Human Capital, Life Satisfaction, and the Floating Population’s Urban Settlement Intention in Cities—A Case Study of Six Cities in the Pearl River Delta

Abstract

The urban settlement intention of the floating population and its influencing factors have received widespread attention, but there is less literature on the relationship between human capital, life satisfaction, and the urban settlement intention of the floating population. Employing 2146 questionnaire data from the Pearl River Delta’s floating population, this study establishes measurement indicators for human capital and life satisfaction, constructs a structural equation model, and examines the influence of human capital on life satisfaction, as well as their collective impact on the floating population’s urban settlement intentions. The findings reveal that human capital influences multiple facets of the floating population’s life satisfaction, which subsequently shapes its desire to settle. Considering individual life satisfaction, the economic well-being and social security contentment of the floating population exert a notable impact on its urban settlement intentions, whereas satisfaction with emotional life and public service facilities appears to have a less pronounced effect. Further investigation into how varying levels of human capital modulate the influence of life satisfaction on urban settlement intentions, alongside the transition from fundamental economic security to psychological needs (about life satisfaction), holds substantial referential value for research on population migration and the development of citizenship policies in China.

1. Introduction

The floating population (migrants) plays a pivotal role in urbanization, with its selection of a definitive and enduring settlement location post-migration constituting a significant scholarly focus. Within China, the burgeoning social capital and progressive relaxation of the household registration (hukou, “户口”) system have met the fundamental needs of the floating population. Nevertheless, a segment of the floating population exhibits a diminished inclination to settle, attributed to the incongruity between its personal human capital and its destination, coupled with reduced life satisfaction, culminating in the prevalent trend of returning to a hometown or other places [1]. Amidst the intensification of China’s urbanization, the foundational economic and policy conditions for population mobility are maturing; however, elevated needs like life satisfaction remain comparatively overlooked. As China’s economy advances and living standards ascend, the influence of the floating population’s life satisfaction on its residential choices and urbanization warrants additional scrutiny. Therefore, exploring the role of human capital and life satisfaction and their impact on the retention of migrants in cities is of great significance for the development of China’s new urbanization, which is characterized by people’s livelihood, sustainable development, and the pursuit of quality.

The determinants of migrant movement can be categorized into macro-, meso-, and micro-level factors based on their scale of influence. Macro-level theories, such as neoclassical macro-migration theory, emphasize the role of regional disparities and institutional contexts in influencing population migration. Population migration is deemed an inescapable consequence of socio-economic progress, where inter-regional socio-economic discrepancies foster market supply and demand imbalances, precipitating a strategic reallocation of resources and labor to more advantageous regions. The “push–pull theory” posits that the combined forces of rural “push” factors and urban “pull” factors initiate migrant relocation towards cities, often culminating in permanent urban settlement.

Meso-level theories mainly include social capital theory, social network theory, etc., focusing on the family or community level, that is, the influence of the composite of interpersonal relationships in which the floating population interacts with families or friends regarding migration intention. Social network theory underscores the significance of social capital throughout the migration journey [2]. Meso-level theoretical frameworks propose that the rationale behind migrants’ settlement, formerly grounded solely in regional economic disparities, is now insufficient; notions of urban belonging and collective identity offer substantial explanatory power for migrants’ settlement behaviors [3]. Robust bonds, a profound sense of belonging, and a shared sense of identity between the floating population and the indigenous populace enhance migrant welfare, elevate their life quality evaluation, and ultimately bolster their propensity to establish roots in the destination city [4,5]. Concurrently, a steadfast nexus persists between the floating population and its kin back home throughout the migratory phase, with the concomitant relocation of family members impinging on migrants’ earnings, spending, social protection, and educational resource needs at the destination, thereby shaping migrants’ settlement choices [6].

Micro-level theories, such as neoclassical micro-migration theory, primarily focus on individual-level factors. According to Nahapiet and Ghoshal, social capital is embedded in the network of relationships possessed by an individual or social organization, and the sum of actual and potential resources derived from, and accessible through, that network, and consists of three dimensions: structural, relational, and cognitive. The intersection of social relations constitutes a social network that has a significant impact on the consciousness and behavior of the social actors within it [7]. Firstly, at the micro-level, individual economic and skill-factor-related studies believe that the floating population decides to settle down in a destination city or return to a hometown or other places in order to maximize its economic value, and economic factors are considered to be the fundamental motivation for the occurrence of the population’s settlement behavior [8]. Among them, factors such as wages and the type of occupation influence migrants’ willingness to migrate. Income and expenditure stand out as critical indicators of migrants’ living conditions at the migration site, with the lack of substantial income or an unfavorable income-to-expenditure ratio deterring permanent urban settlement. Economic incentive theory emphasizes that the floating population makes settlement decisions based on the maximum value of its human capital. For example, the interaction between human capital and the labor market has a significant effect on migration willingness [9]. As far as market factors are concerned, job stability, employment opportunities, and the type of occupation also influence the population’s willingness to migrate. Lin Sainan proved that a stable job with a labor contract can reduce the cost of choosing another job due to unemployment in the middle of the job, and increasing the job stability of the floating population is conducive to increasing the willingness of the floating population to settle down [10]. In addition, Liu Yuqi believed there are differences in the migration intentions of new migrants seeking different types of work, among which the investment migrant has the highest urban settlement intention, followed by the intellectual type, and the labor type is the weakest [11]. In summation, the preponderance of research regarding migrants’ urban settlement intentions focuses on market and economic factors since migrants’ migratory actions are frequently motivated not just by the aspiration to shift from rural to urban status but equally by the quest for economic benefits.

Overall, the primary focus has been on the influence of external factors on migrants’ intentions to settle. Generally speaking, when the economic income is sufficient to support the floating population to settle in the place of relocation, the floating population will start to consider spiritual pursuits. However, there are still few studies on the impacts of differences in individual life satisfaction on the floating population. Currently, some scholars have begun to pay attention to the impact of life satisfaction (or happiness differences) caused by urban disparities. Another investigation into the happiness variance between urban and rural inhabitants across multiple dimensions revealed that their subjective happiness indicators diverge in areas [12]. A separate study underscored the significance of social networks, informal finance (the main forms of informal finance include private lending, small loan companies, and commercial credit. In the historical process of China’s rapid economic development in the past, informal finance has played an important role in explaining the miracle of China’s economic growth), and leisure time in shaping residents’ happiness [13,14].

In recent years, with the increasing attention of residents to their own health, the elements of mental health and life satisfaction have begun to be used in residential migration studies. Such studies predominantly examine the influence of mental well-being and overall life contentment on patterns of family migration. Research consistently indicates that the enhancement of residents’ physical and psychological health contributes to greater residential stability [15]. In terms of mental health, most of the available empirical studies focus on the effects of long-term psychological stress and community reputation. The findings reveal that economically disadvantaged inhabitants are prone to sustained psychological stress, subsequently leading to a heightened propensity to relocate compared to others [16], which was evident during the COVID-19 period, and that home financing costs and the built environment contribute to a higher level of depression among residents [17]. Consequently, the aspiration to mitigate psychological issues like anxiety and depression often drives individuals to move to more prosperous communities [18]. In addition, community reputation has been found to be significantly associated with residential relocation. The better the perceived reputation of the community, the lower the intention to move [19]. Notably, residential relocation also has an inverse effect on residents’ mental health. It is well documented that elevated levels of residential migration within a community can precipitate a range of psychological issues, including anxiety, loneliness, a sense of insecurity, and challenges of self-identity among its inhabitants [20]. Individuals exhibit lower relational trust when they live in areas with higher residential mobility [21].

At the same time, with the social and economic development and the popularization of compulsory education, the human capital of the Chinese population has been greatly improved, which directly drives the improvement in the employment and wage levels of the floating population, and the migration of agriculture from rural (household registration is agricultural, or the occupation engaged in is agriculture) to urban areas provides the necessary labor force and human capital for the urban non-agricultural industry, thus promoting the effective allocation of resources [22]. However, the long-term household registration segmentation system has resulted in part of the floating population being engaged in heavy physical labor and low-paying and undignified work. Low-paying jobs are characterized by the high intensity and low education level of the practitioners, which makes them unable to integrate into the city [23], and with low life satisfaction, the probability of this contingent of low-skilled migrant workers returning to their hometowns is high [24]. Higher levels of socio-economic status, such as being male and elderly, are associated with vertical or upward residential mobility. Conversely, low socio-economic status, females, and youth are associated with downward or horizontal residential mobility [25]. Even if all other levels improve successively, the floating population will still face serious challenges in integrating into the city. The improvement in the material level does not represent an improvement in migrants’ psychological sense of acquisition. Especially, when the material level is satisfied, individual psychological needs will slowly emerge; at this time, the floating population will pursue the happiness and life satisfaction of city life, because life satisfaction represents the fulfilment of both material and psychological needs. Therefore, in order to explore the issue of migrants’ willingness to settle down, we need to focus not only on the wage level of the floating population, but also on the life satisfaction of the floating population in the process of migration and settlement. Moreover, as China’s economic growth has entered a new normal, Chinese governments at all levels have begun to emphasize the quality and efficiency of urban development, placing more emphasis on improving people’s livelihoods and increasing residents’ life satisfaction. Whether the life satisfaction of the floating population affects their urban settlement intention also deserves further thinking and research. Based on this, we put forward two basic Hypothesis 1 and Hypothesis 2. The following text will present the basic assumptions and literature, and further refine the hypotheses:

Hypothesis 1.

The life satisfaction of the floating population in the destination cities will affect its urban settlement intention. The higher the life satisfaction of the floating population in a place, the stronger its chances of staying in the city.

Hypothesis 2.

The improvement in the economic income and work ability of the floating population will increase its satisfaction with life in a region, thus making it more inclined to stay in the destination cities.

2. Concepts and Measurement Indicators of Model Structure of Human Capital, Life Satisfaction, and Urban Settlement Intention

2.1. The Concept and Measurement Indicators of Life Satisfaction

Life satisfaction is a subjective feeling used to reflect people’s quality of life. People often mix life satisfaction and life happiness. This paper delineates life satisfaction as a personal appraisal of life quality, contingent upon individual standards, and is a perception of happiness [26,27]. Life satisfaction is a crucial construct for gauging subjective happiness, with the current empirical research predominantly employing questionnaire surveys to assess it [28]. Previous studies on the measurement indicators and inter-relationships of life satisfaction mainly focus on both subjective and objective factors. From the perspective of objective factors, factors such as economic status, life events, health status, and marital status affect people’s life satisfaction. Some studies have shown that life satisfaction is higher in high-income groups than in low-income groups [29], but others have shown that income has no effect on life satisfaction [30]. Considering these disparate findings, it is essential to recognize the distinctions between the demographic groups of the respective studies. The marginal utility of life satisfaction decreases with the increase in the number of needs, so when the basic needs of life are satisfied, the marginal utility of income for life satisfaction becomes smaller. According to Maslow’s theory of needs, when the level of need is higher, the economic impact will be smaller. Subjective happiness also affects the perception of life events [31]. Some far-reaching life events will affect people’s well-being. At the personal level, studies have shown that bad childhood experiences will lead to poor life satisfaction and more frequent depressive symptoms in adulthood [32]. At the group level, a number of studies have shown that during COVID-19, life satisfaction generally declined due to increased social pressure [33,34,35]. In terms of the relationship between health status and people’s subjective feelings, the health status of the elderly has a significant impact on their life satisfaction [36]. Marriage is an important indicator used to measure well-being, and at that time the results of studies on the relationship between the two were very divergent, with some of the literature suggesting that married people have a higher quality of life than unmarried people; however, others have questioned this view, arguing that the correlation between the two is not strong [37]. The discrepancy between these two results may be attributed to the variability in the views of marriage among different groups [38].

Subjective well-being can also affect the life satisfaction of residents, including three main aspects: social support, individual characteristics, and cognitive mechanisms. The mechanism by which social support affects life satisfaction is mainly reflected in the fact that good social support can make residents happy, and ultimately affect their cognition, thereby improving their life satisfaction. Research has demonstrated that providing assistance to others can bolster one’s sense of autonomy, subsequently diminishing depressive episodes [39]. A person’s subjective happiness is highly dependent on individual characteristics [40], and the interplay between one’s personality and their environment impacts both life satisfaction and subjective well-being [41]. The cognitive process—entailing the analysis and construction of perceptions—plays a pivotal role in determining an individual’s happiness or discontent [42].

Synthesizing the aforementioned studies, we utilized questionnaire data to categorize migrants’ life satisfaction into variables such as satisfaction with family economy, work situation, housing situation, physical health, medical situation, children’s education, daily transport, leisure and recreation, marital life, social interaction, and financial services.

2.2. Indicators for Measuring the Human Capital of the Floating Population

Neoclassical economics emphasizes the economic incentives behind decisions to migrate and settle down [43]. The theory describes immigrants’ decisions to settle down as the process of maximizing the value of human capital and expanding economic prospects. Through education in schools or professional training, migrants may obtain stable jobs and increased income at migration destinations [44,45]. Therefore, migrants are more likely to settle down in their destination places if they obtain a high position in the labor market [43]. Based on the above research, we define human capital as the combination of individual intellectual knowledge accumulation and external training skills, the technical means and abilities possessed by workers themselves, and the overall income they ultimately obtain [43,44].

Through migration and settlement, the floating population can accrue enhanced benefits within improved environments and bolster its survival capabilities. This is a critical aspect of migrants’ human capital, reflecting the economic value and marketability of their capabilities. Therefore, there is a conduction effect between human capital and the formation of migration ability, and improving human capital makes it possible to enhance the work skills of the floating population, which in turn has a positive impact on its ability to stay in the city. Consequently, we consider that the core elements of the human capital of the floating population include its occupational ability and education level, which affect its economic income. Hence, we use three indicators to characterize the human capital of individuals: education, occupation, and income.

The occupations of the floating population are divided as follows: migrant workers, generally engaged in unskilled or low-skilled jobs, who make a living by manual labor, including agriculture, mining and construction workers, transportation and beauty, waiters, security, and other service industry personnel; self-employed and private business owners; professional personnel, that is, personnel with professional skills, including enterprise management personnel, company staff, translators, salespeople, lawyers, accountants, planners, etc. Generally speaking, self-employed individuals, private entrepreneurs, and technicians possess a relatively higher level of human capital compared to traditional migrant laborers.

The educational attainments of the floating population are divided into three groups: primary school and below; junior and senior high school; college and above. The income in 2016 of the floating population was divided into four groups: less than 30,000; from 30,000 to 60,000; from 60,000 to 100,000; more than 100,000 (see Appendix B,

Table A2. Detailed description and descriptive statistics of variables in the structural equation model.

Attribute	Classification	Frequency	Proportion(%)	Attribute	Classification	Frequency	Proportion(%)
Personal income	≤30	684	29.9	Daily transportation	0–2	61	2.7
	30–60	1105	48.4		3–4	126	5.5
	60–100	337	14.8		5–6	597	26.1
	>100	158	6.9		7–8	960	42.0
Personal education	Primary school and below	327	14.3		9–10	540	23.6
	Junior and senior high school	1424	62.3		Average	7.13
	College and above	533	23.3	Leisure and recreation	0–2	109	4.8
Household economy	0–2	133	5.8		3–4	171	7.5
	3–4	242	10.6		5–6	680	29.8
	5–6	915	40.1		7–8	910	39.8
	7–8	744	32.6		9–10	414	18.1
	9–10	250	10.9		Average	6.73
	Average	6.15		Married life	0–2	109	4.8
Work situation	0–2	90	3.9		3–4	171	7.5
	3–4	158	6.9		5–6	680	29.8
	5–6	828	36.3		7–8	910	39.8
	7–8	908	39.8		9–10	414	18.1
	9–10	300	13.1		Average	7.83
	Average	6.55		Social interaction	0–2	53	2.3
Housing situation	0–2	167	7.3		3–4	143	6.3
	3–4	351	15.4		5–6	634	27.8
	5–6	818	35.8		7–8	961	42.1
	7–8	672	29.4		9–10	493	21.6
	9–10	276	12.1		Average	7.06
	Average	5.97		Financial services	0–2	90	3.9
Physical health	0–2	33	1.4		3–4	130	5.7
	3–4	60	2.6		5–6	631	27.6
	5–6	388	17.0		7–8	965	42.3
	7–8	952	41.7		9–10	468	20.5
	9–10	851	37.3		Average	6.93
	Average	7.83		Overall situation	0–2	11	0.5
Medical condition	0–2	109	4.8		3–4	67	2.9
	3–4	169	7.4		5–6	589	25.8
	5–6	709	31.0		7–8	1300	56.9
	7–8	933	40.8		9–10	317	13.9
	9–10	364	15.9		Average	7.17
	Average	6.65
Children’s education	0–2	90	3.9
	3–4	123	5.4
	5–6	402	17.6
	7–8	1423	62.3
	9–10	246	10.8
	Average	6.53

).

2.3. Model Structure of Human Capital, Life Satisfaction, and Urban Settlement Intention for the Floating Population

Economic factors, serving as the primary impetus for population migration, stand as the focal point in immigration research. A number of studies have shown that the improvement in family economy, job income, and housing conditions will significantly increase the urban settlement intention of the floating population [46,47,48,49,50,51,52]. This paper proposes Hypothesis 1-1: The economic life satisfaction of the floating population will have a significant positive impact on their urban settlement intention, which is closely related to family economy, job status, and housing status. Higher human capital can boost the floating population’s ability to economically integrate into the place of migration, gain a higher status in the labor market, obtain better or more stable occupations and higher incomes, and be more willing to settle down in the destination city [53,54,55,56]. Therefore, Hypothesis 2-1 is proposed: Higher levels of human capital will enhance the floating population’s satisfaction with its economic life and thus its urban settlement intention.

The medical security and educational provisions available in the migration destination frequently exert a direct influence on the floating population’s life satisfaction, which is one of the reasons why China’s large cities are more likely to attract the floating population to settle permanently than small and medium-sized cities [7]. The household registration system prevents some of the floating population from obtaining comprehensive social security, and there are social phenomena such as being forced to return to their hometowns to seek medical services [57] and restrictions on their children’s enrollment in schools of their choosing [58,59], which affect the willingness of the floating population to settle permanently. Based on this, this paper proposes Hypothesis 1-2: Good physical health, medical condition, and children’s education have a positive driving effect on the social security satisfaction of the floating population, increasing its urban settlement intention. Additionally, a floating population with higher levels of human capital usually has more favorable economic conditions and enjoys more policy benefits, so as to obtain better social security. This results in Hypothesis 2-2: A floating population with a higher level of human capital has higher social security satisfaction.

With the increase in per capita disposable income, the emotional needs of the floating population after migration become more and more prominent. Existing studies indicate that marital relationships [46], family migration [50,60,61], social relationships [62], and cultural integration [56,63] have an impact on urban settlement intention, although leisure and recreation are less frequently addressed in the related research. At the same time, related studies show that a considerable portion of the floating population chooses to settle in cities for a long period of time in order to obtain high income despite the conditions of family separation [61,64] and social discrimination [65]. To sum up, we propose Hypothesis 1-3: Marital life and social interaction have a more significant effect on emotional life satisfaction compared with leisure and entertainment, and the impact of emotional life satisfaction on urban settlement intention is less pronounced than that of economic life satisfaction, but it will still have a positive effect on urban settlement intention. Moreover, human capital accumulation can effectively enhance the ability to integrate into the place of migration. A survey based on the subjective well-being of immigrants in Guangzhou reflects that immigrants with a higher socio-economic status usually have good mental and emotional health [66], and some studies have also shown that when the floating population has accumulated economic resources to become self-employed, it is more likely to have a sense of belonging in the city [54], which suggests that a floating population with better human capital tends to have the right to autonomy in leisure and recreation, marital life, and social interaction, which collectively translates into a higher degree of emotional life satisfaction. This is put forward as Hypothesis 2-3: Human capital has a positive driving effect on emotional life satisfaction.

Public services are daily services that directly affect residents’ lives. The results of several studies show that the improvement in various services in the city, such as housing support [47], transport facilities [7,67], financial, and entertainment [7,68] can enhance the urban settlement intention of the floating population. Based on the existing research, this paper proposes Hypothesis 1-4: The increase in satisfaction towards public services can increase the floating population’s urban settlement intention. In the construction of public services, the government’s policy introduction and financial investment play a leading role, which concerns public welfare and focuses more on the vulnerable groups in society [68]. Therefore, the individual level of human capital has less of an effect on the level of public services enjoyed, but more on the other three dimensions of life satisfaction, which in turn affect the urban settlement intention. In summary, Hypothesis 2-4 of this paper is proposed: Among the four dimensions of life satisfaction, human capital and public service satisfaction are weakly associated.

In structural equation modeling, there are two main variables: the latent variable and manifest variable. Latent variables are variables that cannot be directly measured in practical work, including abstract concepts and variables that cannot be accurately measured for various reasons. A latent variable often corresponds to multiple explicit variables, which can be seen as an abstraction and generalization of its corresponding explicit variables, while explicit variables can be seen as response indicators for specific latent variables. Based on the correlation between the four latent variables (economic factors, social security, emotional life, and public services) and the observed variables, we constructed the model structure of human capital, life satisfaction, and urban settlement intention for the floating population (Figure 1 and Figure 2). The life satisfaction of the floating population is a comprehensive perception of four dimensions, and there are interactions among the dimensions. Integrating the existing relevant studies cited above, this paper proposes Hypothesis 1-5: Economic life satisfaction is dominant, the increase in economic income can improve the satisfaction with social security, emotional life, and public services, and there is a strong correlation among the latter three; and Hypothesis 2-5: The three dimensions of human capital in this paper (education, occupation, and income) follow the influence path of “individual education → individual occupation → individual income → urban settlement intention”. The level of human capital is more a reflection of an individual’s economic situation.

3. Study Area, Data, and Model

3.1. Study Area and Survey Target

Since its economic reforms, the Pearl River Delta’s (PRD) open economic structure, alongside its export-driven and labor-intensive industrial traits, has established it as a pivotal area representing China’s substantial migrant population. The PRD region officially surpassed Tokyo in 2015 to become the world’s largest urban agglomeration in terms of area and population. Nowadays, as the Pearl River Delta urban agglomeration has become an important spatial carrier for China’s economic development and enhancement of national competitiveness, pertinent research into the settlement inclinations of the floating population within the Pearl River Delta urban cluster holds significant referential and instructional worth for policy deliberations on China’s novel urbanization issues.

In this paper, based on the primary data collected from the large-scale questionnaire survey, the number of research questionnaires for each city and each district and street within the city were roughly distributed using a stratified, multi-stage, and proportional-to-size methodology (Figure 3). Our research team carried out migrant surveys spanning May to October 2017 within six central cities of the PRD region. We interviewed members of the floating population who had lived and worked in the research site for at least three months.

Among the nine cities in the PRD, Guangzhou, Shenzhen, Zhuhai, Foshan, Dongguan, and Zhongshan have high levels of economic development, offer a large number of different types of jobs, and have a relatively large floating population. At the same time, these six cities represent three types of cities with different resident population sizes: (1) cities with more than ten million people: Guangzhou and Shenzhen; (2) five to ten million people: Foshan City and Dongguan City; (3) less than five million people: Zhuhai and Zhongshan. Therefore, the survey sites selected for this study were determined to be the six core PRD region cities of Guangzhou, Dongguan, Foshan, Zhongshan, Zhuhai, and Shenzhen.

A total of 2468 questionnaires were prepared for distribution. After excluding discrepancies and incomprehensible answers, our dataset comprised 2416 cases for analysis, with an effective rate of 97.8%. All the interviewees were migrants (members of the floating population) who did not have registered households in the urban area of the city (had households registered in other counties or cities, whether rural or urban) and who worked and lived in the research area for more than three months. The survey mainly combined quota sampling, snowball sampling introduced by acquaintances, random sampling, and stratified sampling methods to investigate the migrants in various regional government units, factories, parks, supermarkets, and streets. Investigators conducted face-to-face interviews.

Of the 2416 respondents, males accounted for 54.06% by gender. In terms of age distribution, the majority of respondents were between 21 and 50 years old, with an average age of 34.59 years. In terms of educational attainment, the respondents had an average of 10.64 years of schooling; respondents from Shenzhen and Guangzhou, who were employed, had higher educational levels, reaching 11.99 and 11.24 years, respectively. In terms of registered residence, 80.38% of the respondents held rural household registration. In terms of personal income, the average monthly income of respondents was 4802.72 yuan, with the level being significantly higher in Shenzhen than in the other five cities, at 6934 yuan. The basic demographic information of respondents from six cities in the Pearl River Delta is presented in

Table 1. The demographic characteristics of the respondents.

	Dongguan	Foshan	Guangzhou	Shenzhen	Zhongshan	Zhuhai	Total
Number of questionnaires	499	384	560	363	317	293	2416
Proportion of males (%)	58.52%	51.82%	56.43%	53.99%	49.21%	50.17%	54.06%
Avg. age	36.32	34.15	34.81	32.97	33.22	35.30	34.59
S.D. of age	11.84	9.62	11.01	10.15	10.58	10.16	10.75
Avg. years of schooling	9.56	9.99	11.35	12.00	10.36	10.60	10.64
S.D. of years of schooling	3.61	3.73	4.00	3.44	3.56	3.54	3.78
Proportion of rural household registrations (%)	87.17%	83.85%	76.25%	76.31%	79.50%	78.16%	80.38%
Avg. monthly individual income (unit: Yuan)	4086.38	4288.14	4743.45	6934	4987.12	3922.46	4802.72
S.D. of monthly individual income (unit: Yuan)	3144.05	2912.86	4264.92	11,691.35	6879.08	2682.78	6040.65

below.

3.2. Urban Life Satisfaction of Floating Population in Pearl River Delta

In order to analyze the satisfaction of the floating population with its current urban life, the following question was set in the questionnaire M table: “Are you currently satisfied with these aspects of your life (on a scale of 1–10; 1 = very dissatisfied, 10 = very satisfied)”. The analysis of the questionnaire data shows that the average score of the respondents’ evaluation of their comprehensive satisfaction with their current life is 7.18, and the average score of their satisfaction with various aspects of their family’s economy is 6.87, which still represents general satisfaction with their current state of life. Among them, the respondents’ satisfaction with their own physical health and married life is the highest (7.84 and 7.82 respectively), while their evaluations of the current situation regarding housing, work, household economy and children’s education are relatively low, all below 6.6 points.

Among the six cities surveyed, respondents in Shenzhen City express relatively high contentment with their overall living conditions, with the highest levels of physical health, medical condition, children’s education, leisure and recreation, social interaction, and financial services among all cities; the average scores of each item and the overall situation are the highest in each city. This may be related to the fact that Shenzhen is a young immigrant city: On the one hand, Shenzhen’s financial, IT, and other high-tech industries are developing rapidly, and there are a lot of young, highly educated members of the floating population working in these high-income industries, such as Huawei, Tencent, etc., with high income levels and high social strata in general. On the other hand, the floating population accounts for more than half of the total population in Shenzhen, and in order to attract these migrants, the city has relatively complete policies for floating populations. In addition, respondents in Zhongshan City are the most satisfied with their family’s financial and housing situation; meanwhile, respondents in Zhuhai City are the most satisfied with their daily transportation and marital life among the six cities.

From the inner-city space, the satisfaction of the floating population with their current life in the city centers is generally higher than that of those in the suburbs. Among the six cities, Dongguan, Foshan, Guangzhou, and Zhuhai, where the municipal government is located, have the highest average values of life satisfaction among all districts in the city. Futian District, host to the Shenzhen Municipal Government, holds the second rank in satisfaction scores among Shenzhen’s districts, only behind Guangming New District, which has a relatively small sample size of respondents. Additionally, the satisfaction of the floating population in Zhongshan City Center District with their current lives ranks in the middle of the list of all districts, with a small gap between it and the other clusters. Across different satisfaction levels, respondents residing in central districts generally report higher incomes and express greater satisfaction with their family’s economic status and employment conditions. Moreover, the central districts typically offer superior conditions for medical care, education, public transportation, leisure activities, and financial services.

3.3. Measurements for Structural Equation Modeling

The structural equation model is divided into measurement and structural models, with the following specific forms:

3.3.1. Measurement Models

x = Λxξ + δ

(1)

y = Λyη + ε

(2)

In Equations (1) and (2), x is a vector composed of exogenous indicators, Λx is the factor loading matrix of the exogenous indicators on the exogenous latent variables, δ is the error term for x, y is a vector composed of endogenous indicators, Λy is the factor loading matrix of the endogenous indicators on the endogenous latent variables, and ε denotes the error term for the endogenous indicatory.

3.3.2. Structural Models

η = Bη + Γξ + ζ

(3)

In Equation (3), η is the endogenous latent variable, ξ is the exogenous latent variable, B is the relationship between endogenous latent variables, Γ is the effect of exogenous latent variables on endogenous latent variables, and ζ is the residual term of the structural equation.

4. Results

4.1. Correlation Analysis of Observed Variables in Life Satisfaction

The exploratory Pearson correlation analysis of the comprehensive life satisfaction of the floating population using SPSS 19.0 reveals that the respondents’ personal annual income and education level are weakly correlated with comprehensive life satisfaction, and a similarly weak correlation exists between the variance in satisfaction with urban living conditions and those of the respondents’ hometowns. Only the level of education is compared with the hometown, and the correlation coefficient of the improvement in children’s education score is higher than 0.3. This suggests that the elevation in comprehensive and life satisfaction is not significantly associated with the respondents’ actual income or educational levels. Rather, it depends on the mutual influence of respondents’ satisfaction perception of various dimensions such as economic conditions and public services in life. In addition, the overall correlation between the comprehensive satisfaction with life and the degree of improvement in the life satisfaction of the floating population is also not strong, so the correlation between the comprehensive satisfaction with life of the floating population is mainly analyzed, and the results are as follows:

On the whole, except for the strong correlation between household economy and work situation (R > 0.6, p < 0.05), and the almost non-existent correlation between marital life and housing situation and medical condition (R < 0.2, p < 0.05), the correlation coefficients pertaining to the floating population’s comprehensive life satisfaction span from moderate to weak (0.2–0.6). The strongest correlation between the respondents’ satisfaction with overall living conditions and family economic and working conditions indicates that the respondents’ satisfaction with the economic and working conditions plays a fundamental role in the evaluation of the satisfaction with the overall living conditions. Married life, on the other hand, has a weaker correlation with satisfaction with the overall situation.

The exploratory analysis of the correlation between the comprehensive satisfaction with each aspect of life can be roughly summarized into four dimensions: economic factors, social security, emotional life, and public services. Therefore, the latent variables in the life satisfaction part of the structural model of human capital, life satisfaction, and urban settlement intention of the floating population are set as the four latent variables of economic factors, social security, emotional life, and public services. The inter-relations among the four latent variables—economic factors, social security, emotional life, and public services—and the observed variables manifest as follows (

Table 2. Correlation matrix of overall life satisfaction of mobile population.

	Household Economy	Work Situation	Housing Situation	Physical Health	Medical Condition	Children’s Education	Daily Transportation	Leisure and Recreation	Married Life	Social Interaction	Financial Services
Household Economy	1	0.605 **	0.529 **	0.330 **	0.394 **	0.345 **	0.284 **	0.338 **	0.248 **	0.304 **	0.336 **
Work Situation	0.605 **	1	0.544 **	0.329 **	0.415 **	0.347 **	0.295 **	0.339 **	0.245 **	0.330 **	0.346 **
Housing Situation	0.529 **	0.544 **	1	0.331 **	0.386 **	0.365 **	0.295 **	0.284 **	0.191 **	0.296 **	0.304 **
Physical Health	0.330 **	0.329 **	0.331 **	1	0.420 **	0.266 **	0.278 **	0.254 **	0.285 **	0.332 **	0.258 **
Medical Condition	0.394 **	0.415 **	0.386 **	0.420 **	1	0.395 **	0.390 **	0.357 **	0.189 **	0.285 **	0.380 **
Children’s Education	0.345 **	0.347 **	0.365 **	0.266 **	0.395 **	1	0.341 **	0.324 **	0.309 **	0.299 **	0.336 **
Daily Transportation	0.284 **	0.295 **	0.295 **	0.278 **	0.390 **	0.341 **	1	0.427 **	0.228 **	0.295 **	0.375 **
Leisure and Recreation	0.338 **	0.339 **	0.284 **	0.254 **	0.357 **	0.324 **	0.427 **	1	0.254 **	0.413 **	0.379 **
Married Life	0.248 **	0.245 **	0.191 **	0.285 **	0.189 **	0.309 **	0.228 **	0.254 **	1	0.394 **	0.281 **
Social Interaction	0.304 **	0.330 **	0.296 **	0.332 **	0.285 **	0.299 **	0.295 **	0.413 **	0.394 **	1	0.397 **
Financial Services	0.336 **	0.346 **	0.304 **	0.258 **	0.380 **	0.336 **	0.375 **	0.379 **	0.281 **	0.397 **	1
Overall Situation	0.533 **	0.518 **	0.476 **	0.404 **	0.459 **	0.414 **	0.424 **	0.424 **	0.332 **	0.486 **	0.504 **

** p is significantly correlated at the 0.05 level (bilateral).

):

(1)

The correlation between household economics and the satisfaction scores of work status and housing status are all strong. The correlation coefficients of all three are above 0.5, indicating an indissoluble relationship that underpins the floating population’s perceptions of economic and living standards. The correlation between these three variables and medical status is also relatively strong, with a correlation coefficient R of around 0.4, a relationship that can be attributed to the higher incomes from comparatively superior employment and the presence of supplementary health insurance. In addition, the correlation coefficients R of these three variables with other variables are generally around and below 0.3.

(2)

There is a correlation between the satisfaction scores of physical health, medical condition, and children’s education related to the social security system. A moderate correlation is observed between the respondents’ physical health and medical condition (R = 0.42, p < 0.05); meanwhile, the correlation coefficient between medical condition and children’s education is close to 0.4 (R = 0.395, p < 0.05).

(3)

There is a correlation between the satisfaction scores of leisure and recreation, marital life, and social interaction related to emotional life. Among them, the correlation coefficient linking leisure and recreation with social interaction exceeds 0.4 (R = 0.413, p < 0.05), and the correlation coefficient between marital life and social interaction also approaches the 0.4 mark (R = 0.394, p < 0.05).

(4)

Concurrently, the scores for leisure and recreation, as well as social interaction, bear a relationship with the satisfaction levels regarding public service facilities such as daily transportation and financial services. Among them, the correlation coefficient between leisure and recreation and daily transportation is more than 0.4 (R = 0.427, p < 0.05), and the correlation coefficients of the other factors are also close to 0.4, suggesting that public service facilities exert an influence on the floating population’s daily recreational and social interaction activities.

4.2. Model Estimation Results: Human Capital, Life Satisfaction, and Urban Settlement Intention

Measurement error terms have been incorporated for all variables in the specified model within Mplus v8.11, which is specialized software for structural equation modeling, resulting in an operational structural equation model (refer to Appendix B for the detailed code). This model encompasses 14 observed variables and 61 free parameters. Typically, the comparative fit index (CFI) and Tucker–Lewis index (TLI) range between 0 and 1, with values approaching 1 indicating a better fit of the hypothesized model to the actual data. The goodness of fit for the model, as indicated by the CFI, has a value of 0.945, while the TLI registers at 0.922, suggesting an overall satisfactory model fit. The Chi-squared test for model fit yields a value of 5682.582, with degrees of freedom (DF) standing at 105 and a p-value of less than 0.00001. Although not all tests for the path coefficients are entirely successful, this does not compromise the model’s explanatory power.

In the measurement model of human capital, life satisfaction, and urban settlement intention, the relationships between economic life satisfaction and the three observed variables of household economy, work status, and housing status all pass the significance test, but we find that economic life satisfaction is negatively correlated with work status and housing status, which suggests that the better the household economy is, the more dissatisfied the floating population may be with its work status and housing status. Regarding social security satisfaction, the association between the floating population’s satisfaction with social security and its physical health, medical condition, and children’s education is notably robust. With respect to emotional life satisfaction, this aspect of the floating population is most strongly associated with marital life, followed by leisure and recreational activities, while social interaction exerts a lesser impact. As for public service satisfaction, the floating population’s contentment with this facet is most significantly influenced by daily transportation, with leisure and recreational services coming in second, and financial services playing a smaller role. In terms of human capital factors, the correlation between the human capital factors of the floating population and the three observed variables of personal income, personal occupation, and personal education are all very strong, and the path coefficients of these three variables reach 0.890, 0.625, and 0.439. When juxtaposed with the sub-hypotheses delineated in the preceding section, the outcomes of the structural equation modeling measurements align fundamentally with these hypotheses, albeit with some deviations in the magnitude of influence pertaining to several factors. Of particular note is the inverse correlation within the economic life satisfaction dimension, which will be subjected to further analysis in the subsequent section (

Table 3. Correlation matrix of overall life satisfaction of floating population.

	Path Coefficient (Estimate)	Standard Error (S.E.)	Critical Ratio (st./S.E.)	Significance Probability (p-Value)
Economic life satisfaction by household economy	0.069	0.023	2.935	0.003
Economic life satisfaction by work situation	−0.768	0.013	−58.439	0.000
Economic life satisfaction by housing situation	−0.788	0.013	−61.090	0.000
Social security satisfaction by physical health	0.661	0.015	43.322	0.000
Social security satisfaction by medical condition	0.520	0.019	28.023	0.000
Social security satisfaction by children’s education	0.625	0.017	37.770	0.000
Emotional life satisfaction by leisure and recreation	0.295	0.097	3.041	0.002
Emotional life satisfaction by married life	0.645	0.049	13.100	0.000
Emotional life satisfaction by social interaction	−0.023	0.026	−0.876	0.381
Public service satisfaction by daily transportation	0.639	0.036	17.768	0.000
Public service satisfaction by leisure and recreation	0.307	0.096	3.182	0.001
Public service satisfaction by financial services	0.035	0.026	1.366	0.172
Human capital factors by personal income	0.890	0.087	10.203	0.000
Human capital factors by personal occupation	0.625	0.104	6.017	0.000
Human capital factors by personal education	0.439	0.041	10.723	0.000
Economic life satisfaction → urban settlement intention	0.176	0.089	1.964	0.050
Social security satisfaction → urban settlement intention	0.199	0.088	2.267	0.023
Human capital factors → urban settlement intention	0.560	0.169	3.321	0.001
Personal income → urban settlement intention	−0.229	0.177	−1.296	0.195
Personal occupation → personal income	−0.209	0.075	−2.784	0.005
Personal education → personal occupation	−0.067	0.067	−1.009	0.313
Economic life satisfaction ↔ human capital factors	−0.146	0.033	−4.431	0.000
Social security satisfaction ↔ human capital factors	0.142	0.032	4.497	0.000
Social security satisfaction ↔ economic life satisfaction	−0.905	0.017	−53.303	0.000
Emotional life satisfaction ↔ human capital factors	0.210	0.037	5.616	0.000
Emotional life satisfaction ↔ economic life satisfaction	−0.620	0.052	−11.975	0.000
Emotional life satisfaction ↔ social security satisfaction	0.722	0.058	12.513	0.000
Public service satisfaction ↔ human capital factors	0.028	0.038	0.729	0.466
Public service satisfaction ↔ economic life satisfaction	−0.619	0.043	−14.533	0.000
Public service satisfaction ↔ social security satisfaction	0.843	0.046	18.142	0.000
Public service satisfaction ↔ emotional life satisfaction	0.790	0.094	8.447	0.000

).

4.3. Outcome Analysis: Human Capital, Life Satisfaction, and Urban Settlement Intention

Variables that demonstrate higher path coefficients and meet the significance threshold within the measurement model—pertaining to human capital, life satisfaction, and urban settlement intention among floating population—are identified in the structural model, with the finalized version depicted in Figure 4. An analysis of the structural model yields the following conclusions:

4.3.1. Human Capital, Economic Life Satisfaction, and Social Security Satisfaction among the Floating Population Exert a Discernible Influence on Its Urban Settlement Intentions

The migration decisions of the floating population are directly related to its personal characteristics, such as age, skills, education, and experience. One of the key reasons migrant workers exhibit considerable differentiation in the final results is that their human capitals are different [69,70]. Our empirical results confirm the previous conclusion that the floating population’s human capital significantly impacts its intention to settle, evidenced by a path coefficient of 0.560. Generally speaking, human capital and the formation of migration ability influence each other, and increased investment in the human capital of the floating population will promote the enhancement of the floating population’s work skills, which will have a positive impact on the formation of the various abilities required for the floating population to settle in the city. At the same time, we find that the multiple coefficients of the path of “personal education → personal occupation → personal income → urban settlement intention” in Hypothesis 2-5 do not pass the significance test, and some of the parameter directions are opposite to the expected ones. This finding implies that an elevation in personal education levels does not invariably lead to a commensurate rise in income.

As previously hypothesized, educational attainment is a significant factor for predicting migrants’ intention to settle down. This effect is especially evident for those with higher education. Similar to Europe, college graduates in China are expected to show an above-average propensity to migrate [71,72,73]. However, few studies have focused on the impact of personal life satisfaction on the willingness of the floating population to settle down. Our empirical results confirm that the floating population’s economic life satisfaction and social security satisfaction have an impact on its urban settlement intention. The path coefficients for these two factors stand at 0.176 and 0.199, respectively, aligning with Hypothesis 1-1 and Hypothesis 1-2 as per significance testing. This indicates that the floating population’s satisfaction with economic life and social security exerts a positive influence on its intentions to settle. Contrary to Hypothesis 1-3, emotional life satisfaction does not achieve significance; however, public service satisfaction, failing the significance test as well, adheres to the anticipated outcome of Hypothesis 1-4. This implies that the floating population’s primary considerations for permanent settlement in a city revolve around the adequacy of its employment, income, housing, and other economic dimensions, in addition to its satisfaction with the city’s social security and educational provisions for children. In relative terms, migrants are less inclined to factor in their contentment with emotional life, transportation, and entertainment amenities. Overall, with respect to permanent urban settlement, the floating population prioritizes satisfaction with survival-related elements like economic stability, healthcare, and education, while placing comparatively less emphasis on lifestyle-related elements such as emotional well-being, transportation, and leisure facilities.

4.3.2. A Negative Correlation between the Economic Life Satisfaction of the Floating Population and Multiple Latent Variables Exists

Contravening the initial segment of Hypothesis 1-5, an inverse correlation exists between the floating population’s economic life satisfaction and several latent factors. The path coefficient linking economic life satisfaction with the human capital factor stands at −0.146, whereas it registers at −0.620 with emotional life satisfaction, −0.619 with public service satisfaction, and −0.905 with social security satisfaction. The association between the floating population’s satisfaction regarding economic aspects, employment, and housing conditions, and factors such as daily transportation, marital life, and social interaction is feeble. The path coefficients are negative, potentially due to the external environment, like transportation, having minimal relevance to the respondents’ employment and income; in addition, it is worth noting that the data show that the respondents’ evaluation of the economic factors, such as work, income, and housing, do not positively or even negatively affect the factors related to emotional life, such as marital life and social interactions. This phenomenon could stem from the fact that higher income levels among the floating population may lead to increased expectations, thereby amplifying its dissatisfaction with aspects of emotional life, public services, and social security.

4.3.3. A Robust Correlation among Satisfaction with Social Security, Emotional Life, and Public Services Exists

In alignment with the latter part of Hypothesis 1-5, a pronounced correlation is observed among the latent variables of the floating population’s satisfaction with social security, emotional life, and public services. The path coefficient interconnecting social security satisfaction and emotional life satisfaction stands at 0.210, with that between public service satisfaction at 0.843 and human capital at 0.142. The path coefficient linking satisfaction with public services to satisfaction with emotional life is recorded at 0.639. Comparing the path coefficients of the latent variables of social security, emotional life, and satisfaction with public services of the floating population, the path coefficients of public services with social security and emotional life are high, respectively, indicating that public service satisfaction may play an important role in the floating population’s leisure and marital life.

5. Conclusions and Policy Recommendations

Based on the questionnaire survey data of the floating population in the Pearl River Delta, this paper establishes a structural equation model to explore how human capital factors affect life satisfaction, and quantitatively analyzes the impact of both factors on the urban settlement intention of the floating population. The results show that human capital has a positive effect on social security satisfaction and emotional life satisfaction, and a negative effect on economic life satisfaction, which has a significant impact on migrants’ urban settlement intention. Regarding individual life satisfaction, the floating population’s satisfaction with its economic situation and social security appears to influence its urban settlement intentions, whereas satisfaction with emotional life and public service facilities exerts a lesser impact. This study’s insights offer the following recommendations for the Pearl River Delta and other urban clusters to enhance the floating population’s life satisfaction and facilitate its successful settlement and integration as citizens:

Firstly, it is essential to advance industrial development and promote access to high-quality job opportunities for the floating population. The Pearl River Delta should fully leverage its post-reform advantages, cultivate high-tech and advanced manufacturing sectors, elevate the level of industrial sophistication, and steer the aggregation of talent. With the expansion of higher education, China has emerged as the leading global producer of higher-education graduates. Nonetheless, currently, many industries in the Pearl River Delta remain positioned at the middle and lower tiers of the global industrial value chain, offering relatively few high-skill and high-value-added positions. Our findings reveal that an elevated level of human capital, such as educational attainment, correlates with lower economic life satisfaction among the floating population. This may be due to the fact that the floating population does not have actual economic income that meets its expectations as its skills improve, and on the contrary, this group is less economically satisfied. Consequently, the PRD urban agglomeration should persist in its transformation towards more sophisticated, skill-intensive industries to generate a greater number of well-paying jobs. Simultaneously, it is advisable for local governments to enact legislation governing the utilization and management of employee training funds; to incentivize businesses to invest in their workforce; to offer financial incentives to training providers; to encourage the government procurement of training services; to accelerate the establishment of vocational skill development centers to facilitate the floating population’s transition into employment; to support both pre-employment and on-the-job vocational training and enhance the support systems for retraining the transitioning workforce; to establish a mechanism that ties wages and benefits to skill levels, promoting the floating population’s access to high-quality job opportunities and thereby elevating various facets of life satisfaction among this population.

Secondly, enhancing the floating population’s life satisfaction should be a priority, with an emphasis on the diversity of its needs. During the phase of high-quality economic transformation and progress, a gradual increase in per capita disposable income is observed, and the social security offered by urban areas is set to become an increasingly significant magnet for the floating population. It is suggested that while providing high-quality vocational jobs, the state and local governments should provide more support in housing purchases, children’s education, medical and healthcare, and other economic incentives and social security; fully open up urban service functions and issue corresponding policy guidance; establish a sound social integration mechanism for the floating population; and strengthen communication and interaction between the floating population and local residents to enhance the sense of belonging of the floating population after migration. In terms of public service satisfaction, the Pearl River Delta urban agglomeration needs to pay more attention to traffic demand and better infrastructure construction. We will continue to enhance all types of social security and facilitate high-quality economic advance. Accordingly, the differentiation of urban human capital will lead to the differences in the floating population’s perception of life satisfaction. This requires cities in the Pearl River Delta to pay attention to the heterogeneity of different migrant groups, establish a sound assessment mechanism for the life satisfaction of the floating population, regularly investigate and assess their living conditions and satisfaction, and further explore the impact of the life satisfaction of the floating population on its urban settlement intention under different levels of human capital. In different dimensions of life satisfaction, the satisfaction with the household economy and housing situation of the floating population is relatively low. It is recommended to increase the household economic income of the floating population and improve its living conditions. Especially in Guangzhou and Shenzhen, due to the high housing prices, the satisfaction of the floating population with the housing situation is relatively low. Therefore, it is important to focus on addressing the housing conditions in these two cities.

Across different satisfaction levels, respondents residing in central districts generally report higher incomes and express greater satisfaction with their family’s economic status and employment conditions. Moreover, the central districts typically offer superior conditions for medical care, education, public transportation, leisure activities, and financial services. Therefore, each province promotes regional balanced development and promotes the level of industrial development and infrastructure construction in the peripheral areas of cities in order to improve the life satisfaction of the floating population in relevant areas. It has referential significance to the study of population migration and the formulation of citizenship policies in China.