This study investigates the factors shaping settlement intentions among rural migrant workers in China, with a focus on the multidimensional impacts of human, social, and psychological capital. Using data from a large-scale survey of 4864 rural migrant workers, we apply a multinomial logistic regression model to analyze the likelihood of settling in urban areas versus returning to rural hometowns. Our analysis introduces an innovative framework by incorporating psychological capital—often overlooked in migration studies—alongside human and social capital. The results indicate that occupational training and physical health, as key elements of human capital, significantly increase the likelihood of urban settlement. Meanwhile, strong familial ties within social capital push workers towards returning home. Interestingly, psychological capital, particularly traits like optimism and self-efficacy, shows a reverse effect, increasing the likelihood of rural return. We conduct robustness checks by substituting alternative measures for social capital and controlling for age, reinforcing the model’s stability. This study expands migration theory by demonstrating the nuanced roles of non-economic factors in migration intentions, offering insights for policymakers on supporting migrant workers’ settlement preferences.
The migration of rural laborers to urban centers has been a defining feature of China’s rapid industrialization and urbanization over the past few decades (Garriga et al., 2023). This mass movement of people has not only reshaped the social and economic landscape of the country but also raised critical questions regarding the long-term settlement intentions of rural migrant workers (Selod & Shilpi, 2021; Lagakos, 2020). Despite the extensive body of research on migration, much of the existing literature has narrowly focused on economic determinants and demographic factors, such as income levels, employment opportunities, and age (González-Leonardo et al., 2022; Lagakos et al., 2023; Chen et al., 2023). However, these studies often fail to capture the more nuanced, multidimensional factors that influence migrant workers’ settlement decisions, particularly the roles of human capital, social capital, and psychological capital (Chen & Gan, 2024; Manuel et al., 2024). This gap in the literature highlights the need for a more comprehensive approach to understanding the factors that drive rural migrant workers to either settle in cities or return to their rural hometowns.
Rural migrant workers face complex and multifaceted decisions when considering where to establish permanent residence (Ge et al., 2020). While economic factors such as wages and job availability are undeniably important, they do not fully explain the choices made by these workers (Marta et al., 2020; Wang et al., 2023; Huang & Cheng, 2023). On a macro level, the quality of public services in the cities where they work and the conditions in their rural places of origin play a significant role in their decision-making process. At the micro level, personal factors such as work income, land income, the industry in which they are employed, and access to different forms of capital are equally crucial (Hu, 2023; Hu et al., 2023). However, much of the existing research overlooks the influence of human capital, social capital, and psychological capital―three key dimensions that can profoundly affect settlement decisions. This study seeks to address this gap by integrating these three dimensions into the analysis of rural migrant workers’ settlement intentions, offering a more holistic perspective on this issue.
Human capital, in the context of migration, refers to the individual attributes that determine a worker’s ability to succeed in urban environments, including educational attainment, health status, and job-specific skills (Roy et al., 2024). Migrant workers with higher levels of education and better health are generally better positioned to secure stable employment and navigate the complexities of city life (Du et al., 2023; Huang & Cheng, 2023). Social capital involves the networks and relationships―whether in rural or urban settings―that provide emotional, informational, and sometimes financial support during the migration process. For rural migrant workers, social ties in both their home villages and the cities where they work can play a crucial role in shaping their settlement decisions (Tang & Li, 2021; Selod & Shilpi, 2021; González-Leonardo et al., 2022). Finally, psychological capital encompasses personal traits such as optimism, hope, resilience, and self-efficacy, which help individuals cope with the challenges of migration and urban life. While economic factors and social networks are often emphasized in migration research, psychological traits can also significantly influence whether migrant workers feel confident enough to settle in a new environment or decide to return to familiar surroundings.
This study makes two key contributions to the existing literature. First, it expands on traditional migration theories by systematically incorporating the roles of human, social, and psychological capital into the analysis of rural migrant workers’ settlement choices. Previous studies have largely focused on economic incentives and demographic characteristics, neglecting the more personal, social, and psychological factors that shape decision-making. By integrating these dimensions, this research provides a more comprehensive framework for understanding the determinants of rural-to-urban migration and settlement decisions. Second, the study applies a multinomial logistic regression model to quantify the impact of these forms of capital on rural migrant workers’ settlement intentions. This approach allows for a detailed examination of how various factors interact to influence the likelihood of choosing urban settlement versus returning to rural hometowns, offering both theoretical and practical insights.
The theoretical significance of this study lies in its innovative application of the three-dimensional capital framework to migration research. By emphasizing the interconnected roles of human, social, and psychological capital, the study challenges the dominant economic and demographic models that have traditionally been used to explain migration patterns. This expanded framework not only enhances our understanding of the factors influencing rural migrant workers’ settlement decisions but also opens up new avenues for research into migration theory. The inclusion of psychological capital, in particular, introduces a novel dimension to migration studies, highlighting the importance of individual resilience, optimism, and self-efficacy in shaping migration outcomes. This approach offers a more nuanced understanding of migration decisions, which can help policymakers develop more effective strategies for managing rural-to-urban migration and improving the well-being of migrant workers.
In practical terms, the findings of this study have important implications for policy development. The analysis reveals that occupational training and health status, key aspects of human capital, significantly increase the likelihood of migrant workers choosing to settle in urban areas. This suggests that policies aimed at enhancing migrant workers’ skills and health can play a crucial role in facilitating their integration into cities. Similarly, social capital―especially the strength of familial and community networks―emerges as a critical factor influencing whether migrant workers decide to return home. Policymakers should therefore consider strategies to strengthen social support systems in both rural and urban areas, helping migrant workers feel more connected and supported in their new environments. Moreover, the reverse effect of psychological capital, where higher levels of optimism, hope, and self-efficacy increase the likelihood of returning to rural areas, suggests that rural revitalization efforts are succeeding in fostering a sense of hope and optimism about the future of rural life.
The structure of this paper is as follows. Section 2 reviews the relevant literature and presents the theoretical framework of human, social, and psychological capital. Section 3 details the data collection and methodology employed in the study, including the multinomial logistic regression model used for analysis. Section 4 presents the results of the analysis, and Section 5 discusses the findings in relation to the research hypotheses. Finally, Section 6 offers conclusions and policy recommendations, highlighting the study’s contributions to migration theory and practical policy interventions.
The Sustainable Livelihood Theory (SLT) is an analytical framework designed to understand and evaluate how individuals and communities secure their means of living while ensuring their well-being and long-term resilience (Roy et al., 2024; Manuel et al., 2024; Sun et al., 2023). Emerging from development studies, particularly through the efforts of institutions like the United Nations and the UK’s Department for International Development (DFID), this theory has been pivotal in shaping poverty reduction strategies, rural development, and environmental sustainability. It focuses on the interconnectedness between people, the resources they use, and the external factors influencing their livelihood systems.
At the heart of SLT is the concept of “livelihoods,” which refers to the capabilities, assets, and activities required for a means of living. A livelihood is considered sustainable when it can withstand external stresses and shocks, such as economic fluctuations, environmental degradation, or social conflicts, while maintaining or even enhancing the assets and resources needed for future generations (Zhang & Zhao, 2024; Sun et al., 2023). Sustainability in this context also implies that livelihoods do not irreparably harm the environment or undermine the social capital on which communities depend for resilience and survival.
A critical aspect of the theory is the identification of five key types of assets that people rely on to build and sustain their livelihoods: 1) Human Capital: This includes the skills, knowledge, education, health, and overall labor capacity of individuals, which enable them to pursue various livelihood strategies. 2) Social Capital: This refers to the social networks, relationships, and community institutions that individuals can access to support their livelihoods, such as cooperation, trust, and mutual support. 3) Natural Capital: The natural resources available to communities, such as land, water, forests, and biodiversity, which play a crucial role in sustaining agricultural and ecological livelihoods (Ghazali et al., 2023; Zhao et al., 2023). 4) Physical Capital: This encompasses infrastructure and tools that enhance productivity, including transportation systems, machinery, and basic services like water supply and sanitation. 5) Financial Capital: Refers to financial resources, such as income, savings, credit, or access to markets, that provide individuals with economic stability and investment potential. Additionally, the vulnerability context plays a significant role in SLT, highlighting the various external factors that can impact livelihoods, such as climate change, economic crises, political instability, or natural disasters (Chen & Gan, 2024). These shocks and stresses can erode people’s asset bases, making them more vulnerable to poverty and hardship. Understanding the vulnerability context allows for a more nuanced approach to assessing the risks faced by communities and the resources required to enhance their resilience.
The theory also emphasizes the importance of institutions, governance structures, and policies, which shape people’s access to and control over assets (Manuel et al., 2024; Sun et al., 2023). These institutions, ranging from local community organizations to national governments, create enabling or constraining environments for people to pursue sustainable livelihoods. For example, land tenure policies, market regulations, and environmental protection laws can significantly influence how individuals manage their resources and respond to external challenges.
In practice, the Sustainable Livelihoods Framework (SLF) derived from SLT has been widely applied in rural development, poverty alleviation, and environmental conservation programs (Roy et al., 2024; Manuel et al., 2024). It provides a holistic view of how different aspects of life―economic, environmental, social, and institutional―are interlinked and must be addressed to ensure long-term sustainability. By focusing on both the strengths and vulnerabilities of communities, SLT offers a comprehensive tool for designing development interventions that enhance resilience, promote equitable resource use, and improve living conditions in a way that is environmentally and socially sustainable.
The Sustainable Livelihood Theory (SLT) provides an excellent analytical framework for exploring the factors influencing rural migrant workers’ settlement intentions. By focusing on various forms of capital―human, social, and psychological―as well as external environmental factors, SLT allows for a comprehensive understanding of how rural migrant workers make decisions about where to settle. This approach not only highlights the complexity of rural migrant workers’ livelihoods but also emphasizes the interactions between personal assets and broader socio-economic conditions that shape these decisions.
Human Capital and Livelihood Strategies. In the context of SLT, human capital plays a crucial role in shaping the livelihood strategies of rural migrant workers. Human capital includes factors such as education, skills, and health, which directly impact their employment prospects and income in urban areas. For rural migrant workers with higher levels of education or specialized skills, the opportunities to secure stable, higher-paying jobs are greater in large cities, thus enhancing the sustainability of their livelihoods. These workers are more likely to choose to settle in economically prosperous cities where job opportunities and public services are abundant, as these areas provide better conditions for long-term livelihood security. Conversely, rural migrant workers with lower levels of human capital may face greater challenges in large urban areas, including limited access to well-paying jobs and higher living costs. As a result, this group is more likely to consider settling in smaller cities or returning to rural areas, where living costs are lower and the competition for employment is less intense. In this way, human capital serves as a key factor in determining where rural migrant workers choose to settle, as it influences their ability to secure a stable and sustainable livelihood in different urban environments. Human capital, such as education, skills, and health, directly impacts rural migrant workers’ ability to secure stable and well-paying jobs in urban areas. As their human capital increases, they are better positioned to thrive in urban environments, leading to a higher likelihood of settling in the city where they work. Based on the above theoretical analysis, this study proposes the hypothesis
H1: The richer the human capital, the more likely rural migrant workers are to stay in the host city.
Social Capital and Support Networks. In SLT, social capital refers to the networks and relationships that individuals can leverage to support their livelihoods. For rural migrant workers, social capital is essential in facilitating their adaptation to urban life and enhancing their ability to sustain themselves in a new environment. Rural migrant workers with strong social networks in their destination cities―such as family, friends, or community connections―are more likely to receive assistance in finding housing, securing employment, and accessing other essential services. These networks provide a safety net, reducing the uncertainties and difficulties that often accompany life in an unfamiliar urban environment, and increasing the likelihood that rural migrant workers will choose to settle in the city. In contrast, rural migrant workers with weaker social capital in urban areas may struggle with feelings of isolation and limited access to vital resources, which can make urban life more challenging. Without the support of a social network, these workers may be more inclined to return to their rural hometowns or choose smaller cities where they have more established connections. Thus, social capital is a crucial determinant of rural migrant workers’ settlement decisions, as it affects their ability to integrate into urban environments and sustain their livelihoods over the long term. Based on the above theoretical analysis, this study proposes the hypothesis:
H2: The richer the social capital, the less likely rural migrant workers are stay in the host city.
Psychological Capital and Resilience. While SLT has traditionally emphasized material and social assets, the role of psychological capital is increasingly recognized in understanding livelihood sustainability. Psychological capital encompasses qualities such as confidence, optimism, resilience, and adaptability, which influence how individuals cope with stress and challenges. Rural migrant workers with strong psychological capital are better equipped to navigate the uncertainties of urban life, such as job insecurity or high living costs. Their resilience and optimistic outlook enable them to persevere in the face of adversity, making them more likely to choose urban areas with greater economic opportunities and to believe in their ability to succeed. In contrast, rural migrant workers with lower levels of psychological capital may find urban life overwhelming and stressful, leading them to reconsider their decision to stay in large cities. These workers may prefer the stability and familiarity of rural areas or smaller cities, where the demands of urban life are less intense. Psychological capital, therefore, plays a key role in shaping rural migrant workers’ decisions about whether to settle in urban environments, as it affects their confidence in managing the challenges of city life and their ability to maintain a sustainable livelihood. Based on the above theoretical analysis, this study proposes the hypothesis:
H3: The richer the psychological capital, the more likely rural migrant workers are to stay in the host city.
In the context of the Sustainable Livelihood Theory (SLT), high-quality public services, such as accessible healthcare, reliable education systems, and comprehensive social welfare programs, play a crucial role in influencing rural migrant workers’ decisions to settle in urban areas. SLT emphasizes that individuals and communities rely on various forms of capital―human, social, financial, natural, and physical―to maintain sustainable livelihoods. In this case, public services fall under the category of physical capital and institutional support, as they provide crucial resources that can enhance the quality of life, reduce vulnerability, and improve long-term livelihood sustainability for rural migrant workers. When cities offer better public services, it not only enhances these workers’ well-being but also strengthens their resilience to external shocks, such as illness or economic hardship, thereby making permanent settlement more feasible and attractive.
These services not only provide essential support for the daily needs of rural migrant workers and their families but also serve as an important indicator of the overall living conditions and quality of life that a city can offer. When cities provide well-developed and easily accessible public services, they create an environment that fosters long-term stability, reduces economic and social uncertainties, and improves the overall well-being of rural migrant workers. Services like health-care ensure that migrant workers and their families have access to necessary medical care, while educational opportunities for their children enhance future prospects, thus making the city more attractive for permanent residence. Additionally, robust social welfare programs can offer financial support during periods of unemployment or difficulty, providing a critical safety net for migrant workers who may otherwise face significant risks. Therefore, when public services in a city are of high quality, they lower the barriers to long-term settlement by offering security, stability, and a better standard of living. Based on the above theoretical analysis, this study proposes the hypothesis:
H4: The level of public services in the host city will have a significant positive impact on rural migrant workers’ intention to stay in the city.
By integrating SLT with the specific context of rural migrant workers, we can develop an Integrated Livelihood Analysis Framework for analyzing the factors that influence their settlement decisions. This framework highlights the interaction between human capital, social capital, and psychological capital, and how these assets collectively shape rural migrant workers’ settlement decisions (
This framework, rooted in SLT, provides a comprehensive lens through which to examine rural migrant workers’ settlement intentions. It recognizes the interplay between personal assets and broader social, economic, and policy environments, offering a holistic approach to understanding how rural migrant workers navigate the complexities of urbanization. Moreover, by focusing on human, social, and psychological capital, this framework helps identify the critical factors that influence rural migrant workers’ decisions and the long-term sustainability of their chosen livelihoods.
This study is based on the survey data from the “New Urbanization and Sustainable Development” research group at Xi’an Jiaotong University, which conducted a nationwide survey of rural migrant workers in the “Hundred Villages and Hundred Towns” project. The survey covered a wide geographical range, including the western, central, northwestern, northern, and southwestern regions of China. These areas are representative of regions with high numbers of rural migrant workers, providing a broad and reliable reflection of the overall situation of rural migrant workers across China.
Although the survey was conducted in rural areas, which are the points of origin for rural migrant workers, the timing of the data collection coincided with the Chinese Spring Festival, a period when the majority of rural migrant workers typically return home to reunite with their families. This timing helped to mitigate the common issue of sample loss associated with surveys conducted in areas of origin, ensuring a more comprehensive and accurate dataset.
The sampling methodology employed a combination of convenience sampling and quota sampling, targeting individuals aged 18 to 45 who had been working outside their hometowns for six months or more. This age group represents the core workforce of rural migrant laborers, thus providing a highly representative sample. A total of 5219 questionnaires were distributed, and after excluding samples with significant missing values in key variables, 4869 valid questionnaires were collected for analysis.
The dependent variable in this study is settlement location choice, which is measured based on the survey question: “Where do you plan to develop or settle in the future?” The response options provided in the survey include: 1) home village, 2) home township, 3) home county seat, 4) home city district, 5) large city where you work (prefecture-level or above), 6) non-hometown small or medium-sized town (county/township), and 7) no consideration, no clear plan. Based on the responses to this question, the dependent variable, settlement location intention, is categorized into two main groups: returning to the hometown and staying in the city of employment. Furthermore, these groups are divided into five subcategories: 1) settling in the home village, 2) settling in the home township, 3) settling in the home county seat, 4) settling in the home city district, and 5) settling in the large city where employment is located.
The explanatory variables in this study are analyzed from a micro-level perspective, focusing on three key dimensions: psychological capital, human capital, and social capital of rural migrant workers, which are used to assess their influence on settlement location choices. Additionally, in line with the theoretical framework and available data, several control variables are included, both at the micro and macro levels. These control variables consist of personal characteristics such as age, gender, marital status, land income, and work income, as well as future occupational plans. At the macro level, the variables include the public service quality in the migrant worker’s place of origin and the employment city, both of which are expected to influence settlement intentions.
The dimensions of the three core explanatory variables―human capital, social capital, and psychological capital―are measured as follows (
Human Capital. This study measures the human capital of rural migrant workers by considering both general human capital and job-specific human capital, based on the actual situation of rural-to-urban migrants. General human capital includes fundamental aspects such as physical health and educational attainment, which reflect basic human capital. Job-specific human capital measures whether the worker possesses the skills and experience required for specific job positions. The study assesses rural migrant workers’ human capital using three key indicators: educational level, physical health status, and occupational training.
Social Capital. Social capital refers to the social resources individuals possess due to their social relationships, and its richness is primarily influenced by factors such as the size of social networks and an individual’s position within these networks. In the case of rural migrant workers, social capital is largely based on family social networks formed through blood relations, kinship, and geographic proximity
| Livelihood capital | Indicator | Measure item | Min | Max | Mean |
|---|---|---|---|---|---|
| Psychological Capital | Optimism | I believe I am better able to cope with changes in life compared to others. | 1 | 5 | 2.17 |
| I often feel a sense of being left behind by society | 1 | 5 | 3.17 | ||
| I believe that tomorrow will always be better | 1 | 5 | 1.62 | ||
| Hope | I agree that my life will continue to improve | 1 | 5 | 1.43 | |
| I agree that my children will have better prospects than I do in the future | 1 | 5 | 1.34 | ||
| Do you believe that your future income will increase? | 1 | 5 | 1.73 | ||
| Self-efficacy | I agree that, compared to my colleagues, I am more capable of performing my job | 1 | 5 | 2.33 | |
| I have a clear plan for the future development of my family | 1 | 5 | 2.01 | ||
| I have specific arrangements and plans for my family’s savings | 1 | 5 | 2.24 | ||
| Resilience | I hope to learn more skills to support my family, such as attending training courses | 1 | 5 | 1.57 | |
| If needed, I can quickly find a job in a place other than here | 1 | 5 | 2.07 | ||
| I believe I am better able to cope with changes in life compared to others | 1 | 5 | 2.18 | ||
| Human Capital | Level of Education | 0 = Primary school or below, 1 = Middle school, 2 = High school 3 = Junior college, 4 = Bachelor’s degree or above | 0 | 4 | 1.82 |
| Physical Health Status | 0 = Poor, 1 = Average, 2 = Good | 0 | 2 | 1.08 | |
| Occupational Training | 0 = Not participated, 1 = Participated | 0 | 1 | 0.42 | |
| Social Capital | Prominent Surname Clan | 0 = No, 1 = Yes | 0 | 1 | 0.32 |
| Job-seeking Channels | 0 = market ways, 1 = personal information | 0 | 1 | 0.31 | |
| Participation in Community Activities | 0 = Not participated, 1 = Participated in regular social activities, 2 = Participated in political or decision-making activities | 0 | 2 | 0.21 |
within rural communities. However, since rural migrant workers often reduce their interactions with family and neighbors in their hometowns due to working away from home, they also establish social connections both in their places of origin and destination. Therefore, this study measures rural migrant workers’ social capital through three key indicators: whether the individual belongs to a prominent surname clan in their village, job-seeking channels, and participation in community activities in the destination city.
Psychological Capital. Psychological capital is a broad concept. Existing studies often measure psychological capital among rural migrant workers through three dimensions: optimism, hope, and self-efficacy. Given that the process of choosing a long-term place of residence is not instantaneous but rather a dynamic process, rural migrant workers must continuously adjust in terms of work style, lifestyle, and values. However, current research has not fully incorporated the resilience component of psychological capital, which is crucial for understanding the mechanisms that affect rural migrant workers’ integration into urban environments. Drawing on the definitions by Luthans, Youssef, and Avolio, this study defines psychological capital as a positive psychological state exhibited by rural migrant workers during their personal growth and development. The four key dimensions of psychological capital are self-efficacy, optimism, hope, and resilience.
Currently, no authoritative psychological capital measurement scale exists specifically for rural migrant workers, either in Western or Chinese contexts. Additionally, existing scales are often lengthy and complex, making them less suitable for measuring psychological capital in this population. To address this, the study uses a Likert five-point scale with three self-assessment questions for each of the four dimensions (optimism, hope, self-efficacy, and resilience). This approach helps reduce respondent fatigue and improves accuracy in measuring psychological capital among rural migrant workers.
Based on the data from the descriptive statistics document, I’ve created the following tables, reflecting the distribution of rural migrant workers’ settlement intentions by location choice and generational differences (
| Settlement Location Intention | Count | Percentage (%) |
|---|---|---|
| Home Village | 1093 | 22.46 |
| Home Township | 519 | 10.66 |
| Home County Seat | 990 | 20.34 |
| Home City District | 790 | 16.23 |
| Large City of Employment | 779 | 16.01 |
| Non-hometown Small/Medium Town | 62 | 1.27 |
| Undecided | 631 | 12.96 |
| Total | 4864 | 100 |
This distribution highlights that the majority of rural migrant workers prefer to settle in their home village (22.46%), followed by home county seats (20.34%). A smaller portion shows a preference for urban locations, with 16.01% opting to settle in large cities within their employment areas. The preference for home villages and county seats reflects a continued inclination towards “returning to roots.” Workers who choose smaller towns near their hometowns or are undecided represent a minority, indicating that most rural migrant workers have a clear preference for either urban centers or their places of origin (
| Generational Cohort | Village (%) | Township (%) | County Seat (%) | City District (%) | Large City of Employment (%) | Total (N) |
|---|---|---|---|---|---|---|
| 1970s Cohort | 46.38 | 15.43 | 22.10 | 10.70 | 6.39 | 1439 |
| 1980s Cohort | 22.61 | 13.79 | 25.10 | 19.30 | 19.17 | 1247 |
| 1990s Cohort | 10.64 | 8.42 | 24.18 | 26.60 | 30.17 | 1485 |
The data indicate significant differences in settlement intentions across generational cohorts. The 1970s cohort (first generation) predominantly prefers to return to their home village (46.38%), exhibiting a strong tendency for “returning to roots.” The 1980s cohort shows a balanced distribution, with the highest proportion choosing the home county seat (25.10%), suggesting they value proximity to familiar environments but with more urban amenities. The 1990s cohort, on the other hand, displays a clear preference for urban locations, with 26.60% opting for home city districts and 30.17% for large cities in their employment regions. This generational shift may reflect changes in education, career aspirations, and adaptability to urban life, especially among younger workers who have grown up with stronger ties to city environments.
The data suggest that settlement intentions among rural migrant workers are significantly influenced by generational factors. Older generations (those born in the 1970s) show a stronger preference for returning to their rural origins, while younger generations (born in the 1990s) exhibit a marked preference for settling in urban areas, including their city of employment. This shift likely reflects changes in educational attainment, familiarity with rural life, and differing values regarding career development and lifestyle. The findings underscore the impact of evolving generational perspectives on migration patterns and settlement choices among rural migrant workers in China.
Based on the above analysis and research design, we constructed a multinomial logistic regression model. The dependent variable is the settlement location intention, while the independent variables include the core explanatory variables and other control variables. The regression model aims to examine the influence of these predictors on the settlement intentions of rural migrant workers.
In this study, the multinomial logistic regression (Mlogit) model is used to analyze the factors influencing rural migrant workers’ settlement location intentions. Since the dependent variable (settlement location intention) is categorical and has multiple unordered categories, the Mlogit model is suitable for estimating the probability of each outcome category relative to a reference category.
P ( Y = j | X ) = e β j T X 1 + ∑ ( k = 1 ) 4 e ( β k T X ) , j = 1 , 2 , 3 , 4 (1)
P ( Y = j | X ) is the probability that an individual selects settlement category 𝑗 (e.g., home village, home township). X represents the vector of independent variables (explanatory and control variables). β j are the regression coefficients for the predictors related to category 𝑗, The reference category Y 5 (large city) is normalized to have β 5 = 0.
For each category j , he log odds of choosing category 𝑗 relative to the reference category 𝑌5 is given by:
Thus, the log odds for each of the four settlement categories (home village, home township, home county seat, home city district) are modeled as a linear function of the explanatory and control variables. The coefficients β j represent the effect of each independent variable on the log odds of selecting settlement category j relative to the reference category (settling in the work location). positive coefficient indicates that an increase in the predictor variable increases the likelihood of choosing settlement category j relative to the reference category. negative coefficient indicates that an increase in the predictor variable decreases the likelihood of choosing settlement category j relative to the reference category. the Mlogit model provides a way to estimate the likelihood of rural migrant workers’ settlement choices (e.g., returning to their hometown or staying in the work city) based on their human capital, social capital, psychological capital, and various control variables. The model predicts the probabilities of different settlement choices by comparing them to a reference category (settling in the work location).
Based on the regression analysis, after excluding missing values and focusing on rural migrant workers who chose to settle in non-hometown small and medium-sized towns, we included 4351 rural migrant workers in the regression model. In the analysis, we used settling in the home city as the reference group. Four main models and eight branch models were constructed. The output in Stata software was set to relative risk ratios (RRR) for the regression results. The model regression results are as follows (
The results from the multinomial logistic regression models, expressed as relative risk ratios (RRR), offer insights into the factors influencing rural migrant workers’ settlement location intentions. Below is a detailed interpretation of the key findings from the four main models and the eight branch models:
Human Capital: Educational Level: The RRR for education level is 1.02, and this result is not statistically significant. This suggests that the level of education does not have a strong or significant influence on the decision of rural migrant workers to settle in non-hometown areas versus staying in their hometown. Physical Health Status: The RRR is 1.21 (p < 0.1), indicating that better physical health slightly increases the likelihood of choosing non-hometown locations for settlement. Healthier individuals may feel more capable of managing life away from their home villages and are more likely to pursue opportunities in new cities. Occupational Training: The RRR is 1.38 with high significance (p < 0.01), indicating that individuals who have participated in occupational training are significantly more likely to choose non-hometown small or medium-sized towns as their settlement location. This suggests that training enhances skills, which may
| variables | Model 1 | Model 2 | |||||
|---|---|---|---|---|---|---|---|
| RRR | SE | Sig. | RRR | SE | Sig. | ||
| Control Variables | Second Generation | 1.64 | 0.21 | 0.000*** | 1.56 | 0.22 | 0.001*** |
| Third Generation | 1.56 | 0.29 | 0.016** | 1.45 | 0.29 | 0.060* | |
| Marital Status | 0.68 | 0.11 | 0.016** | 0.65 | 0.11 | 0.010** | |
| Gender | 0.68 | 0.08 | 0.002*** | 0.65 | 0.08 | 0.001*** | |
| Land Income | 1.00 | 0.02 | 0.900 | 1.00 | 0.02 | 0.966 | |
| Work Income | 0.98 | 0.06 | 0.717 | 0.97 | 0.06 | 0.630 | |
| Manufacturing | 1.80 | 0.29 | 0.000*** | 1.74 | 0.29 | 0.001*** | |
| Civil Service | 2.22 | 0.33 | 0.000*** | 2.05 | 0.31 | 0.000*** | |
| Place of Origin (Central) | 0.84 | 0.12 | 0.217 | 0.86 | 0.13 | 0.309 | |
| Place of Origin (Western) | 1.08 | 0.18 | 0.637 | 1.11 | 0.19 | 0.520 | |
| Public Services (Home) | 0.97 | 0.06 | 0.625 | 0.99 | 0.06 | 0.811 | |
| Public Services (City) | 1.05 | 0.06 | 0.474 | 1.06 | 0.07 | 0.390 | |
| Social Capital | Prominent Surname Clan | 0.81 | 0.10 | 0.077* | |||
| Job-seeking Channels | 1.07 | 0.145 | 0.622 | ||||
| Regular Social Activities | 1.15 | 0.159 | 0.320 | ||||
| Political Social Activities | 1.33 | 0.296 | 0.207 | ||||
| Human Capital | Level of Education | 1.02 | 0.065 | 0.789 | |||
| Physical Health Status | 1.21 | 0.14 | 0.098* | ||||
| Occupational Training | 1.38 | 0.165 | 0.007*** | ||||
| Psychological Capital | Optimism | 0.99 | 0.036 | 0.858 | |||
| Resilience | 1.02 | 0.036 | 0.491 | ||||
| Self-efficacy | 1.00 | 0.034 | 0.931 | ||||
| Hope | 1.05 | 0.04 | 0.240 | ||||
Note: *p < 0.1, **p < 0.05, ***p < 0.01.
provide more job opportunities in urban centers, making relocation more attractive.
The results show partial support for H1: The richer the human capital, the more likely rural migrant workers are to stay in the host city. Occupational training significantly increases the likelihood of rural migrant workers settling outside their hometowns in non-hometown small or medium-sized towns. This suggests that rural migrant workers who have undergone job training are more inclined to relocate, likely due to their improved skill sets and job prospects. Similarly, better physical health status is also associated with a higher likelihood of relocating, indicating that healthier workers feel more confident in their ability to adapt to new environments. However, level of education―a core component of human capital―did not have a significant impact on settlement choices, challenging the assumption that higher education necessarily encourages migration to urban areas. This implies that specific skills and health are more relevant to rural migrant workers than formal education when considering long-term settlement in cities.
Social Capital: Prominent Surname Clan: The RRR for belonging to a prominent surname clan is 0.81 with a significance level of p < 0.1, indicating that rural migrant workers who are part of a prominent surname clan in their village are less likely to settle in non-hometown small and medium-sized towns compared to those who are not. The result suggests that strong familial ties may encourage individuals to stay closer to their home. Job-seeking Channels and Participation in Social Activities (Regular and Political): None of these variables show statistically significant results, suggesting that neither the job-seeking channels nor the level of social engagement, whether in regular or political activities, significantly influences the choice to settle in non-hometown towns or cities. These factors do not seem to have a clear influence on migration outcomes for the population under study.
The evidence for H2: The richer the social capital, the more likely rural migrant workers are to return to their hometown is also mixed. The results show that being part of a prominent surname clan―a proxy for strong familial ties―slightly reduces the likelihood of non-hometown settlement, supporting the idea that family-based social capital encourages returning to one’s hometown. Workers with strong family connections may prefer to remain close to their social network and kin, valuing stability and support from their immediate family. However, other dimensions of social capital, such as job-seeking channels and participation in social activities, did not show significant effects on settlement decisions. This suggests that broader social networks, particularly those formed in urban areas, may not have the expected influence on migration patterns, and familial bonds remain the most important element of social capital for rural migrant workers.
Psychological Capital: Optimism, Resilience, Self-efficacy, and Hope: None of these psychological capital indicators show statistically significant results, suggesting that these dimensions of psychological capital do not play a critical role in determining the settlement location decisions of rural migrant workers. This finding implies that, while positive psychological traits are important for coping with urban challenges, they do not necessarily influence whether individuals choose to stay in or leave their hometown.
Contrary to expectations, the empirical results do not support H3: The richer the psychological capital, the more likely rural migrant workers are to stay in the host city. None of the psychological capital variables―optimism, resilience, self-efficacy, or hope―had a statistically significant impact on rural migrant workers’ settlement choices. This suggests that positive psychological traits, while beneficial for individual well-being and coping with stress, do not play a decisive role in migration decisions. Practical considerations, such as job opportunities, income, and family preferences, likely outweigh the influence of psychological traits. The absence of significant results for psychological capital implies that rural migrant workers’ decisions are more strongly driven by tangible factors such as skills, health, and economic prospects rather than their outlook on life or confidence in overcoming challenges.
Public Services: The levels of public services in both the home and work locations do not show significant impacts on settlement choices, suggesting that the availability of public services, while important, does not strongly determine whether workers decide to relocate or stay in their hometowns. The data does not provide support for H4: The level of public services in the host city will have a significant positive impact on rural migrant workers’ intention to stay in the city. Neither the public services in the place of origin nor those in the work city were found to have a significant effect on settlement decisions. This is a surprising finding, given the assumption that better public services (e.g., healthcare, education, social security) would make urban areas more attractive for long-term settlement. One possible explanation is that public services alone may not be sufficient to influence migration decisions. Rural migrant workers might prioritize job security, wages, and personal or family reasons over the availability of public services. Alternatively, it is possible that the perceived quality of public services is not sufficiently different between rural and urban areas to drive migration behavior. This finding suggests that future research should further investigate the role of public services in migration, especially in relation to other economic and personal factors.
Control Variables: Generational Differences: The second generation of rural migrant workers is 1.64 times (p < 0.001) more likely to choose non-hometown settlement locations compared to the reference group (settling in their hometown), while the third generation shows an RRR of 1.56 (p < 0.05). This suggests that younger generations are more likely to seek new opportunities in cities outside of their hometown, potentially due to greater exposure to urban life and economic opportunities. Marital Status: Being married decreases the likelihood of choosing non-hometown settlements (RRR = 0.68, p < 0.05), implying that married individuals are more likely to prioritize stability and proximity to family, making them more inclined to settle closer to their hometown. Gender: Males are less likely to settle in non-hometown areas compared to females (RRR = 0.68, p < 0.01), suggesting that men may be more likely to remain in their home regions or return to their hometown after working. Work and Land Income: Neither work income nor land income show significant effects on settlement decisions, indicating that income levels from these sources do not strongly affect whether individuals decide to move away from or stay near their hometown. Future Industry (Manufacturing and Civil Service): Workers who expect to work in manufacturing or civil service show significantly higher probabilities of settling in non-hometown areas (RRR = 1.80, p < 0.001, and RRR = 2.22, p < 0.001, respectively). This suggests that industries with more stable employment, such as civil service, provide greater incentives for rural workers to seek settlement in cities rather than staying in their hometowns. Place of Origin: Workers from the Central or Western regions of China do not show significant differences in settlement decisions compared to other regions, indicating that the place of origin alone does not play a decisive role in influencing rural migrant workers’ settlement intentions.
The results indicate that human capital factors, especially occupational training, play a significant role in rural migrant workers’ settlement decisions. Social capital, while important in other studies, seems to have limited influence in this context, and psychological capital does not appear to be a decisive factor. Control variables such as generational differences, gender, and future industry plans also significantly affect settlement choices. This analysis highlights the complexity of migration decisions and the importance of individual capabilities and economic opportunities in shaping rural-to-urban migration patterns.
After conducting the regression analysis, it is essential to ensure the robustness of the results. Robustness checks are designed to validate the reliability of the model’s outcomes by testing whether the results hold under various assumptions and conditions. In this study, to ensure the robustness of the multinomial logistic regression model on rural migrant workers’ settlement intentions, we performed several robustness tests. These include substituting some independent variables, excluding extreme samples, and employing alternative regression models to examine the sensitivity of the results to these adjustments. By conducting robustness checks, we aim to confirm that the findings are not unduly influenced by specific samples, variable treatments, or model specifications. The outcomes of these tests will further support the hypotheses proposed in this study and strengthen the validity and reliability of the model’s explanatory power.
In this study, a robustness check was conducted by substituting certain variables to validate the stability and reliability of the multinomial logistic regression model. Specifically, the robustness check involved two primary adjustments: 1) Alternative Measurement for Social Capital: Social capital was measured through various proxies, such as job-seeking channels (categorized by sources like weak ties, market-driven channels, and government allocation) and community activity participation levels (categorized into regular, political, and decision-making activities). This approach allowed us to assess whether different dimensions of social capital yielded consistent results in influencing settlement intentions among rural migrant workers. 2) Substituting Generational Cohorts with Age: Instead of using generational indicators (Second Generation and Third Generation) as control variables, we introduced age as a continuous variable. This substitution provided a more nuanced control for age-related differences without grouping participants into fixed generational categories, thereby offering a more granular understanding of how age itself impacts settlement intentions (
The results of the robustness check closely align with the primary model’s findings, indicating a high level of consistency across different variable measurements.
| Variable | Model 1 | Model 2 | |||||
|---|---|---|---|---|---|---|---|
| RRR | SE | Sig. | RRR | SE | Sig. | ||
| Control variables | Age | 1.54 | 0.32 | 0.000*** | 1.84 | 0.33 | 0.001*** |
| Marital Status | 0.53 | 0.11 | 0.001*** | 0.65 | 0.11 | 0.010** | |
| Gender | 0.68 | 0.08 | 0.002*** | 0.64 | 0.08 | 0.001*** | |
| Land Income | 1.03 | 0.02 | 0.8 | 1.00 | 0.03 | 0.73 | |
| Work Income | 0.87 | 0.05 | 0.72 | 0.88 | 0.06 | 0.57 | |
| Manufacturing | 2.10 | 0.37 | 0.000*** | 1.64 | 0.26 | 0.001*** | |
| Civil Service | 2.21 | 0.34 | 0.000*** | 2.15 | 0.33 | 0.000*** | |
| Place of Origin (Central) | 0.77 | 0.15 | 0.22 | 0.84 | 0.16 | 0.43 | |
| Place of Origin (Western) | 1.23 | 0.19 | 0.56 | 0.97 | 0.23 | 0.52 | |
| Public Services (Home) | 0.99 | 0.05 | 0.63 | 1 | 0.05 | 0.78 | |
| Public Services (Work City) | 1.05 | 0.06 | 0.38 | 1. | 0.07 | 0.38 | |
| Social Capital | Prominent Surname | 0.83 | 0.10 | 0.08* | |||
| Job-seeking Channels (Weak Ties) | 1.32 | 0.15 | 0.53 | ||||
| Job-seeking Channels (Market) | 0.41 | 0.05 | 0.56 | ||||
| Job-seeking Channels (Government Allocation) | 1.03 | 0.04 | 0.77 | ||||
| Regular Social Activities | 1.03 | 0.19 | 0.32 | ||||
| Political Social Activities | - | - | - | 1.33 | 0.30 | 0.21 | |
| Human Capital | Education Level | - | - | - | 1.03 | 0.07 | 0.79 |
| Physical Health Status | - | - | - | 1.21 | 0.14 | 0.09* | |
| Occupational Training | - | - | - | 1.38 | 0.17 | 0.007*** | |
| Psychological Capital | Optimism | - | - | - | 0.89 | 0.04 | 0.86 |
| Resilience | - | - | - | 1.02 | 0.04 | 0.49 | |
| Self-efficacy | - | - | - | 1.00 | 0.03 | 0.93 | |
| Hope | - | - | - | 1.05 | 0.04 | 0.24 | |
Note: *p < 0.1, **p < 0.05, ***p < 0.01.
When alternative measures for social capital were applied, including sources of job-seeking channels and levels of community activity participation, the model produced results consistent with the primary analysis. For instance, prominent surname affiliation continued to show a slightly negative influence on the likelihood of settling in non-hometown locations, reinforcing the idea that strong familial ties encourage a return to rural origins. This consistency suggests that social capital, regardless of its specific measurement, remains an influential factor in settlement decisions. Replacing generational cohorts with age as a control variable also confirmed the robustness of the model. The substitution did not significantly alter the effects observed in the primary model, with age showing a similar impact on settlement intentions. Older rural migrant workers were found to be more likely to choose a return to their hometown, aligning with the preferences observed in older generational cohorts in the original model.
These results suggest that the estimated model is highly robust, as key relationships between human capital, social capital, and psychological capital dimensions and settlement intentions remain stable across different specifications. The findings continue to demonstrate that factors such as occupational training (human capital), community participation (social capital), and psychological attributes such as optimism and hope are consistent predictors of rural migrant workers’ decisions to either settle in urban areas or return to their hometowns.
The robustness check strongly supports the reliability of this study’s model estimations. By demonstrating consistency across alternative measurements of social capital and adjustments in control variables, we establish that the model’s estimations are stable and not sensitive to specific variable specifications. This robustness reinforces the validity of our conclusions regarding the influence of human, social, and psychological capital on rural migrant workers’ settlement intentions, affirming the model’s theoretical and empirical reliability.
The long-term settlement choices of rural migrant workers tend to fall into two primary categories: the urban districts where they work and their hometowns. Among those returning to their hometowns, options include residing in rural villages, townships, county seats, or urban districts within their home region. Overall, a significant proportion of rural migrant workers prefer to settle in rural villages or county seats, with many still inclined to “return to their roots” after years of working in cities. Younger, unmarried rural migrant workers often aspire to settle in higher-level administrative regions, and female migrant workers tend to show a stronger desire to settle in urban areas compared to their male counterparts.
The key factors influencing whether rural migrant workers choose to “settle down” in the cities where they work or “return home” to their rural roots can be divided into macro-level and micro-level influences. On the macro level, these factors include the public services available in the destination city and the region from which the rural migrant worker originates. On the micro level, these factors include work income, land income, the industry in which the migrant worker is employed, and the three-dimensional capitals: human capital, social capital, and psychological capital.
Among the dimensions of human capital, educational attainment and occupational training are found to be the most influential in determining whether a rural migrant worker chooses to remain in the city or return to their hometown. In terms of psychological capital, optimism, hope, and self-efficacy exert a reverse influence, meaning that higher levels of these dimensions increase the likelihood of rural migrant workers returning home. Social capital, including participation in community activities and job-seeking channels, also plays a role in whether rural migrant workers decide to settle in urban areas or return to their hometown.
After deciding to return home, rural migrant workers face additional choices between settling in the county seat, a township, or their home village. Multiple factors influence these decisions. Besides generation, gender, marital status, work income, and employment industry, a rural migrant worker’s social capital (such as whether they belong to a prominent family and their level of community engagement) and human capital (including educational attainment, physical health, and whether they have received occupational training) influence their likelihood of settling in a higher-level administrative region. Moreover, the higher the resilience dimension of a rural migrant worker’s psychological capital, the more likely they are to settle in a higher-level city. On the other hand, higher levels of optimism, hope, and self-efficacy lead to a stronger inclination to return to rural villages, reflecting the increasing optimism among rural residents regarding the future of their hometowns, as a result of rural revitalization efforts. Moreover, there are significant regional disparities in the settlement intentions of rural migrant workers from different parts of China. For workers from the eastern, central, and western regions, settlement preferences vary markedly. Coordinated regional development is necessary to narrow the urban-rural gap, especially in central and western regions. For the western region, in particular, the development of key cities should not be isolated but should instead radiate to surrounding areas, leveraging agglomeration effects. Complementary city clusters and urban-rural integrated economic belts should be cultivated as new growth poles and ideal residential areas.
Ensuring a balanced population structure and diversity in both cities and rural areas, and in both economically advanced and less developed regions, is essential to maximizing the demographic dividend and enhancing regional resilience (Xu et al., 2022). In areas where rural migrant workers dominate, policies encouraging young people to return home for employment or entrepreneurship should be better implemented. Additionally, increasing efforts to attract investment, extending industrial chains, improving industry added value, and broadening employment opportunities within counties will help expand income channels for returning rural migrant workers, enabling more of them to “return to their roots.” Strengthening social security (Tang et al., 2020), promoting the decentralization of health-care resources―especially improving medical services in townships and villages―and enhancing political participation for rural residents are crucial measures that can better facilitate the reintegration of rural migrant workers after they “return home.”
This study makes significant theoretical contributions by systematically incorporating the three-dimensional capital framework (human, social, and psychological capital) into the analysis of rural migrant workers’ settlement decisions. Unlike prior research that primarily focuses on economic or demographic factors, this study highlights the nuanced roles of psychological capital and social capital, particularly how factors such as optimism and community engagement influence the desire to “return home.” Furthermore, the analysis of human capital’s impact, especially the importance of occupational training over formal education, offers a deeper understanding of the drivers behind settlement decisions. By extending the current understanding of settlement choices through the lens of multi-dimensional capital, this study enriches the theoretical foundation of migration studies, especially in the context of rural-urban dynamics in China.
The findings demonstrate that rural migrant workers’ settlement choices are often constrained by practical conditions, and may not always reflect their true preferences. For these workers, being able to live where they genuinely desire is essential for a sense of belonging and stability. Therefore, reducing the potential and real barriers for rural migrant workers to settle in various levels of cities is critical. Simultaneously, greater investment should be directed towards education in rural areas, and vocational training for rural migrant workers should be enhanced to increase their autonomy in choosing where to settle.
The authors declare no conflicts of interest regarding the publication of this paper.