Subjective well-being is a metric for assessing the effectiveness of public policy. However, the relationship between depression, an important indicator of subjective well-being, and access to clean water (ACW) has received scant attention. This study investigates the effect of ACW on depression using the 2014–2018 waves of the China Family Panel Studies (CFPS). Using ordinary least squares (OLS) two-way fixed effects (FE) estimation, the results indicate that ACW leads to lower levels of depression. The ACW–depression relationship is mediated by individual self-reported health and household food expenditure, but the mechanism varies across subsamples, as determined by structural equation modelling of the underlying mechanisms. The results of the heterogeneity analysis demonstrated that the total effect of the absence of ACW on depression is mitigated when rural migrants reside in cities, and that the effect disappears entirely when the migrant has an urban hukou. These findings demonstrate the negative impact of urban–rural disparities and hukou issues on mental health.

  • Access to clean water (ACW) is significantly associated with a lower depression score.

  • Food expenditure and health are important transmission channels through which ACW mitigates depression.

  • This study constructs a comprehensive insight to explain the phenomenon that the mediation on the ACW-depression nexus is heterogeneous in subsamples.

The United Nations' 2030 Agenda for Sustainable Development includes 17 Sustainable Development Goals (SDGs), which serve as a ‘shared framework for peace and prosperity for people and the planet’ (United Nations, 2021). Since their inception, these goals have guided numerous governmental undertakings, commercial policies, educational endeavours, and research activities, among others. The current research is no exception, since it focuses on mental health and clean water access (SDG: 3; 6). SDG3 (Good health and Well-being) clearly aims to greatly promote mental health and well-being by 2030. One in three people worldwide lacks access to safe drinking water. SDG6 (Clean Water) based on this context and emphasis on the priority achieve universal and equitable access to safe, affordable drinking water for all.

Water insecurity, defined as inadequate or uncertain access to safe water for an active and healthy lifestyle, has negative health effects (Hadley & Wutich, 2009). Access to clean water (ACW) has a similar effect on mental health as does the availability of food (Aihara et al., 2016). Lack of ACW has all known implications for communicable disease risk, but the effect of ACW on mental health impact is less well understood. Although the pathogenesis of depression is unknown, it has been suggested that genetic and environmental factors influence the disorder (Malhi & Mann, 2018). Mental disorders, specifically depressive disorders, are the leading cause of years lived with disability worldwide. The rising prevalence of depression is not only a global public health concern but a significant concern for economic development and social welfare (Hsieh & Qin, 2018). Due to its rapid economic development and lifestyle changes in the past decades, China is now facing the challenge of a growing prevalence in depression, which has become one of the leading causes of disability-adjusted life years in China (Hsieh & Qin, 2018). Depression places a heavy economic burden on Chinese society (Que et al., 2019). Moreover, dementia is always associated with symptoms of depression (Hansen et al., 2021; Hsieh et al., 2021). Therefore, more scholarly studies have explored the factors influencing depression, for example, religion and culture (Schimmack et al., 2002; Diener & Diener, 2009; Tov & Diener, 2009), unemployment (Binder & Coad, 2015), income (Frijters et al., 2004; Deaton, 2008), social capital (Kroll, 2011), energy poverty (Nie et al., 2021), wealth (Lindqvist et al., 2020), and health (Herman et al., 2013).

However, existing literature pays little attention to the linkage between ACW and depression. Studies regarding the channel variables that mediate the ACW-depression nexus have not yet been reported. Households with ACW are more likely to use modern infrastructure that is good for household health status. The inability of poor households' ACW leads to the ‘drink or eat’ dilemma, which forces them to make tradeoffs. The difficulty of accessing clean water causes them to spend more time collecting clean water, reducing the time available for engaging in economic activities. This reduction in economic activity reduces the income available to acquire more food. As a result, it leads to a significant reduction in food spending. If people do not have access to tap water, they are considered unable ACW. This group uses more groundwater or lake water. Groundwater may be polluted by heavy metals, radioactive elements, and other toxins, and groundwater is more susceptible to pollutants than tap water (Siddiqi et al., 2021). As a result, household members who use groundwater or lake water would be more likely to report poor health, especially in rural areas. It is interesting to analyse how the ACW–depression relation is mediated by health and household food expenditure.

Productive use of the rich China Family Panel Studies (CFPS) data in prior research confirms its ability to shed light on China's contemporary issues, including subjective well-being (Zhang & Awaworyi Churchill, 2020; Nie et al., 2021). China has made great achievements in poverty alleviation. But there are still a small number of people in rural areas who live in a state with a low level of basic infrastructure, although they have been lifted out of poverty economically. Along with the economic progress, the mental health of this group also deserves more and more attention. Difficulties in accessing clean water tend to occur only in rural areas, among people engaged in agricultural production, and are less likely to occur in cities. This study employs OLS and FE techniques to examine the ACW–depression relationship in order to gain an in-depth understanding of the ACW-depression nexus. Using data from CFPS, this study then examines potential mediators to provide useful insight into the mechanisms by which ACW impacts depression.

DATA

This study's analysis utilised data from the CFPS, which is administered by the Institute of Social Science Survey at Peking University. Due to the survey's coverage of 25 provinces, municipalities, or autonomous regions representing 95% of the Chinese population, the sample of Chinese households is nationally representative. The CFPS encompasses a vast array of topics that capture socioeconomic development as well as economic and noneconomic well-being.

The CFPS conducts detailed surveys at both the household and individual levels. At the household level, one member of the household – typically the household head – will respond to two questionnaires, one on the information of individual household members, such as gender and education, and the other on the information of the entire household, such as members' relationships and household expenditures.

All waves contain demographic, socioeconomic, and depressive information. These data allow us to analyse the relationship between depression and ACW. I attempted to accomplish this using the three most recent data waves. The final sample consists of 35,145 individuals and 74,962 observations that are unbalanced. The data used in this study are detailed in the supplementary materials.

Depression measurement

Depression was assessed using a 20-question version of the Center for Epidemiologic Studies Depression (CES-D) questionnaire to calculate the CES-D score (Radloff, 1977).

On a 4-point scale, respondents indicate how frequently they experienced the indicated feeling in the previous week: 0 = seldom, 1 = little, 2 = occasionally, and 3 = frequently. The score on the CES-D is then computed as follows:
formula
  • is the score for the ath question of the somatic-retarded activity.

  • is the score for the bth question of the interpersonal relations.

  • is the score for the cth question of the depressed affect.

  • is the score for the dth question of the positive affect.

Given an overall CES-D score of 0–60, with higher scores indicating a higher probability of depression.

Empirical strategy

Ordinary least squares estimation

Ordinary least squares (OLS) is a common method for conducting analysis in econometrics, and the dependent variable in this study approximates a continuous variable. Therefore, the use of OLS as the baseline regression is well suited. So, this study employed the standard OLS regression method first. It can be expressed as follows:
formula
where denotes the CES-D score of individual i in terms of depression. is a dummy variable representing ACW if value = 1. is a vector of individual i 's characteristics. is a vector of household characteristics, is a vector of provincial dummies, is a vector of wave dummies with 2014 as reference, is an error term.

Two-way fixed effects model

Given the potential for bias from individual time-invariant unobservables, the following two-way fixed effects (FE) model was developed to better identify the impact of ACW on depression:
formula
where denotes the CES-D score of individual i at time t in terms of depression. is a dummy variable representing individual i's ACW at time t. The availability of clean water in this study is a dummy variable that equals 1 if the household has ACW (e.g. tap water) and 0 for those who use other sources (e.g. rain, lake water, well water). is a set of time-variant controls, is a set of time-invariant controls, is a vector of provincial dummies, is a vector of wave dummies, is an error term.

Structural equation modelling

To investigate the potential channels through which ACW influences depression, this study employs structural equation modelling (SEM) to examine the effects of two hypothesised channels variables: individual health and household food expenditure. I estimate these variables using self-reported health (SRH, on a 5-point scale ranging from 1 = extremely unwell to 5 = extremely healthy) and the amount of RMB (yuan) spent on food in the preceding month. This study utilises subsamples to analyse heterogeneity and control for age, age squared, gender, education, job status, marital status, household size, and homeownership to investigate mediation on the ACW–depression relationship. RMSEA 0.08 and CFI > 0.90 imply a satisfactory overall fit (Schermelleh-Engel et al., 2003).

Descriptive statistics

As Table 1 shows, that the average values of depression in our sample are 12.988. The share of ACW was nearly 70%. For the population who lack ACW, 31% of them use well water, 5.16% of them use pond water, 2.39% of them use cellar water, and 0.73% use rain water. The share of the employed (1 = currently employed, 0 = otherwise) was 75.4%. The descriptive statistics of household ownership (1 = property is completely or partly owned, 0 = otherwise), household size, urban (1 = urban, 0 = rural), agricultural hukou (1 = agricultural hukou, 0 = non-agricultural hukou, urban hukou), and other variables are shown in Table 1. This study divided the sample into three subsamples. Group A: subsample living in rural; Group B: subsample with agricultural hukou living in urban; Group C: subsample with non-agricultural hukou living in urban. From Figure 1, we can see that the population living in rural areas has a lower share of ACW, at less than 60%. Figure 2 shows that people living in rural areas have higher levels of depression than those living in cities.
Table 1

Summary statistics.

VarNameObsMean/percentageSD
Depression (0–60) 74,898 12.988 8.063 
ACW (access to water) 74,962 0.707 0.455 
Individual characteristics    
Age 74,962 48.208 15.475 
Gender 74,962 0.503 0.500 
Educational levels    
Illiterate 74,962 0.258 0.437 
Primary 74,962 0.217 0.412 
Middle 74,962 0.285 0.451 
High 74,962 0.139 0.346 
Vocational 74,962 0.058 0.235 
University or higher 74,962 0.043 0.203 
Employed 74,962 0.754 0.431 
Married 74,962 0.845 0.361 
Household characteristics    
Household size 74,962 4.239 2.017 
Home ownership 74,962 0.900 0.300 
Urban 74,962 0.488 0.500 
Agricultural Hukou 74,867 0.721 0.448 
Channel variables    
Log (food cost) 74,319 9.480 0.976 
Self-reported health (SRH)    
Poor 74,957 0.163 0.370 
Fair 74,957 0.158 0.365 
Good 74,957 0.377 0.485 
Very good 74,957 0.171 0.376 
Excellent 74,957 0.130 0.337 
VarNameObsMean/percentageSD
Depression (0–60) 74,898 12.988 8.063 
ACW (access to water) 74,962 0.707 0.455 
Individual characteristics    
Age 74,962 48.208 15.475 
Gender 74,962 0.503 0.500 
Educational levels    
Illiterate 74,962 0.258 0.437 
Primary 74,962 0.217 0.412 
Middle 74,962 0.285 0.451 
High 74,962 0.139 0.346 
Vocational 74,962 0.058 0.235 
University or higher 74,962 0.043 0.203 
Employed 74,962 0.754 0.431 
Married 74,962 0.845 0.361 
Household characteristics    
Household size 74,962 4.239 2.017 
Home ownership 74,962 0.900 0.300 
Urban 74,962 0.488 0.500 
Agricultural Hukou 74,867 0.721 0.448 
Channel variables    
Log (food cost) 74,319 9.480 0.976 
Self-reported health (SRH)    
Poor 74,957 0.163 0.370 
Fair 74,957 0.158 0.365 
Good 74,957 0.377 0.485 
Very good 74,957 0.171 0.376 
Excellent 74,957 0.130 0.337 
Fig. 1

Share of population with ACW over different groups. (Group A: subsample living in rural; Group B: subsample with agricultural hukou living in urban; Group C: subsample with non-agricultural hukou living in urban).

Fig. 1

Share of population with ACW over different groups. (Group A: subsample living in rural; Group B: subsample with agricultural hukou living in urban; Group C: subsample with non-agricultural hukou living in urban).

Close modal
Fig. 2

Mean of depression score over different groups (Group A: subsample living in rural; Group B: subsample with agricultural hukou living in urban; Group C: subsample with non-agricultural hukou living in urban).

Fig. 2

Mean of depression score over different groups (Group A: subsample living in rural; Group B: subsample with agricultural hukou living in urban; Group C: subsample with non-agricultural hukou living in urban).

Close modal

Impact of ACW on depression: OLS and FE estimates

Table 2 presents the regression results. The estimation results showed that ACW is significantly associated with a lower level of depression score. People who did not have ACW were more likely to be depressed. As regards sociodemographic factors, the estimation results showed that males have lower levels of depression than females. Lower depression is associated with larger households. Lower depression is associated with being married and employed, indicating that social support and marriage may shield individuals from exposure to stress to some extent.

Table 2

OLS/FE estimates of the ACW impact on depression in the 2014–2018 CFPS sample.

(1)(2)
OLSFE
VariablesDepressionDepression
ACW (access to clean water) −0.693*** (0.0780) −0.239** (0.110) 
Age 0.147*** (0.0154) 0.272*** (0.0879) 
Age squared −0.143*** (0.0160) 0.0351 (0.0594) 
Gender −1.406*** (0.0732)  
Primary −1.287*** (0.115) −0.0955 (0.320) 
Middle −1.944*** (0.112) 0.363 (0.411) 
High −2.230*** (0.132) 0.484 (0.545) 
Vocational −2.172*** (0.168) 0.517 (0.621) 
University −2.337*** (0.182) 0.693 (0.712) 
Employed −0.824*** (0.0950) −0.504*** (0.125) 
Married −1.998*** (0.114) −2.145*** (0.243) 
Household size −0.153*** (0.0183) −0.102*** (0.0348) 
Home ownership −0.544*** (0.114) −0.114 (0.162) 
Urban −0.334*** (0.0827) 0.499** (0.208) 
Hukou 0.468*** (0.0982) −0.122 (−0.221) 
Constant 14.93*** (0.513) 2.794 (3.676) 
Observations 74,898 74,898 
R-squared 0.069  
Number of individuals  35,118 
Individual FE No Yes 
Provincial FE Yes Yes 
Wave FE Yes Yes 
(1)(2)
OLSFE
VariablesDepressionDepression
ACW (access to clean water) −0.693*** (0.0780) −0.239** (0.110) 
Age 0.147*** (0.0154) 0.272*** (0.0879) 
Age squared −0.143*** (0.0160) 0.0351 (0.0594) 
Gender −1.406*** (0.0732)  
Primary −1.287*** (0.115) −0.0955 (0.320) 
Middle −1.944*** (0.112) 0.363 (0.411) 
High −2.230*** (0.132) 0.484 (0.545) 
Vocational −2.172*** (0.168) 0.517 (0.621) 
University −2.337*** (0.182) 0.693 (0.712) 
Employed −0.824*** (0.0950) −0.504*** (0.125) 
Married −1.998*** (0.114) −2.145*** (0.243) 
Household size −0.153*** (0.0183) −0.102*** (0.0348) 
Home ownership −0.544*** (0.114) −0.114 (0.162) 
Urban −0.334*** (0.0827) 0.499** (0.208) 
Hukou 0.468*** (0.0982) −0.122 (−0.221) 
Constant 14.93*** (0.513) 2.794 (3.676) 
Observations 74,898 74,898 
R-squared 0.069  
Number of individuals  35,118 
Individual FE No Yes 
Provincial FE Yes Yes 
Wave FE Yes Yes 

Robust standard errors in parentheses.

***p < 0.01, **p < 0.05, *p < 0.1.

It is worth mentioning that OLS and FE have different estimates of the impact of living in urban areas on depression. People living in urban areas may be more prone to depression due to a faster pace of life, but better living conditions compared to living in rural areas may also reduce depression. OLS estimates showed that people with agricultural hukou were significantly associated with higher levels of depression. It is worth continuing to explore the heterogeneity between groups with different hukou and different groups of people who live in urban or rural areas.

Underlying mechanisms

The structural models are presented in Table 3. These results indicate that there is heterogeneity in the mechanism of ACW's impact on depression. This study divided the sample into three subsamples: those who live in rural areas, those who move from rural areas to urban areas with agricultural hukou, and those who live in urban areas and have urban hukou. Urban hukou determines, to some extent, access to urban public services. Most people who live in urban areas but do not have urban hukou are rural migrant workers urban areas. In 2020, the number of rural migrant workers in cities in China was 285.6 million.

Table 3

The structural model (standardised coefficients).

Food costHealthDepression
Panel A: Subsample living in rural obs: 37,979 
Food cost   −0.0623*** (0.0507) 
Health   −0.304*** (0.0494) 
ACW 0.0427*** (0.00484) 0.0301*** (0.00478) −0.0332*** (0.00479) 
Goodness-of-fit statistic 
 22.68   
RMSEA 0.024   
CFI 0.999   
Panel B: Subsample with agricultural hukou living in urban obs:18,221 
Food cost   −0.0641*** (0.00744) 
Health   −0.316*** (0.00709) 
ACW 0.0841*** (0.00696) 0.0323*** (0.00702) −0.00743 (0.00706) 
Goodness-of-fit statistic 
 17.501   
RMSEA 0.030   
CFI 0.998   
Panel C: Subsample with non-agricultural hukou living in urban obs: 18,005 
Food cost   −0.0912*** (0.00729) 
Health   −0.316*** (0.00711) 
ACW 0.0416*** (0.00711) −0.00226 (0.00702) −0.00872 (0.00699) 
Goodness-of-fit statistic 
 1.034   
RMSEA 0.001   
CFI 1.000   
Control variables Yes 
Food costHealthDepression
Panel A: Subsample living in rural obs: 37,979 
Food cost   −0.0623*** (0.0507) 
Health   −0.304*** (0.0494) 
ACW 0.0427*** (0.00484) 0.0301*** (0.00478) −0.0332*** (0.00479) 
Goodness-of-fit statistic 
 22.68   
RMSEA 0.024   
CFI 0.999   
Panel B: Subsample with agricultural hukou living in urban obs:18,221 
Food cost   −0.0641*** (0.00744) 
Health   −0.316*** (0.00709) 
ACW 0.0841*** (0.00696) 0.0323*** (0.00702) −0.00743 (0.00706) 
Goodness-of-fit statistic 
 17.501   
RMSEA 0.030   
CFI 0.998   
Panel C: Subsample with non-agricultural hukou living in urban obs: 18,005 
Food cost   −0.0912*** (0.00729) 
Health   −0.316*** (0.00711) 
ACW 0.0416*** (0.00711) −0.00226 (0.00702) −0.00872 (0.00699) 
Goodness-of-fit statistic 
 1.034   
RMSEA 0.001   
CFI 1.000   
Control variables Yes 

Standard errors in parentheses.

***p < 0.01, **p < 0.05, *p < 0.1.

In rural areas, that is, those engaged in agricultural production, ACW negatively impacts depression directly and indirectly through two mediators. ACW significantly increased food costs, and increased food costs reduced depression (see Table 4, Panel A). ACW had a significantly positive impact on health, and health significantly reduced depression (see Table 3, Panel A). As previously hypothesised, when people have ACW, it contributes to their health. When people do not have ACW, they spend time getting it. ACW could help them to spend more time on the production activities, thus improving their income and quality of life, one of the performances is to spend more on food.

Table 4

Estimated indirect effects and total effects (standardised coefficients).

Indirect effects ACW → Mediators → DepressionDirect effects ACW → DepressionTotal effects
Panel A: Subsample living in rural (Group A) obs: 37,979 
Food cost −0.00267*** (0.000371) − 0.0332*** (0.00479) − 0.0450*** (0.00500) 
Health −0.00914*** (0.00146) 
Panel B: Subsample with agricultural hukou living in urban (Group B) obs: 18,221 
Food cost −0.00539*** (0.000769) − 0.00743 (0.00706) − 0.0230*** (0.00738) 
Health −0.0102*** (0.00223) 
Panel C: Subsample with non-agricultural hukou living in urban (Group C) obs: 18,005 
Food cost −0.00379*** (0.000717) − 0.00872 (0.00699) − 0.0118 (0.00736) 
Health 0.000716 (0.00222) 
Indirect effects ACW → Mediators → DepressionDirect effects ACW → DepressionTotal effects
Panel A: Subsample living in rural (Group A) obs: 37,979 
Food cost −0.00267*** (0.000371) − 0.0332*** (0.00479) − 0.0450*** (0.00500) 
Health −0.00914*** (0.00146) 
Panel B: Subsample with agricultural hukou living in urban (Group B) obs: 18,221 
Food cost −0.00539*** (0.000769) − 0.00743 (0.00706) − 0.0230*** (0.00738) 
Health −0.0102*** (0.00223) 
Panel C: Subsample with non-agricultural hukou living in urban (Group C) obs: 18,005 
Food cost −0.00379*** (0.000717) − 0.00872 (0.00699) − 0.0118 (0.00736) 
Health 0.000716 (0.00222) 

Standard errors in parentheses.

***p < 0.01, **p < 0.05, *p < 0.1.

Rural migrant workers in cities continue to lack ACW, which reflects their inability to better enjoy urban public services due to China's hukou problem. The direct effect of ACW on depression is insignificant, and the effect of ACW on depression is mediated primarily by channel variables (see Table 3, Panel B). One possible explanation is that tap water is a basic and common public service in cities, and people are accustomed to it, so ACW has no direct effect on depression. Although people without urban hukou cannot fully enjoy urban public services, rural migrant workers have easier ACW than rural residents. It also saves them time searching for clean water, allowing them to work in higher-paying jobs in cities.

The total impact of ACW on depression for urban residents with urban hukou is insignificant and is only through the single channel of food cost (see Table 4, Panel C). The lack of health channels is due in part to the fact that urban residents with urban hukou are more likely to receive better medical care.

Globally, depression is an urgent public health issue with a high disease burden. As the prevalence of this health condition increases globally, it is necessary to investigate its various causes. Despite widespread awareness of the negative effects of depression on sustainable development and social welfare, empirical research provides few insights into the relationship between ACW and depression, particularly in China. My analysis of nationally representative data from the three waves of the CFPS is intended to shed light not only on the ACW–Depression relationship in China but also on the extent to which ACW may transmit depression via the two channels of health and household food expenditure.

The analysis reveals several significant findings: First, ACW reduces the likelihood of developing depression. Second, the relationship between ACW and depression is partially mediated by health and household food expenditures. Third, the mediation of the ACW–depression relationship varies across groups. For the rural subsample (Group A), ACW has direct and indirect negative effects on depression via two mediators. This ACW–depression nexus is partially mediated by food price and health, which account for 5.93 and 20.31%, respectively. For the subsample with agricultural hukou residing in urban areas (Group B), the direct effect is insignificant, indicating that the association between ACW and depression is entirely mediated by channel variables. The direct effect of ACW on depression is not significant for the subsample of urban residents (Group C) with non-agricultural hukou, nor is the total effect of ACW on depression. When farmers migrate to cities, a lack of ACW is less likely to cause depression, indicating that urban areas have better public services than rural areas. However, only with urban hukou did ACW-related poverty-related depression completely vanish.

Our findings have significant policy implications. Above all, they emphasise the pressing need to ensure clean water accessibility and affordability for all Chinese people by increasing investment in and access to modern social services and technologies, a goal consistent with China's current public health strategy. The Healthy China 2030 Plan, issued by the Chinese government, stipulates the promotion of mental health and the treatment of depression. By encouraging infrastructure development to provide clean water and accelerating urbanisation, economic development can effectively combat depression. Since 2019, Chinese policymakers have incorporated the promotion of mental health into the Healthy China Action (2019–2030), which aims to prevent mental diseases (Ren et al., 2020). The ‘Basic Healthcare and Health Promotion Law’ also intends to develop and improve mental health services and systems (Lancet, 2020). However, the implementation and efficacy of these plans are uncertain. Therefore, promoting ACW could be an effective method for reducing depression in China.

Based on our findings that health and household food expenditure at least partially mediate the ACW–depression relationship, there is an immediate need to investigate all possible mechanisms through which ACW operates on development in order to better inform China's social economic and developmental policies. My findings indicate that ACW has beneficial effects on mental health. In general, our findings suggest that policymakers must consider the role of ACW and the identified transmission channels when developing anti-depression policies and programmes.

Policies and programmes aimed at eliminating the lack of ACW may help reduce mental health issues, increasing access to quality food and clean water for all citizens by reducing food costs or boosting household incomes. The finding that food expenditure and health are significant channels suggests that policies aimed directly at improving health may have the desired effect on mental health. Other pertinent policies could include making it easier for economically vulnerable individuals to access public services, such as by providing subsidies to poor households. Such efforts would reduce the likelihood of households developing mental health issues.

ACW, which is essential for improved quality of life, is a fundamental requirement for economic growth. In addition to shedding light on the significance of ACW and the mechanisms by which it affects well-being, our study identifies a number of promising avenues for future research. Given China's rapid urbanisation, focusing on people who live in cities but have agricultural hukou is an important possibility. These individuals, also known as rural migrant workers in cities, may not have full access to infrastructure services, and their ACW is 20% lower than that of city residents with urban hukou. Therefore, the actual situation of rural-to-urban immigrants should also be considered in the urbanisation process. Especially in large cities, all types of expenses are relatively high; if migrants could not enjoy the infrastructure due to the hukou problem, then it would unquestionably increase their living expenses and life pressure.

In 1958, the People's Republic of China's Household Registration Regulations institutionalised the ‘division of urban and rural hukou’ and established the privileged status of urban hukou. The urban–rural dual structure was formed, and the vast rural area fed back into the city, especially with regard to the development of the country's heavy industry, creating a landscape that was unique to China. A thin piece of hukou paper then unintentionally determined the life course of hundreds of millions of people and their descendants. In 1978, China's urbanisation rate was only 17.9%, and more than 80% of the population had an agricultural hukou tied to a few acres of marginal farmland. Now that China's economy is experiencing rapid growth, the country should not only improve infrastructure in rural areas but also make it a priority for rural migrant workers in cities to have access to the same urban public services. This is not only beneficial to their physical health but also their mental health.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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