that fishers’ membership to informal fishing groups or fishing cooperatives significantly increased the likelihood of fishers’ participation in surveillance of their peers. The coefficient of social norms cannot pass the significance test in willingness to monitor equation, but it is significantly positive in actual monitoring behavior equation, revealing that social norms only affect respondent’s actual behaviors. This discrepancy might be easy to understand. Social norms are constraints or encouragements of social behavior, and provide a cognitive shortcut for people to comply with even though they don't realize the influence of social norms on themselves. Nevertheless, they are not directly related to people’s willingness. The crucial role of social norms in waste governance has attracted the attention of some researches, such as the impact of social 54

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Table 6 The impacts of social capital components on respondents’ waste disposal monitoring. Separate estimation

Social network Social norm Institutional trust Interpersonal trust Biophysical conditions Community attributes Rules in use Other attributes of participant ρ Log-likelihood AIC BIC Observations

Joint estimation

Willingness to monitor

Actual monitoring behavior

Willingness to monitor

Actual monitoring behavior

0.649** 0.071 0.073 0.739*** Yes Yes Yes Yes 0.000(fixed) −400.407 882.813 1046.871 404

0.473*** 0.294*** 0.128 0.513*** Yes Yes Yes Yes

0.423*** 0.125 0.098 0.703*** Yes Yes Yes Yes 0.813*** (0.077) −350.969 783.938 947.996 404

0.492*** 0.290*** 0.112 0.500*** Yes Yes Yes Yes

(0.301) (0.111) (0.099) (0.123)

(0.117) (0.094) (0.091) (0.103)

(0.138) (0.104) (0.088) (0.113)

(0.117) (0.092) (0.091) (0.103)

Notes: figures in parentheses are clustered standard errors of estimates; ** and *** indicate statistical significance at the 5% and 1% levels, respectively; AIC denotes Akaike's information criterion, and BIC denotes Bayesian information criteria; to save the space, this table does not report the estimation results of other independent variables, and the cut points in ordered probit model, as well as the constants in probit model.

4.3.2. Endogeneity problem of social capital In IAD framework, biophysical conditions, community attributes, and application rules are external variables, which are less likely to have endogenous problem in household decisions on waste disposal supervision. As for attributes of participant, social capital might be faced with an endogeneity problem. Two potential sources of endogeneity may lead to estimation bias in establishing a causal impact of social capital on waste disposal monitoring, namely, simultaneity (reverse causality) and omitted variable bias (Durlauf and Fafchamps, 2005; Han et al., 2016). There might be a simultaneous relationship between social capital and waste disposal surveillance activities since the process of surveillance can strengthen social networks, reinforce norms of reciprocity, build trust, and possibly encourage participation in other civic organizations. Meanwhile, the problem of omitted variable bias may also arise because the decisions on cultivating social capital and engaging in waste disposal surveillance activities may be related to unobserved characteristics such as individual disposition and community attachment. In order to eliminate the potential endogeneity of social capital derived from simultaneous effects and omitted variable, we estimate the original model separately with two groups of instrumental variables for social capital. First, we select respondent’s residency length within the community and whether respondent is the original inhabitant or not as the instrumental variables of social capital. The reason to choose this group of instrumental variables is that length of residency and original inhabitant have a massive impact on the investment of social capital (Barnes-Mauthe et al., 2015; Kesler and Bloemraad, 2010) while these two variables are not immediately associated with decisions on participating in waste disposal surveillance activities. In order to verify that instrument variables have no direct effect on respondent’s decisions upon waste disposal supervision, we adds residency length and original inhabitant into preceding models as control variables and find that their coefficients are not significant at the conventional significance levels. Table 7 reports the estimation results using residency length and original inhabitant as instrumental variables for social capital. One can see that the coefficients of residency length and original inhabitant in social capital equation are significant at the 1% level, indicating the selection of instrumental variables is reasonable. Meanwhile, the estimation results of endogenous auxiliary parameters (atanhrho_13, namely the correlation coefficient of disturbance terms between Eqs. (1) and (3); and atanhrho_23, namely the correlation coefficient of disturbance terms between Eqs. (2) and (3)) are significant at the 5% level, and the null hypothesis that social capital is exogenous variable is thus rejected in both willingness to monitor and actual monitoring behavior equations. Therefore, employing the above-

norms on household waste recycling (Abbott et al., 2013; Viscusi et al., 2011) and on compliance with waste management policies (Jones et al., 2011). The impact of institutional trust is positive but insignificantly at the 10% level. This result roughly supports the work of Nenadovic and Epstein (2016), who found that fishers’ trust in those government agencies responsible for fisheries management has no immediate impact on the likelihood of participation in surveillance activities. The different influence of institutional trust on voluntary monitoring and waste separation behavior at source is also noteworthy. While trusting local authorities are competent to effectively treat sorted waste is an important driving force behind waste source separation (Nguyen et al., 2015; Loan et al., 2017), trusting in local government capacities concerning monitoring and rule enforcement inhibits households’ voluntary monitoring. This discrepancy in results might be attributed to the following reasons. People with higher levels of institutional trust generally believe that local waste management officials have the ability to handle the waste pollution problem and consider that they do not need to participate in waste disposal supervision activities. However, as for people with lower levels of institutional trust, they regard waste disposal surveillance as the responsibility of local government or neighborhood committee and therefore are reluctant to perform supervision activities. To sum up, both people with higher levels of institutional trust and those with lower levels of institutional trust are unwilling to participate in supervision activities. Interpersonal trust significantly enhances respondents’ willingness to monitor waste disposal and the likelihood of actual participation in surveillance activities at the 1% level. This result seems to counterintuitive since individual with higher levels of social trust towards other members tends to supervise less (Langfred, 2004). Whereas interpersonal trust is a relational trust based on mutual interaction. In strong interpersonal relationships, people would believe that other party will not harm their own interests in spite of uncertainty and opportunities for defection and selfish behavior (Rus and Iglič, 2005). This interpersonal trust is similar to the words describing one’s moral characters such as “reliable”, “honest” and “rest assured” commonly used in our daily life (Yang, 1999) and does not necessarily mean trusting in waste disposal of other people. In fact, respondents with a reliable relational trust acquire understanding and support from their peers more easily when they conduct waste disposal monitoring. Therefore, interpersonal trust indeed boosts respondents’ willingness to monitor waste disposal and the likelihood of implementing waste disposal monitoring as opposed to reducing them.

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Table 7 Joint estimation of using residency length and original inhabitant as instrumental variables. Willingness to monitor Social capital Residency length Original inhabitant Biophysical conditions Community attributes Rules in use Other attributes of participant ρ atanhrho_13 atanhrho_23 Log-likelihood Prob > chi2 Observations

1.145

***

Actual monitoring behavior ***

(0.294)

1.284

Yes Yes Yes Yes 0.751*** (0.076) −0.397** (0.193) −0.375** (0.183) −807.466 0.000 404

Social capital (0.213) 0.020*** 0.242*** Yes Yes Yes Yes

Yes Yes Yes Yes

(0.005) (0.087)

Notes: figures in parentheses are clustered standard errors of estimates; ** and *** indicate statistical significance at the 5% and 1% levels, respectively; to save the space, this table does not report the estimation results of other independent variables, and the cut points in ordered probit model, as well as the constants in probit model.

community has a significant positive effect on social capital of the respondent, manifesting the instrumental variable is reasonably selected. In addition, the endogenous auxiliary parameters reject the null hypothesis that social capital is exogenous variable in both willingness to monitor and actual monitoring behavior equations at the 5% level. This results are compelling evidence that social capital indeed matters in respondent’s decisions on waste disposal supervision.

mentioned instrumental variables to estimate the impact of social capital on household waste disposal monitoring decisions is necessary and appropriate. The result of instrumental variable specification still supports our previous results that social capital plays a prominent role in facilitating respondent participation in waste disposal supervision. Second, we select average social capital stock of peers in the same community as the instrumental variable for social capital of the respondent. The choice of this instrumental variable is mainly out of the following considerations. First, social capital has obvious externalities and social capital of peers in the same community will have a profound impact on social capital of the respondent (Durlauf and Fafchamps, 2005), while social capital of peers in the same community has no direct impact on respondent’s decision-making in waste disposal monitoring. Next, similar to this method, in order to solve the endogenous problem of individual decision-making, some previous studies have used the mean value of other people in the same community as the instrumental variable. For example, Gao and Lu (2010) used average social trust of other households in the same community as the instrumental variable for social trust of the interviewed household when they examined the impact of social trust on labor mobility. In the study of the impact of financial knowledge on family business decision-making, Yun et al. (2015) used average financial knowledge of other people in the same income stratum in the same community as the instrumental variable for financial knowledge of the surveyed family. Their empirical results have uniformly shown the validity of the instrumental variables. Table 8 displays the estimation results using the corresponding instrumental variable. As shown in Table 8, average social capital of peers in the same

5. Further discussion on household waste disposal monitoring In this section, in order to further explore underlying factors influencing households' decisions on waste disposal monitoring, we provide primary reasons for respondent implementing monitoring or not implementing monitoring. The detailed statistical results are shown in Fig. 3. From Fig. 3 (a), among respondents who did not implemented waste disposal supervision, the largest number of respondents (57.2%) argued that waste disposal monitoring was the responsibility of sanitation cadres and has nothing to do with them. This phenomenon echoes our above-mentioned findings that staffing community with fulltime cadres for sanitation management reduces respondent’s willingness to supervise waste disposal and the possibility of conducting waste disposal supervision. Meanwhile, 53.5% of respondents considered that they had few social contacts with their neighbors, and it was difficult to monitor neighbors’ waste disposal behaviors, which also coincide exactly with our previous findings that the lower the stock of social capital, the less likely the respondent participate in surveillance activities of waste disposal. However, it is also worth mentioning that nearly a quarter (25.8%) of respondents maintained that in order to

Table 8 Joint estimation of using average social capital of peers as instrumental variable. Willingness to monitor Social capital Social capital of peers Biophysical conditions Community attributes Rules in use Other attributes of participant ρ atanhrho_13 atanhrho_23 Log-likelihood Prob > chi2 Observations

1.230

***

Actual monitoring behavior ***

(0.183)

1.232

Yes Yes Yes Yes 0.764*** (0.069) −0.407**(0.202) −0.383**(0.191) −798.682 0.000 404

Yes Yes Yes Yes

Social capital (0.166) 0.868*** Yes Yes Yes Yes

(0.110)

Notes: figures in parentheses are clustered standard errors of estimates; ** and *** indicate statistical significance at the 5% and 1% levels, respectively; to save the space, this table does not report the estimation results of other independent variables, and the cut points in ordered probit model, as well as the constants in probit model. 56

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Fig. 3. Respondents’ reasons for not monitored or monitored.

community attributes and participant attributes are important factors in explaining household participation in waste disposal supervision. Specifically, population density and modernization of community, and being male significantly increase the likelihood of households supervising waste disposal while community size and heterogeneity suppress it. More importantly, this study also finds that staffing community with full-time sanitation cadres undermines households’ enthusiasm to conduct waste disposal supervision, but social capital and peer monitoring substantially increase the willingness to supervise waste disposal and the possibility of household conducting waste disposal supervision. Lastly, household income, social norms and householder age are primary predictors of the gap between hypothetical willingness to monitor and actual monitoring behavior. Several policy implications are drawn from the above conclusions. First, local governments should actively improve community infrastructure construction and the whole household income. According to Maslow’s hierarchy of needs, only after basic needs of life (demand for food, water, energy, socializing and others) have been satisfied, will higher needs for good environmental quality be aroused. Second, local governments should decentralize and empower community inhabitants with a certain degree of autonomy, reduce the excessive dependence of administrative intervention, and consequently achieve inhabitant selfdetermined participation in environmental governance. Finally, strengthening social capital cultivation is an important approach to stimulate individuals to engage in surveillance activities spontaneously. Social capital can not only provide inhabitants with regular communication opportunity and reduce transaction costs in the process of environmental governance, but also subtly reshape the preferences of inhabitants towards community environment management.

keep neighborhood relationship harmonious, it is inadvisable to supervise others’ waste disposal behaviors, indicating that higher levels of social capital may also lead to a small part of respondents are reluctant to implement waste disposal supervision. This is similar to the results of Bodin and Crona (2008). They found that, for averting off ;ender embarrassment and themselves risking social rejection, villagers had low willingness to monitor and report rule breaking of other fishermen in the village with relatively high levels of social capital. In addition, Fig. 3(b) shows that among respondents who have supervised waste disposal of others, 84.1% of respondents considered that participation in environmental governance affairs was the right and duty of each resident, and 83.7% of respondents maintained that favorable sanitary condition was publicly owned and individuals has no right to destroy it, respectively. This to some extent demonstrates that respondents living in communities with good economic conditions and high degrees of modernization have an increasing demand for higher environmental quality, and thus have a strong desire to engage in environmental governance. On the other hand, 41.2% of respondents thought they had extensive social interactions with their neighbors and were easy to supervise waste disposal behaviors of each other, and 27.8% of respondents indicated that they were living here since they were born and had a strong feeling of attachment to local community. This confirms the view of previous literature that social capital can not only reduce the cost of conducting waste disposal supervision, but also might reshape individual preferences over surveillance activities through enhancing inhabitants’ community identity (Bowles and Gintis, 2002; Durlauf and Fafchamps, 2005; Passy and Giugni, 2000). 6. Conclusions As a concrete form of public participation in environmental governance, autonomous supervision of local inhabitants can effectively increase the total supply of supervision for environmental governance, and thus help to alleviate pressure on increasing severe environmental pollution. This paper empirically assesses the potential drivers and restraining factors of household waste disposal supervision under IAD framework. In particular, we mainly examines the effect of social capital as well as external variables on household participation in waste disposal surveillance activities. Using household and community level survey data from four suburbs in China, our estimation results reveal that willingness to monitor and actual monitoring behavior of households are not immediately associated with biophysical conditions while

Conflict of interest The authors declare they have no conflict of interest. Acknowledgements We thank Hongyun Han for her helpful suggestions, and appreciate the help provided by Hanning Li, Sheng Xia, Ye Jiang, Shuang Lin, and Shu Wu in our data collection. We also acknowledge the financial support from the National Natural Science Foundation of China (Grant No. 71803175; 71773114). 57

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