The Effects of Vocational-Skills Training on Migrant Workers’ Willingness to Settle in Urban Areas in China

Abstract

Using the micro data of the China Labor Force Dynamics Survey (CLDS), this study constructed an employment quality index of migrant workers by using the factor analysis method, it used the binary logistic model and the intermediary effect model to explore the urban-settlement intention of 1451 migrant workers in 29 cities in China, and it empirically analyzed the impact and internal mechanism of vocational-skills training on migrant workers’ willingness to settle in urban areas. The results show that (1) there is a significant positive relationship between the vocational-skills training and the migrant workers’ settlement intention. A further analysis showed that the empirical results remain robust after correcting endogeneity bias by using the instrumental variable model; and (2) participating in vocational-skills training can improve the employment quality of migrant workers, which would result in the increase of migrant workers’ willingness to settle in urban areas, and employment quality plays the significant intermediary role in this path. (3) The heterogeneity test results show that participating in vocational-skills training has a larger effect on the willingness to settle in urban areas of older-generation, female, and married migrant workers. Therefore, this paper makes the following suggestions: the government should do a good job of top-level design of vocational-skills training for migrant workers, increase investment in human capital of migrant workers, and improve the employment quality of migrant workers.

1. Introduction

Rural-to-urban migration in China has increased rapidly since the early 1980s, with many rural residents moving to cities in pursuit of better job and life opportunities [1]. As a special group in the period of socioeconomic development and transformation in China, migrant workers have made great contributions to the development of urban economy and urbanization [2,3]. As shown in Figure 1, according to the National Bureau of Statistics, the total number of migrant workers has increased from 225.42 million in 2008 to more than 292.51 million in 2021 [4]. However, a large number of migrant workers do not settle permanently in urban areas, failing to truly integrate into urban life, and forming a “migratory bird group” between urban and rural areas [5]. Farmers’ bird-like migration not only reduces the income of migrant workers but also excludes them from the urban social-security system. The “semi-urbanization” status of migrant workers has become a major bottleneck in China’s modernization development [6]. The thirteenth National People’s Congress of the People’s Republic of China proposed the “acceleration of citizenization of rural migrant workers”. Improving the social integration and promoting the citizenization of migrant workers has always been a focus of the government and academia, as well as a fundamental part of China’s new urbanization strategy [7,8,9]. Therefore, it is great and far-reaching significance to promote the settlement of migrant workers in cities, whether to improve the social security level and wage income of migrant workers in cities or to promote new urbanization strategy and socioeconomic stability [10].

There is a low level of education and lack of professional skills of migrant workers generally in China. The low level of human capital has become the important factor hindering migrant workers from transferring to urban non-agricultural sectors [11]. The low level of migrant workers’ human capital is also associated with the unequal diffusion of knowledge. Migrant workers grow up in rural areas; the education level in rural areas lags behind that in urban areas, and migrant workers have fewer channels to acquire knowledge [12]. Vocational-skills training plays a “bridge” role between formal school education and occupation [13], and vocational-skills training can make up for the inferior position of migrant workers in terms of education level. Vocational-skills training for migrant workers is an important measure to break through the bottleneck of human capital and alleviate the uneven knowledge transmission [14]. In recent years, the government has paid more and more attention to vocational-skills training for migrant workers; it has carried out various vocational-skills-training programs for migrant workers and other vulnerable groups in the market and provided a large amount of financial funds and various training subsidies. The government has successively issued policies and measures on vocational-skills training for migrant workers. In 2018, “the opinions of the State Council on the implementation of the lifelong vocational skills training system” identified the new generation of migrant workers as the key employment and entrepreneurship groups to build the lifelong vocational-skills-training system. In January 2019, the Ministry of Human Resources and Social Security issued the “New generation migrant workers vocational skills improvement plan (2019–2022)”, which emphasized that it is necessary to increase the education and training opportunities for the new generation of migrant workers and give full play to the advantages of this group of human resources. The Ministry of Human Resources and Social Security carried out subsidized vocational-skills training for 27.005 million people in 2020, of which 10.466 million were for migrant workers [15]. Under the background of the orderly promotion of vocational-skills training for migrant workers by the state, a very realistic question is, will the vocational-skills training provided by the government influence migrant workers’ willingness to settle in urban areas? This question is worthy of further study.

Although some studies believe that vocational training can bring positive external effects to the floating population and promote their social integration [16], the internal mechanism has not been studied. Some scholars believe that migrant workers with stable employment are more likely to achieve citizenization, but there is a lack of theoretical analysis on the impact of vocational-skills training on improving the employment stability of migrant workers [17]. So, does vocational-skills training affect migrant workers’ willingness to settle in cities and towns? Furthermore, are there group differences in this effect? How does vocational-skills training affect migrant workers’ willingness to settle in cities and towns? In order to answer the above questions, this study measured the employment-quality indicators of migrant workers based on the data of China’s Labor Force Dynamic Survey in 2018, estimated the impact of vocational-skills training of migrant workers on their willingness to settle in cities and their group differences by establishing a regression model, and focused on the role mechanism of vocational-skills training on migrant workers’ settlement in cities, so as to deepen the study on the association between the vocational-skills training of migrant workers and their willingness to settle in cities.

Compared with the previous literature [5,16,17], the main contributions of the study are as follows: First, from the perspective of research, taking human capital as the starting point, this paper focuses on discussing the important influencing factor, vocational-skills training, which has been neglected by the existing research on migrant workers’ willingness to settle in cities. Second, in terms of the research content, the paper not only estimates the impact of vocational-skills training the intentions of rural migrants to settle in cities but also analyzes the heterogeneity in terms of age, gender, and marital status. In addition, this paper also examines and discusses the influencing mechanism, which further deepens the knowledge and understanding of the relationship between vocational-skills training and migrant workers’ willingness to settle in cities.

2. Theoretical Analysis

The academic circle has conducted a large number of comprehensive studies on the factors affecting the willingness of migrant workers to settle in cities and towns, including institutional factors, individual factors, family factors, and socioeconomic factors [18,19,20,21]. The obstacle of the urban settlement of migrant workers has mainly been attributed to the hindrance caused by China’s unique household registration (hukou) system. In recent years, many local urban governments, especially those of medium-sized and small cities, have made great efforts to reform the hukou system in the hope that most members of the migrant workers will settle down in the cities after the removal of the hurdles created by the hukou system [11]. Scholars pay more attention to the factors of influence at the individual, family, and social-economy levels on migrant workers’ willingness to settle in urban areas [18,19]. At the individual level, age, gender, and marital status significantly affected the willingness of migrant workers to settle in cities [22], and migrant workers’ willingness to settle in urban areas is also related to education level [23], monthly income, and migration experience [24]. At the household level, household income, proportion of non-agricultural income, family development ability, and children’s education expectation affected migrant workers’ willingness to settle in urban areas [25,26,27], and the new generation of migrant workers was more willingness to settle in urban areas than the older generation of migrant workers. Li et al. [28] explored the impact of agricultural land transfer on migrant workers’ willingness to settle in urban areas, using data from the dynamic monitoring of China’s floating population, and the study found that there was a positive impact between agricultural land transfer and migrant workers’ willingness to settle in urban areas, and this is mainly realized through migrant workers’ housing choice. Moreover, the increase of agricultural land transfer rent was conducive to improving the housing situation of migrant workers, thus enhancing their willingness to settle in urban areas. At the level of social economy, it is generally believed that migrant workers’ willingness to settle in urban areas is related to the level of social security, regional economic development [29], public-service satisfaction, housing provident fund, labor-market policy, and living environment [30,31,32]. Liu et al. argued that high housing prices and rising housing prices generally reduced farmers’ willingness to settle in urban areas, and rising housing prices led to the increase in the cost of living and, thus, reduced the willingness to settle in urban areas of migrant workers; meanwhile, the rising housing prices also promoted migrant workers’ willingness to settle in urban areas through the stabilization expectation effect and wealth effect [33,34]. Some researchers also have examined the relationship between social discrimination and urban settlement of migrant workers from the perspective of identity and found that social discrimination inhibited migrant workers’ willingness to settle in urban areas, while identity can enhance migrant workers’ willingness to settle in urban areas. Identity played an intermediary role in this path [35,36].

Scholars have earlier studied the migration of floating population, and a series of classical theories have been formed, including the two-sector model based on the macroeconomic perspective [37], the cost-benefit theory at the microlevel [38], as well as the new migration economics based on the maximization of household economic benefits [39], the “push-pull model” based on the push of the outgoing and the pull of the incoming places [40], etc. The traditional population-migration theory mainly examines the influence of migration costs and the social and economic environment of the two places of migration on population migration, arguing that rural laborers are constantly moving to the urban non-farm sector due to the disparity between urban and rural socioeconomic environments, and rural laborers will migrate to the city even at risk of unemployment, as long as the expected income from urban jobs is higher than rural and migration costs [41]. According to the human-capital theory, migrant populations choose to transfer from a rural to an urban area based on the variety of productive knowledge, labor and management skills, and physical qualities that it possesses, as these enable migrant populations to live in urban areas. Migration is usually divided into two stages in China: the first stage is to achieve non-agricultural employment, and the second stage is to settle in urban areas [42]. Only with the ability to survive in urban areas can migrant workers achieve the transfer from rural to urban, and only with the human capital to adapt to urban life are migrant workers more willing to settle in cities and towns. Human capital is an important factor influencing the migration of migrant population, and vocational-skills training, as one of the main manifestations of human capital, can improve human capital and enhance the vocational competitiveness of migrant workers, thus helping migrant workers integrate into urban life and promoting the process of migrant workers’ citizenship [43,44]. Thus, this paper proposes Hypothesis 1:

Hypothesis 1 (H1).

Participating in vocational-skills training has a positive impact on migrant workers’ willingness to settle in urban areas.

Employment quality is the comprehensive reflection of workers’ employment status; Kalleberg et al. argued that the evaluation indicators of employment quality include wage income, endowment insurance, and medical insurance [45]. Handel selected employment mobility, labor-management relations, working environment, social security, and salary level to measure the employment quality of workers [46], and some scholars also took employment environment and employment satisfaction as the measure of employment quality [47]. Although the measurement standards of employment quality are different, they basically include social security, wage income, and job stability. Social insurance is the important component of social security in China, and compared with migrant workers who did not participate in the training, migrant workers who participated in the training had a higher participation rate in social insurance [48]. The wage income of migrant workers depends on marginal productivity, and vocational-skills training can improve the marginal productivity of migrant workers, thus raising the wage level, and the wage rate of return is about 9% to 11% [49]. Whether the farmers have the ability to obtain stable jobs and how to ensure the stability of those jobs are the main factors affecting the stability of the farmers’ jobs. For the former, participating in vocational-skills training can improve the level of human capital of migrant workers, and this will enhance the occupational competitiveness and, thus, increase the probability of finding stable jobs. For the latter, participating in vocational-skills training can enhance the knowledge and awareness of the protection of the rights of migrant workers and prompt migrant workers to consider the stability of work when choosing a job.

A series of measures of employment quality, such as social security, wage income, and job stability, are closely related to migrant workers’ willingness to settle in urban areas [50]. Social security provides institutional support for migrant workers to live in urban areas, which effectively reduces and counteracts the social risks encountered by migrant workers in urban areas. Social security is also a symbol of citizenship. When migrant workers enjoy various forms of social welfare in urban areas, the sense of belonging in urban areas will be enhanced and the willingness to settle in urban areas will be improved [3]. Wage income is the core issue of employment quality, and migrant workers transfer to urban areas as the substitute for low income in agricultural production, which is a kind of migration in pursuit of higher income; migrant workers with higher income often have stronger occupational competitiveness, which is the basis for migrant workers to live and settle in urban areas [51]. Job stability is the basic need for migrant workers to work and can accumulate a solid social relationship network, enhance the familiarity with urban residents [52], promote mutual help with urban residents, enhance the sense of identity of urban life, and thus improve migrant workers’ willingness to settle in urban areas. Thus, the author proposes the following hypothesis:

Hypothesis 2 (H2).

Employment quality plays a mediating role in the influence of vocational-skills training on migrant workers’ willingness to settle in urban areas.

3. Data and Methodology

3.1. Data Source

The data used in this paper are taken from the fourth round of the China Labor Force Dynamics Survey (CLDS) conducted nationwide by Sun Yat-sen University in 2018. The China Labor Force Dynamics Survey is a comprehensive database, with the Chinese labor force as the survey object. It conducts a follow-up survey on the urban and rural household labor force in China every two years and establishes three levels of follow-up survey data: village, family, and individual. The China Labor Force Dynamics Survey adopts a multi-stage, multilevel scientific sampling design proportional to the size of the labor force. The content focuses on the information related to education, work, migration, health, social participation, economic activities, and grassroots organizations. The survey covers 29 provinces and cities in China (except for Hong Kong, Macao, Taiwan, Tibet, and Hainan), and the survey respondents are laborers aged 15–64. This paper focuses on the impact of vocational-skills training on migrant workers’ willingness to settle in urban areas, and the 2018 China Labor Force Dynamics Survey detailed survey of the rural household labor force provides an important basis for this study. Based on the needs of the research, we used the family code of rural individual data and the village code of family data as key variables to match the data in a many-to-one manner; then we selected the sample of employees by occupation type, and in order to reduce the influence of extreme values, all continuous variables were tail-tailed at 1% level. After excluding irrelevant and missing samples, 1451 valid samples were obtained.

3.2. Variable Definition and Measurement

3.2.1. Dependent Variable

The dependent variable is the migrant workers’ willingness to settle in urban areas in this paper. Referring to previous studies [17,53], the indicator of migrant workers’ willingness to settle in cities and towns comes from the reply to “Do you plan to settle in cities and towns in the next five years?” The options include “yes”, “no”, and “already settled in urban areas”. The samples of “already settled in urban areas” are deleted, and the samples with the answer “yes” are assigned 1; the samples with the answer “no” are assigned 0.

3.2.2. Independent Variables

The independent variable of this study is vocational-skills training. Since this paper focuses on the impact of the training provided by the government on the willingness of migrant workers to settle in cities, the government is the main body that organizes or supports the training, and the recipients of the training are migrant workers. The questionnaire asks, “Have you received vocational skills training provided by the government or subsidized by the government?” to represent the vocational-skills training, and the options include “participated” and “not participated”, which are assigned 1 and 0, respectively [54].

3.2.3. Intermediate Variables

This paper uses employment quality as the intermediary variable to explain the influence mechanism of vocational-skills training on migrant workers’ willingness to settle in urban areas. Employment quality is a comprehensive index that contains different dimensions, and this study uses the factor analysis method to complete the comprehensive evaluation of employment quality. Based on the availability of data, and with reference to the study of Tang et al. [55], this study selected eight variables to measure the employment quality of migrant workers: income satisfaction, job satisfaction, work environment, work safety, work respect, labor contract, nature of work unit, and housing provident fund.

The factor analysis method was used to perform principal component analysis on the employment quality index, and common factors with eigenvalue greater than 0.8 were extracted by orthogonal rotation based on variance maximization method. A total of three common factors were extracted by factor analysis. As shown in

Table 1. Employment quality index’s description and factor analysis.

Variables	Indicator Description	Mean Value	Standard Deviation	Communality
Income satisfaction	1 = very dissatisfied; 2 = dissatisfied; 3 = in general; 4 = satisfied; 5 = very satisfied	3.083	0.950	0.554
Job satisfaction	1 = very dissatisfied; 2 = dissatisfied; 3 = in general; 4 = satisfied; 5 = very satisfied	3.579	0.729	0.697
Work environment	1 = very dissatisfied; 2 = dissatisfied; 3 = in general; 4 = satisfied; 5 = very satisfied	3.469	0.852	0.678
Work safety	1 = very dissatisfied; 2 = dissatisfied; 3 = in general; 4 = satisfied; 5 = very satisfied	3.562	0.850	0.615
Work respect	1 = very dissatisfied; 2 = dissatisfied; 3 = In general; 4 = satisfied; 5 = very satisfied	3.606	0.747	0.623
Labor contract	Whether to sign a labor contract: 1 = yes; 0 = no	0.356	0.479	0.677
Nature of work unit	1 = state-owned/public institution; 0 = other	0.165	0.371	0.792
Housing provident fund	Whether to pay housing provident fund: 1 = yes; 0 = no	0.070	0.256	0.568
Eigenvalues	>0.8
Cumulative variance contribution rate	65.058%

Note: The data are taken from 2018CLDS, with 1451 observations.

, the KMO value is 0.766, and the p-value of Bartlett’s spherical test is 0.000; the cumulative variance contribution rate reached 65.058%, indicating that the variables were suitable for factor analysis. According to the principal-component-matrix results, job satisfaction, work environment, work safety, and work respect have the highest load on factor 1, so factor 1 was named “job stability”. Labor contract and housing provident fund had the highest load on factor 2, so factor 2 was named “social security”. Income satisfaction and nature of work unit had the highest load on factor 3, so factor 3 was named “wage income”. In order to express the employment quality with a variable, this paper multiplies the values of the three common factors by the proportion of variance contribution rate to the cumulative variance contribution rate and adds up to obtain the total score of the employment quality of migrant workers, the total score of the employment quality = job stability factor value × 0.551 + social security factor value × 0.259 + wage income factor value × 0.190. In order to intuitively analyze the relationship between dependent variables, independent variables, and intermediary variables in the regression model, the employment-quality factor value is converted into an index between 1 and 100 [56]. It is indicated that the higher the employment quality index is, the better the quality of employment will be.

3.2.4. Control Variables

On the basis of previous studies, this paper divides the variables that affect the settlement intention into individual characteristic variables, family characteristic variables, and socioeconomic characteristic variables. Previous studies have found that the human-capital factors of the heads of floating population households have an impact on labor migration [57], among which female-headed households and the young and middle-aged labor force are conducive to migration. In addition, the religious beliefs and work experience of migrant workers are the factors affecting the labor force’s willingness to settle in cities [58]. Therefore, the individual characteristic variables of this paper include gender, age, religious belief, and work experience. Previous studies have shown that family income, family size, and family arable-land area are important factors affecting migrant workers’ willingness to settle in cities [57,59]. Therefore, the family characteristics include the number of children, the number of family members, the proportion of non-farm income, and whether to contract land in rural areas. The social-insurance system serves as a risk-dispersion system, which can effectively reduce the uncertainty risk of the future development of migrant workers, enhance their confidence in future income, help to improve the social welfare level and quality of life of migrant workers, and further improve their willingness to settle in cities [1]. Thus, two variables were selected for the socioeconomic characteristics: endowment insurance and medical insurance. The definitions of specific variables and descriptive statistic results are shown in

Table 2. Descriptive statistics of variables.

Variable Name	Assignment Definition	Mean Value	Standard Deviation	Minimum Value	Maximum Value
Urban-settlement intention	Willing to settle in urban areas: 1 = yes; 0 = no	0.164	0.370	0	1
Vocational-skills Training	Whether attended any vocational training: 1 = yes; 0 = no	0.072	0.259	0	1
Employment quality	Factor values	51.138	13.555	0	100
Gender	1 = male; 0 = female	0.565	0.495	0	1
Age	Respondent’s actual age (years)	41.274	12.324	16	65
Religious beliefs	1 = yes; 0 = no	0.125	0.331	0	1
Work experience	1 = yes; 0 = no	0.325	0.469	0	1
Number of children	Actual number of children of the respondents (persons)	1.009	1.022	0	7
Number of family members	Total actual number of persons in the household (persons)	4.889	1.972	0	15
Proportion of non-farm income	Household wage income/total household income in 2017	0.697	0.394	0	1
Land	Whether contracted land: 1 = yes; 0 = no	0.082	0.276	0	1
Endowment insurance	Whether to purchase urban employee pension insurance: 1 = yes; 0 = no	0.116	0.321	0	1
Medical insurance	Whether to buy urban employee medical insurance: 1 = yes; 0 = no	0.133	0.340	0	1

Note: The data are taken from 2018CLDS, with 1451 observations.

.

Table 2 shows the essential characteristics of all the samples; 16.4% of the migrant workers are willing to settle in urban areas. In the sample, the mean value of migrant workers’ participation in vocational-skills training is 0.072, which shows that the migrant workers’ enthusiasm to participate in government-supported vocational-skills training is low. In addition, males account for 56.5%, and the average age of migrant workers is 41.27. A total of 12.5% of migrant workers have religious beliefs, and 32.5% of migrant workers have work experience. The average size of children in the sample is 1.009; the mean of the number of family members in the sample is 4.889; the proportion of non-farm income in the sample accounts for 70%; and 8.2% of migrant workers have contracted land in rural areas. At the socioeconomic level, 11.6% of the sample has urban workers’ pension insurance and 13.3% have urban workers’ medical insurance.

3.3. Research Models

First of all, since the dependent variable of this paper, the willingness of migrant workers to settle in cities, is a binary variable, it cannot be directly regressed in the OLS model; otherwise, it may lead to heteroscedasticity, and the binary selection model is more suitable for estimating nonlinear effects. Therefore, this paper selects the binary logit model for estimation, and the model expression is shown in Equation (1).

$$migratio{n}_i={\alpha }_0+{\alpha }_{1}trai{n}_i+{\alpha }_{2}controls+{\partial }_{1i}$$

(1)

According to the previous analysis, vocational-skills training may affect migrant workers’ willingness to settle in urban areas by improving the quality of employment. Therefore, in order to verify the mediating effect of the mediating variable (the employment quality of migrant workers), on the basis of estimating the total effect of vocational-skills training on migrant workers’ willingness to settle in cities by Equation (1), this paper uses the stepwise method of Baron and Kenny to construct a mediating-effect model [60]:

$$emplo{y}_i={\beta }_0+{\beta }_{1}trai{n}_i+{\beta }_{2}controls+{\partial }_{2i}$$

(2)

$$migratio{n}_i={\lambda }_0+{\lambda }_{1}trai{n}_i+{\lambda }_{2}emplo{y}_i+{\partial }_{3i}$$

(3)

where $migratio{n}_i$, $emplo{y}_i$ and $trai{n}_i$, respectively, represent the urban-settlement intention, employment quality, and vocational-skills training of the migrant workers; $controls$ represents all control variables; ${\partial }_{1i}$, ${\partial }_{2i}$, and ${\partial }_{3i}$ are random disturbance terms; ${\alpha }_0$, ${\beta }_0$, and ${\lambda }_0$ are model constants; ${\alpha }_1$ is the total effect of vocational-skills training on migrant workers’ intention to settle in urban areas; ${\beta }_1$ is the effect of vocational-skills training on employment quality as an intermediate variable; ${\lambda }_1$ is the direct effect of vocational-skills training on employment quality as the intermediate variable after controlling for the effect of employment quality; and ${\lambda }_2$ is the direct effect of vocational-skills training on migrant workers’ willingness to settle in urban areas after controlling for the effect of vocational-skills training. In the model, the indirect effect is the intermediate effect, which is equal to the product of the coefficients of ${\beta }_1{\lambda }_2$. At the same time, the ratio of the mediation effect to the total effect is used to reflect the relative size of the mediation effect, namely ${\beta }_1{\lambda }_2/{\alpha }_1$.

4. Results

4.1. Impact of Vocational-Skills Training on Migrant Workers’ Willingness to Settle in Urban Areas

Based on Equation (1), this paper firstly discusses the overall effect of vocational-skills training on migrant workers’ willingness to settle in urban areas. The regression results are shown in

Table 3. Benchmark regression results of the effect of vocational-skills training on migrant workers’ intention to settle in urban areas.

Variables	Urban-Settlement Intention	Urban-Settlement Intention	Urban-Settlement Intention	Urban-Settlement Intention
	Logit (1)	Logit (2)	Probit (3)	Probit (4)
Vocational-skills training	0.449 * (0.244)	0.507 ** (0.256)	0.255 * (0.142)	0.295 ** (0.148)
Gender		−0.097 (0.152)		−0.041 (0.084)
Age		−0.061 *** (0.007)		−0.034 *** (0.004)
Religious beliefs		−0.002 (0.224)		−0.004 (0.125)
Work experience		0.458 *** (0.155)		0.246 *** (0.087)
Number of children		−0.005 (0.083)		−0.010 (0.051)
Number of family members		−0.015 (0.039)		−0.008 (0.022)
Proportion of non-farm income		−0.111 (0.186)		−0.072 (0.104)
Land		0.402 * (0.242)		0.226 (0.139)
Endowment insurance		−0.005 (0.315)		−0.024 (0.172)
Medical insurance		−0.136 (0.305)		−0.082 (0.164)
Constant term	−1.666 *** (0.075)	0.672 * (0.345)	−0.999 *** (0.041)	0.320 (0.197)
R2	0.002	0.098	0.002	0.098
Observations	1451	1451	1451	1451

Note: ***, **, and * denote 1%, 5%, and 10% significance levels, respectively, and robust standard errors are in parentheses.

. Columns (1) and (3) control only for the core explanatory variable. The results of Columns (1) and (3) show that participating in vocational-skills training has a significant positive impact on migrant workers’ willingness to settle in urban areas at the level of 10%, indicating that the model fits well with the data. Columns (2) and (4) are added with control variables. The regression results of both models passed the significance test after adding control variables and positively affect the vocational-skills training on migrant workers’ willingness to settle in urban areas at the level of 5%, indicating that the model results are relatively robust. The regression model shows that there is a significant positive relationship between migrant workers’ participation in vocational-skills training and their willingness to settle in cities, and hypothesis H1 is confirmed. On the one hand, it may be because the participation in vocational-skills training has improved the human capital level of migrant workers, and they are more likely to obtain stable jobs and higher incomes in cities. On the other hand, when migrant workers intend to settle in cities, participating in vocational-skills training can also enhance their comprehensive ability and help them integrate into urban life as soon as possible.

As for other control variables, age and work experience significantly affect migrant workers’ willingness to settle in urban areas. Specifically, the influence of age on the migrant workers’ willingness to settle in urban areas is significant at the 1% level, but the regression coefficient of the age is negative, which may be due to the close emotional connection between migrant workers and rural areas, and elderly migrant workers are used to living in rural areas and prefer to live in peaceful rural areas as they grow older. The influence of work experience on migrant workers’ willingness to settle in urban areas is positive and significant at the 1% level, indicating that the richer the work experience, the more willing the rural labor force is to settle in urban areas. The main reason is that, for migrant workers, working in cities is also a process of adapting to urban life. The more migrant workers work outside the city, the stronger the ability to adapt to urban life will be, which is conducive to promoting the urban settlement of migrant workers.

4.2. Endogenous Issues

According to the previous findings, participating in vocational-skills training will increase the willingness of migrant workers to settle in urban areas. At the same time, migrant workers who are willing to settle in cities and towns hope to improve the human capital, and they are more willing to participate in vocational-skills training. Therefore, the model estimation may have an endogenous problem due to the reverse causal relationship between migrant workers’ urban-settlement intention and vocational-skills training. This paper adopts the instrumental variable method (IV-probit) to correct the model estimation result and solve the problem of estimation-result bias, so as to obtain a consistent and unbiased estimation. Based on the selection condition that the instrumental variable should be highly correlated with the endogenous explanatory variable, but not related to disturbance terms, this paper uses the average vocational-skills training of other migrant workers in the same village as the instrumental variable of the model. The behavior of migrant workers is often influenced by other migrant workers, and this is the so-called “peer effect” [61]. There is a strong relationship between the vocational-skills training of migrant workers and the migrant workers of the same village participating in vocational-skills training. However, the vocational-skills training of the surrounding people does not directly affect the migrant worker’s willingness to settle in urban areas, and migrant workers cannot control other migrant workers’ participation in vocational-skills training. Therefore, the selection of this instrumental variables meets the requirements of relationship and exogeneity theoretically.

Table 4. IV-probit estimation results of vocational skills-training and urban-settlement intentions of migrant workers.

Variable Name	Vocational-Skills Training		Urban-Settlement Intention
	Coefficient	Robust Standard Error	Coefficient	Robust Standard Error
Vocational-skills training			0.408 **	0.163
The average vocational-skills training of other migrant workers in the same village	0.999 ***	0.012
Control variables	under control	under control	under control	under control
Constant term	0.078	0.015	0.324	0.206
F-statistics	628.84
Observations	1451	1451	1451	1451

Note: *** and ** represent the significance level at 1% and 5%, respectively.

reports IV-probit results. The F-values estimated in the first stage are much greater than 10, thus indicating that the selected instrumental variable is not a weak instrumental variable. After correcting for potential endogenous bias by using the IV-probit model, the coefficient estimates of the independent variables are improved compared with the benchmark regression, indicating that the influence of migrant workers’ participation in vocational-skills training on their willingness to settle in cities and towns is underestimated due to the existence of endogenous bias. The regression results of the instrumental variables still support the conclusion that vocational-skills training has a positive effect on migrant workers’ intention to settle in urban areas, and the regression results of this paper are reliable.

4.3. Robustness Test

In order to further ensure the reliability of the research conclusions, this study conducted a sample robustness test on the main-effect model from the aspects of variables and samples.

4.3.1. Substitution of Dependent Variable

Referring to the previous practice [53], the willingness of migrant workers to purchase houses in urban areas is used to replace the willingness to settle in urban areas, and the binary logistic regression is performed with Equation (1) to test the robustness of the impact of participating in vocational-skills training on migrant workers’ willingness to settle in urban areas. The regression results of Columns (1) and (2) in

Table 5. Sample stability tests.

Variable Name	Willingness to Buy a House in Urban Areas (1)	Willingness to Buy a House in Urban Areas (2)	Urban-Settlement Intention (3)	Urban-Settlement Intention (4)
Vocational-skills training	0.669 *** (0.248)	0.774 *** (0.264)	0.405 * (0.245)	0.506 ** (0.256)
Control variables		under control		under control
Constant term	−1.753 *** (0.078)	0.346 (0.335)	−1.584 *** (0.075)	0.552 (0.360)
R2	0.005	0.108	0.002	0.084
Observations	1409	1409	1347	1347

Note: ***, **, and * represent the significance level at 1%, 5%, and 10%, respectively; robust standard error in parentheses; all regressions are logit regressions.

show that participating in vocational-skills training has a significant positive impact on migrant workers’ willingness to buy houses in urban areas, and the other control variables are basically consistent with the analysis of the main-effect model.

4.3.2. Partial Sample Exclusion

Elderly people are usually not suitable for excessive physical-labor activities due to physical reasons. Moreover, the service targets of vocational-skills training are mainly the working-age population from the perspective of the government. People over the age of 60 are classified as the elderly according to the age-classification criteria for the elderly in China. Thus, this study removed the sample of old members. The results are shown in Columns (3) and (4) of Table 5. The obtained result is still significant at the 5% significance level (see Table 5), which shows that the sample has good robustness.

4.4. Mechanism Test

The above regression results verified that participating in vocational-skills training increases migrant workers’ willingness to settle in urban areas, but what is the intermediate mechanism by which vocational-skills training improves migrant workers’ willingness to settle in urban areas? In order to explore the mechanism of vocational-skills training and migrant workers’ willingness to settle in urban areas, this study constructed the mediating-effect model by introducing the mediating variable (employment quality) combined with the theoretical analysis of the previous article. In this paper, the stepwise test of Wen et al. [62] is used to further verify the transmission mechanism of vocational-skills training on migrant workers’ willingness to settle in urban areas based on Equations (1)–(3). The first step is to test whether the total effect, $a_1$, of vocational-skills training on migrant workers’ willingness to settle in urban areas is significant; if it is significant, proceed to the next step. The second step is to test the significance of the influence coefficient, ${\beta }_1$, of vocational-skills training on employment quality and the influence coefficient, ${\lambda }_2$, of employment quality on migrant workers’ willingness to settle in urban areas. If both are significant, the intermediary effect is significant, and skip to the fourth step to continue the test; if one of the two is not significant at least, then proceed to the third step of the test. The third step is to test whether the indirect effect is significant by using the bootstrap method. If it is significant, then carry out the fourth step of the test. If it is not significant, stop the analysis, indicating that there is no intermediary effect. The fourth step is to compare the signs of ${\beta }_1{\lambda }_2$ and $a_1$ to determine whether there is a is mediating effect or a masking effect.

As shown in

Table 6. Mediating-effect test.

Variable Name	Urban-Settlement Intention	Employment Quality	Urban-Settlement Intention
	Logit (1)	Logit (2)	Logit (3)
Vocational-skills training	0.295 ** (0.148)	8.911 *** (1.318)	0.242 (0.151)
Employment quality			0.006 * (0.003)
Control variables	under control	under control	under control
Constant term	0.320 (0.198)	51.975 *** (1.787)	0.223 (0.138)
R2	0.098	0.094	0.101
Observations	1451	1451	1451

Note: ***, **, and * represent the significance level at 1%, 5%, and 10%, respectively; robust standard error in parentheses.

, the regression results of the first step show that there is a positive relationship between migrant workers’ participation in vocational-skills training and their willingness to settle in cities and towns, and it is significant and positive at the level of 5%, indicating that the mediating-effect analysis can be performed. The regression results of the second step show that participating in vocational-skills training improves the employment quality of migrant workers significantly, and employment quality has a significant positive role in promoting migrant workers’ willingness to settle in urban areas at the level of 10%. Hypothesis 2 is proved, indicating that employment quality has a significant mediating effect in the process of vocational-skills training on migrant workers’ willingness to settle in urban areas. The proportion of mediating effect to total effect is 0.006 × 8.911/0.295 ≈ 0.181. To some extent, the positive impact of vocational-skills training on migrant workers’ willingness to settle in urban areas is about 18.1% through the mediating effect of migrant workers’ employment quality. This is consistent, to some extent, with the results of Checa-Olivas et al. [63], who found that the higher the employment quality of migrant workers, the stronger their willingness to settle down. However, Checa-Olivas et al. discussed only the relationship between employment quality and migrant workers’ willingness to settle in cities, and they did not discuss the relationship between vocational-skills training and migrant workers’ willingness to settle in cities, thus leading to the difference with the results of this paper.

4.5. Heterogeneity Analysis

The unified study of the whole sample of migrant workers draws the conclusion with general characteristics. However, there are differences within migrant workers’ groups that lead to differences in the vocational-skills training and urban-settlement intention of different migrant workers [64]. Therefore, the impact mechanism of vocational-skills training on urban-settlement intention can be better discovered through the heterogeneity analysis. Thus, we discuss the heterogeneity of the impact of vocational-skills training on the urban-settlement intention of migrant workers from the perspective of migrant workers’ generation, gender, and marital status.

4.5.1. Distinguishing between Migrant Workers of Different Generations

Different generations of migrant workers have different values, cognition, and off-farm employment ability. This study examined the heterogeneity of the impact of vocational-skills training on migrant workers’ willingness to settle in urban areas in different generational groups and divided the new generation and the old generation of migrant workers according to the age of the respondents. Migrant workers who were born before 1980 are defined as the old generation, and those born after 1980 are defined as the new generation. The results of the study show that, whether or not the control variable is added, the old generation of migrant workers participating in vocational-skills training has a significant and positive impact to settle in urban areas, but it is not significant for the new generation. This is consistent with the results of Meng et al. [11] and Arif et al. [64]. The possible explanation is that, compared with the new generation of migrant workers, the old generation has a lower education level, is less competitive in the labor market, and pays more attention to and place more trust in vocational-skills training. Therefore, the old generation of migrant workers is more willing to participate in vocational-skills training.

4.5.2. Distinguishing between Migrant Workers of Different Genders

Taking into account the urban-settlement intentions between women and men, this study further investigated the heterogeneity of the impact of vocational-skills training on migrant workers’ willingness to settle in urban areas under different genders, and the results are shown in

Table 7. Heterogeneity analysis: generation, gender, and marital status.

Variable Name	New Generation	Old Generation	Female	Male	Unmarried	Married
	Logit (1)	Logit (2)	Probit (3)	Probit (4)	Probit (5)	Probit (6)
Urban-settlement intention	0.315 (0.317)	0.773 * (0.409)	0.525 ** (0.213)	0.092 (0.211)	0.121 (0.358)	0.309 * (0.160)
Control variables	under control	under control	under control	under control	under control	under control
Constant term	0.115 (0.633)	−1.180 (1.360)	−1.186 *** (0.207)	−0.306 (0.254)	−1.050 *** (0.296)	1.337 *** (0.155)
Gender	0.108	0.038	0.048	0.092	0.066	0.031
Age	608	843	631	820	259	1192

Note: ***, **, and * represent the significance level at 1%, 5%, and 10%, respectively; robust standard error in parentheses.

. Participating in vocational-skills training has a more significant effect on the urban-settlement intention of the female migrant workers compared to the male migrant workers, indicating that the influence of vocational-skills training on migrant workers’ willingness to settle in urban areas is mainly reflected in the female group. Women are more responsible for taking care of children and the elderly according to traditional in China, and there are few opportunities for rural women to work in urban areas. Rural female laborers have more opportunities to work in urban areas with the development of the times, and rural female laborers can improve the human capital and enhance the employment competitiveness by participating in vocational-skills training and make it easier to find jobs in urban areas. Working in cities helps to increase the economic income and realize personal value of female migrant workers, who prefer to stay in urban areas.

4.5.3. Distinguishing between Migrant Workers with Different Marital Statuses

Since the different marital status of migrant workers often leads to different willingness to settle in urban areas, this paper examines the heterogeneity of the effect of vocational-skills training on the urban-settlement intention of migrant workers with different marital status. The empirical results show that, whether or not the control variables are added, the vocational-skills training of married migrant workers have a significant effect on willingness to settle in urban areas compared to the unmarried migrant workers. The reason is that married migrant workers face greater economic pressure, and married migrant workers need to earn more money to support their families. Therefore, married migrant workers are more willing to improve the human capital level by participating in vocational training, so as to obtain a higher salary income.

5. Discussion

In the context of high-quality urban development, the migrant mode of working by relying on cheap and low-skilled labor is unsustainable. Effective vocational-skills training is an important measure to improve personal human capital, enable migrant workers to settle in cities, and promote new urbanization. There are many studies on migrant workers’ willingness to settle in cities and towns in the existing literature, but there are few empirical studies that specifically focus on the impact of vocational-skills training on their willingness to settle in cities. In this study, based on a large sample size survey, 1451 Chinese migrant workers were selected to study the impact of vocational-skills training on willingness to settle in cities. This study not only focused on the impact of vocational-skills training on willingness to settle in cities, but it also investigated the settlement intention of different groups of vocational-skills training and tested the intermediary impact of vocational-skills training on willingness to settle in cities. We constructed a binary logit model to ensure the rationality of model selection; the instrumental variable method was used to deal with the endogeneity problem of variable selection, and the intermediary model was used as the mechanism test, which is more comprehensive. As the largest developing country with the largest number of migrant workers in the world, the research results for China have strong practical significance.

This study found that vocational-skills training has a significant positive impact on the urban-settlement intentions of migrant workers. This is consistent with the results of Yang et al. [65], who found that the social-integration level of migrants who received vocational-skills training can be effectively improved. Our findings are also consistent with those of Wan et al. [49], who found that vocational training can effectively increase individual income, increase their probability of participating in community activities, and enhance their recognition of urban lifestyles. Additionally, our results are consistent with Nie et al. [50]. Based on the 1436 migrant workers survey data conducted in the 19 cities of the Pearl River delta and the Yangtze River delta, they found that training was significantly and positively associated with urban-settlement intentions, and every time the training number increased, the willingness of migrant workers to settle in cities and towns increased by 4.3%. However, they mainly studied the impact of the number of migrant workers participating in training on their willingness to settle in cities, which leads to the difference from the results of this paper. The results of our study also show that if endogeneity is not controlled, the effect of vocational-skills training on migrant workers’ willingness to settle in urban areas is underestimated.

The results of heterogeneity analysis show that migrant workers’ urban-settlement decisions are made in a specific group, which shows that female, older, and married migrant workers have higher willingness to settle in cities. It shows that migrant workers’ urban-settlement decision is an active choice that is based on individual characteristics and the consideration of maximizing benefits, thus verifying the effectiveness of the selective theory of population flow.

By exploring the impact of vocational-skills training on migrant workers’ willingness to settle in cities, it provides theoretical support and practical reference for migrant workers to improve their employment quality and successfully realize people-oriented urbanization. The effect of vocational training on the willingness of migrant workers to settle in cities makes up for the theoretical deficiencies of previous studies on rural floating population in urban areas to a large extent and broadens the research horizon of economics on the citizenization of migrant workers [54]. The study enriches the existing research perspective of migrant workers’ urban settlement and provides a theoretical basis for the government to select and formulate appropriate policies for migrant workers’ urban integration. At the same time, based on the human capital theory, this paper constructs the measurement index of the employment quality of migrant workers and discusses the impact of various factors on migrant workers’ willingness to settle in urban areas, as this is helpful to understand the mechanism of migrant workers’ urban settlement.

This study has several deficiencies that can be addressed in future studies. First, the impact of vocational-skills training on migrant workers’ willingness to settle in urban areas is subtle and complex in reality. Although this paper attempts to find and verify the mechanism of employment quality on migrant workers’ willingness to settle in urban areas, there still exist many unknown transmission mechanisms which are worthy of further study in the future. Second, we selected the cross-sectional data of 2018 CLDS as the research sample for this paper. However, the influence of vocational-skills training on migrant workers’ willingness to settle in urban areas is a dynamic process. Thus, future research could use panel data to further expend and verify the relationship in greater detail. Finally, studying the relationship between vocational-skills training and migrant workers’ willingness to settle in cities also involves many missing variables, such as town size, risk appetite, and living environment [52,66]; if these variables can be controlled, the processing effect will be cleaner, but it is beyond the scope of this article to discuss them due to the availability of data and samples.

6. Conclusions

Based on the data from the China Labor Force Dynamics Survey in 2018, this paper studies the relationship between migrant workers’ participation in vocational-skills training and their willingness to settle in cities and towns. The results of the binary logistic model show that migrant workers’ participation in vocational training positively affects their willingness to settle down in cities. The heterogeneity analysis found that the older-generation, female, and married migrant workers are more significantly affected by vocational-skills training than the new-generation, male, and unmarried migrant workers. Employment quality plays the intermediary role in the relationship between vocational-skills training and urban-settlement intentions of migrant workers, and about 18.1% of the improvement of migrant workers’ willingness to settle in urban areas by participating in vocational skills-training is achieved through the intermediary role of employment quality.

According to the research conclusions of this paper, the following insights can be stated. First, the government should strengthen its guiding role in migrant workers’ training; do a good job in the top-level design of vocational-skills training for migrant workers; and assume more responsibilities in terms of increasing subsidies for vocational-skills training, stimulating the enthusiasm of training subjects, innovating training content, and improving training market supervision. Second, the government should increase investment in migrant workers’ human capital and improve the employment quality of migrant workers. Improving the employment quality of migrant workers is an effective way to promote the urban-settlement intention, and in addition to increasing training content, it is also necessary to optimize training content, improve training quality, improve the quality of migrant workers, enhance the competitiveness in the labor market, and improve the employment quality of migrant workers.