Social Support Improves the Employment Quality of Returning Migrant Workers in China—From the Perspective of Skill-Matching

Abstract

From the macro perspective of social equity and sustainable economic development, exploring the employment quality of returning rural migrant workers is an important approach to addressing the global issue of income inequality. Drawing on survey data of returning rural migrant workers in China, this study investigates the impact of social support on their employment quality and examines the underlying mechanism. The results show that social support significantly improves the employment quality of returning rural migrant workers. Mechanism analysis reveals that social support promotes the improvement of their employment quality by enhancing skill matching. Further heterogeneity analysis indicates that the promoting effect of social support is more pronounced among passively returning rural migrant workers, while the effect is weakened for those in characteristic protected villages; compared with the central region, social support exerts a stronger promoting effect on returning rural migrant workers in the eastern and western regions. The findings of this study provide new perspectives and empirical evidence for the international academic community to understand labor market dynamics, and offer a practical basis for optimizing employment policy structures and formulating targeted strategies, thereby advancing the global economy toward a more inclusive and balanced direction.

Introduction

There are 290 million rural migrant workers in China, accounting for about 20.1% of the total national population (National Bureau of Statistics of China, 2025). In the early stage of urban development, a large number of rural migrant workers formed a trend of “going out,” actively engaged in urban construction, and became the absolute main force of labor transfer. In recent years, against the backdrop of global urbanization and labor mobility, the total number of rural migrant workers has continued to increase. However, with the continuous improvement of transportation infrastructure and the steady progress of China’s poverty alleviation and rural revitalization strategies, the flow of rural migrant workers has shown a trend of returning to the central and western regions of China; this phenomenon of return migration is both common and structurally significant in China, driven by factors such as rural revitalization policies, urban–rural disparities, and cyclical labor market dynamics (Tong & Lo, 2021). In 2023, about 152.77 million rural migrant workers were absorbed by the eastern region, a decrease of 1.7 million compared with the previous year; the central, western and northeastern regions absorbed 136.5 million rural migrant workers, an increase of 3.56 million compared with the previous year (National Bureau of Statistics of China, 2025). The employment problem of rural migrant workers returning to their hometowns is a common phenomenon in the process of global urbanization (Z. Cao & Liang, 2025). Globally, the employment quality of the large group of rural migrant workers returning to their hometowns is directly related to people’s livelihood. This study defines “returning rural migrant workers” as laborers with rural household registration and prior migrant work experience, who have returned to their registered locality (including towns, counties, and cities) and have stayed for at least 6 months. Stable and high-quality employment can not only help reduce poverty and stimulate consumption, but also ensure social harmony and stability (Omitogun et al., 2024).

Many scholars have conducted in-depth explorations on improving the employment quality of rural migrant workers, demonstrating that human capital accumulation, optimized labor distance (J. Huang, 2023), rational frequency of occupational transitions (Stecy-Hildebrandt et al., 2019) and related factors can effectively enhance the employment quality of the labor force. Existing studies have revealed that the key to improving employment quality lies in the employability of workers themselves and the employment environment. However, the improvement and optimization of these key elements that help improve employment quality are equally difficult for rural migrant workers returning to their hometowns. It is necessary to explore and find an external constraint that is easier to control to help improve the employment quality of rural migrant workers returning to their hometowns. Research has shown that external constraints like social support serve as an effective lever to enhance employment quality (M. Cao et al., 2021). According to the social support theory, social support can provide resources such as emotional support, material assistance, and information for vulnerable groups, thus helping the supported individuals maintain their social identities, relieve stress, and promote development. For improving the employment quality of rural migrant workers returning to their hometowns, verifying the positive role of easily adjustable external factors such as social support is more conducive to transforming theoretical achievements into practical effects. At the same time, in terms of employment difficulties, there are significant differences between rural migrant workers returning to their hometowns and the general migrant worker group. Among them, the unique employment dilemma faced by rural migrant workers returning to their hometowns is the mismatch between their own skill structure and the industrial needs of towns and townships caused by the differences in urban and rural industrial structures, that is, the employment dilemma of low skill matching levels. However, existing studies have not separated rural migrant workers returning to their hometowns from the migrant worker group for separate research, ignoring the important impact of the uniqueness of the employment environment and individual characteristics faced by rural migrant workers returning to their hometowns on the improvement of their employment quality. Therefore, the purpose of this study is to theoretically analyze and empirically examine how social support improves the employment quality of returning rural migrant workers from the perspective of skill matching.

Specifically, based on survey data of returning rural migrant workers in China, this study first aims to explore whether social support can enhance the employment quality of returning rural migrant workers, with a separate discussion on the differential effects of formal and informal social support within the broader construct of social support. Second, it intends to investigate the mediating role of skill matching in the relationship between social support and the employment quality of returning rural migrant workers. Finally, to identify the impacts of individual and regional characteristics on the research conclusions, this study seeks to conduct multi-dimensional heterogeneity analyses to accurately clarify the applicable scenarios of its findings. This study is helpful for understanding the challenges faced by urban and rural development around the world, and providing empirical evidence for optimizing relevant policies to protect the employment rights and interests of returning rural migrant workers and improve their employment quality.

Literature Review and Research Hypothesis

The Impact of Social Support on the Employment Quality of Returning Rural Migrant Workers

In recent years, with the promotion of China’s economic restructuring and rural revitalization strategy, the trend of rural migrant workers returning to China has become increasingly significant. However, returning rural migrant workers face many challenges in the employment process, and their employment quality is generally low. This situation is mainly manifested in problems such as low income, unstable jobs and lack of social security. The employment quality of returning rural migrant workers is restricted by multiple structural factors, including both the lack of resources at the individual level and the institutional barriers at the social level.

First of all, information asymmetry is one of the key factors restricting the improvement of the employment quality of returning rural migrant workers. Due to the lack of effective access to employment information, returning rural migrant workers often struggle to match job opportunities consistent with their skills and needs. This leads to limited employment choices, and some even have to pursue low-skilled and low-pay jobs (Z. Guo, 2025). Secondly, the lack of skills and skill mismatch significantly affect the employment competitiveness of returning rural migrant workers. The skills they accumulate during working in cities may not match the job market needs in their hometown (Xia et al., 2023). Due to the lack of continuous skills training, their skill level is difficult to meet the needs of modern industry development, which further limits the space for their career development (McGuinness et al., 2018).

In addition, the lack of social network resources is also an important factor restricting the employment quality of returning rural migrant workers. The social network of rural migrant workers returning to their hometowns is often concentrated in their fellow villagers or colleagues during the urban period, while the social network in their hometown is relatively weak. This lack of social capital limits their ability to obtain support in the employment process (Y. Zhou, 2024). Besides, insufficient psychological comfort and emotional support further weaken the employment quality of returning rural migrant workers. When returning to their hometowns from cities, returning rural migrant workers need to adapt to the new living and working environment. This adaptation process may bring anxiety, and the mental health and employment satisfaction of returning rural migrant workers without emotional support may be negatively affected (D. Wang et al., 2024).

As an important social resource, social support can provide various assistance to rural migrant workers returning to their hometowns through formal and informal channels, so as to effectively alleviate the above constraints, and this forms the core logical basis for social support to affect the employment quality of returning rural migrant workers.

Formal social support comes from formal channels such as the government, enterprises, community organizations and markets, including skills training, public transport services, government subsidies, information platforms and other resource support (Zhu & Li, 2024). First of all, skill training is an important way to improve the employment quality of returning rural migrant workers. By providing targeted skills training such as agricultural technology and e-commerce, the government and social organizations can help returning rural migrant workers master the skills needed by modern agriculture or emerging industries. This enables them to better adapt to the job market demand of their hometown, thus improving their competitiveness in employment (C.-C. Huang et al., 2020), and this process directly addresses the constraint of lack of skills and skill mismatch faced by returning rural migrant workers.

Convenient public transportation expands the employment choice range of rural migrant workers returning to their hometowns (Q. Zhou et al., 2025) and improves the stability and sustainability of employment (L. Guo & Xiao, 2024). At the same time, the improvement of transportation infrastructure can attract enterprise investment, drive regional economic development, and indirectly create more local employment opportunities (Banerjee et al., 2020), which eases the problem of limited employment options caused by narrow regional employment space. In addition, the government establishes employment information platforms or holds job fairs to provide rich employment information for returning rural migrant workers. This helps them find more suitable job opportunities and reduces the negative impact caused by information asymmetry (Meng, 2012), and this measure directly mitigates the constraint of information asymmetry.

Informal social support refers to support from various informal channels based on blood ties, kinship, industry connections, geographical ties or private relationships, including emotional support, information support and practical help. First of all, emotional support can relieve the psychological pressure of returning rural migrant workers and enhance their employment satisfaction. Informal social networks such as family, friends and fellow townspeople can provide emotional support to returning rural migrant workers, help them relieve psychological pressure and enhance their self-confidence and adaptability. Emotional support not only helps to improve their mental health status, but also improves their employment satisfaction and work motivation (Judge & Kammeyer-Mueller, 2012), and this kind of support addresses the constraint of insufficient emotional support and psychological pressure.

In addition, information support provided by family and friends can help returning rural migrant workers find suitable job opportunities faster. Especially in rural areas, the information transmission of informal social networks tends to be more efficient and direct (Liu & Yeo, 2021), which supplements the role of formal information platforms in alleviating information asymmetry. Members of informal social networks are usually able to introduce jobs, provide temporary accommodation or financial assistance. These actions provide more employment options and support for returning rural migrant workers (Bloch & McKay, 2015). This practical help can provide more employment options for returning rural migrant workers and help them find suitable jobs more quickly, and it also fills the gap of insufficient social network resources and expands employment channels.

Overall, social support alleviates the core constraints faced by returning rural migrant workers in employment through the dual paths of formal support and informal support. Formal support covers skills training, public transportation, and information platforms, while informal support includes emotional support, information support, and practical help. The employment constraints addressed in this process specifically involve insufficient skills, limited employment options, information asymmetry, lack of social resources, and psychological pressure. These alleviating effects further help returning rural migrant workers gain access to high-quality employment opportunities by improving their skills, and find more suitable jobs by obtaining adequate employment information, ultimately boosting their employment quality. Based on the above analysis, the present study proposed the following research hypotheses:

• H1: Social support can significantly improve the employment quality of rural migrant workers returning to their hometown.

The Influence Mechanism of Social Support on the Employment Quality of Returning Rural Migrant Workers

Social support contributes significantly to enhancing the skill levels of returning rural migrant workers, and it operates through two dimensions: formal support and informal support. Formal support includes resources from the government, such as skills training, public transport services, subsidies, and information platforms—these can systematically help returning rural migrant workers acquire skills aligned with market needs (Kumar et al., 2024), thereby boosting their skill matching. On one hand, government-led skills training programs offer targeted vocational education based on local economic traits and industrial demands, making the skill structure of returning rural migrant workers more compatible with market requirements (Fang et al., 2024). On the other hand, on-the-job training from enterprises and skill-upgrading courses offered by community organizations can further address gaps in technology updates and career adaptability among these workers (Jiang et al., 2016).

Informal social support primarily offers emotional support, information support, and practical assistance to returning rural migrant workers (Baig & Chang, 2020). While this type of support does not directly involve skills training, it can indirectly foster skill improvement among them by boosting their learning motivation and expanding their social capital. Thus, both formal and informal social support help returning rural migrant workers enhance their skills and master the competencies required by their hometown’s market more quickly—this forms the foundation for improving skill matching between these workers and the local labor market.

Meanwhile, information asymmetry stands as one of the major barriers for returning rural migrant workers in the job market (R. Wang, 2021), and social support plays a key role in easing this issue. Employment information platforms, job referral services, and career guidance—jointly provided by the government, enterprises, and social organizations—can effectively lower the information-search costs for these workers in the job market. Government-led employment information platforms can integrate regional employment demands, provide real-time and accurate job information, and help returning rural migrant workers gain a better understanding of market trends (Battistella, 2018). Community organizations offer personalized career guidance to these workers through employment service centers and career counseling activities. Informal social support can also provide employment information recommendations and practical assistance to returning rural migrant workers. This trust-based information transmission mechanism can make up for gaps in formal information channels, helping these workers find jobs that match their skills more rapidly (Cai et al., 2023). Building on this, social support enables returning rural migrant workers to find suitable jobs and enhances their ability to achieve skill matching.

Higher levels of skill matching exert a significant positive influence on the employment quality of returning rural migrant workers. When workers’ skills are highly aligned with market demands, their employment stability, job satisfaction, and income levels all rise notably (Li et al., 2024). Through providing systematic skills training and employment information services, formal social support can directly improve skill matching among returning rural migrant workers, thereby boosting their competitiveness in the job market. Informal social support, through emotional support and practical assistance, indirectly fosters skill improvement and employment adaptability among these workers. Improved skill matching not only reduces the unemployment risk for returning rural migrant workers, but also provides more career development opportunities for them (McGuinness & Sloane, 2011). Thus, higher skill matching among returning rural migrant workers supports the improvement of their employment quality. Based on the above analysis, the present study proposed the following research hypotheses:

• H2: Social support improves the employment quality of returning rural migrant workers by enhancing skill matching.

Data and Methods

Sampling and Data Collection

The research data of this paper comes from the comprehensive survey of “Improving the Policy Guarantee Mechanism of Talents going to the countryside” carried out by the Network and Social Governance Research Group of Xi’an Jiaotong University in 2023. In order to reflect the differences between urban and rural areas and between regions, the survey adopted multi-stage stratified sampling, convenient sampling and random sampling methods by comprehensively considering the geographical location, economic development level and population mobility. First of all, they are classified according to different types of villages, including agglomeration and promotion, suburban integration, characteristic protection, and relocation and merger. On this basis, large areas were stratified to cover as many provinces as possible to increase the representativeness of the sample. Secondly, the use of mobilizable social resources to select specific villages by convenient sampling. Finally, within the selected villages, random sampling was conducted for college graduates, rural migrant workers, talented people and entrepreneurs to ensure the randomness and diversity of the sample. After the extreme value and invalid samples were re-screened, 1,281 valid samples that met the study of the research group were retained. Based on the core research content of this paper, the samples with missing important variables were excluded, and 512 samples of returning rural migrant workers were selected.

Variable Selection

Dependent Variable

The dependent variable in this paper is the quality of employment index. This paper measures the employment quality of returning rural migrant workers, based on the multidimensional employment quality index adopted by Leschke and Watt (2014), combined with the questionnaire design, and finally chooses to construct the employment index system from four aspects: working income, working hours, work stability and social security in this regard, The four sub-indicators are respectively: (a) Working income, Measured by the average actual monthly income, This income includes wages, bonuses, cash benefits, subsidies, income in kind, And deduct the five social insurance and one housing fund and taxes; (b) Working hours, Is expressed by the average monthly working hours, Multiplied by the average working days per month and the average working hours per day; (c) Work stability, With whether to sign a fixed or long-term labor contract to judge, The workforce who answer “fixed worker” or “long-term contract” in the questionnaire is considered to have a stable job; (d) Social security, Use whether to attend endowment insurance to decide with, Treat the workforce with pension insurance as social security, The main reasons for choosing endowment insurance are: First, Endowment insurance is one of the most important types of insurance used in urban society, Therefore, whether to enjoy endowment insurance can be used to measure their employment status; next, With the implementation of the national pooling of basic pensions, Compared to other social insurance policies, There may be some advantages to the benefits of pensions and the workforce participation initiative.

In the calculation of the employment quality index, the sub-index is first standardized, and then the equal weight average method is adopted to determine the weight of each index, so as to obtain the employment quality index. The specific formula is as follows:

$$x_{\mathrm{ij}}^{\mathrm{nor}}=\frac{(x_{\mathrm{ij}}-\mathrm{mi}{n}_j)}{(\mathrm{ma}{x}_j-\mathrm{mi}{n}_j)}$$

(1)

$$\mathrm{qualit}{y}_i=\frac{1}{4}\sum _{j=1}^4{x}_{\mathrm{ij}}^{\mathrm{nor}}\times 100$$

(2)

In formula (1), $x_{\mathrm{ij}}^{\mathrm{nor}}$ represents the standardized indicator, where are i represents the sample individual, and j represents the four indicators in the employment quality system, which are working income (j = 1), working hours (j = 2), work stability (j = 3) and social security (j = 4). $\mathrm{ma}{x}_j$ represents the maximum value of the j item index and $\mathrm{mi}{n}_j$ represents the minimum value of the i item index. In addition, since longer working hours will reduce employment quality, this paper uses 1 minus the difference of standardization index to represent the negative relationship between working hours and employment quality. In order to explain the meaning of the employment quality index, this paper times the original employment quality index by 100 in formula (2) to obtain the new employment quality index“ $\mathrm{qualit}{y}_i$ ,” which represents the employment quality of the i-th labor force.

Independent Variable

The independent variable in this paper is social support. Drawing on (Wunder, 2008) research and combining with the research topic of this paper, we measure social support into formal social support and informal social support. Among them, formal social support includes skills training, tool support and various forms of subsidies from the government or social institutions; informal support refers to the emotional support or information support of family friends. Finally, the scores of the above 2 dimensions and the combined scores were calculated using the entropy method for the social support variables.

Mediator Variable

The mediator Variable in this paper is skill matching. Human capital theory points out that the longer a worker works, the lower the probability of obtaining alternative jobs with higher matching quality. Based on this, the academic community also regards the working term as an important indicator to measure the quality of matching (Chen & Deng, 2019). Referring on relevant literature, this paper shows the working years of rural migrant workers after returning to their hometown. In addition, if the higher the fitness of the worker to his existing work, the better the adaptation, the more the worker’s skills match his work. Therefore, this paper also chooses working fitness to measure skill matching. Finally, the entropy method is used to calculate the comprehensive score of skill matching.

Controlled Variables

This paper will control the individual characteristics and working characteristics of the rural migrant workers returning home. Among them, individual characteristic variables include gender, age, marital status, physical condition, village cadres and education level; work characteristic variables are the nature of work unit. Descriptive statistics for the relevant variables are presented in detailed Table 1.

Table 1.

Definitions of the Variables and the Descriptive Statistics.

Type of variable	Variable name	Assignment and meaning	Mean	Standard deviation
Dependent variable	quality	Comprehensive indicators of employment quality	44.786	15.997
	wage	Average monthly income/yuan	4,459.590	2,236.949
	time	Average working hours per month	71.073	27.62
	contract	Have signed a fixed or long-term contract = 1, no = 0	0.270	0.444
	insurance	Enjoy pension insurance = 1, no = 0	0.742	0.438
Independent variable	support	The comprehensive index	0.542	0.189
	for_support	Have skills training = 1, no = 0	0.566	0.497
		With public transport = 1, no = 0	0.625	0.485
		Is it difficult to get government resource support (subsidies, loans, land, technology, etc.)? Take the value of 1∼5, the greater the value, the greater the support	2.600	0.807
	infor_support	When encountering difficulties in life and work, family members, relatives and friends can always help you solve your problems. The value is 1∼5, the greater the value, the greater the support	3.485	0.798
		Is there so much information about contacts, policy, business and so on from family, relatives and friends? Value of 1∼5, the greater the value, the greater the support	2.816	0.939
Mechanism variables	skill	The comprehensive index	0.176	0.226
	year	Years of local work (years)	12.865	12.364
	adaptability	Now the adaptation of the work, the value of 1∼5, the larger the value, the higher the fitness	3.931	0.642
Controlled variable	gender	Male = 1 and female = 0	0.604	0.490
	age	Age/year of life	46.566	10.49
	marriage	Married = 1, unmarried = 0	0.863	0.344
	health	Physical health level: 1∼5	3.848	0.842
	village cadre	A village cadre = 1, not a village cadre = 0	0.207	0.406
	education level	Take the value of 1∼10, the greater the value, the higher the education level	3.766	1.688
	job nature	The value of government organs and public institutions is 1, the value of state-owned or collective enterprises is 2, the value of individual industrial and commercial households is 3, the value of private enterprises is 4, the value of “foreign-funded enterprises” is 5, and the value of other units is 6	4.767	1.474

Empirical Model

This study employs Stata software for empirical analysis, utilizing OLS and Probit models based on the types of dependent variables. It also incorporates a combined application of instrumental variable methods, mediation effect tests, and subgroup regression to address endogeneity, explore underlying mechanisms, and examine heterogeneity.

In order to examine the impact of social support on the employment quality of rural migrant workers returning home, it is necessary to test the effect of social support on the employment quality composite index of returning rural migrant workers. Based on the characteristics of the data at the micro level. The model is set as follows: because the employment quality index, monthly salary income, and monthly working hours are continuous spacing variables.

$$\mathrm{qualit}{y}_i={\alpha }_0+{\beta }_0\mathrm{suppor}{t}_i+{\gamma }_0\mathrm{contro}{l}_i+{\gamma }_0\mathrm{contro}{l}_i+{\epsilon }_i$$

(3)

Among them, the dependent variable quality is the comprehensive employment quality index of returned rural migrant workers; support is the core independent variable, social support; control is the control variable; is the random error term; and the subscript i represents the sample of returned rural migrant workers.

Further, we need to test the impact of social support on the sub-indicators in the employment quality of rural migrant workers returning to their hometowns. Since the monthly salary income and monthly working hours in the sub-index are continuous spacing variables, the regression model is set as follows:

$$\mathrm{wag}{e}_i={\alpha }_1+{\beta }_1\mathrm{suppor}{t}_i+{\gamma }_1\mathrm{contro}{l}_i+{\epsilon }_i$$

(4)

$$\mathrm{tim}{e}_i={\alpha }_2+{\beta }_2\mathrm{suppor}{t}_i+{\gamma }_2\mathrm{contro}{l}_i+{\epsilon }_i$$

(5)

Among them, the dependent variable wage is the monthly wage income of returned rural migrant workers, and time is the monthly working hours of returned rural migrant workers. The rest of the variables are the same as those in Model (3).

Since work stability and social insurance are the sub-indicators of employment quality of returning rural migrant workers, we used Probit regression model to test:

$$P(\mathrm{contrac}{t}_i=1|m)={\alpha }_3+{\beta }_3\mathrm{suppor}{t}_i+{\gamma }_3\mathrm{contro}{l}_i+{\epsilon }_i$$

(6)

$$P(\mathrm{insuranc}{e}_i=1|m)={\alpha }_4+{\beta }_4\mathrm{suppor}{t}_i+{\gamma }_4\mathrm{contro}{l}_i+{\epsilon }_i$$

(7)

Among them, $P(\mathrm{contrac}{t}_i=1|m)$ represents the probability that rural returned laborers sign fixed—term or long—term labor contracts, and $P(\mathrm{insuranc}{e}_i=1|m)$ represents the probability of enjoying social insurance. The remaining variables are consistent with those in Model (3).

After examining the relationship between social support and the employment quality of returning rural migrant workers and their sub-indicators, we will examine the different effects of formal and informal support in social support on the employment quality of returning rural migrant workers. This part mainly adopts the OLS model, and the model is set as follows:

$$\mathrm{qualit}{y}_i={\alpha }_5+{\beta }_5\mathrm{for}\_\mathrm{suppor}{t}_i+{\gamma }_5\mathrm{contro}{l}_i+{\epsilon }_i$$

(8)

$$\mathrm{qualit}{y}_i={\alpha }_6+{\beta }_6\mathrm{infor}\_\mathrm{suppor}{t}_i+{\gamma }_6\mathrm{contro}{l}_i+{\epsilon }_i$$

(9)

Among them, quality represents the comprehensive employment quality index of returned rural migrant workers. In Equation 8, $\mathrm{for}\_\mathrm{support}$ represents formal social support; in Equation 9, $\mathrm{infor}\_\mathrm{support}$ represents informal social support. The remaining variables are the same as those in Model (3).

In order to further explore the influence mechanism of social support on the employment quality of returning rural migrant workers, we need to test the influence of social support on the skill matching of mechanism variables, also using OLS model. The model is set as follows:

$$\mathrm{skil}{l}_i={\alpha }_7+{\beta }_7\mathrm{suppor}{t}_i+{\gamma }_7\mathrm{contro}{l}_i+{\epsilon }_i$$

(10)

Among them, skill is the mediating variable, skill matching. The remaining variables are the same as those in Model (3).

Empirical Results

Benchmark Regression

Analysis of the Influence of Social Support on the Employment Quality of Returning Rural Migrant Workers

This study adopts the OLS regression model with robust standard errors to address potential heteroscedasticity. As shown in Table 2, social support significantly promotes the employment quality of returning rural migrant workers at the 1% significance level, regardless of whether control variables are included—verifying Hypothesis 1. Specifically, social support improves employment quality mainly by increasing working income and reducing working hours, as reflected in Columns 3 to 4 of the table, while it exerts no significant impact on job stability and social security, which is shown in Columns 5 to 6.

Table 2.

Results of Model Estimates for the Impact of Social Support on the Employment Quality of Returning Rural Migrant Workers.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
	Quality		Wage	Time	Contract	Insurance
support	23.326*** (7.163)	25.560*** (11.371)	26.545*** (3.153)	−85.079*** (−47.992)	0.266 (1.011)	0.397 (1.032)
gender		0.132 (0.098)	−6.921** (−2.376)	−0.744 (−0.818)	−0.088 (−0.504)	−0.026 (−0.171)
age		0.062 (0.874)	0.133 (0.841)	0.018 (0.392)	−0.014 (−1.501)	0.017* (1.948)
marriage		0.131 (0.057)	−0.290 (−0.041)	−1.680 (−1.193)	−0.002 (−0.006)	−0.231 (−0.878)
health		−2.248*** (−2.660)	−1.008 (−0.611)	−0.316 (−0.656)	−0.065 (−0.675)	−0.211** (−2.164)
village cadre		−1.575 (−1.073)	−10.805** (−2.586)	−2.363** (−2.355)	−0.605*** (−2.649)	−0.159 (−0.531)
education level		0.353 (0.804)	−0.088 (−0.125)	0.075 (0.253)	−0.042 (−0.733)	0.064 (1.251)
job nature		−4.155*** (−8.489)	−1.745 (−1.410)	−4.321*** (−13.535)	−0.712*** (−10.185)	0.071 (1.412)
_cons	24.187*** (13.914)	51.755*** (8.057)	52.090*** (5.013)	153.053*** (36.392)	3.921*** (4.585)	−0.152 (−0.210)
N	314	314	314	382	382	382
R 2	.352	.484	.039	.906	.405	.079

Note.t Value is in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively.

Analysis of the Impact of Formal and Informal Social Support on the Employment Quality of Returning Rural Migrant Workers

As can be seen from Table 3, the influence of formal social support and informal social support on the employment quality of returning rural migrant workers is significantly improved, and the employment quality of returning rural migrant workers is significantly improved at the level of 1%. Hypothesis 1 has been verified again. From the results in columns (2) and (4), it can be found that the informal social support has a stronger impact on the employment quality of returning rural migrant workers than the formal social support.

Table 3.

Model Estimation Results for the Impact of Formal & Informal Social Support on the Employment Quality of Returning Rural Migrant Workers.

Variable	(1)	(2)	(3)	(4)
	Quality
for_support	29.674*** (12.552)	25.399*** (11.176)
infor_support			319.715*** (10.155)	184.197*** (4.916)
controlled variable	No	Yes	No	Yes
_cons	25.500*** (14.805)	53.249*** (8.213)	33.248*** (22.349)	55.705*** (7.001)
N	314	314	314	314
R 2	.328	.475	.208	.334

Note.t Value is in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively.

Robustness Test

Tool Variable Method

The empirical analysis using OLS regression model may be disturbed by the problems of reverse causality, missing variables, measurement error and autocorrelation, leading to inaccurate model estimation. Generally speaking, the instrumental variable method is the best choice to solve the above endogenous problems. In order to solve the possible endogenous problems in the regression model, this paper selects the mean of street-level social support as the instrumental variable of the endogenous independent variable “social support,” and the two-stage least squares (2 SLS) model is used for regression analysis. The validity of the instrumental variables was tested before the analysis using the instrumental variables.

The regression results of the first stage show that the influence of street social support mean on social support is significant at 1%, indicating a significant correlation between instrumental variables and endogenous independent variables; the F statistical value is 181.864; when the F value of the first stage of 2 SLS is greater than 16.38 at the critical value of 10% bias, the null hypothesis that “tool variable is weak tool variable” (Stock & Yogo, 2002) is rejected, which indicates that the tool variables selected in this paper are not weak tool variables, so the selection of the tool variable is effective. After correcting for the endogenous independent variables, the second stage regression results was obtained, as shown in Table 4.

Table 4.

Results of Regression of the Instrumental Variable Method.

Variable	(1)	(2)	(3)
	Quality
Average street-support	29.008*** (9.361)
Average street-for_support		28.987*** (9.113)
Average street-infor_support			237.027*** (4.796)
controlled variable	Yes	Yes	Yes
The first stage F value	432.353	419.841	533.621
_cons	49.646*** (7.626)	51.251*** (7.852)	54.377*** (7.078)
N	314	314	314
R 2	.480	.471	.307

Note.t Value is in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively.

The results in column (1) of the table show that social support significantly promotes the quality of employment at the 1% level. The analysis results are generally consistent with the previous benchmark regression results, further verifying hypothesis 1. Street-level mean was also used as the instrumental variable for formal and informal social support, and the results are listed in columns (2) and (3) of the table. It can be found that the two types of social support still significantly improve the employment quality of rural migrant workers returning to their hometowns, and consistent with the above, the role of informal social support is significantly stronger than that of formal social support.

To support the validity of the instrumental variables, we have conducted an exogeneity test of the instrumental variables in Appendix B, with detailed results shown in Table B.1.

Replace the Employment Quality Index

In the benchmark regression part, the equal right average method is used to calculate the employment quality index. In order to overcome the limitation that the equal weight average method may ignore the information difference between the indexes, the comprehensive employment quality index adopts the entropy value method and performs the regression. The results are shown in Table 5. The first (1) in the table lists the estimated results of the impact of social support on the employment quality of returning rural migrant workers after replacing the employment quality index, which still shows that social support can significantly improve the quality of employment, which is basically consistent with the benchmark regression results. Column (2) (3) shows that both formal social support and informal social support still have a significant positive impact on employment quality after replacing the quality of employment index, and the strength of informal social support is greater than that of formal social support. The estimation results of this part further confirm the robustness of the conclusions of the above study.

Table 5.

Robustness Test: Replacement of Employment Quality Indicators.

Variable	(1)	(2)	(3)
	Quality (entropy method)
support	0.978*** (31.692)
for_support		0.978*** (30.135)
infor_support			5.843*** (7.168)
controlled variable	Yes	Yes	Yes
_cons	0.849*** (10.667)	0.904*** (10.948)	1.124*** (6.775)
N	382	382	382
R 2	.831	.817	.372

Note.t Value is in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively.

Eliminate the No-Work Plan Sample

In view of the possibility that workers’ employment enthusiasm may affect the employment quality, this section will exclude the sample of rural migrant workers with no work plan after returning home, that is, the special sample who are not planning to find jobs or prepare to start their own business, and conduct model regression again.

The results are shown in Table 6. Three columns in the table results respectively said after excluding no work plan returning rural migrant workers, social support, formal social support and informal support the regression of the influence of employment quality.

Table 6.

Robustness Test: Samples Without Work Plan Were Excluded.

Variable	(1)	(2)	(3)
	Quality
support	25.560*** (11.371)
for_support		25.399*** (11.176)
infor_support			191.324*** (5.142)
controlled variable	Yes	Yes	Yes
_cons	51.755*** (8.057)	53.249*** (8.213)	56.888*** (6.942)
N	314	314	314
R 2	.484	.475	.310

Note.t Value is in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively.

To ensure the robustness of the core findings, this study reconstructs the employment quality index using principal component analysis for verification. The results indicate that the signs, significance, and economic interpretations of the core explanatory variables remain highly consistent with those in the baseline model. Detailed results are provided in Appendix A, Table A.1, further validating the reliability of the research conclusions.

Mechanism Inspection

With the above theoretical analysis and empirical test of the direct impact of social support on the employment quality of returning rural migrant workers, this part will continue to explore the internal mechanism. In order to test whether the social support by improving returning rural migrant workers and market skills matching level to improve the quality of employment, this paper first check whether the social support for rural migrant workers skills matching level, and then on this basis, in order to avoid the causal effect of mechanism variables for independent variables theory argument may not be sufficient, this paper further test the influence of test mechanism variables on the independent variables, namely the influence of returning rural migrant workers skills matching on the quality of employment. Relevant model estimation results are listed in Table 7.

Table 7.

Results of the Mechanism Testing Results.

Variable	(1)	(2)	(3)	(4)
	Skill			Quality
support	0.070** (2.237)
for_support		0.057* (1.788)
infor_support			3.367*** (6.247)
skill				31.084*** (7.102)
controlled variable	Yes	Yes	Yes	Yes
_cons	0.533*** (5.950)	0.545*** (6.086)	0.388*** (4.101)	49.504*** (6.177)
N	382	382	382	314
R 2	.384	.381	.459	.373

Note.t Value is in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively.

From columns (1) to (3) of the table, we can see that the coefficient of social support, formal social support and informal social support are significantly positive, indicating that they can significantly improve the skill matching level of returning rural migrant workers; in column (4), the improvement of the skill matching level means that social support can improve the skill matching of returning rural migrant workers and thus improve their employment quality; the formal and informal social support also have a positive impact on the employment quality of returning rural migrant workers through this mechanism.

Heterogeneity Analysis

Analysis of Returning Home Motivation Heterogeneity

Due to different opportunities to return home, rural migrant workers have significant differences in resource endowment, psychological state, social network utilization ability, which may affect the impact of social support on employment quality. Therefore, this paper draws lessons from the existing literature, comprehensive data characteristics, according to the questionnaire, “ What is the main reason why you went to the countryside?,” Answer” personal feelings, want to contribute to the hometown; policy attraction, the country policy support for talents to the countryside; find business opportunities, seek opportunities; friends influence, introduced by friends “for voluntary homecoming; answer” family factors, family wants to go home to work; bad economic situation, return to find work; other “for passive homecoming. This paper divides the return motivation into active and passive return, and analyzes the effect of social support on employment quality under different return motivation. The specific regression results are shown in Table 8.

Table 8.

Analysis of the Motivation Heterogeneity of Returning Rural Migrant Workers.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
	Voluntary homecoming			Passive homecoming
support	23.374*** (3.743)			25.761*** (10.491)
for_support		23.372*** (3.544)			25.526*** (10.386)
infor_support			279.992*** (3.331)			172.676*** (4.078)
controlled variable	Yes	Yes	Yes	Yes	Yes	Yes
_cons	50.214*** (2.741)	52.091*** (2.815)	42.821* (1.963)	52.362*** (7.479)	53.878*** (7.624)	59.177*** (6.666)
N	52	52	52	262	262	262
R 2	.323	.309	.255	.501	.492	.316

Note.t Value is in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively.

It can be seen from (1) (2) and (4) (5), both social support and formal social support significantly affect the employment quality of active and passive rural migrant workers returning to their hometowns. Compared with rural migrant workers returning to their hometowns, social support and formal social support have a stronger effect on improving the employment quality of passive rural migrant workers. And (3) (6) shows that informal social support promotes the employment quality of active rural migrant workers significantly better than passive rural migrant workers.

Analysis of the Village-Type Heterogeneity

In China, there are differences among villages in terms of resource endowment, economic development, geographical location, and policy support. These differences may lead to variations in the impact of social support on the employment quality of returning rural migrant workers in different villages. Therefore, this paper attempts to explore the differences in the role of social support in different types of villages. This paper refers to the “Rural Comprehensive Revitalization Plan (2024–2027)” issued by the Chinese government in January 2025 and divides village types into four categories: agglomeration and promotion villages, suburban integration villages, characteristic protection villages, and relocation and merger villages.

Specifically, agglomeration and promotion villages mainly refer to large—scale central villages and other general villages that will still exist, accounting for the majority of rural types. Suburban integration villages are those located in the suburban areas of cities and the locations of county—seat towns. They have the advantage of becoming the “backyard” of cities and also have the conditions for transformation into urban areas. Characteristic protection villages refer to villages rich in natural, historical, and cultural resources, such as famous historical and cultural villages, traditional villages, ethnic—minority—characteristic villages, and famous villages for characteristic landscape tourism. Relocation and merger villages include those located in areas with harsh living conditions, fragile ecological environments, and frequent natural disasters, villages that need to be relocated due to major project construction, and villages with a particularly serious population loss. This village classification standard can effectively classify Chinese villages based on their resource endowment, geographical location, and development potential. This paper conducts grouped regressions on samples of the four types of villages respectively. The results of the impact of comprehensive social support on employment quality are shown in Table 9, and the relevant results of formal and informal support are shown in Table 10.

Table 9.

Heterogeneity Analysis of Village Types: The Impact of Comprehensive Social Support on the Employment Quality of Returning Labor Force.

Variable	(1)	(2)	(3)	(4)
	Agglomeration and promotion	Suburban integration	Characteristic protection	Relocation and merger
support	20.476*** (5.102)	26.757*** (8.342)	36.781** (3.181)	25.611*** (10.116)
controlled variable	Yes	Yes	Yes	Yes
_cons	56.427*** (5.026)	48.294*** (5.205)	84.794 (1.724)	60.019*** (6.915)
N	149	125	16	24
R 2	.360	.603	.455	.914

Note.t Value is in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively.

Table 10.

Heterogeneity Analysis of Village Types: The Impact of Formal and Informal Social Support on the Employment Quality of Returning Employment.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Agglomeration and promotion		Suburban integration		Characteristic protection		Relocation and merger
for_support	20.363*** (5.010)		26.566*** (8.197)		36.076*** (3.507)		25.975*** (10.907)
infor_support		170.589*** (3.007)		177.150*** (3.066)		185.976 (0.816)		44.748 (0.216)
controlled variable	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
_cons	57.260*** (5.102)	64.691*** (5.504)	50.641*** (5.377)	45.025*** (3.393)	88.493** (2.536)	124.128 (1.753)	60.164*** (7.195)	78.097*** (3.278)
N	149	149	125	125	16	16	24	24
R 2	.354	.275	.593	.401	.432	−.187	.919	.501

Note.t Value is in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively.

It can be seen from Table 9 that social support can play a role in improving the employment quality of returning rural migrant workers in all types of villages. In particular, social support plays a relatively strong role in characteristic protection villages.

According to the results in columns (1), (3), (5), and (7) in Table 10, it can be seen that formal social support can also play a significant role in all types of villages, especially in characteristic protection villages. However, the results in columns (2), (4), (6), and (8) show that informal support only plays a role in agglomeration and promotion villages and suburban integration villages.

Regional Heterogeneity

Different areas of the social resources, cultural background and policy environment together shape the unique employment ecology, is likely to lead to the effect of social support for regional economic development level, industrial structure and the difference of social network density presents a significant spatial differentiation characteristics, the influence of the social support on employment quality of regional heterogeneity analysis to better play to the positive role of social support in employment. Therefore, according to the classification criteria of the fourth national economic census, on the basis of dividing the eastern, central and western regions, the heterogeneous impact of social support on the employment quality of returning rural migrant workers in different regions was investigated. The regression results are shown in Table 11.

Table 11.

Heterogeneity Analysis of Regions: The Impact of Social Support on the Employment Quality of Returning Labor Force.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	Estern China			Central China			Western China
support	29.609*** (5.521)			19.327*** (5.064)			29.021*** (9.187)
for_support		28.798*** (5.404)			19.425*** (5.089)			29.013*** (8.944)
infor_support			197.241*** (2.656)			98.761 (1.240)			289.978*** (5.355)
controlled variable	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
_cons	68.996*** (5.074)	71.742*** (5.247)	68.432*** (3.574)	40.576*** (3.380)	41.970*** (3.464)	46.573*** (2.893)	48.895*** (4.828)	49.807*** (4.867)	59.242*** (4.618)
N	90	90	90	87	87	87	137	137	137
R 2	.399	.383	.264	.406	.404	.207	.568	.559	.375

Note.t Value is in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively.

According to the empirical results in columns (1), (4), (7) and (2), (5), (8), the positive effects of both social support and formal social support on the employment quality of returning rural migrant workers are significantly stronger in the eastern and western regions compared to the central region. But it is worth noting that (3) (6) (9) indicates that the coefficient of informal social support is only significantly positive in the eastern and western regions, and the western region is significantly greater than the eastern region. This shows that informal social support can better improve the employment quality of rural migrant workers returning home in western China.

Conclusions and Discussion

Research Conclusions

This study focuses on the employment quality of rural migrant workers returning to their hometowns, aiming to explore the influence mechanism of formal and informal social support in rural society on this quality from the perspective of skill matching. Empirical tests are conducted using comprehensive survey data from the “Improving the Policy Guarantee Mechanism for Talents Going to the Countryside” project implemented by the Network and Social Governance Research Group of Xi’an Jiaotong University in 2023. The key research findings are as follows.

First, social support plays a significant role in enhancing the employment quality of rural migrant workers returning to their hometowns, and this enhancement is mainly achieved through specific paths. Specifically, social support can improve employment quality by increasing working income and reducing working hours, but it has no significant impact on job stability and social security. This indicates that current social support has a clear effect on improving the core material conditions of employment for returning rural migrant workers, while its role in employment security-related dimensions still needs to be strengthened.

Second, both formal and informal social support can improve the employment quality of rural migrant workers returning to their hometowns, but there are differences in their effectiveness, and skill matching plays a mediating role between social support and employment quality. On the one hand, the improvement effect of informal social support is stronger than that of formal social support. This result is closely related to the acquaintance society attribute of rural China and the underdeveloped state of the formal support system—informal networks based on blood ties and geographical proximity in rural society are more likely to quickly transmit employment resources, while the coverage and service quality of formal support still have room for improvement. On the other hand, social support does not directly affect employment quality; instead, it first improves the skill matching level of returning rural migrant workers, and then indirectly promotes the improvement of employment quality through the enhancement of this skill matching level. This verifies the mediating transmission role of skill matching.

Third, there is significant heterogeneity in the impact of social support on the employment quality of rural migrant workers returning to their hometowns, and the heterogeneous characteristics are clearly manifested in different dimensions. From the perspective of return motivation, social support and formal support can both work for rural migrant workers who return voluntarily and those who return involuntarily, but the benefits of informal support are more prominent for the voluntarily returning group. From the perspective of village types, social support and formal support are effective in all four types of villages, with the strongest effect in characteristic protection villages, while informal support only takes effect in agglomeration and promotion villages and suburban integration villages. From the perspective of regions, the positive effects of social support and formal support in the eastern and western regions are significantly stronger than those in the central region; informal support only plays a significant role in the eastern and western regions, and its effect in the western region is more prominent than that in the eastern region.

Research Contributions

On the theoretical side, this study enriches the theoretical framework of research on the employment quality of rural migrant workers returning to their hometowns in multiple ways. First, it incorporates the typological analysis of social support into the research framework, breaking through the limitations of traditional studies that either focus on a single type of support or ignore differences between formal and informal channels. This classification not only aligns with the actual social support structure in rural China but also provides a more detailed analytical perspective. Second, it verifies the mediating role of skill matching, filling the gap in existing literature that mostly explores the direct relationship between social support and employment quality while neglecting intermediate transmission paths. This further deepens the theoretical understanding of the causal chain of “social support—skill matching—employment quality.” Third, it systematically analyzes the heterogeneous effects of social support from three dimensions—return motivation, village type, and region—surpassing the uniform conclusions of previous studies and revealing the contextual dependence of social support’s role.

On the empirical side, this study enhances the reliability and validity of its findings through detailed primary data and rigorous methodological design. First of all, it adopts large-scale survey data from a national-level research project, covering different regions and village types, which makes the empirical results more representative and generalizable to the group of rural migrant workers returning to their hometowns in China. Furthermore, to address potential endogeneity issues such as reverse causality and omitted variables, it uses the street-level average of social support as an instrumental variable and employs two-stage least squares (2SLS) regression. This avoids the bias of ordinary least squares (OLS) regression and enhances the credibility of the causal relationship.

Research Implications

The research conclusions provide targeted references for improving the employment quality of rural migrant workers returning to their hometowns through social support. First, for voluntarily returning rural migrant workers who are more adept at utilizing informal support, policies should focus on optimizing the integration of informal networks and formal support—such as establishing community-based informal support platforms and providing supplementary formal services like vocational training. For involuntarily returning rural migrant workers who rely more on formal support, the government needs to strengthen targeted measures, including expanding the coverage of employment assistance and improving public job-matching platforms.

Second, in agglomeration and promotion villages as well as suburban integration villages where informal support is effective, policies should encourage the development of community-based informal support organizations. In characteristic protection villages where formal support is crucial, the government should increase investment in formal support, such as launching tourism-related vocational training. In relocation and merger villages where social networks have been disrupted, policies should prioritize reconstructing social networks and enhancing formal support.

Finally, in the eastern region with a mature formal support system, policies should improve the precision of formal support. In the western region where informal support is prominent but formal support is insufficient, the government needs to balance the development of both types of support. In the central region where both types of support are weak, policies should accelerate the improvement of the formal support system and break the closure of local social networks.

Research Limitations and Future Directions

This study still has limitations. First, the data used has shortcomings in terms of timeliness and coverage. The 2023 survey data does not include variables related to post-epidemic economic recovery, which may affect the comprehensiveness of the analysis. Moreover, the regional coverage of the data is not sufficiently balanced, and there is a lack of cross-national comparisons, making it difficult to generalize the conclusions to other developing countries. Second, the analysis of the skill matching mechanism lacks depth. Although the mediating role of skill matching has been verified, the specific action paths (such as the impact of different training models or resource allocation methods) have not been explored, which limits the in-depth understanding of the mechanism. Third, there is a lack of in-depth international dialogue. The study does not compare the social support mechanisms for returning rural migrant workers between China and other countries, making it difficult to fully reflect the uniqueness and universality of China’s experience in the global context.

To address the above limitations, future research can advance in four directions. First, establish longitudinal databases and expand cross-national samples. Long-term tracking can capture the dynamic impact of social support and skill matching; cross-national samples facilitate comparative studies; and cross-lagged panel models enable more rigorous causal testing. Second, adopt mixed research methods to explore the micro-mechanisms of skill matching. Combining quantitative and qualitative methods (such as in-depth interviews and case studies) can complement quantitative analysis and enhance the in-depth understanding of the mechanism. Third, strengthen international comparative research. Comparing social support policies between China and other countries can enrich the international perspective and provide references for optimizing China’s policies. Fourth, explore new forms of social support driven by the digital economy. Studying online skill training platforms, digital informal employment information networks, and intelligent job-matching systems can keep the research up-to-date and provide practical solutions for the digital era.