Small Acts With Big Impacts: Does Garbage Classification Improve Subjective Well‑Being in Rural China?
Junpeng Li1 · Puneet Vatsa2 · Wanglin Ma2
Received: 4 October 2022 / Accepted: 28 December 2022
© The Author(s) 2023
Abstract
Solid waste has surged in rural China, home to more than 540 million people. To preserve the environment, the Chinese government has piloted garbage classifica- tion programs. However, little is known about whether and to what extent classify- ing garbage affects people’s subjective well-being—should its effects be positive, people would be more amenable to classifying garbage, making it easier to entrench garbage classification practices and programs and ultimately improve the environ- ment. Accordingly, we analyze the impact of garbage classification on subjective well-being using the 2020 China Land Economic Survey data. An endogenous treat- ment regression model is utilized to address self-selection into garbage classifica- tion programs. We find that this simple and somewhat mundane practice can sig- nificantly improve people’s happiness and life satisfaction. These results reaffirm the compound benefits of allocating more public resources to accelerate the adoption of garbage classification in rural areas.
Keywords Garbage classification · Happiness · Life satisfaction · Endogenous treatment regression model · China
JEL Classification C31 · I31 · Q56
* Wanglin Ma
Wanglin.Ma@lincoln.ac.nz Junpeng Li
LJP549@163.com Puneet Vatsa
Puneet.Vatsa@lincoln.ac.nz
1 School of Economics and Management, Huaiyin Normal University, Huaian 223301, China
2 Department of Global Value Chains and Trade, Faculty of Agribusiness and Commerce, Lincoln University, Christchurch, New Zealand
Introduction
Rapid economic progress pulled billions out of poverty; at the same time, it has also wrought unprecedented environmental degradation (Ahmad & Wu, 2022; Fidelis et al., 2020; Shi et al., 2021; Wu et al., 2020). Global production and consumption have risen, and with them, waste. The accumulation of waste is one of the most significant contemporary environmental issues hindering human development and well-being (Li & Zhou, 2020; Shi et al., 2021; Vyas et al., 2022). In response, poli- cymakers are designing waste-management interventions to reduce, recycle, and reuse waste.
That China is the largest emitter of greenhouse gases (GHG) globally is well- publicized in the media and policy circles. However, the surge in China’s rural solid waste has been largely under the radar—it has grown from 46.7 billion tons in 2013 to 52.2 billion tons in 2019 (Intelligence Research Group, 2020). At the same time, a considerable proportion (around 30–60 percent) of rural garbage has been misman- aged (Wang & Hao, 2020), leading to economic losses and environmental degrada- tion. The garbage has contaminated arable land and drinking water, having deleteri- ous effects on rural residents’ health and economic welfare (Liu et al., 2020). The rapid increase in solid waste and its adverse effects on society underscore the signifi- cance of effective waste management practices in rural China.
Recognizing the gravity of this situation, the Chinese government is implement- ing policies to increase the uptake of garbage classification—an integral aspect of waste management—in rural areas. In China, garbage classification entails sepa- rating the waste into different pre-defined categories, packaging it, transporting it to disposal sites, and finally processing it for further use or dumping it in landfills (Chen et al., 2020; Liu et al., 2020). These practices have shown promise and proven effective in keeping cities, towns, and villages clean and mitigating environmental degradation. Understandably, China’s government is pressing forward to establish these practices. For instance, at the beginning of the "Thirteenth Five-Year Plan"
(2016–2020), eight cities were selected by the central government to pilot garbage classification programs.1 In 2019, China introduced a law on garbage disposal: "Law of the People’s Republic of China on the Prevention and Control of Environmen- tal Pollution Caused by Solid Wastes." Nevertheless, at 44 percent, the participa- tion rate of garbage classification in rural China remains very low (Wang & Hao, 2020). Thus, there is a pressing need to accelerate the adoption of garbage classifica- tion practices among rural residents for environmental preservation and sustainable development in rural China.
Research devoted to garbage classification abounds and can be broadly classi- fied into two strands. The first strand investigates the determinants of residents’
garbage classification. Most studies of these studies show that sociodemographic characteristics (Fan et al., 2019; Ma & Zhu, 2020; Pakpour et al., 2014; Zhang et al., 2015) and social norms (Lee et al., 2019; Park & Ha, 2014; Peng et al.,
1 The programs were piloted in Beijing, Shanghai, Nanjing, Hangzhou, Guilin, Guangzhou, Shenzhen, and Xiamen.
2021) significantly influence the garbage classification practices of residents. For instance, Zhang et al. (2017) and Fan et al. (2019) found that urban residents’
awareness of garbage classification contributes significantly to this practice in China and Singapore. Nepal et al. (2020) reported that adherence to social norms has also proven to be significantly and positively associated with the participa- tion of urban residents in garbage classification. Ma and Zhu (2020) suggested that Internet use can make rural residents more willing to classify their domestic waste in China. Furthermore, other studies argue that urban garbage classifica- tion could be driven by political interventions such as publicity, subsidies, regula- tions, and infrastructure (Ao et al., 2022; Kirakozian, 2016; Lee et al., 2019; Li et al., 2019; Peng et al., 2021; Torres-Pereda et al., 2020). For instance, Ao et al.
(2022) found that publicity influenced pariticipation in garbage classification in rural areas. Li et al. (2019) showed that sufficient financial support was a key driver for rural residents adopting garbage classification.
The second strand emphasizes the positive impacts of garbage classification (e.g., Babalola, 2015; Ghisellini et al., 2016; Giannis et al., 2017; Tong et al., 2020; Wang and You 2021; Xiong, 2019). Generally, the benefits of processing garbage classi- fication are two-fold: economic development and improvements in environmental quality. Previous studies have shown that garbage classification reduces the volume of garbage and accelerates garbage disposal, thereby improving environmental qual- ity (Giannis et al., 2017), reducing GHG emissions (Calabrò, 2009; Wang and You 2021), and mitigating water and soil contamination (Nie et al., 2018; Tong et al., 2020). Previous studies have also found that garbage classification promotes recy- cling (Nie et al., 2018; Ning & Cao, 2019; Pei, 2019; Xiong, 2019), which helps avert and attenuate resource crises and stimulate the economy. Taking Tianjin (China) as an example, Wang and You (2021) showed that a one percent increase in the participation of garbage classification could reduce the area allocated to landfills by more than 500 m2 while contributing to the province’s GDP.
Notwithstanding the considerable research on garbage classification, previous studies have not explored the association between garbage classification and peo- ple’s subjective well-being. One exception is Qi et al. (2022), who documented that environment protection behaviour (e.g., garbage classification and donating money for environmental protection) can enhance farmers’ subjective well-being in China.
However, they only considered garbage classification as an element of the synthe- sized key explanatory variable (i.e., environmental protection behaviour), shedding no light on the association between garbage classification and subjective well-being.
Accordingly, the impact of garbage classification on farmers’ subjective well-being remains undetermined. Whether and to what extent garbage classification influences people’s subjective well-being has implications for the design and effectiveness of waste management policies. For instance, should classifying garbage contribute pos- itively to people’s subjective well-being, they would be more amenable to policies designed to curb the mismanagement of garbage; how the populace perceives gar- bage classification can also inform the messaging of awareness programs promoting this practice. This study addresses both of these gaps, making two significant contri- butions to the literature on garbage classification.
First, we explore the determinants of rural residents’ garbage classification. Inad- equate management of rural garbage aggravates environmental pollution, contrib- utes to adverse health outcomes, and strains natural resources, thereby hindering rural development (Li et al., 2019; Liu et al., 2020; Sosna et al., 2019). This is espe- cially true for rural China, home to 540 million people. Furthermore, compared with urban areas, the unique features of rural domestic garbage, i.e., wide dispersion, a steep rise in volume, and composition variability, impede garbage classification and disposal (Shi et al., 2021). Therefore, rural residents’ garbage classification partici- pation warrants attention. We provide the first attempt to investigate the determi- nants of rural garbage classification participation.
Second, we examine the linkages between garbage classification and subjective well-being. The latter is vital to one’s quality of life and foundational to lasting and effective rural development (Hu et al., 2021; Zheng & Ma, 2021). Whether and how garbage classification— a practice that also mitigates environmental degradation and improves the standard of living—affects subjective well-being has direct impli- cations for developmental and environmental policies. For instance, having lasting engagement with garbage classification practices is more likely to take hold should people feel positively about these practices and derive meaning, purpose, happiness, and satisfaction from them. It may be unnecessary for local governments to monitor adherence to garbage disposal practices of people intrinsically motivated to classify their garbage, say to preserve the environment, reduce landfills, save energy, or set a good example for their children. On the other hand, should people find garbage classification onerous, inconvenient, and futile, frequent monitoring and stringent regulations may be necessary to ensure compliance with garbage disposal mandates.
In this study, we analyze how garbage classification affects the subjective well- being of rural households in Jiangsu province in China. To this end, we analyze the 2020 China Land Economic Survey (CLES) data collected by Nanjing Agricultural University, located in Jiangsu. It bears emphasis that pro-environmental practices are not randomly assigned among rural residents; garbage classification is no excep- tion. The residents decide on their own whether to classify their garbage. Thus, there are systematic differences between those who classify their garbage and those who do not. Moreover, these differences may stem from observed factors (e.g., age, edu- cation level, sex, and household size) and unobserved factors (i.e., motivations and preferences), rendering garbage classification endogenous. To account for this endo- geneity, we employ the endogenous treatment regression (ETR) model to estimate the effects of classifying garbage on the subjective well-being of rural residents. The modeling proceeds in two stages. First, we identify the determinants of participa- tion in garbage classification. Then, we study the effects of garbage classification on subjective well-being. We show that garbage classification participation significantly improves rural residents’ happiness and life satisfaction, two measures of subjective well-being.
The remainder of this paper is structured as follows.“Why Study the Jiangsu Province?“ section discusses why studying Jiangsu is apt for this study. "Analyti- cal Framework: How Classifying Garbage Affects Subjective Well-Being" section outlines the theoretical framework. "Empirical Strategy" and " Data and Descrip- tive Statistics " sections describe the empirical strategy and data, respectively. The
empirical results are presented and discussed in "Empirical Results and Discus- sions" section. The final section concludes this study, laying out its policy implica- tions, drawing attention to its limitations, and offering avenues for future research.
Why Study the Jiangsu Province?
Jiangsu is an interesting case study on the effects of garbage classification on subjec- tive well-being. The province has long been a testing ground for various pilot studies related to economic and environmental policies. For instance, programs designed to abolish the agricultural tax, reform state-owned enterprises, and build beautiful and livable cities were first piloted in Jiangsu. Apropos garbage classification, Nan- jing, the capital city of Jiangsu, was chosen as one of the eight pilot cities for test- ing waste management practices in 2015. So far, garbage classification participation rates among urban residents have improved in 13 cities in Jiangsu (China Construc- tion News Network 2021). However, like the rest of China, garbage classification is not taking hold in the province’s rural areas. Data show that only 40.14 percent of its administrative villages have implemented garbage classification (China Construc- tion News Network 2021), which is close to the national rate. In this sense, Jiangsu is nationally representative of garbage classification uptake in China.
Furthermore, Jiangsu is emblematic of China’s happiness paradox: even though China has made rapid economic progress since joining the World Trade Organiza- tion in 2001, the subjective well-being of the Chinese has not risen commensurately (Cheng et al., 2018). Although Jiangsu had the third-highest GDP per capita (121.23 thousand yuan) in 2020 (NSBC, 2021), suicide by pesticide self-poisoning was dis- concertingly high in the province (Wang et al., 2020). Thus, it is important to devise policies that simultaneously promote economic growth and subjective well-being and demonstrate that neither has to be sacrificed to attain the other. How garbage classification affects subjective well-being in Jiangsu may provide valuable insights that may be leveraged to roll out such policies in the rest of the country.
Analytical Framework: How Classifying Garbage Affects Subjective Well‑Being
Research has uncovered several pathways through which garbage classification can potentially influence the subjective well-being of rural residents. Leaning on previ- ous studies, we illustrate these pathways in Fig. 1.
The top half of the figure shows four pathways through which garbage classifica- tion can positively affect subjective well-being. Studies have shown that classifying garbage improves people’s physical and mental health by reducing their exposure to garbage (Li & Zhou, 2020; Orru et al., 2016; Tanaka, 2015); it also protects the environment by promoting reuse and recycling, mitigating pollution and environ- mental contamination (Eriksson et al., 2005; Fidelis et al., 2020; Wang and You, 2021). Being in good health and living in a clean environment enhances subjective well-being (Li & Zhou, 2020; Zheng & Ma, 2021).
Garbage classification also affects subjective well-being by reducing the cost of living. Meng et al. (2019) have shown that garbage classification helps rural resi- dents reuse solid waste such as shopping bags and carton boxes, thereby reduc- ing their household expenses, leaving them with more financial resources to allo- cate toward education, leisure, and healthcare, in turn, improving their subjective well-being.
Although individuals classify garbage mainly for personal reasons, this practice also generates significant positive externalities for their communities, earning them praise and respect from others. According to the theory of social recognition, prac- tices such as garbage classification that yield positive externalities can enhance peo- ple’s reputations and help them build harmonious interpersonal relationships, thus improving subjective well-being (Chen et al., 2021; Fidelis et al., 2020; Meng et al., 2019; Ghisellini et al., 2016).
Nevertheless, classifying garbage can also compromise subjective well-being.
The bottom half of Fig. 1 points out three mechanisms through which this can hap- pen. Rural Chinese are wont to discard their garbage carelessly, without classifying it (Liu et al., 2009)—they find it convenient. On the other hand, garbage classifica- tion guidelines call for separating the garbage into different categories (i.e., recy- clable waste, hazardous waste, food waste, and residual waste) and dropping it into designated bins. Thus, classifying garbage can be time-consuming and inconvenient, leading to lower subjective well-being.
Although garbage classification can help households save money (as noted above), it can also increase their cost of living. Garbage classification guidelines require people
Garbage classification
Improves physical and mental health
Builds reputation and harmonious interpersonal
relationships Reduces living costs
Increases living expenses Makes life inconvenient
Invites criticism
Subjective well- being +
- Improves environmental
quality
Fig. 1 How garbage classification affects subjective well-being: potential pathways
to separate garbage into small bags (Tong et al., 2020). Because people may have to purchase bags and bins for proper garbage disposal, adherence to these guidelines may increase living expenses, requiring people to curb their expenditure on leisure, food, and healthcare, thereby reducing their subjective well-being.
The logistics and practical realities of classifying garbage bear emphasis, as they affect how people engage in this mundane practice. Often, the signage on the desig- nated bins is illegible and not standardized, causing confusion and misleading people.
And if people dispose of the garbage incorrectly, they may draw criticism from the gov- ernment and their neighbors. Exposure to criticism, even just anticipating it, can induce stress and anxiety, thus lowering one’s subjective well-being. To summarize, it remains an open question whether garbage classification improves or compromises subjective well-being.
Empirical Strategy
Participation in garbage classification is dichotomous: either people participate, or they do not. We assume that rural residents decide between participating and not participat- ing in garbage classification to maximize their expected utilities. Let U1 be the utility of a rural resident derived from participating in garbage classification, while U0 be the utility derived from not participating. A rational and risk-neutral rural resident will par- ticipate in garbage classification only if the resident perceives a positive net utility ( G∗i ) between participation and non-participation, that is, G∗i =U1−U0>0 . Although G∗i is unobservable, rural residents’ decisions to participate in garbage classification can be expressed by a latent variable model as follows:
where G∗i is a latent variable that indicates the probability of household head i decid- ing to classify household garbage. It is denoted by a dummy variable ( Gi ), which represents the garbage classification participation status of rural residents (1 for gar- bage classification participants and 0 otherwise). Zi refers to a vector of exogenous variables, such as age, sex, education, and asset ownership. 𝛾 refers to a vector of parameters to be estimated, and 𝜇i refers to the error term.
Following Zheng and Ma (2021) and Yuan et al. (2021), we assume that happi- ness and life satisfaction, the two measures of subjective well-being, are linear func- tions of garbage classification participation ( Gi ), as well as a vector of exogenous variables. The empirical specification is expressed as follows:
where Si measures the level of subjective well-being (happiness or life satisfaction) of household head i . Xi is a vector of exogenous variables. 𝛼 and 𝛽 are parameters to be estimated. 𝜀i is the error term.
If the treatment variable, i.e., garbage classification, is randomly assigned, the impact of garbage classification on subjective well-being can be estimated using (1)
G∗i = 𝛾Zi+ 𝜇i,with Gi= {
1,if G∗i >0 0,if G∗i ≤0
(2) Si= 𝛼iGi+ 𝛽iXi+ 𝜀i
ordinary least square (OLS) regressions as specified in Eq. (2). However, rural residents’ decisions to participate in garbage classification are influenced by both observed factors and unobserved factors, which may also influence their subjec- tive well-being. That is, those rural residents who classify garbage and those who do not may differ systematically, and these differences can lead to observed and hidden selection bias—an unbiased impact assessment cannot be made without addressing the selection bias.
Several empirical strategies have been developed and utilized to address selec- tion bias. For analyzing cross-sectional data with endogenous binary treatment variables and discrete outcomes, empirical strategies such as propensity score matching (PSM), the augmented inverse probability weighted (AIPW) estima- tor, the inverse probability weighted regression adjustment (IPWRA) estimator, and the endogenous treatment regression (ETR) model have been widely used to account for selection bias (Kurz, 2021; Li et al., 2020; Ma et al., 2020a, 2020b;
Manda et al., 2018; Zhou & Ma, 2022). Among them, PSM, AIPW, and IPWRA help address selection bias arising from observed factors, but they fail to address selection bias originating from unobserved factors. In comparison, the ETR model addresses both observed and unobserved selection bias and estimates the treatment variable’s direct impact on the outcome variable (Belissa et al., 2020;
Dedehouanou et al., 2018; Hodjo et al., 2021; Yuan et al., 2021). Therefore, the ETR model is used in this study to analyze the direct effects of garbage classifica- tion on subjective well-being.
The ETR model jointly estimates Eqs. (1) and (2) using a maximum likelihood estimator (Ma et al., 2020a, b; Stata, 2019). The error terms in Eqs. (1) and (2) are assumed to have zero means and bivariate normal distributions, which can be speci- fied as,
where 𝜌𝜀𝜇 is the correlation between 𝜀i and 𝜇i . 𝜎𝜀2 and 𝜎𝜀 refer to the variance and standard deviation of 𝜀i , respectively. The variance of 𝜇i (i.e., 𝜎2𝜇 ) is normalized to one. A significant 𝜌𝜀𝜇 points to the presence of selection bias stemming from unob- served factors, confirming the benefits of using the ETR model (Hodjo et al., 2021;
Vatsa et al., 2022; Yuan et al., 2021).
To specify the ETR model, we include an identifying instrument in Zi but not in Xi . Specifically, we leverage the theory of reasoned action (TRA) proposed by Fishbein and Ajzen (1975) to select rural residents’ access and exposure to the pro- motion of garbage classification by the media as the instrumental variable. The the- ory hypothesized that an increase in people’s knowledge improves their awareness, which then helps them form behaviors (Sussman & Gifford, 2019). With respect to environmental behaviors, Gao et al. (2019) have confirmed that improving rural residents’ access to promotional activities via different media can enhance knowl- edge, awareness, and adoption of environmentally-friendly practices. In the present study, the instrumental variable, i.e., rural residents’ exposure to governmental ini- tiatives promoting garbage classification, is based on the survey question: “Have
(3) (𝜀i
𝜇i )
∼N [(0
0 )
,
( 𝜎𝜀2 𝜌𝜀𝜇𝜎𝜀 𝜌𝜀𝜇𝜎𝜀 1
)]
you received any promotional information regarding garbage classification from the government?”. It is measured as a dichotomous variable (1 = Yes; 0 = No).
Given the TRA and the empirical findings showing that the chosen instrument drives pro-environmental behaviors (Gao et al., 2019; Zhang et al., 2019), we believe that it may also lead to the adoption of garbage classification. Furthermore, exposure to governmental promotions does not directly influence rural residents’ subjective well-being, except through its impact on people’s perception and adoption of pro- environmental practices. That is to say that rural residents’ exposure to governmen- tal initiatives promoting garbage classification meets the theoretical criteria for an appropriate instrument. We also statistically test for the validity of the instrument.
Following Adhvaryu and Nyshadham (2017) and Li et al. (2020), a falsification test is performed to this end. The falsification test, reported in Table A1 in the online Appendix, suggests that the instrument is positively and significantly correlated with garbage classification but is uncorrelated with happiness and life satisfaction. The results of the falsification test confirm the validity of the chosen instrument.
Data and Descriptive Statistics Data
We use the 2020 China Land Economic Survey (CLES) data collected by Nanjing Agricultural University, Nanjing, China. The data were collected using a three-stage probability proportional to size (PPS) sampling procedure. In the first stage, two counties were randomly selected from each of the 13 cities in Jiangsu, resulting in 26 sampled counties. In the second stage, two villages or communities were randomly chosen within each county. In the third stage, around 50 household heads were ran- domly selected from each selected village for face-to-face interviews, resulting in a sample of 2,600 rural households. We analyzed 2,254 out of the 2,600 observations by removing observations with missing values and anomalous answers.2 Specifi- cally, first we deleted 18 observations with missing values on garbage classification participation. Then, we dropped 21 observations with missing values or anomalous answers on indicators of subjective well-being. Furthermore, we also removed 307 observations with anomalous and missing values for the control variables.
The CLES was conducted by a team of postgraduate students from Nanjing Agricultural University who can speak both Mandarin and regional dialects of the selected counties. Utilizing a detailed structured questionnaire, this survey col- lected information on multiple aspects of household heads, such as demographic and household characteristics (e.g., age, sex, and household size), asset owner- ship, agricultural management, and living conditions. Following prior studies
2 Anomalous answers refer to those that are highly unlikely, potentially erroneous, or both. For instance, the values of happiness and life satisfaction variables are bounded between 1 and 10. The observa- tions containing answers that are higher than 10 or have decimals are deemed anomalous answers and excluded from the analysis.
(e.g., Nie et al., 2021; Sujarwoto et al., 2018; Zheng & Ma, 2021), we use two indicators, happiness and life satisfaction, to measure subjective well-being. The information was collected based on two survey questions: “How happy are you?”
and “How satisfied are you with your life?”. Specifically, the survey assigns an ordered variable for measuring rural residents’ happiness and life satisfaction, quantified using a 10-point Likert scale, ranging from 1, denoting very unhappy or very unsatisfied, to 10, denoting very happy or very satisfied.
Responsibly disposing of waste is a household decision (Li et al., 2019; Peng et al., 2021). However, it is common for specific household members to dispose of waste on the household’s behalf. Thus, waste disposal is as much an individual decision as a household decision (Kip Viscusi et al., 2011; Kuang & Lin, 2021).
Following previous studies (Liu et al., 2020; Meng et al., 2019), we use a dichot- omous variable to capture whether a rural resident classifies household garbage.
To construct this variable, we rely on the answers to the following question: “Do you classify garbage?”. Individuals who classified garbage are assigned a value of 1, whereas those who did not are assigned a value of 0.
We lean on previous studies on people’s pro-environment actions and subjec- tive well-being (e.g., Ma et al., 2020a, 2020b; Ma & Zhu, 2020; Sujarwoto et al., 2018; Zheng & Ma, 2021) to select the control variables for analyzing the asso- ciation between garbage classification and subjective well-being. Specifically, we control for demographic factors such as age, sex, education, health status, marital status, and household size. There is a general agreement that as people age, they tend to become set in their ways and more unwilling to give up habits and behav- iors to which they are accustomed (Gebrezgabher et al., 2015). Therefore, we expect age to be negatively associated with classifying garbage, as this practice is relatively new in rural China. Consistent with Zhou and Turvey (2018), who argued that females tend to shoulder responsibility for housework (e.g., clean- ing), we expect a negative correlation between sex (a dummy variable assigned a value of one for males and zero for females) and garbage classification. Edu- cated individuals tend to be more informed and aware of the importance of pro- environment behaviors and have the skills to learn new tasks relatively quickly;
they tend to be more adaptable (Ma et al., 2020a, 2020b). Therefore, we control for household heads’ education measured in years and expect it to be positively associated with garbage classification. Previous studies have shown that being in good health improves subjective well-being (Li & Zhou, 2020; Nie et al., 2021;
Taşkaya, 2018); thus, we have included the self-reported health status of the household heads, measured on a 5-point Likert scale. We have also controlled for the marital status of the household heads. Married couples tend to have more labor within the household at their disposal, which they can allocate to different tasks. Married couples also tend to live in larger households relative to unmarried individuals, reaping the benefits of household economies of scale (Bimber et al., 2003). Given this, we expect being married to be positively associated with clas- sifying garbage. Household economies of scale suggest that large households are likely to have lower per capita costs of classifying garbage, making them more likely to do so. With this in mind, we also control for household size.
We also include how trusting of others the household head is and whether the household head owns assets (denoting wealth) to control for the effects of societal expectations and financial factors. Trust is foundational to building social capital;
it engenders cooperation and fosters a sense of social responsibility. The desire to do what is in the best interest of society may nudge people to engage in activities—
such as garbage classification—that have positive externalities. Thus, we expect the variable respresenting trust to be positively associated with garbage classifica- tion. TWealth enhances people’s purchasing power, making them early adopters of novel products, services, and ways of life to improve their living standards (Charles et al., 2019; Lim et al., 2020; Zheng & Ma, 2021). Accordingly, asset ownership is included and expected to foster garbage classification. Moreover, we use three dummies representing rural household income tertiles to serve as wealth proxies and expect subjective well-being to be positively associated with higher household income. We also control for adversity, distress, and hardship using a dichotomous variable (i.e., negative shock), as the death of loved ones, injury, and ill health can profoundly decrease subjective well-being (López-Feldman & Porro, 2021).3 We expect to find a negative association between the variable negative shock and sub- jective well-being.
Studies have shown a link between people’s attitudes toward risk and their pro- pensity to adopt pro-environment behaviors. Gong et al. (2016) noted that risk- averse people are less likely to lead environmentally friendly lives. Given this, we incorporate a dummy variable representing rural residents’ risk attitudes (i.e., risk- averse) into our regression model.4 We expect risk-aversion to be negatively associ- ated with garbage classification. Studies have also shown that those who perceive the environmental quality to be poor report lower subjective well-being and are more likely to adopt pro-environmental practices (Li & Zhou, 2020; Sulemana et al., 2016). Therefore, we also include a binary variable representing rural residents’ per- ception of pollution and expect it to affect garbage classification positively.5
Whether rural residents classify garbage also depends on village-level factors such as economic conditions and democratization. Rural residents in economically
3 Negative shocks are measured based on the following question: “What were the major adverse events that your family encountered in 2019?”. Possible answers to this question were as follows: “1 = None;
2 = death of family members; 3 = household members suffered a serious illness, accidental injury, or violent injury; 4 = job loss or business loss; 4 = livestock death, crop loss due to diseases and pests, droughts, and floods; 5 = victims of burglary, theft, assault, robbery, or vandalism; 6 = wedding in the family”. Negative shock is expressed as a binary variable, which equals 1 if the household heads chose one or more of options 2, 3, 4, and 5, and 0 if they chose 1, 6, or both.
4 The risk-averse variable is measured based on the survey question, “Which of the following invest- ments would you prefer?”. The household heads were asked to choose one of the following three options:
“1 = an investment with a high risk (high risk, high return, and high loss); 2 = an investment with a mod- erate risk (moderate risk, moderate return, and moderate loss); 3 = an investment with a low risk” (low risk, low return, and low loss). We measured risk-averse as a binary variable, which equals 1 if house- hold heads choose 3, and 0 if they choose 1 or 2.
5 Pollution perceptions are measured based on the survey question, “What is your perception of the envi- ronment in your village?”. The question had four possible answers: “1 = not polluted at all; 2 = slightly polluted; 3 = polluted; 4 = highly polluted”. We measured pollution perception as a binary variable, which equals 1 if the household heads chose 2, 3, or 4, and 0 if they chose 1.
developed villages have greater access to public amenities (such as community centers and sanitary facilities), promoting pro-environmental behaviors. Thus, we expect people living in economically developed regions to be more likely to classify garbage than those living in economically disadvantaged regions. Following Wang et al. (2019), we use a dummy variable denoting the presence of various industries in a village to reflect its economic condition. Rural democratization allows people to get involved in community decision-making—research shows that the greater the involvement, the higher the subjective well-being (Radcliff & Shufeldt, 2016). We control for the degree of democratization by incorporating a dummy variable, which is assigned a value of one for villages in which important decisions are made in consultation with village members or their representatives and zero otherwise. Fur- thermore, the control variables also include three regional dummies to capture the unobserved disparities in institutional arrangements, resource endowment, and eco- nomic conditions.
Descriptive Statistics
Table 1 presents the definitions, means, and standard deviations of all selected vari- ables. Our data show that the mean values of household heads’ self-reported happi- ness and life satisfaction are 7.81 and 7.69 out of 10, respectively, suggesting that, in general, rural Chinese in Jiangsu report relatively high levels of subjective well- being. These results are consistent with Zheng and Ma (2021) and Nie et al. (2021).
As noted above, only 47 percent of rural households in the province participated in garbage classification; this is close to the national participation rate of 44 percent (Wang & Hao, 2020).
Table 1 shows that 69 percent of the household heads were males, and 89 percent were married. The household heads’ average age was about 61 years, and overall, they reported being in good health; they had, on average, 6.85 years of education, and only 36 percent of household heads perceived the environment to be polluted, suggesting that they may have become inured to environmental degradation. This points to opportunities to inculcate pro-environment behaviours in a large number of people through education and awareness initiatives and, in doing so, improve the environment. However, given their potential desensitization to environmental deg- radation, it may be challenging to impel rural households to adopt pro-environment behaviours. Table 1 also shows that only a small fraction of rural household heads (around 15 percent) experienced a negative shock in 2019. Interestingly, 74 percent of household heads are risk-averse—this is in line with Ma et al., (2020a, 2020b).
Table 2 presents the differences in the mean values of the variables for those who classify garbage and those who do not. As for the two variables represent- ing subjective well-being, the significant mean differences suggest that garbage classification participants are happier and more satisfied with their lives than non- participants. Relative to non-participants, garbage-classification participants tend to live in smaller households and are more likely to be younger, healthier, more educated, and married; they also perceive the environment to be less polluted.
Regarding the differences between the two groups apropos household income
Table 1 Variable definitions and descriptive statistics VariablesDefinitionMeanS.D Dependent variables HappinessSelf-reported happiness level: from 1 = very unhappy to 10 = very happy7.811.67 Life satisfactionSelf-reported life satisfaction level: from 1 = very unsatisfied to 10 = very satisfied7.691.60 Garbage classification1 if the household head (HH) classifies garbage at home, 0 otherwise0.470.50 Independent variables AgeAge of the HH (years)61.2411.23 SexSex of the HH: 1 if male, 0 otherwise0.690.46 EducationSchooling years of the HH6.853.91 Health StatusSelf-reported health status of the HH: from 1 = very poor to 5 = very good3.881.06 Marital status1 if the HH is married, 0 otherwise0.890.32 Household sizeNumber of people residing in a rural household6.471.44 TrustingHow trusting of others the HH is: from 1 = not trusting at all to 5 = very trusting4.060.79 Household income tertile 11 if household income (1,000 yuan/capita) is categorized as tertile 1, 0 otherwise0.330.47 Household income tertile 11 if household income (1,000 yuan/capita) is categorized as tertile 2, 0 otherwise0.330.47 Household income tertile 11 if household income (1,000 yuan/capita) is categorized as tertile 3, 0 otherwise0.330.47 Asset ownership1 if the household owns a washing machine, 0 otherwise0.950.22 Negative shock1 if the HH experiences negative shocks (e.g., member death, health deterioration, agricultural shocks, prop- erty loss, and job loss), 0 otherwise0.150.36 Risk-averse1 if the HH is risk-averse, 0 otherwise0.740.44 Pollution perception1 if the HH perceives the rural environment is polluted, 0 otherwise0.360.48 Village industry1 if there are one or more industries in the village (e.g., rural tourism enterprise and E-commerce enterprise), 0 otherwise0.230.42 Village meetings1 if meetings of all villagers or villagers’ representatives are held for major events (e.g., pollution control and land acquisition), 0 otherwise0.800.40 Northern Jiangsu1 if the household is located in northern Jiangsu, 0 otherwise0.430.49
Table 1 (continued) VariablesDefinitionMeanS.D Central Jiangsu1 if the household is located in central Jiangsu, 0 otherwise0.240.43 Southern Jiangsu1 if the household is located in southern Jiangsu, 0 otherwise0.330.47 Instrument1 if the HH is exposed to governmental initiatives promoting garbage classification, 0 otherwise0.820.39 Sample size2,254 S.D. refers to standard deviation
tertiles and asset ownership, the results indicate that garbage classification par- ticipants are more likely to be better off financially than non-participants. Spe- cifically, those who classify garbage are more likely to have incomes in the third income tertile, while the incomes of non-participants are likely to fall in the sec- ond income tertile. Classifying garbage is also associated with a greater likeli- hood of owning a washing machine. It also shows that, relative to non-partici- pants, participants are less likely to be risk-averse but more likely to trust others.
Moreover, the significant differences in the variables representing the democrati- zation (village meetings) and the level of economic development (village indus- try) of the villages for participants and non-participants suggest that the former reside in more economically developed and democratic villages. In addition, the significant mean difference in the instrumental variable indicates that the prob- ability of those who classify garbage being exposed to government initiatives to promote garbage classification is much higher than that of those who do not—this lends credence to the aptness of the instrumental variable.
Table 2 Mean differences in selected variables between garbage classification participants and non-par- ticipants
*** < 0.01, ** < 0.05, and * < 0.10
Variables Participants Non-participants Mean difference t-value
Happiness 8.052 7.607 0.446*** 6.383
Life satisfaction 7.961 7.449 0.512*** 7.670
Age 59.404 62.856 -3.452*** -7.368
Sex 0.694 0.687 0.008 0.401
Education 7.489 6.282 1.207*** 7.402
Health status 3.989 3.785 0.204*** 4.572
Marital status 0.903 0.875 0.028** 2.123
Household size 6.402 6.529 -0.127** -2.085
Trusting 4.163 3.972 0.192*** 5.769
Household income tertile 1 0.332 0.334 -0.002 -0.105
Household income tertile 2 0.314 0.351 -0.037** -1.849
Household income tertile 3 0.354 0.315 0.039** 1.955
Asset ownership 0.956 0.938 0.018** 1.902
Negative shock 0.153 0.152 0.001 0.072
Risk-averse 0.721 0.766 -0.045*** -2.435
Pollution perception 0.330 0.387 -0.056*** -2.791
Village industry 0.252 0.207 0.046*** 2.583
Village meetings 0.847 0.768 0.080*** 4.788
Northern Jiangsu 0.397 0.455 -0.058*** -2.801
Central Jiangsu 0.229 0.255 -0.026* -1.456
Southern Jiangsu 0.375 0.290 0.085*** 4.287
Instrument 0.946 0.705 0.242*** 15.630
Sample size 1,054 1,200
Evidently, the simple mean comparisons highlight systematic differences between those who classify their garbage and those who do not. Nevertheless, caution is necessary for interpreting these simplistic results, as they neither account for con- founders nor the endogeneity of garbage classification. Therefore, a more rigorous approach is in order. Next, we present the results obtained from the ETR model explained above.
Empirical Results and Discussions Diagnostic Tests
Following previous studies (Zheng & Ma, 2021), we conduct multiple diagnostic tests to ensure that our empirical models are correctly specified. We conduct Ram- sey’s regression equation specification error test (RESET) to check for model mis- specification, examine variance inflation factors (VIF) to test for multicollinearity, and use White’s test to see if the residuals are homoscedastic. The results for the RESET and VIFs presented in Table A2 in the online Appendix suggest that the model is not misspecified and multicollinearity is not a concern. However, White’s test points to heteroscedastic residuals. With this in mind, we use village-level clus- tered standard errors.
We also estimate the correlation coefficient (i.e., 𝜌𝜀𝜇= cor r(𝜀i,𝜇i) ) utilizing the maximum likelihood estimator and present the results in the lower part of Table 3.
All the estimates of 𝜌𝜀𝜇 are statistically significant, indicating the presence of selec- tion bias arising from unobserved factors. This highlights the utility of using the ETR model.
Determinants of Garbage Classification
Columns 2 and 4 of Table 3 show the estimated coefficients of the explanatory vari- ables. The signs of the estimated coefficients largely align with our expectations.
Since Columns 2 and 4 report similar results, we will explain them together. The negative and statistically significant coefficients of the age variable suggest that older household heads are less likely to classify their garbage. Older individuals may be acclimated to traditional garbage disposal practices that do not require them to classify garbage. Furthermore, they may not be as well informed as younger indi- viduals about classifying garbage and the benefits of doing so. These factors may underpin the negative association between age and classifying garbage.
The coefficient of education is positive and significant, suggesting that the likeli- hood of classifying garbage rises with the education level of the household head.
This finding chimes with that of Ma and Zhu (2020), who argued that education could improve people’s environmental awareness and encourage them to adopt environmentally friendly practices. Marital status also has a positive and signifi- cant coefficient, suggesting that households headed by married individuals are more likely to classify garbage than those headed by unmarried household heads. This is
Table 3 Impac The reference region is southern Jiangsu; The reference household income category is household income tertile 1; Standard errors in parentheses; *** < 0.01, ** < 0.05, and * < 0.10.t of garbage classification on the subjective well-being of rural residents: ETR model estimations VariablesImpact on happinessImpact on life satisfaction Garbage classification (coefficients) Happiness (coefficients)
Garbage classification (coefficients)Life satisfaction (coefficients) Garbage classification0.955 (0.290)***0.905 (0.274)*** Age-0.014 (0.003)***0.009 (0.004)**-0.013 (0.003)***0.010 (0.004)*** Sex-0.018 (0.066)0.239 (0.078)***-0.021 (0.066)0.164 (0.074)** Education0.015 (0.008)*0.025 (0.010)**0.015 (0.008)*0.024 (0.010)** Health status0.014 (0.029)0.249 (0.034)***0.014 (0.029)0.268 (0.033)*** Marital status0.150 (0.091)*0.070 (0.108)0.149 (0.091)*-0.035 (0.102) Household size-0.001 (0.021)-0.010 (0.025)-0.002 (0.021)-0.034 (0.024) Trusting0.124 (0.037)***0.223 (0.047)***0.124 (0.037)***0.270 (0.045)*** Household income tertile 20.021 (0.070)0.306 (0.084)***0.019 (0.071)0.236 (0.079)*** Household income tertile 30.014 (0.071)0.456 (0.085)***0.014 (0.071)0.408 (0.080)*** Asset ownership-0.132 (0.131)0.713 (0.151)***-0.130 (0.131)0.665 (0.143)*** Negative shock0.018 (0.078)-0.333 (0.093)***0.022 (0.078)-0.288 (0.088)*** Risk-averse-0.097 (0.065)0.000 (0.078)-0.094 (0.065)-0.069 (0.074) Pollution perception-0.101 (0.059)*-0.088 (0.071)-0.100 (0.059)*-0.127 (0.068)* Village industry0.072 (0.068)-0.033 (0.081)0.076 (0.068)0.011 (0.077) Village meetings0.073 (0.076)0.166 (0.090)*0.070 (0.076)0.197 (0.085)** Northern Jiangsu-0.218 (0.067)***0.012 (0.084)-0.219 (0.067)***0.059 (0.079) Central Jiangsu-0.103 (0.077)0.378 (0.094)***-0.104 (0.077)0.357 (0.089)*** Instrument1.095 (0.088)***1.097 (0.088)*** Constant-0.696 (0.331)**3.571 (0.408)***-0.697 (0.332)**3.488 (0.385)*** 𝜌𝜇𝜀-0.285 (0.113)**-0.252 (0.114)**
The reference region is southern Jiangsu; The reference household income category is household income tertile 1; Standard errors in parentheses; *** < 0.01, ** < 0.05, and * < 0.10 Table 3 (continued) VariablesImpact on happinessImpact on life satisfaction Garbage classification (coefficients)
Happiness (coefficients)
Garbage classification (coefficients)Life satisfaction (coefficients) Log-likelihood-5,554.898-5,438.707 LR test of indep. eqns𝜒2(1) = 5.46; Prob >𝜒2=0.020𝜒2(1) = 4.49; Prob >𝜒2=0.034 Sample size2,2542,254
unsurprising on two accounts. First, married household heads tend to have larger households and thus more labor within the household to perform different household chores and duties, including classifying garbage. Second, due to household econo- mies of scale, the per capita cost of classifying garbage is likely to be lower among larger households, making them more likely to adopt this practice. Trust in others is also positively associated with the likelihood of classifying garbage. This is consist- ent with our expectations: trust engenders cooperation and fosters a sense of social responsibility (Irwin et al., 2015), prompting people to engage in pro-environmental practices (Harring et al., 2019), such as garbage classification.
Contrary to the finding of Ma and Zhu (2020), we find a negative association between pollution perception and garbage classification—those who deem rural pol- lution severe are less likely to classify their garbage than those who do not. Clean- ing up excessively polluted rural areas may seem insurmountable, thus discouraging people from taking pro-environmental action. Overwhelmed by the magnitude of the problem, people may feel that only their actions would be insufficient to mitigate environmental degradation. Consequently, they may follow their peers and not adopt pro-environmental practices such as garbage classification. The positive and signifi- cant coefficients of the instrumental variable suggest that exposure to governmental initiatives promoting garbage classification improves the participation rate, corrobo- rating the instrument’s admissibility.
Impacts on Subjective Well‑Being
Columns 3 and 5 of Table 3 report the determinants of household heads’ happiness and life satisfaction, respectively. Let us first consider the variable of primary inter- est: garbage classification. The results show that it is associated with improvements in both happiness and life satisfaction. Specifically, the results suggest that should people who do not classify garbage switch to classifying it, their happiness and life satisfaction would increase by 0.955 and 0.905 points, respectively, on a 10-point Likert scale. In "Analytical Framework: How Classifying Garbage Affects Subjec- tive Well-Being" section, we discussed the possibility that classifying garbage can affect subjective well-being positively or negatively—our results confirm that the effects are positive.
Turning our attention to the control variables, we find that, in general, all the coefficients are consistent with economic theory and previous studies. For instance, education and health improve happiness and life satisfaction; asset owners and higher-income earners report higher levels of happiness and life satisfaction than non-owners. A one-year increase in household heads’ education is associated with increases of 0.025 and 0.024 points in happiness and life satisfaction, respectively.
These findings are consistent with Ma and Zhu (2020), Zheng and Ma (2021), and Lai et al. (2021). Relative to those with relatively low incomes (at the household income tertile 1), rural residents with higher incomes (at the household income ter- tile 2 and 3) are more likely to report greater subjective well-being. This finding aligns well with the conclusions of Lim et al. (2020) and Pleeging et al. (2021), who found that income is positively associated with people’s subjective well-being.
Understandably, experiencing adversity reduces subjective well-being. On average, the happiness and life satisfaction of household heads who experienced negative shocks are 0.333 and 0.288 points lower than those of household heads who did not experience such shocks. This result is in line with the findings of Charles et al.
(2019) and López-Feldman and Porro (2021).
Heads of relatively large households report lower life satisfaction than those of smaller households. Larger households may be more prone to conflict—intra- household conflicts may arise from competing for household public goods; the more members there are, the greater the competition and thus the potential for conflict. A shortage of household public goods can undermine cooperation between household members, which stems from mutual affection and shared norms (Bjorvatn et al., 2020). Trust in others is also associated with greater happiness and life satisfaction.
Trust is foundational to societal harmony and cooperation. It is critical to forming lasting meaningful relationships thereby fostering health, longevity, and well-being (Miething et al., 2020).
Unlike previous studies that reported a negative relationship between age and subjec- tive well-being (e.g., Van den Broeck & Maertens, 2017), we find a positive association between the two. A one-year increase in age is associated with increases in happiness and life satisfaction of 0.009 and 0.010 points, respectively. Many researchers have reported a U-shaped relationship between age and well-being (Blanchflower & Oswald, 2004).6 Others have suggested that happiness increases after the age of 60, whereas it remains relatively stable between ages 20 and 50 (Frijters & Beatton, 2012; Laaksonen, 2018).
The increase in well-being has been attributed to reduced stress after age 60. We want to emphasize that the average age of the household heads in our sample is 61 years—the average sample age skews older than the national average. Moreover, if well-being either stays constant or increases with age, a positive link between age and subjective well- being, on the whole, stands to reason. Pollution perception has a negative and statistically significant impact on household heads’ life satisfaction, a finding that is evidenced by the work of Sulemana et al. (2016) and Li and Zhou (2020). Pollution leads to emotional and physical distress and thus reduces life satisfaction. The coefficient of the variable village meetings is positive and significant, indicating that democratization improves happiness and life satisfaction. Specifically, the levels of happiness and life satisfaction are 0.166 and 0.197 points higher for household heads living in villages where major decisions germane to the village are made inclusively through meetings involving the villagers or their representatives—democratizing decision-making increases transparency, cultivates perceptions of social fairness, and strengthens self-affirmation, thus, leading to greater subjective well-being.
Robustness Test
We estimate a two-stage residual inclusion (2SRI) model for confirming the robust- ness of our findings reported in Table 3. The 2SRI model can address selection bias
6 Our initial model comprised the age-squared term. However, it was statistically insignificant and thus removed from subsequent analyses.
