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Decomposition analysis of differences in depressive symptoms between agricultural and non-agricultural workers in China

BMC Public Health volume 25, Article number: 1503 (2025) Cite this article

Abstract

Background

Chinese workers are confronted with severe mental health issues. This study aimed to investigate the reasons for the differences in depressive symptoms between agricultural and non-agricultural workers in China, and to measure the contribution of relevant influencing factors.

Methods

The data used in this study came from the 2018 China Family Panel Studies (CFPS) data. We used the brief 8-item Centre for Epidemiological Studies Depression Scale (CES-D8) to measure participants’ depressive symptoms, and Fairlie decomposition model was used to analyze the influencing factors for the differences in depressive symptoms between agricultural and non-agricultural workers and their contribution.

Results

The percentage of employed people with depressive symptoms was 14.44%. The percentage of agricultural workers (18.68%) with depressive symptoms was higher than that of non-agricultural workers (11.33%).The results of Fairlie decomposition analysis showed that 74.68% of the differences in depressive symptoms between agricultural and non-agricultural workers was due to observed factors, which were education level (39.63%), self-rated health (25.59%), marital status (-23.93%), residence (12.04%), job satisfaction (8.39%), chronic disease (5.52%), gender (5.11%), life satisfaction (3.59%), and body mass index (-1.28%) (all P < 0.05).

Conclusions

The percentage of depressive symptoms was higher in agricultural than in non-agricultural workers, which was primarily associated with differences in educational level, self-rated health, marital status, residence, job satisfaction, chronic disease, gender, life satisfaction, and body mass index between them.

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Introduction

Global mental health problems are increasing. Globally, mental disorders account for 1 in 6 years lived with disability [1]. Depression and anxiety disorders are two of the most common mental illnesses, costing the global economy 1 trillion dollars a year [2]. 85% of people with mental disorders in low and middle-income countries receive no treatment at all [3]. As one of the developing countries, China also faces significant mental health problems. A national study [4]reported that the prevalence of depressive symptoms among adults in China was as high as 37.9%, and the prevalence of depression was 4.1%. A 2019 data showed that less than 1% of people with depression in China were adequately treated [5]. At the same time, China’s population has continued to grow in recent years. The seventh national census shows that the total population of China has reached 1.443 billion in 2020 [6]. The prevalence of depressive symptoms does not change dramatically over a short period of time. Therefore, as the population increases, the number of people with depressive symptoms will increase. This indicates that the demand for depression treatment resources in China will be enormous.

Agricultural workers experience worse depressive symptoms than the general population [7,8,9,10,11]. Factors associated with the occurrence of depressive symptoms among agricultural workers mainly include socio-demographic characteristics, personal lifestyle, health status, job satisfaction, and life satisfaction. A study with Western Australian farmers showed that higher financial/external trade and societal pressures, family/relationship tension, use of coping strategies, lack of succession planning and considering selling the farm, and lower social support and sense of belonging, were associated with higher depression [12]. At the same time, a study with Brazilian farmers reported that the associated factors were: female, not owning the land, professional dissatisfaction, previous pesticide poisoning, complex multimorbidity and occurrence of previous depressive episodes [10]. A systematic literature review showed that increased stress, poor physical health, compromised financial position, and previous injury were the leading indicators of depression among agricultural workers [13].

The available studies of inequality in depressive symptoms mostly focused on gender [14,15,16] and residence [17,18,19]. Occupational inequalities in depressive symptoms have been less well studied. A French study showed that the factors related to decision latitude, social support, reward, and other exposures of physical and biomechanical nature contributed to explain occupational inequalities in depressive symptoms [20]. A UK study reported that serving military personnel are more likely to endorse symptoms of common mental disorder compared to those employed in other occupations, which may be partly explained by the role of predisposing characteristics [21].

And it is well known that China is a large agricultural country. According to data from the third national agricultural census (2017) [22], the total number of agricultural production and operation personnel in the country was as high as 314.22 million. These personnel were mainly distributed in the central and western regions, while the more economically developed eastern region was less. This distribution reflected the unbalanced and insufficiency of development in China. Some studies have found that there were serious mental health disparities in China [23], with large urban-rural differences in depressive symptoms [24, 25]. The main constituent population of the countryside is agricultural workers, and the main constituent population of the towns is non-agricultural workers. Therefore, we speculate whether there exists a disparity in depressive symptoms between these two groups.

In order to explore the differences in depressive symptoms between agricultural and non-agricultural workers in China, and to provide a policy basis for precise prevention and control of adult depressive symptoms to alleviate adult mental health problems in China, this study utilized data from the 2018 China Family Panel Studies (CFPS), and the Fairlie model was used to analyze the depressive symptom differences between agricultural and non-agricultural workers. We conducted a preliminary exploration of the extent to which factors such as sociodemographic characteristics, personal lifestyle, and health status explain the differences in depressive symptoms between agricultural and non-agricultural workers in China.

Methods

Data sources and sample composition

The data were obtained from the 2018 CFPS national resample data [26]. Institution of Social Science Survey (ISSS) at Peking University conducted the CFPS, utilizing a sample that represents 25 provinces, municipalities, and autonomous regions. The study aimed for a target sample size of 16,000 households, with all family members in the sampled households included as respondents. 8,000 households were over-sampled from five separate sub-sample frames (called ‘large provinces’) in Shanghai, Liaoning, Henan, Gansu and Guangdong. Another 8,000 households were sampled from a separate sub-sample frame (called ‘small provinces’) consisting of 20 other provinces. After secondary sampling, the five ‘large province’ sample frames, together with the ‘small province’ sample frames, form a nationally representative total sample frame. Four types of questionnaires were utilized: community, household, adult, and children [27]. The initial sample size for the 2018 CFPS national resample data was 24,456. As required for the study, we excluded 8,930 samples whose work status was non-employed, 9 samples whose age was less than 16 years old, and 1,394 samples with missing values on the depressive symptoms indicators. Additionally, we excluded 1,248 samples with missing values on demographic, sociological, personal lifestyle, or health status characteristics. 3 participants were excluded due to suffer from a doctor-diagnosed depression in the past six months. Finally, we included 12,872 samples in the study. Figure 1 illustrates the exclusion process.

Fig. 1
figure 1

Flowchart of study participants

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Depressive symptoms

This study used the brief 8-item Centre for Epidemiological Studies Depression Scale (CES-D 8) to measure depressive symptoms in the study population. Responses are assigned the following values: 0 = little or no time (< 1 day), 1 = some or little time (1–2 days), 2 = occasional or moderate time (3–4 days), and 3 = most or all of the time (5–7 days). The items ‘I feel happy’ and ‘I live a happy life’ are scored in reverse. Scores range from 0 to 24. Higher scores indicate more severe depressive symptoms. With reference to several previous studies, a score of 10 was defined as the threshold score, and respondents with scores greater than or equal to 10 were defined as having depressive symptoms, and scores less than 10 were defined as not having depressive symptoms [28, 29]. The internal consistency reliability of the scale (Cronbach’s alpha) was 0.84. The scale showed good reliability and validity in adults [30, 31].

Grouping variables

Based on the respondents’ answer to the question ‘Is your job agricultural or non-agricultural?’, respondents who chose ‘agricultural work (farming, forestry, animal husbandry, sideline production and fishery)’ were defined as agricultural workers, and those who chose ‘non-agricultural work’ were defined as ‘non-agricultural workers’. Agricultural workers as defined in this study were those engaged in farming, managing fruit trees, collecting agricultural and forestry products, fish farming, fishing, raising livestock, and going to the market to sell agricultural products.

Covariates

To obtain more reliable results, we controlled for a number of potential confounding factors. Demographic characteristics, sociological characteristics, personal lifestyle, and health status were incorporated with reference to other studies on depressive symptoms. Demographic characteristics included age, gender, body mass index (BMI), and education level. Sociological characteristics included residence, marital status, self-rated income and medical insurance. Personal lifestyle included smoking, drinking and exercise. Health status included self-rated health (SRH) and chronic disease. Satisfaction included job satisfaction and life satisfaction.

Demographic characteristics

Age was classified as 16–44 years, 45-64 years, and ≥ 65 years. Gender was categorized as male and female. BMI was calculated by dividing body weight (kg) by the square of height (m) and was divided into four categories: <18.5, 18.5–23.9, 24.0-27.9, and ≥ 28.0. Education level was categorized as illiteracy/semiliterate, primary school, middle school, high school/junior college, and bachelor or above.

Sociological characteristics

Residence was categorized into rural and urban. Marital status was categorized into married and not married. Self-rated income status was divided into three categories: poor, so so, and rich. Medical insurance status was categorized as yes and no.

Personal lifestyle

Based on participants’ responses to the questions ‘Have you smoked in the past month?’, ‘Have you been drinking alcohol more than three times a week for the past month?’, and ‘How many times did you exercise in the past week?’, smoking, drinking and exercise were categorized as yes or no.

Health status

SRH was based on the answer to the question ‘How do you rate your health at present?’. The results were categorized as good (perfectly healthy, very healthy or relative healthy) and bad (so so or unhealthy). Based on the respondents’ answer to the question ‘Have you suffered from a chronic disease diagnosed by a doctor in the past six months?’, the chronic disease was categorized as yes and no.

Satisfaction

Job satisfaction was based on the answer to the question ‘Overall, how satisfied are you with the job?’. The results were categorized as satisfied (very satisfied, satisfied or so so) and dissatisfied (dissatisfied or very dissatisfied). Based on the answers to the questions ‘1 is very dissatisfied. 5 is very satisfied. How would you rate your level of satisfaction with your life?’ and ‘1 is not confident. 5 is very confident. How confident do you feel about your future?’, responses were rated on a 5-point scale from 0 (1) to 4 (5). Life satisfaction scores range from 0 to 8.

Statistical analysis

Firstly, descriptive statistics were used to analyze general information on demographic characteristics, sociological characteristics, personal lifestyle, health status and satisfaction. Secondly, the chi-square or Wilcoxon rank sum test was used to analyze the distribution characteristics of depressive symptoms among agricultural and non-agricultural workers. In addition, the binary logistic regression model was used to explore the main factors associated with depressive symptoms in agricultural and non-agricultural workers. Finally, the Fairlie model was used to analyze the factors contributing to the differences in depressive symptoms between agricultural and non-agricultural workers. The analysis was conducted using Stata MP17.0 software. The level of statistical significance was defined as 0.05.

As the dependent variable is dichotomous, we used the Fairlie nonlinear decomposition method to decompose the depressive symptoms differences into the contributions of various factors [32]. According to Fairlie [33], the decomposition of the nonlinear equation \(\:Y=F\left(X\widehat{\beta\:}\right)\) can be written as

$${\eqalign{\:{\stackrel{-}{Y}}^{a}-{\stackrel{-}{Y}}^{b}& =\left[\sum\:_{i=1}^{{N}^{a}}\frac{F\left({X}_{i}^{a}{\beta\:}^{b}\right)}{{N}^{a}}-\sum\:_{i=1}^{{N}^{b}}\frac{F\left({X}_{i}^{b}{\beta\:}^{b}\right)}{{N}^{b}}\right]\cr & +\left[\sum\:_{i=1}^{{N}^{a}}\frac{F\left({X}_{i}^{a}{\beta\:}^{a}\right)}{{N}^{a}}-\sum\:_{i=1}^{{N}^{a}}\frac{F\left({X}_{i}^{a}{\beta\:}^{b}\right)}{{N}^{a}}\right]}}$$
(1)

\(\:{\stackrel{-}{Y}}^{a}\:\)and \(\:{\stackrel{-}{Y}}^{b}\) were the mean probabilities of the binary outcomes of depressive symptoms in the two groups, F was the cumulative distribution function of the logistic distribution, \(\:{\stackrel{-}{Y}}^{a}-{\stackrel{-}{Y}}^{b}\)represents the total difference due to group differences, and \(\:{N}^{a}\) and \(\:{N}^{b}\) were the sample sizes of the two population samples. The first term in parentheses in Eq. (1) represented the portion of the gap due to group differences in observed characteristics and the portion attributable to differences in estimated coefficients. The second term represented the portion due to differences in Y levels.

Results

General data of the respondents

The total sample size of this study was 12,872. Table 1 shows the results of descriptive statistical analyses for agricultural and non-agricultural workers. We found that 14.44% of employed people experienced depressive symptoms, and 85.56% had no depressive symptoms. A higher proportion of agricultural workers (18.68%) experienced depressive symptoms than non-agricultural workers (11.33%) (P < 0.001). The results of chi-square test and Wilcoxon rank sum test showed that there were differences in the distribution of 14 covariates between agricultural and non-agricultural workers in age, gender, residence, BMI, marital status, education level, self-rated income, SRH, chronic disease, smoking, exercise, medical insurance, job satisfaction, and life satisfaction. There was no difference in the distribution of drinking.

Table 1 Distribution of the variables in agricultural and non-agricultural respondents
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Comparison of variable distributions of agricultural and non-agricultural workers

Table 2 indicates the distribution of covariates in different depressive symptoms between agricultural and non-agricultural workers. The results showed that among agricultural workers, the differences in age and smoking status between those with and without depressive symptoms were statistically significant. However, these differences cannot be observed in non-agricultural workers. Meanwhile, the difference in exercise status among people with and without depressive symptoms was only statistically significant among non-agricultural workers.

Table 2 Distribution of the variables in agricultural and non-agricultural workers with and without depressive symptoms
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Logistic model results

Figure 2 displays the results of logistic model calculation for depressive symptoms in agricultural and non-agricultural workers. Among agricultural workers, gender (male, OR = 0.64), residence (urban, OR = 0.84), BMI (24.0–27.9 kg/m2, OR = 0.71), marital status (married, OR = 0.44), education level (primary school, OR = 0.77, middle school, OR = 0.72, high school/junior college, OR = 0.51), self-rated income (so so, OR = 0.72), SRH (good, OR = 0.48), job satisfaction (satisfied, OR = 0.59), and higher life satisfaction scores (OR = 0.78) respondents were less likely to have depressive symptoms than references; age (45–64 years, OR = 1.25), BMI (< 18.5 kg/m2, OR = 1.45), and chronic disease (yes, OR = 1.38) respondents were more likely to have depressive symptoms than references. Among non-agricultural workers, age (45–64 years, OR = 0.81), gender (male, OR = 0.67), residence (urban, OR = 0.83), marital status (married, OR = 0.64), education level (middle school, OR = 0.69, high school/junior college, OR = 0.56, bachelor or above, OR = 0.43), self-rated income (so so, OR = 0.71), SRH (good, OR = 0.52), exercise (yes, OR = 0.84), job satisfaction (satisfied, OR = 0.47), and higher life satisfaction scores (OR = 0.71) respondents were less likely to have depressive symptoms than references; and chronic disease (yes, OR = 1.62) respondents were more likely to have depressive symptoms than references.

Thus, the differences in depressive symptoms between agricultural and non-agricultural workers were reflected in the following three main areas. First, BMI (24.0–27.9 kg/m2, OR = 0.71) and education level (primary school, OR = 0.77) respondents were less likely to have depressive symptoms than references only among agricultural workers. Second, age (45–64 years, OR = 0.81), education level (bachelor or above, OR = 0.43), and exercise (yes, OR = 0.84) respondents were less likely to have depressive symptoms only among non-agricultural workers. Third, age (45–64 years, OR = 1.25) and BMI (< 18.5 kg/m2, OR = 1.45) respondents were more likely to have depressive symptoms than references only among agricultural workers.

Fig. 2
figure 2

Results of logistic model in agricultural and non-agricultural workers

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Decomposition analysis results

To ensure the stability of the results, we repeated the decomposition model 100 times using Stata MP17.0 software. Table 3 shows the results of the decomposition model of the differences in depressive symptoms between agricultural and non-agricultural workers. The results showed that 74.68% of the differences in depressive symptoms was due to observed factors, and 25.32% was due to agricultural and non-agricultural factor and unobserved factors. Educational level (39.63%), SRH (25.59%), marital status (-23.93%), residence (12.04%), job satisfaction (8.39%), chronic disease (5.52%), gender (5.11%), life satisfaction (3.59%), and BMI (-1.28%) were significant in explaining differences in depressive symptoms (P < 0.05).

Table 3 Results of fairlie decomposition analysis of differences in depressive symptoms between agricultural and non-agricultural workers
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Discussion

This study investigated the relationship between some factors (such as sociodemographic characteristics, personal lifestyle, health status, and satisfaction) and depressive symptoms among Chinese agricultural and non-agricultural workers. Additionally, it quantified the extent to which these factors could explain persistent differences in depressive symptoms between agricultural and non-agricultural workers in China. Our study confirmed that there were indeed differences in depressive symptoms between agricultural and non-agricultural workers in China.

This study showed that the percentage of depressive symptoms among Chinese employed people (age ≥ 16) was 14.44%, which was lower than the overall percentage of depressive symptoms among Chinese adults (age ≥ 16) of 23.4% [34]. However, it exceeded the reported percentage of depressive symptoms among Brazilian adults (age ≥ 18) of 10.3% [35], the reported percentage of depressive symptoms among South Korean adults (age ≥ 19) of 11% [36], and the reported percentage of depressive symptoms among German adults (age 18–79) of 6.4% [37].Moreover, the percentage of depressive symptoms was higher among agricultural workers (18.68%) than non-agricultural workers (11.33%), indicating that there were significant differences in depressive symptoms between agricultural and non-agricultural workers in China. Other studies have revealed higher levels of depressive symptoms among workers in rural areas of China than in urban areas [38, 39]. The agricultural production is closely tied to the natural environment and is a way of life for agricultural workers. It could be a challenge for agricultural workers to achieve a work-life balance. Compared with non-agricultural workers, agricultural workers are more likely to have physiological illnesses, such as lung and musculoskeletal diseases. In addition, climate change and adverse weather conditions that affect seasonal harvests can lead to economic hardship. All of these can increase stress and anxiety [40, 41] and may be related to the high prevalence of depressive symptoms among agricultural workers.

The logistic regression analysis further revealed the differences in factors associated with depressive symptoms between agricultural and non-agricultural workers in China. Age, gender, residence, BMI, marital status, education level, self-rated income, SRH, chronic disease, exercise, job satisfaction, and life satisfaction were factors associated with the presence of depressive symptoms. A longitudinal study published in JAMA Psychiatry by Angelina R Sutin et al. showed that depressive symptoms followed a U-shaped pattern throughout adulthood, with depressive symptoms being highest in young adulthood, decreasing through middle age, and increasing again in old age [42]. Among non-agricultural workers, respondents aged 45–64 years were less likely to have depressive symptoms compared to those aged 16–44 years, consistent with previous studies. However, among agricultural workers, respondents aged 45–64 years were more likely to have depressive symptoms compared to those aged 16–44 years. The reasons need to be further investigated. We also noted that exercise respondents were less likely to have depressive symptoms than non-exercise respondents among non-agricultural workers, but not among agricultural workers. This may be due to the fact that agricultural workers were more physically demanding in their daily work compared to non-agricultural workers. It has served as an exercise. Thus, specialized exercises were not as effective for agricultural workers. Rural female agricultural workers with characteristics such as 45–64 years old, BMI < 18.5 kg/m2, not married, illiteracy/semiliterate, poor self-rated income, poor SRH, suffering from chronic diseases, job dissatisfaction, and poor life satisfaction and rural female non-agricultural workers with characteristics such as 16–44 years old, not married, illiteracy/semiliterate, poor self-rated income, poor SRH, suffering from chronic diseases, lack of exercise, job dissatisfaction, and poor life satisfaction had higher likelihoods of depressive symptoms. This may be due to the lower socio-economic status and poorer living conditions of this group. They belonged to a vulnerable group and were less likely to have access to qualified livelihood and health care.

There were significant differences in depressive symptoms between agricultural and non-agricultural workers in China. The results of the Fairlie model showed that this part of the differences was mainly caused by nine factors: education level (39.63%), self-rated health (25.59%), marital status (-23.93%), residence (12.04%), job satisfaction (8.39%), chronic disease (5.52%), gender (5.11%), life satisfaction (3.59%), and body mass index (-1.28%). All factors other than gender were intervenable. It should be noted that the education level of agricultural workers was significantly lower than that of non-agricultural workers. Other research has demonstrated that education had a significant impact on regions undergoing rapid social change, and educational level was negatively correlated with depressive symptoms [43]. Today, China is experiencing a new era of economic development. The government is leading the transformation of the economy with a strong push for digital villages and digital agriculture. Chinese agricultural workers have to face the enormous challenges brought by the agriculture modernization [44, 45]. Some agricultural workers, especially those with low literacy and socio-economic status, may not able to adapt quickly to this change. Therefore, the high prevalence of depressive symptoms among Chinese agricultural workers may be related to low education levels.

Mental health services in China suffer not only from a lack of resources, but also from inequitable distribution of those resources in urban vs. rural areas [46,47,48]. It remains difficult for people with mental health problems to seek medical attention. The government should increase support for the training of psychiatrists and strengthen mental health education for the population. The focus of attention should be on rural women with a BMI < 18.5 kg/m2, illiteracy, poor SRH, suffering from chronic diseases, not married, and work-life dissatisfaction. The Government should give them preferential medical insurance policies and formulate targeted support and assistance programs to slow down the rising trend of the prevalence of depressive symptoms.

Limitations

This study has several limitations. Firstly, the definition of depressive symptoms was based on the CES-D 8 scale. This widely validated scale has good reliability. However, it relies on self-report, which reduces accuracy compared to clinical diagnosis. Secondly, there are many factors influencing depressive symptoms, but only some of these indicators were included in this study. Finally, it is important to note that there is a large population of adults in China beyond what was covered by the CFPS survey data we analyzed. Therefore, our findings are limited to the subset of adults who were surveyed and may not be representative of the entire population.

Conclusions

This study confirmed that the percentage of depressive symptoms was higher in agricultural than in non-agricultural workers. The difference was primarily associated with their differences in educational level, SRH, marital status, residence, job satisfaction, chronic disease, gender, life satisfaction, and BMI. Therefore, improving the socio-economic and health status of agricultural workers was particularly important to eliminate their differences in depressive symptoms.

Data availability

The datasets generated and/or analysed during the current study are available in the China Family Panel Studies repository, http://www.isss.pku.edu.cn/cfps/en/.

Abbreviations

CFPS:

China Family Panel Studies

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Acknowledgements

We thank the Institution of Social Science Survey (ISSS) at Peking University for organizing the CFPS and all the participants, investigators, and assistants of the CFPS.

Funding

This work was funded by the National Social Science Fund of China (grant number 23BRK008).

Author information

Author notes

  1. Zhe Zhao, Weijuan Lan and Yangyi Li contributed equally to this work.

  2. Jinhai Sun, Lijuan Liu and Lei Yuan contributed equally as corresponding authors.

Authors and Affiliations

  1. Department of Health Management, Faculty of Military Health Service, Naval Medical University, Shanghai, China

    Zhe Zhao, Weijuan Lan, Qinqin Jiang, Yijun Liu, Jinhai Sun, Lijuan Liu & Lei Yuan

  2. Department of Ultrasound, Lianyungang Maternal and Child Health Hospital, Lianyungang, Jiangsu, China

    Yangyi Li

Authors
  1. Zhe Zhao
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  2. Weijuan Lan
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  5. Yijun Liu
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  6. Jinhai Sun
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Contributions

ZZ: methodology, formal analysis, and writing. WJL: formal analysis and writing. YYL: writing and data curation. QQJ: methodology and validation. YJL: editing and validation. LJL: writing—review and editing. JHS: funding acquisition and supervision. LY: conceptualization and project administration. All authors contributed to manuscript revision, read, and approved the submitted version.

Corresponding authors

Correspondence to Jinhai Sun, Lijuan Liu or Lei Yuan.

Ethics declarations

Ethics approval and consent to participate

The studies involving human participants were reviewed and approved by the Ethics Committee of Peking University (No. IRB00001052-14010). The patients/participants provided their written informed consent to participate in this study.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Zhao, Z., Lan, W., Li, Y. et al. Decomposition analysis of differences in depressive symptoms between agricultural and non-agricultural workers in China. BMC Public Health 25, 1503 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-025-22687-3

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BMC Public Health

ISSN: 1471-2458

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