Keywords

1 Introduction

In the past 40 years of reform and opening up, China’s economy has grown rapidly. At the same time, the gap between regions has also widened, resulting in large-scale migration in China. Populations in rural or underdeveloped areas have chosen to migrate to developed metropolises in order to obtain better employment, education, health care and other benefits. Such groups play a vital role in reshaping China’s urban space and economic pattern [1]. Large-scale migration has also profoundly affected China’s urbanization process. In 2011, the urbanization rate of China’s long-term residents exceeded 50% for the first time, reaching 51.3%. In 2018, the figure further rose to 59.58%. However, taking the urbanization level of 80% in developed countries is a reference, there is still a big gap between China and developed countries [2]. In addition, the urbanization rate of China’s registered population is only 43.37% till 2018. China is currently in the new stage of urbanization based on people. The regional choice of migrants and their settlement intention are of great significance to China's urbanization process. How to narrow the gap between the urbanization rate of the resident population and the urbanization rate of the registered population is also regarded as the major practical problems facing by the central and the local government.

There exists big difference of economic development in different regions. Zhiyong [3] pointed out that the most important factor causing this gap is the difference in industrial structure and industrial efficiency between regions, and this difference is mainly attributed to the differences in human capital caused by differences in urbanization levels. The decentralized management of the central government has made the local governments independent of each other and consider the development of their own jurisdiction as the main direction. In order to better develop the regional economy and transform the economic growth momentum. However, the most fundamental way to retain them for a long time is to excavate the domestic demand of the floating population. Dadao and Hua [4] pointed out that the endogenous power represented by the labor force within the local administrative jurisdiction is the main driving force of China’s urbanization process, and the floating population is the core target group in the recent “talent introducing war”. The instability and long-term plans for settlement are widely controversial. Therefore, in this context, clarifying their settlement intention and influencing factors will help to further promote the urbanization process at the local and even the whole country.

2 Literature Review

Population migration is an extremely important research topic in academic field. International research on population migration often equates migration and settlement with two simultaneous completion processes. In China, due to the economic income gap between urban and rural areas, rural surplus labor began to migrant like migratory birds between urban and rural areas [5], and the population is often in a temporary situation after moving into cities [6]. These migrants make decisions on whether to settle in the local area after considering various factors. Neoclassical economics believes that urban-rural migration is driven by individual rational choices, and immigrants will make a decision on whether to migrate after considering the cost and benefits of migration [7]. Based on this theory, the new migration economics proposes that population migration is not only an individual’s own decision-making, but also influenced by factors such as the family environment [8]. Recently, Findlay A (2015) [9] pointed out that the floating population will decide whether to migrate based on their past experience according to the life cycle theory. These theories together explain how the migrants’ settlement intentions are influenced by social network relationships, including home and relocation cities.

The floating population’s settlement intention has changed significantly in recent years. In the early days, due to the influence of the household registration system, the floating population often circulated between rural-urban or urban-urban, and it was difficult to settle down [10]. With the gradual liberalization of the household registration system from the national to the local level in recent years, more immigrants tend to settle in the immigration areas [11]. Based on this, many scholars have also conducted in-depth research on relevant factors affecting their settlement intentions. Population structure, family, economic status and social factors are often listed as the main influencing factors of the floating population’s settlement intentions [12, 13]. These factors will affect their adaptation to urban life by affecting the personal stability of the floating population. As far as the population structure is concerned, the academic community generally believes that with the increase of age, the migrants’ settlement intentions will gradually decline. Older people tend to return to their hometowns after experiencing inter-urban circulation, and the educational level of migrants and their willingness to stay shows a significant positive correlation [13]. Some people in the floating population will carry their families together, and their settlement intention will be affected by their family members in addition to their personal circumstances [9]. In addition, Khraif [14] also pointed out that economic conditions (such as work, etc.) can only affect the temporary residence of migrants, and the social and cultural factors of the inflow can truly affect the floating population to stay permanently. This is mainly because China’s household registration system has prevented migrants from enjoying the same social benefits as urban residents. Although the current household registration system has relaxed access to migrants, the corresponding welfare packages still lag behind local residents. The floating population’s settlement intention is still not high. In addition, the characteristics of the inflowing area also have an important impact on the willingness of the floating population to settle. The floating population tends to pay more attention to the type of city when choosing to move the city. Hao and Tang [15] found that the floating population is more willing to settle based on the level of social and economic development, and the factors affecting their settlement in different types of cities are mainly related to their education level, income status, housing and other factors [16].

The existing literature has quantitatively analyzed the migrant’s settlement intention in many aspects. However, the research mainly focuses on the floating population where the inflowing land is located. It is rare to compare the intention of migrants from the same region to settle in different regions. When the labor force of the same household registration chooses the migration direction, they will make different choices according to their own situation. If the population flowing out from the same place are distributed in different regions, is there a significant difference in the settlement intention? What factors have affected their intention to settle? As a municipality directly managed under the central government, Chongqing has long been the exporter of population. In recent years, as the industry in the developed coastal areas gradually shifts to the central and western regions, Chongqing’s own clustering effect appears. Therefore, this paper takes the floating population from Chongqing to other areas as the research object, and attempts to analyze the impact factors of this group, in this way to provide corresponding policies for the government to retain local people and attract foreigners.

3 Data Source and Methodology

3.1 Data Source

The data used in this paper is derived from the 2017 China Migrants Dynamic Survey of Chongqing household registration data. The questionnaire is sampled by a stratified, multi-stage, scale-oriented PPS method. The survey’ object is those who live in the inflow area for one month and above, non-local (county, city) households with 15 years of age and above will flow into the population. The content of the questionnaire mainly includes four sections: (1) family members, income and expenditure; (2) employment situation; (3) mobility and settlement intention; (4) health and public services. This data is by far the most representative sample survey of the floating population in the country, and has important reference value for analyzing the level of migrants’ settlement intentions and the scope of change. Through screening, 4307 valid samples were finally obtained.

3.2 Methodology

3.2.1 Modeling Method

The dependent variable of this paper, the floating population’ settlement intention, is a two-category variable. Therefore, the binary logistic regression model is used for research. The model can be written as:

$$ \ln \left( {\frac{{P_{i} }}{{1 - P_{i} }}} \right) = {\text{b}}_{0} + {\text{b}}_{1} {\text{x}}_{1} + {\text{b}}_{2} {\text{x}}_{2} + \cdots + {\text{b}}_{p} {\text{x}}_{p} + \varepsilon $$

Among them, the left side is called Logit, which is the natural logarithm of the probability of occurrence of the event. Pi denotes the p-value of the dependent variable (the value is between 0 and 1). x1, x2, …, xn denote the independent variables. b0 denotes the intercept parameters, and bi denotes the coefficient of xi, If the p-value of xi is less than 0.05, the independent variable xi is an influencing factor.

3.2.2 The Design of the Variables

The empirical part measures the strength of the settlement intentions of different migrants to the regional migrant population in the CMDS2017 questionnaire by asking “How long will it be expected to stay in the local area”? This article refers to the practice of You et al. [12] to divide the respondents’ responses into two parts: settlement and non-settlement, and to explore the main factors affecting the migrants’ settlement intentions in different regions. Interpretation is carried out using 10 variables in five dimensions: individual, work, housing, society, and migration. The specific definitions and basic statistics of model dependent and independent variables are shown in Table 1.

Table 1 Variable definition and description statistics
Full size table

4 Analyses on the Floating population’s Settlement Intention from Different Directions Based on the Sample Data

According to the sample data, only 30.35% of the floating population of Chongqing’s population migrating to different regions intends to settle in the inflow area. Even with 6.25% of the intention to live for more than 6 years, the overall settlement intention is still lower than 40%, which was at a lower level. With the two-way differentiation of migrants returning to the countryside and the flow between urban and urban areas, understanding the factors affecting the settlement intention of such groups has important reference significance for attracting populations in Chongqing and similar cities. Table 2 provides a basic overview of the floating population of the province where the household registration is located in Chongqing.

Table 2 The results of the chi-square test
Full size table

It is not difficult to see from the statistical sample data that the migrants’ settlement intention in different flow areas is significantly different due to the different factors. From a personal point of view, according to Fig. 1, the age distribution of the floating population, the proportion of migrants between the ages of 26 and 35 is the largest, and such new generations are living in a rising period due to their young age. Therefore, they are less willing to settle in the present area and choose to move elsewhere to seek better development. In addition, as can be seen from Table 2, the settlement intention of this group is also the highest in all ages, and with the increase of the age level, the proportion of the floating population and their settlement intentions are gradually decreasing. By comparing the education level of the outflow population, the proportion of the junior high school population is the largest, accounting for 41.8%, which also reflects the fact that the floating population is generally not high in education. However, the floating population with college education or above only accounts for 18.5%, and such groups’ settlement intentions are the same as that of the junior high school level, indicating that such groups are more capable of achieving their settlement intentions. The proportion of married people and their settlement intention in the floating population are very high, accounting for 81.9% and 85.0% respectively. This feature is also consistent with the basic characteristics of the flow of rural areas in China: Most migrant workers go to big cities to make a living after marrying and having children in their hometowns. After living in the hometown and big city, they are more willing to settle in a big city for the development of the next generation.

Fig. 1
figure 1

Age distribution

Full size image

From the perspective of work and housing factors, the proportion of outflows from different industries is very different, and the proportion of outflows in the tertiary industry is the largest, accounting for 56.7%. This is because Chongqing relies mainly on manufacturing to develop the economy, which leads to the population engaged in the tertiary industry can only migrant to different places to find work and settle in different places. The proportion of different salary treatments of outflow population is inverted U-shaped, and the average monthly total income is between 4000 and 6000 yuan, accounting for the largest proportion of migrants, which is 30.6%. The income level is higher than the national average, and the migration of migrants is for better work, the higher the income level, the greater the intention to settle. In addition, the monthly housing expenditure of the floating population also shows a certain difference. The monthly housing expenditure of 0–500 accounts for the largest proportion, which also indicates that the floating population has poor living conditions.

Finally, from the perspective of social and migration factors, a large amount of floating population does not have urban residents’ medical insurance, which accounts for 81.9%. This indicator can reflect the social security of the floating population in the inflow area to a certain extent. Hukou has always been an important factor restricting the settlement of migrant populations. The sample data shows that agricultural households account for 76.7% of the total migrant population in different regions. Compared with non-agricultural households, the willingness of such groups to settle inflows is smaller. Nearly 50.2% of the population moving from Chongqing to different regions choose to move within the province. The populations who choose to move to the east and the surrounding western regions are similar, each accounting for about one-fifth, and the least is the central region. The flow occupies the main body of this part of the floating population, and the migration direction shows obvious regional differences in the floating population’s settlement intention. The floating population moving in the province and moving to the western region has higher willingness to settle in the central and eastern regions (as shown in Fig. 2). In addition, the proportion of migrants whose main purpose is to work is the largest, reaching 69.3%, and this group of people has the highest willingness to settle, indicating that such groups may not be able to obtain good employment opportunities in Chongqing and prefer to settle down in their working places.

Fig. 2
figure 2

Settlement intention distribution

Full size image

5 Results

Before performing the binary logistic model for further analysis, it is necessary to determine whether there is a correlation between the independent variables in the sample and the dependent variables. As shown in Table 2, the p-value of the 10 variables are all less than 0.05, indicating that the sample has a significant correlation between dependent variables and independent variables.

Next, the binary logistic regression analysis was performed using 10 variables that pass chi-square test. The model estimation results are shown in Table 3. In this model, β denotes whether the effect of the coefficient is positive or negative, and the EXP (β) denotes the value of the regression coefficient, if this value is more than one, then it means that the parameter has the positive effect when compared with the reference parameter. First, in terms of personal factors, age, marital status, and education level have a significant impact on the settlement intention. Unlike previous studies, this study found that married people are more likely to settle in inflow areas than unmarried people. The educational level has a significant impact on the floating population’s settlement intentions. Taking the floating population with primary school education and below as the reference system. The floating population of high school/secondary school, college degree or above has a 43.5% higher intention to settle, which indicates that the higher the level of education, the stronger the intention to settle, which is similar to most previous studies [17]. Well-educated individuals usually have strong professional skills and will be more likely to work in the local job market. Different from previous studies, this study found that only floating populations over the age of 56 has a significant effect on their intention to settle, which may be because the floating population of this group has accumulated higher capital.

Table 3 Results on the estimation
Full size table

Furthermore, from the perspective of work factors and housing factors, the working factors include two aspects, one is the occupational characteristics of the floating population, and the other is the average monthly total income of the floating population. Occupational characteristics are important factors affecting the migrants’ settlement intention. Compared with individuals engaged in the tertiary industry, individuals who engage in the secondary industry have a 32.5% lower intention to settle. This indicates that individuals engaged in the tertiary industry have a significant intention to settle. The first-tier and new-tier cities usually have a relatively high tertiary industry, which is why most migrants choose to migrate to these regions. In addition, the personal income level also has an important impact on their intention to settle. Compared with individuals whose income levels are above 8000 yuan, the floating population’s intention to settle between 2000 and 4000 is 42.7% lower. The living conditions in the current place of residence are poor, so they want to seek better salary treatment. Therefore, the income level is an important factor for individuals to decide whether to settle or not. Housing factors also have a significant impact on the settlement intention. Although the liberalization of the household registration system reduces the difficulty of entry for migrants in cities, this does not mean that migrants can enjoy the same housing rights as local residents. Most migrants still live in rented houses in the target cities. Taking 0 yuan as the reference target, the settlement intention of housing expenditure which range is 0–500, 500–1000 and above is lower by 31.5%, 40.1% and 35.7%, respectively. This may be accompanied by poor economic conditions.

Finally, from the perspective of social factors and migration factors, both types of factors have a significant impact on the migrants’ settlement intention. First, look at social factors. Social security and household registration types have a significant impact on the migrants’ intentions to settle in inflow areas. Compared with the hukou, the floating population who enjoys good social security in the inflow area, the individual's intention to settle is 25.3 and 52.9% higher than that of the agricultural hukou and those who do not enjoy good social security in the inflow areas. This is mainly because the non-agricultural hukou is available on the one hand. On the other hand, the agricultural hukou considers the existence of a homestead in the hometown and the possibility of returning home afterwards. Looking at the migration factor, this category includes two aspects, one is the migration direction of the floating population of Chongqing household registration, and the other is the reason for migration. The direction of migration has a significant impact on the migrants’ settlement intention. Taking the eastern region as the reference, the intention to settle in the province and in the western and central regions of the province is 254.1%, 72.1%, and 87.0% higher, respectively, although the eastern region have good economic development conditions, the high cost of living also makes it difficult for this part of the floating population to settle down. Correspondingly, as the policy effect of regional coordinated development gradually emerges, the central and western regions are providing better job opportunities. At the same time, the cost of living is lower than that in the eastern region, and the settlement of “cost-effectiveness” is higher. Taking the work as a reference, it is found that the individual’s intention to migrate due to their family’s migration is 95.3% higher. This may be because the family’s joint efforts are more likely to settle in the city than one alone.

6 Conclusion and Policy Implication

Under the background of new urbanization, local governments mainly focus on attracting all parties to settle in the local area. Through population agglomeration, the short-term rapid growth of the economy is achieved, but it is often overlooked that the optimization of its internal structure is long-term retention for migrants. Taking the floating population of Chongqing to different regions as the research object, and analyzing the five factors affecting such groups’ settlement intention, the policy implications for optimizing the internal structure of the same type of city are mainly reflected in the following aspects:

First of all, most immigrants choose to migrate for work reasons, but they are not really willing to settle, but the floating population with family members will generally have a much stronger intention to settle. In addition, the married status can significantly increase the floating population’s settlement intention. Therefore, when formulating the talent introduction policy, the local government should highlight the welfare policy of the accompanying family members, especially the spouses of married couples, and stabilize their settlement intention by protecting the welfare of the family members.

Secondly, because the age factor does not significantly affect the resident factors of the floating population, local governments should not limit the age when optimizing the talent introduction policy, and more comprehensively protect the coverage of the policy.

Moreover, the reason why people migrate to other place is largely due to the fact that there is no suitable industrial structure for their survival and the salary are not up to their ideal standards. Most migrants are engaged in tertiary industry work in the inflow areas, and if local governments want to attract foreign populations or local outflows and achieve long-term residency, it is also necessary to optimize their industrial structure and provide more competitive compensation and benefits.

Finally, housing and the social security measures given to urban residents by cities are also important factors affecting the migrants’ settlement intention. The negative housing pressure affects their settlement intention, and perfect social security measures can effectively increase their intention. Therefore, in order to successfully attract and retain foreign populations, on the one hand, local policies need to improve the housing supporting policies of regional migrants, and combine the affordable housing policies with other social policies to reduce the housing pressure. On the other hand, they need to optimize social security measures for migrants, such as the full implementation of the residence permit system to replace the dual household registration system, to achieve the basic national security of urban workers as soon as possible. A two-pronged approach makes the city more competitive in the battle for introducing people and process of urbanization.