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Article

Research on the Impact of Environmental Inequality on Labor Mobility—A Study Based on the China General Social Survey (CGSS)

by
Zhaoting Xing
1,2,
Jin Guo
3,*,
Zizhen Zhang
2,
Ting Xue
2,
Ming Yang
4 and
Wenzong Wu
4
1
College of Economics, Shenzhen University, Shenzhen 518060, China
2
College of Economics, Shanxi University of Finance and Economics, Taiyuan 030006, China
3
Data Development Department, Zhejiang Economic & Information Centre, Hangzhou 310012, China
4
China Center for Special Economic Zone Research, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(22), 9813; https://doi.org/10.3390/su16229813
Submission received: 18 September 2024 / Revised: 4 November 2024 / Accepted: 7 November 2024 / Published: 11 November 2024
Browse Figure
Figure 1
<p>Decision-making process for determining labor mobility.</p> ">
Versions Notes

Abstract

:
In recent years, as the capital-driven economic growth model gradually encounters bottlenecks and regional economic competition intensifies, governments at all levels have turned to attracting high-quality talent to promote economic transformation and upgrading. Against this backdrop, the quality of the ecological environment has gradually become an important factor affecting labor mobility. This paper explores the impact of environmental inequality on labor mobility by matching provincial and municipal data with micro-data from the Chinese General Social Survey (CGSS), especially focusing on how environmental inequality affects labor decision-making through cognitive and non-cognitive abilities. The results from the benchmark regression confirm that environmental inequality significantly suppresses labor mobility, while robustness tests further validate these findings. Additionally, the mediation mechanism analysis reveals that environmental inequality influences labor mobility through impairments in both cognitive and non-cognitive abilities. The study also conducts an analysis of the heterogeneity of different social groups, finding that environmental inequality has a particularly significant impact on groups with higher levels of education, property owners, and residents living in economically disadvantaged areas. Based on this, the paper puts forward policy recommendations for different groups, emphasizing the importance of optimizing environmental quality, improving education levels, and enhancing the social support system. The research conclusions provide theoretical support for local governments in enhancing regional competitiveness and attracting high-quality labor.

1. Introduction

In the past few decades, local governments have primarily relied on an investment-led economic growth model, attracting capital by increasing the supply of public goods and offering tax incentives to stimulate regional development [1,2]. However, as the marginal efficiency of capital has gradually declined, this model has shown signs of fatigue, particularly amid intensified regional competition and escalating overcapacity. This trend has led the central government to emphasize the need to shift the development paradigm, accelerate economic transformation, and promote high-quality growth through supply-side structural reforms. A crucial factor in achieving these goals is the demand for highly skilled talent [3,4].
Environmental inequality refers to the unequal distribution of environmental benefits and burdens across different social and economic groups, resulting in disparate access to clean air, water, and other natural resources [5,6]. In China, as economic development has advanced, environmental inequality has become a significant issue. Urban areas often enjoy better environmental conditions compared to rural areas, and wealthier regions tend to invest more in ecological protection, while less developed regions bear a disproportionate share of pollution and environmental degradation [7,8]. This disparity has led to growing social tensions and a heightened awareness of environmental justice issues.
Amidst China’s economic transition towards high-quality growth, understanding the role of environmental factors in labor mobility is critical. While traditional research has focused on economic incentives such as wages and benefits, the role of environmental quality in influencing labor decisions remains underexplored. Perceptions of environmental inequality may impact labor mobility as individuals seek to move to areas with better environmental conditions [9,10]. By examining this relationship, this paper aims to fill a significant gap in the literature, providing insights into how environmental inequality influences migration patterns and contributes to regional disparities. Furthermore, understanding this dynamic can offer practical guidance for policymakers aiming to promote balanced regional development and enhance urban competitiveness through ecological strategies.
In recent years, both central and local governments have introduced policies aimed at enhancing regional competitiveness and attracting high-quality talent. For instance, the “Regulations on Optimizing the Business Environment”, issued by the State Council in 2020, explicitly called for improving the business environment and enhancing market vitality. Similarly, the “14th Five-Year Plan and Long-Range Objectives for 2035” stressed the importance of promoting green development and achieving carbon peaking and neutrality, linking environmental quality to the attraction of talent. The introduction of these policies has driven local governments to consider not only salaries, benefits, and the provision of quality public goods (such as infrastructure, education, healthcare, and social security) when attracting talent, but also environmental quality [11,12]. The central government has explicitly stated that the construction of an ecological civilization is an integral part of the transformation of the economic development model, urging provinces and cities to prioritize environmental protection and improvement alongside high-quality development [13]. Against the backdrop of the increasingly fierce competition for talent, how to enhance urban competitiveness through ecological civilization, attract high-quality labor, and thus accelerate economic transformation has become a critical issue that is worthy of in-depth exploration [14,15]. However, as provincial and municipal economies develop, the issue of the fair allocation of environmental resources has become more prominent [16,17]. According to Adams’ equity theory, perceived fairness can influence attitudes and behaviors [18]. When individuals perceive discrepancies between the environmental benefits they enjoy and the environmental costs they bear compared to others, a sense of injustice may arise, leading to social tensions and reduced well-being, which could affect workers’ migration decisions. Although this trend is becoming increasingly evident, academic research on the relationship between environmental inequality and labor mobility remains limited, lacking in-depth empirical analysis. Does environmental inequality truly impact labor mobility? How does this phenomenon differ across various labor groups? What are the underlying mechanisms at play? These questions will be the focus of this paper’s investigation.
This paper contributes to the literature by providing one of the first empirical analyses of how environmental inequality affects labor mobility in China. It introduces a novel perspective by considering environmental factors alongside traditional economic incentives in migration decisions. Methodologically, this study employs a mixed-methods approach, combining a quantitative analysis of labor migration data with qualitative insights from case studies in regions with varying levels of environmental quality. The research also advances theoretical understanding by exploring the mechanisms through which environmental perceptions influence migration behavior.

2. Literature Review and Research Hypotheses

Environmental inequality describes the unequal distribution of environmental risks and resources across different social groups [19,20]. Early research on environmental inequality focused on racial justice, highlighting racial disparities. Over time, this field expanded to address the spatial inequities in the distribution of environmental resources and risks. In the 1980s, several environmental justice movements in the United States drew widespread attention to these issues, such as the protests in Warren County, North Carolina, in 1982, which exposed the link between environmental pollution and social disparities [21,22].
There is no doubt that the environment is a crucial foundation for human survival and development, and environmental inequality inevitably leads to unequal opportunities for the survival and growth of labor. Understanding the importance of the environment for individuals requires consideration at a broader spatial scale. Essentially, the environment acts as both spatial and material support for human existence. Although the environment is inherently a public resource, it is also limited in terms of its availability. Its uneven distribution converts these public resources into private assets for specific groups or regions. Certain groups or areas gain exclusive access to higher-quality air, water, green spaces, and other natural resources [23]. This uneven distribution leads to environmental benefits being disproportionately enjoyed by these groups or regions. The value of the environment is derived from its unique spatial characteristics and exclusivity, consolidating vast natural resources, access to public services, and spatial accessibility [24].
There are notable differences in the spatial distribution of environmental value. For example, industrial areas with heavy pollution have limited access to clean air, water, and green spaces. Similarly, remote suburban or rural areas often face challenges in accessing public services [25]. Residents in low-value environmental regions experience a form of exploitation, as their rights to survival and development are diminished by spatial environmental differences.
In modern society, the environment is not only a space for human survival but also a key factor in regional economic development [26]. The quality of environmental resources influences a region’s attractiveness to labor. Regions with high-quality environmental resources tend to attract highly skilled labor, while regions with severe environmental inequality may face labor outmigration, exacerbating regional disparities [27]. Numerous studies have already shown that environmental degradation can lead to poor health outcomes for residents, which in turn reduces their labor productivity [28].
Moreover, environmental quality has increasingly become a fundamental marker of regional identity and a key factor in individuals’ ability to achieve personal goals and a sense of accomplishment [29,30]. According to Sen’s capability approach, the environment affects not only individuals’ choices and capabilities but also their functional well-being and subjective well-being through mechanisms such as social comparison [31]. Non-cognitive abilities, such as emotional management, social skills, and behavioral regulation, are particularly affected by environmental conditions. For instance, individuals in heavily polluted areas are more prone to mental health issues, including depression and increased suicide risk [32,33]. These factors may drive residents to migrate to regions with better environmental quality to seek healthier lifestyles. In contrast, residents of low-pollution areas engage more frequently in social activities, increasing their access to job opportunities and the accumulation of social capital [34,35,36].
The inequality in environmental resources also has significant implications for children’s psychological and behavioral development, particularly during critical growth stages. Families often relocate to areas with better environmental quality to safeguard their children’s health and future prospects [37,38]. Cognitive abilities, such as attention, memory, and decision-making, are especially vulnerable to environmental deterioration, which can lead to lower academic performance and diminished future career opportunities [39,40]. Residents of heavily polluted areas may choose to move to regions with better environmental conditions to protect their own and their children’s health and potential [41]. The health issues caused by environmental inequality also lead to increased medical expenditures, particularly among low-income households, which exacerbates financial pressure and limits labor participation, further reducing household income. These economic burdens compel many families to relocate to areas with better healthcare services and higher air quality [42,43].
Environmental inequality reflects broader social disparities, including unequal access to opportunities, public resources, and social rights [44,45]. This inequality shapes both economic development and social reproduction, influencing the attraction and retention of talent [46,47,48]. Researchers have employed various methods to quantify environmental inequality, such as pollution exposure indices, environmental health risk assessments, and spatial analyses of socioeconomic status [49,50,51].
In sum, environmental inequality and labor mobility are multidimensional and complex issues. While the existing literature offers a wealth of definitions, measurement methods, and discussions on the socioeconomic impact of environmental inequality, further research is needed to explore how different social groups respond to environmental changes and to investigate effective policy measures that promote labor mobility and sustainable environmental development.
Based on the literature review presented above, this paper proposes the following research hypotheses:
H1. 
The differences in labor mobility behavior under environmental inequality are determined by individual and household decision-making characteristics.
H2. 
In the context of environmental inequality, labor groups with moderate policy needs are more likely to relocate when perceiving environmental risks.
H3. 
Environmental inequality drives labor mobility from heavily polluted regions to areas with better environmental conditions by impairing both cognitive and non-cognitive abilities.
In summary, this paper primarily tests these three hypotheses. The remainder of the paper is structured as follows: Section 3 outlines the research design, including the empirical model and variable definitions; Section 4 presents the results of the empirical tests; Section 5 examines the underlying mechanisms; and the final section concludes with policy implications.

3. Research Design and Explanation

3.1. Empirical Model Design and Variable Description

The classic “push–pull” model [52] posits that population migration is the result of the combined forces of push factors from the origin and pull factors from the destination. Building on this foundation, this paper focuses on environmental inequality as a critical factor, exploring its impact on labor mobility across regions. Environmental inequality may directly influence labor migration decisions by altering quality of life, health risks, and access to social services.
The factors influencing labor migration decisions can be represented by the following equation:
l n U i = α e n v d i + β 1 X i + β 2 Y i + ε i
where L n U i is the logarithm of the expected utility of migrating to a particular region, which captures the net benefits perceived by individuals when considering whether to migrate [53]. e n v d i denotes the explanatory variable of environmental inequality, X i represents provincial- and municipal-level characteristics, Y i refers to individual characteristic variables, and ε i is the random disturbance term, which follows a standard normal distribution.
Similarly, when migrant populations make decisions to move to other cities, their expected utility can be expressed as follows:
l n U j = α e n v d j + β 1 X j + β 2 Y j + ε j
According to the principle of utility maximization, the labor force makes decisions based on the following rule: when deciding whether to migrate to another province, the decision depends on whether the utility from residing in the destination, L n U j , exceeds the utility from residing in the current location, L n U i . If L n U j > L n U i , the labor force will choose to migrate.
C h o i c e i = 1 , i f ln U i < ln U j 0 , i f ln U i > ln U j
This paper will employ a Probit model for estimations. Specifically, the Probit model established in this paper is as follows:
P ( m i g = 1 f ) = α + β e n v d i + γ X i + ϕ Y i + ε i

3.2. Indicator Design and Data Description

This paper matches micro-level labor mobility data with the macro-level characteristics of provinces and cities, while examining both individual-level and provincial/city-level factors that influence labor mobility.
Dependent Variable: Labor Mobility
The age range for labor mobility is 16–64 years. Micro-level data on labor mobility are matched with macro-level data on provincial and city characteristics, examining the factors influencing labor mobility at both the individual and regional levels. The individual data on labor mobility were sourced from the 2021 China General Social Survey (CGSS). A decision-making process for determining labor mobility was selected, which effectively reflects the real impact of ecological factors on mobility decisions [54]. After cleaning the data and matching them with data from 30 provinces and cities, 8147 samples were successfully identified for analysis, with a focus on the population inflow into the selected provinces and cities. According to the China Migrant Population Development Report 2018 by the National Health Commission, the introduction of the concept of fairness in 2012 led to increased attention on environmental quality and equity in population migration. Consequently, this paper focuses on the migration status of survey samples within the decade preceding 2013, aiming to study the impact of environmental inequality while minimizing the exogenous effects of policy changes [55]. The process for determining migration status in the sample is illustrated in Figure 1.
Independent Variable: Environmental Inequality
Following the approach of Ma, S., and Hu, Z [56], the formula for calculating environmental inequality was derived from the geometric interpretation of the Gini coefficient as follows:
e n v d i = I G i n i = 1 − 1 G e E P e ∑ i = 1 n ( E P i − 1 ′ + E P i ′ ) × G i
where:
E P i ′ is the cumulative industrial pollution emissions up to group i ;
E P e is the total national emissions of a certain type of industrial pollution;
G i denotes the total regional production value of province i ;
G e is the total national production value;
I G i n i is the environmental Gini coefficient, which measures the degree of inequality in industrial pollution emissions across province i . This reflects the extent of environmental inequality, denoted as I G i n i .
Control Variables:
Individual Control Variables:
(1)
Education Level: This reflects the educational attainment of the respondents. The coding is as follows: never attended school = 0; primary school = 1; middle school = 2; high school (including vocational high school, technical secondary school, and technical school) = 3; undergraduate (including college diploma programs) = 4; postgraduate (Master’s or doctoral degrees) = 5. Low education includes respondents who never attended school, attended primary school, middle school, or high school (including vocational high school, technical secondary school, and technical school). High education includes those with undergraduate and postgraduate degrees (Master’s or doctoral).
(2)
Gender: Male = 1; Female = 0.
(3)
Happiness: Respondents rated their life satisfaction on a scale from 1 to 5, where 1 represents the lowest level of happiness and 5 represents the highest.
(4)
Political Affiliation: Whether the respondent is a member of the Communist Party. Yes = 1; No = 0.
(5)
Social Status: The social status score ranges from 1 to 5, where 1 represents the lowest social status and 5 the highest. Low social class: a status score of 1 or 2; middle social class: a status score of 3; high social class: a status score of 4 or 5.
(6)
Property Ownership: Owns property = 1; Does not own property = 0.
(7)
Workplace: Whether the respondent works in a non-agricultural sector. Yes = 1; No = 0.
(8)
Perception of Social Fairness: Respondents rated their perception of fairness in society on a scale from 1 to 5, where 1 represents the lowest level of fairness and 5 the highest.
(9)
Health Insurance: Whether the respondent is enrolled in health insurance. Yes = 1; No = 0.
(10)
Dependent Children: Whether the respondent has minor children. Yes = 1; No = 0.
(11)
Job Satisfaction: Respondents rated their job satisfaction on a scale from 1 to 5, where 1 represents the lowest level of satisfaction and 5 the highest.
Provincial/City Control Variables:
The data for the provincial and city control variables came from the China Provincial and City Statistical Yearbook, CEIC China Economic Database, and the China Regional Economic Statistical Yearbook. This paper selected relevant indicators from three perspectives: economic characteristics, public capacity factors, and other relevant factors [57].
Table 1 presents the descriptive statistics for each variable required by the estimation model in this chapter.

4. Empirical Regression Results

4.1. Baseline Regression

Using labor mobility as the dependent variable and the environmental inequality index (envd) as the key independent variable, individual- and provincial-level control variables are successively introduced into the model. The empirical results are presented in Table 2.
The regression results indicate that the coefficient of envd remains significantly negative, regardless of whether the model includes only the environmental inequality index (column 1), gradually incorporates individual control variables (column 2), or adds provincial-level control variables (columns 3 and 4). This further confirms the robustness of the suppressing effect of environmental inequality on labor mobility. These results suggest that, in addition to individual-level factors such as education and economic status, macro-level provincial factors, including regional economic development and policy environment, play a crucial role in influencing labor mobility.

4.2. Robustness Tests

(1)
Changing the Measurement of Environmental Inequality
To test the robustness of the results, we employed two different methods to measure environmental inequality: the Williamson Coefficient and the Theil Index. These two methods assess the inequality in environmental pollution and economic development from different perspectives.
  • (1)
    Williamson Coefficient
Following Williamson’s approach, the model to measure environmental inequality was constructed as shown in Equation (6):
V W − e n v j = 1 e n v j ¯ ∑ i = 1 n ( e n v i j − e n v j ¯ ) 2 P i j P j
In the formula, e n v i j represents the environmental pollution level of city j in province i , and e n v ¯ j represents the population-weighted environmental pollution level of province j . P i j is the resident population of city j in province i , and P j is the total resident population of province j . V W − e n v j indicates that the larger this variable, the higher the level of environmental inequality in province j .
  • (2)
    Theil Index and Entropy Measurement
The Theil index, also called Theil’s entropy, was first proposed by Theil in 1967 when he introduced the concept of entropy in the context of measuring inequality. The Theil index has two forms: the Theil T index and the Theil L index. These two versions of the Theil index account for population size and economic development in different ways. The index is used to calculate the inequality of industrial pollution emissions based on regional economic development. To calculate the weighted ratio of economic development inequality, we use the following equation:
W i = Y i ∑ i = 1 n Y i
where Y i is the total economic output of region i and n is the total number of regions.
To calculate the pollution emission ratio, we use P i = E i ∑ i = 1 n E i , where E i is the total pollution emissions of region i .
The Theil L index is calculated as follows:
L = ∑ i = 1 n ( P i ln ( P i W i ) )
By comparing the share of pollutant emissions with the share of economic development in each region, the inequality in pollutant emissions is measured. If a region’s share of pollutant emissions is significantly larger than its share of economic development, the value of the Theil L index will increase, indicating a higher level of inequality; the opposite is also true.
Table 3 reports the regression results. The coefficient of the core explanatory variable is significantly negative at the 1% level, indicating that the baseline regression results are robust.
(2)
Replacing the Micro-Level Data Sample
To further verify the robustness of the results, this paper replaced the micro-level data sample. In previous research, data from the 2021 China General Social Survey (CGSS) were used, but this time, individual labor force data from the 2018 CGSS were selected, while maintaining consistency in the individual-level control variables. The regression results show that the coefficient of envd remains significantly negative at the 1% level, consistent with the conclusions of the baseline regression, further supporting the findings of this study.
(3)
Lagging the Macro-Level Data by One Period
Considering that the impact of environmental inequality on labor mobility decisions may have a lagged effect, this study matches labor mobility data with the environmental inequality data lagged by one period (i.e., 2022 data). The regression results show that the coefficient of L. envd is significantly negative at the 1% level, consistent with the baseline regression results, further validating the robustness of the study’s conclusions.

4.3. Endogeneity Discussion

Building on the consideration of the impact of environmental inequality on labor mobility, this paper further employs the instrumental variable (IV) method to address potential endogeneity issues arising from omitted variables and reverse causality. For instance, the migration behavior of the mobile population could also be influenced by regional culture, individual religious beliefs, and environmental pollution. This study selected temperature inversion data as an instrumental variable for environmental inequality [15]. Existing research indicates that environmental quality is closely related to atmospheric meteorological changes. On the one hand, temperature inversion is a natural phenomenon largely unrelated to human activities; on the other hand, regional meteorological factors are determined by natural conditions such as the Earth’s revolution, rotation, and local latitude, satisfying the exogeneity assumption of an effective instrumental variable.
Table 4 reports the results of the endogeneity tests. The p-value of the Kleibergen–Paap rk LM statistic is less than 0.001, rejecting the null hypothesis of the underidentification of the instrumental variable. The Kleibergen–Paap rk Wald F-statistic is 12.4535, which exceeds the threshold of 10, rejecting the null hypothesis of a weak instrumental variable. These results indicate that the regression coefficients of the explanatory variables did not undergo substantial changes, and the inhibitory effect of environmental inequality on labor mobility remains robust. The results are significant at the 1% level.

4.4. Heterogeneity Analysis

4.4.1. Heterogeneous Analysis Based on Individual or Family Characteristics

The data analysis in Table 5 reveals the impact of environmental inequality on the mobility decisions of different social groups. This impact exhibits significant heterogeneity across various dimensions, such as education level, property ownership, social class, and family structure.
First, from the perspective of education level, the data clearly show different reactions to environmental inequality between groups with higher and lower education levels. Specifically, the coefficient for the group with a higher level of education is −0.5657 and is significant at the 1% level. This result indicates that individuals with higher education levels are more sensitive to environmental inequality and are inclined to reduce mobility to avoid potential environmental disadvantages. This tendency may reflect that individuals with higher education often occupy more advantageous positions in society and have more resources and a better ability to choose their living environment; thus, they exhibit stronger responses to changes in environmental quality. In contrast, the coefficient for the group with a lower education level is −0.0057 and is not statistically significant, implying that environmental inequality does not significantly affect mobility decisions in this group. It can be inferred that individuals with lower education levels, due to limitations in economic capacity and access to information, may have their mobility decisions influenced more by other factors than by environmental quality.
Second, regarding property ownership, individuals who own property also exhibit higher sensitivity to environmental inequality. Their coefficient is −0.5708, significant at the 1% level. This result suggests that owning property increases the sunk costs for such individuals, making them more inclined to stay in their current residence and less likely to move when facing environmental inequality. Property ownership implies that these individuals will face higher economic losses in the case of mobility, such as property devaluation or increased difficulty in selling their property. These potential economic costs may inhibit their motivation to move. In comparison, for those without property (see Table 5, column 4), the coefficient is −0.3777. Although still negative and significant, the degree of impact is relatively lower. Individuals without property face lower moving costs, making them more likely to move in response to environmental inequality in search of a better living environment. This reflects the crucial role that property ownership plays in mobility decisions, where owning property significantly influences how individuals react to environmental changes.
Third, from the perspective of social class, there are notable differences in mobility decisions in response to environmental inequality across the lower, middle, and upper classes. The coefficients for the lower and middle classes are −0.5138 and −0.4878, respectively, both significant at the 1% level, indicating that these groups exhibit high sensitivity to environmental inequality and tend to reduce mobility. It can be inferred that lower- and middle-class groups may rely more heavily on their current social networks and accumulated resources due to limited access to resources, and moving would mean rebuilding these networks, potentially incurring substantial economic and social costs. In contrast, the coefficient for the upper class is −0.4850; although also significantly negative, the effect is relatively smaller. This phenomenon may suggest that individuals in the upper class, with their economic and social capital advantages, are better equipped to adapt to environmental inequality, resulting in less drastic responses compared to other social classes.
Lastly, from the perspective of family structure, families with and without minor children also exhibit some differences in their mobility decisions when facing environmental inequality. For families with minor children, the coefficient for environmental inequality is −0.4031, which is significant at the 1% level, indicating higher sensitivity to environmental inequality. This may be due to parents’ concerns for their children’s health and development, making them more attentive to the quality of the living environment, and thus more inclined to stay in their current residence to avoid potential negative impacts on their children. For families without minor children, the coefficient for environmental inequality is −0.5742, which is also significantly negative, suggesting that even in the absence of children, these families are still likely to reduce mobility in response to environmental inequality. This may indicate that, regardless of the presence of minor children, families generally show a tendency to reduce mobility when faced with environmental inequality, though families with minor children exhibit this tendency more prominently.
Overall, the results in Table 5 indicate that environmental inequality plays a crucial and complex role in the mobility decisions of different social groups. These groups’ responses to environmental inequality are influenced by factors such as education level, property ownership, social class, and family structure, reflecting significant differences in access to resources, social capital, and life priorities. These differences reveal the multidimensional complexity and heterogeneity in the impact of environmental inequality on mobility decisions. Hypothesis 1 is supported by the results.

4.4.2. Heterogeneity Analysis Based on Policy Needs

Table 6 analysis shows how environmental inequality affects mobility decisions across different policy needs. From the perspective of job satisfaction, environmental inequality significantly influences mobility decisions when perceived job satisfaction is moderate, highlighting a high sensitivity to environmental inequality among this group. This is reflected in the results for groups with low, medium, and high levels of job satisfaction. For individuals with low job satisfaction, the coefficient for environmental inequality is −0.6060, indicating that this group tends to reduce mobility to avoid further environmental degradation under adverse conditions. The coefficient for the medium-satisfaction group, while slightly lower than that for the low-satisfaction group, is still significant at the 1% level, showing a similarly strong response to environmental inequality. In contrast, the high-satisfaction group is slightly less sensitive to environmental inequality, although the coefficient remains significantly negative at the 1% level, suggesting that this group is somewhat resilient but still may choose to move in response to environmental concerns.
Further analysis of the heterogeneity in perceptions of social fairness reveals that groups with higher perceptions of social fairness are more sensitive to environmental inequality. This is confirmed by the analysis of groups with low, medium, and high perceptions of fairness. For the group with a high perception of social fairness, the coefficient for environmental inequality is −0.6009, which is significant at the 1% level. This suggests that individuals with higher perceptions of social fairness are more likely to reduce mobility in response to unfair environmental conditions to maintain their current quality of life and social status. In contrast, while groups with medium and low perceptions of fairness also show some sensitivity to environmental inequality, their responses are weaker than those of the high-fairness perception group.
Similarly, in terms of happiness perception, the analysis shows significant heterogeneity. The group with a high perception of happiness is the most sensitive to environmental inequality, with a coefficient of −0.5471, which is significant at the 1% level. The medium-happiness group also exhibits a significantly negative response, though its sensitivity is slightly lower than that of the high-happiness group. Notably, while the coefficient for the low-happiness group is negative, it is not statistically significant, indicating a weak response to environmental inequality. This may be due to lower expectations regarding environmental conditions or a lack of willingness to improve their current living situation. Hypothesis 2 is supported by the results.

4.4.3. Heterogeneity Analysis Based on City Characteristics

This study conducted an empirical analysis of the impact of environmental inequality on labor mobility across different regions. The cities were divided into eastern, central, and western regions, as well as regions north and south of the Qinling–Huaihe line. As shown in Table 7, there are significant differences in the impact of environmental inequality on labor mobility across different regions.
The model shows that, in the eastern region, the coefficient for environmental inequality is negative and significant, indicating that environmental inequality significantly reduces the inflow of labor into the eastern region. This may be because the higher economic development in the eastern region raises laborers’ expectations regarding environmental quality. Therefore, when environmental inequality is severe, laborers are more inclined to move to regions with better environmental conditions. In contrast, although environmental inequality also negatively impacts labor mobility in the central and western regions, the effects are not statistically significant. This result may be due to the relatively lower economic development in these regions, meaning that laborers have fewer options and are less sensitive to environmental conditions.
For the regions north and south of the Qinling–Huaihe line, the results similarly reveal significant regional differences. The model shows that in the north of the Qinling–Huaihe line, the coefficient for environmental inequality is negative and significant, reflecting high sensitivity to environmental inequality in this region. This could be related to the industrial structure and winter heating in northern areas, which result in severe environmental pollution, prompting laborers to prioritize environmental concerns when choosing work locations. In contrast, although the coefficient for environmental inequality in the south of the Qinling–Huaihe line is positive, the result is not statistically significant, suggesting that laborers in southern regions are less sensitive to environmental inequality, possibly due to the better climate conditions and environmental quality. In southern regions, labor mobility is more likely driven by economic factors.

5. Mechanism Testing

This paper analyzed the mechanisms through which environmental inequality affects labor mobility, with a particular focus on the role of non-cognitive and cognitive abilities in this process. Although previous research suggests that environmental inequality may affect labor mobility through multiple channels, the specific mechanisms remain unclear [58,59,60]. Therefore, this study further explores how environmental inequality indirectly affects labor mobility by influencing the development of individual capacities. The results are presented in Table 8.
In terms of non-cognitive abilities, environmental inequality has different impacts on social frequency, mood depression, and health status. Specifically, the coefficient for social frequency is 0.1117 and statistically significant, indicating that despite the presence of environmental inequality, individuals may increase their social activities to cope with environmental stress. However, the coefficient for mood depression is significantly negative, at −0.3060, indicating that individuals facing environmental inequality are more likely to experience negative emotions and depression, possibly due to the increased psychological pressure caused by an unequal environment. Similarly, the coefficient for health status is significantly negative, at −0.1583, suggesting that individuals’ health may be adversely affected by environmental inequality, thereby reducing their overall quality of life.
In terms of cognitive abilities, environmental inequality has a significantly negative impact on both listening and speaking English abilities, with coefficients of −0.0889 and −0.0883, respectively. This suggests that under conditions of environmental inequality, individuals’ language abilities may be hindered, possibly due to environmental stress affecting cognitive functions related to information-processing, which in turn impacts language expression and comprehension abilities. This decline in cognitive abilities may further impair individuals’ social adaptability and competitiveness in the labor market.
In the mechanism analysis of labor mobility, the different dimensions of both non-cognitive and cognitive abilities have varying impacts on labor mobility. First, social frequency has a significant negative impact on labor mobility, with a coefficient of −0.2173, indicating that individuals with stronger social abilities may face more obstacles to labor mobility, likely because they rely more on their existing social networks and may feel discomfort regarding migration. Mood depression also has a negative impact on labor mobility, with a coefficient of −0.1112, suggesting that individuals with poorer emotional states may be less likely to relocate due to lower motivation or increased psychological barriers. These results support H3 and suggest that enhancing the cognitive and non-cognitive abilities of vulnerable groups, particularly in terms of emotional regulation and language skills development, can mitigate the negative impacts of environmental inequality to some extent, thereby promoting labor mobility.

6. Conclusions and Implications

From the perspective of individual mobility decisions, this study explores the impact of environmental inequality on labor mobility. Various robustness tests indicate that environmental inequality significantly suppresses labor mobility. Workers tend to avoid areas with poor environmental quality, and this effect is especially pronounced among highly educated groups, who are more sensitive to environmental inequality. Additionally, younger workers and those who own property show a higher willingness to move when faced with environmental inequality. Economically underdeveloped regions and high-environmental-risk areas are particularly affected by environmental inequality, highlighting the urgent need for intensified environmental governance to mitigate these negative impacts.
The study also reveals significant heterogeneity in how environmental inequality influences the mobility decisions of different social groups. Laborers with higher education levels, property owners, and residents of economically disadvantaged areas are particularly sensitive to environmental inequality. These findings suggest that policymakers should consider the needs of different groups and adopt more personalized and targeted measures to better address the challenges posed by environmental inequality. Further analysis could uncover the mechanisms through which environmental inequality affects labor mobility through both non-cognitive and cognitive abilities.
Based on the above conclusions, this paper offers the following policy implications:
(1)
Improving environmental quality, enhancing education, and strengthening social support systems: These are key measures to address the challenges posed by environmental inequality. As the environmental Gini coefficient rises and spatial disparities increase, local governments should strengthen environmental regulation and improve environmental quality, especially in heavily polluted areas, by increasing investment in environmental management. This would enhance the living conditions and health of residents. Moreover, by increasing investment in education, especially for disadvantaged groups, and providing skill training, governments can help these individuals secure more opportunities and increase their competitiveness in the labor market.
(2)
Enhancing the cognitive and non-cognitive abilities of vulnerable groups: The government can improve the cognitive and non-cognitive abilities of vulnerable populations through policy guidance and resource allocation. This includes refining social security systems and ensuring the equitable distribution of resources to help disadvantaged groups overcome the challenges posed by environmental inequality. For example, increasing social support and subsidy policies in impoverished areas can promote diversified and sustainable economic development, thereby improving the living conditions and social status of local residents.
These policy recommendations aim to address environmental inequality while promoting sustainable economic and social development. By considering the diverse needs of different groups and implementing targeted interventions, governments can effectively reduce the adverse effects of environmental inequality on labor mobility and improve overall societal well-being.

Author Contributions

Conceptualization, Z.X. and J.G.; methodology, Z.X.; software, T.X.; validation, M.Y. and W.W.; formal analysis, T.X.; investigation, Z.Z.; resources, Z.X.; data curation, T.X.; writing—original draft preparation, Z.X.; writing—review and editing, Z.X., M.Y. and W.W.; visualization, J.G.; supervision, T.X.; project administration, Z.Z. and J.G.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Ministry of Education Humanities and Social Sciences Research Project: Research on the Transmission Mechanism and Practical Path of Digital Economy Empowering High-Quality Development of Urban-Rural Integration (22YJA790090).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 16 09813 g001
Figure 1. Decision-making process for determining labor mobility.
Figure 1. Decision-making process for determining labor mobility.
Sustainability 16 09813 g001
Table 1. Descriptive statistics.
Table 1. Descriptive statistics.
VariableMeanStd. DevMinMax
Labor Mobility (mig)0.12960.335901
Environmental Inequality Index (envd)10.23830.99316.750411.181
Age (age)47.729111.05642865
Political Affiliation (political)0.10460.306101
Gender (gender)0.4690.499101
Hukou Status (rural)0.36440.481301
Health Insurance (yibao)0.95490.207301
Workplace (workplace)0.47920.499601
Urbanization Rate (city)0.65010.08260.53330.8755
Ln per capita GDP (lnpgdp)11.27940.35810.620712.1417
Industrial Structure (strc)1.46390.83470.86424.5399
Degree of Informatization (web)0.02410.00650.01750.0454
Degree of Openness (open)0.25320.190.00930.7546
Social Frequency (social)2.63771.078215
Depressed Mood (mood)2.06791.19315
English Listening Ability (listen)1.52120.774415
English Speaking Ability (speak)1.4330.703215
Education Level (edu)2.25751.18605
Social Status (status)2.28970.879115
Property Ownership (estate)0.69360.46101
Dependent Children0.42960.49501
Perceived Social Fairness (fair)3.4060.961715
Job Satisfaction (Satisfaction)3.63890.886205
Happiness (happiness)3.61950.787115
Table 2. Baseline regression results.
Table 2. Baseline regression results.
(1)(2)(3)(4)
envd−0.5016 ***−0.4796 ***−0.4392 ***−0.4552 ***
(−13.8393)(−11.7801)(−7.0410)(−6.9200)
age −0.0674 *** −0.0598 ***
(−12.3503) (−4.2044)
political 0.5115 ** 0.5131 *
(3.2495) (2.1564)
gender −0.3378 ** (0.1702)
(−3.0703) (−0.9270)
rual (0.0010) (0.3180)
(−0.0086) (−1.6270)
yibao 0.0191 −1.0194 *
(0.2859) (−2.3870)
workplace 0.1542 0.0889
(1.2869) (0.3910)
city 0.5115 **0.0401
(3.2495)(0.2169)
lngpgdp −0.3378 **0.4083
(−3.0703)(1.5130)
strc (0.0010)0.0889
(−0.0086)(0.8586)
web 0.0191(0.0642)
(0.2859)(−0.6255)
open 0.1542 (0.1138)
(1.2869)(−1.2288)
_cons3.1336 ***5.9523 ***1.9443 *6.3550 ***
(8.6784)(10.6568)(2.4418)(5.4002)
N384238423842 3842
Pseudo R20.05820.14520.0899 0.1244
Note: Robust standard errors are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Table 3. Robustness test.
Table 3. Robustness test.
Revised MethodRevised MethodMicro Data ReplacedMacro Data Lagged by One Period
envd1−0.4351 ***
(−3.6326)
envd2 −0.4858 ***
(−12.3698)
envd −0.2146 *
(−2.4953)
L.envd −0.0369 ***
(−5.4419)
ControlYESYESYESYES
_cons10.4937 ***6.2302 ***48.1029 ***−6.6748 ***
(8.6320)(13.5562)(9.4050)(−18.1899)
Pseudo R20.32740.14400.12100.6292
Note: Robust standard errors are in parentheses. ***, and * indicate significance at the 1%, and 10% levels, respectively.
Table 4. Endogeneity test results.
Table 4. Endogeneity test results.
VariableFirst StageSecond Stage
Instrumental Variable−0.0000 ***
(0.0000)
envd −0.2181 ***
(0.0513)
ControlYESYES
Kleibergen–Paap rk LM 0.0000
Kleibergen–Paap rk Wald F12.4535
Note: Robust standard errors are in parentheses. *** indicate significance at the 1% level.
Table 5. Heterogeneous estimation results based on individual or family characteristics.
Table 5. Heterogeneous estimation results based on individual or family characteristics.
Low EducationHigh EducationProperty OwnersNon-Property OwnersLower ClassMiddle ClassUpper ClassWith Minor ChildrenWithout Minor Children
envd−0.0057−0.5657 **−0.5708 ***−0.3777 ***−0.5138 ***−0.4878 ***−0.4850 ***−0.4031 ***−0.5742 ***
(−0.0087)(−3.0963)(−12.2635)(−6.5148)(−10.5152)(−8.2334)(−3.6490)(−7.2344)(−9.0463)
ControlYESYESYESYESYESYESYESYESYES
_cons−2.53943.31453.6663 ***2.1961 ***3.2375 ***2.9795 ***3.1795 *2.6493 ***3.1002 ***
(−0.3733)(1.7687)(7.9168)(3.7970)(6.6679)(5.0372)(2.3854)(4.7042)(4.9632)
Pseudo R20.13540.01980.07190.03640.06270.05280.05520.03350.0693
Log likelihood −65.4275−198.6773−866.4474−516.8973−746.5786 −542.5000 −105.6215−712.2359−447.3026
chi20.008.02134.2239.0899.8660.4912.3449.4566.62
N260763266511772075150626115021994
Note: Robust standard errors in parentheses. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.
Table 6. Heterogeneous estimation results based on policy needs.
Table 6. Heterogeneous estimation results based on policy needs.
Low Job SatisfactionMedium Job SatisfactionHigh Job SatisfactionLow Perception of FairnessMedium Perception of FairnessHigh Perception of FairnessLow Perception of HappinessMedium Perception of HappinessHigh Perception of Happiness
envd−0.6060 ***−0.4152 ***−0.5034 ***−0.3429 ***−0.4317 ***−0.6009 ***−0.3739−0.4390 ***−0.5471 ***
(−3.5334)(−4.7410)(−12.2644)(−3.8243)(−6.6615)(−11.6192)(−1.4484)(−7.5667)(−11.4965)
ControlYESYESYESYESYESYESYESYESYES
_cons4.3894 **2.7519 **3.0784 ***1.27442.6577 ***4.1196 ***1.49542.5222 ***3.5954 ***
(2.6408)(3.1661)(7.5122)(1.4280)(4.1362)(7.9541)(0.5736)(4.3345)(7.6255)
Pseudo R20.12640.05850.05390.02650.05200.07430.01870.04010.0759
Log likelihood−43.54366−173.9015 −1170.8018−232.0960 −373.7798−780.0615−46.03842−606.5919 −740.7781
chi212.6121.60133.3512.6541.04125.131.7550.70121.70
N1063683368720892223015416442044
Note: Robust standard errors are in parentheses. ***, ** represent significance at the 1%, 5%, levels, respectively.
Table 7. Heterogeneous estimation results based on city characteristics.
Table 7. Heterogeneous estimation results based on city characteristics.
Eastern RegionCentral RegionWestern RegionNorth of Qinling–Huaihe LineSouth of Qinling–Huaihe Line
envd−0.3828 ***−0.3456−0.5812−0.4422 ***0.2493
(−8.4360)(−0.5643)(−1.4555)(−9.3245)(0.8102)
ControlYESYESYESYESYES
_cons7.4589 ***4.22396.57716.7228 ***−1.3282
(9.2227)(0.6523)(1.5580)(8.0442)(−0.4096)
Pseudo R20.15950.05680.05230.17090.0562
Log likelihood−473.95268−180.38691−202.3171171.31−483.08352
chi2179.9121.7322.35−415.6714257.49
N9877835959481417
Note: Robust standard errors are in parentheses. *** represent significance at the 1% level.
Table 8. Mechanism analysis of the impact of environmental inequality on labor mobility.
Table 8. Mechanism analysis of the impact of environmental inequality on labor mobility.
Non-Cognitive AbilityCognitive Ability
(1)(2)(3)(1)(2)
socialmoodhealth6Listenspeak
envd0.1117 ***−0.3060 ***−0.1583 ***−0.0889 ***−0.0883 ***
(3.6099)(−9.5798)(−4.6803)(−4.1952)(−4.4581)
ControlYESYESYESYESYES
_cons1.5612 ***4.0964 ***1.1946 ***1.4398 ***1.4866 ***
(4.1841)(11.7237)(6.9383)(5.1248)(5.6641)
Adj R-squared0.01670.15990.10530.17480.1716
Labor mobility
(1)(2)(3)(1)(2)
social−0.2173 *
(−2.4596)
mood −0.1112 **
(−2.6042)
health6 −0.5007 ***
(−5.0951)
listen 0.4333 ***
(4.0727)
speak 0.5360 ***
(4.7782)
ControlYESYESYESYESYES
_cons−0.6483−1.6815 ***−1.6797 ***−1.2041−1.2743
(−0.9367)(−17.4479)(−26.9315)(−1.7470)(−1.8382)
Pseudo R20.06440.00240.00900.07520.0817
N10751074107510751075
Note: Robust standard errors are in parentheses. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.
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Xing, Z.; Guo, J.; Zhang, Z.; Xue, T.; Yang, M.; Wu, W. Research on the Impact of Environmental Inequality on Labor Mobility—A Study Based on the China General Social Survey (CGSS). Sustainability 2024, 16, 9813. https://doi.org/10.3390/su16229813

AMA Style

Xing Z, Guo J, Zhang Z, Xue T, Yang M, Wu W. Research on the Impact of Environmental Inequality on Labor Mobility—A Study Based on the China General Social Survey (CGSS). Sustainability. 2024; 16(22):9813. https://doi.org/10.3390/su16229813

Chicago/Turabian Style

Xing, Zhaoting, Jin Guo, Zizhen Zhang, Ting Xue, Ming Yang, and Wenzong Wu. 2024. "Research on the Impact of Environmental Inequality on Labor Mobility—A Study Based on the China General Social Survey (CGSS)" Sustainability 16, no. 22: 9813. https://doi.org/10.3390/su16229813

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