Research Articles

The settlement intention of urban-to-urban migrants in China: Spatial differences and driving factors

  • WANG Xinming , 1, 2, 3 ,
  • QI Wei , 1, 2, 3, * ,
  • LIU Shenghe 1, 2, 3 ,
  • LIU Zhen 1, 2, 3 ,
  • GAO Ping 1, 2, 3 ,
  • JIN Haoran 4
  • 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 2. Key Laboratory of Regional Sustainable Development Modelling, CAS, Beijing 100101, China
  • 3. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • 4. Policy Research Center, Ministry of Housing and Urban-Rural Development, Beijing 100835, China
* Qi Wei, PhD and Associate Professor, specialized in urban geography and population geography. E-mail:

Wang Xinming, PhD, specialized in urban geography and population geography, E-mail:

Received date: 2021-11-25

  Accepted date: 2022-03-30

  Online published: 2022-12-25

Supported by

National Natural Sciences Foundation of China(42171237)


China has entered the middle-to-late stage of urbanization. The scale of urban- to-urban migrants, which more refers to the urban-to-urban floating population (UUFP) across China, has significantly increased. UUFP settlement intention is a crucial issue for urbanization development. This study examines the spatial pattern and factors influencing the settlement intention of the UUFP in China based on data obtained through its dynamic monitoring in 2017 and the binary logistic model. The results show that most members of the UUFP were married, older, better educated, and had a higher income than the average person with extensive migration experience. We correlated a high settlement intention with developed economies, coastal areas, good environmental conditions, and more amenities in cities such as Beijing, Tianjin, Shanghai, and Zhuhai. Amenities were more impactful on settlement intention than economic factors, from individual and regional perspectives. The UUFP more often sought equality of education for children and social integration in its choices of destinations. However, the distance was not a hindrance to intention to settle but played a substantial role in influencing it. We suggest optimizing the stock of the UUFP in large cities, improving public education services, and promoting remote urbanization. Likewise, industrial transfer and enhanced public resources may ease the pressure of large UUFP flows into large cities.

Cite this article

WANG Xinming , QI Wei , LIU Shenghe , LIU Zhen , GAO Ping , JIN Haoran . The settlement intention of urban-to-urban migrants in China: Spatial differences and driving factors[J]. Journal of Geographical Sciences, 2022 , 32(12) : 2503 -2524 . DOI: 10.1007/s11442-022-2058-6

1 Introduction

Zelinsky proposed the Hypothesis of Mobility Transition in 1971. It examines the evolution trends of migrants from the macroscopic perspective of the relationship between population migration and the stage of socio-economic development (Zelinsky, 1971). Zelinsky argued that changes in migration were compatible with socio-economic development and were an inherent part of modernization. He divided the transition of migration into five stages: the premodern traditional society, the early transitional society, the late transitional society, the advanced society, and the future super-advanced society. He also divided the forms of migration into five types: international migration, domestic migration to remote areas, rural-to-urban migration, urban-to-urban migration, intracity migration, and circulating flow. Large-scale international migration and migration to remote areas began in the early transitional society, peaked, and then declined in its later period. Large-scale rural-to-urban migration started in the early transitional society, continued to grow to the late transitional society, and then dropped. At the same time, trends in urban-to-urban migration, intracity migration, and circulating flow were significant. As a result, super-advanced society and rural-to-urban migration will decrease, and all population migration will persist in intercity or intracity migration. Developed countries have undergone rapid urbanization, driven by industrialization to form a developed urban system. Therefore, literature on domestic migration in developed countries has focused on urban-to-urban migration and urban-rural migration (Potter and Rossi, 1956; Clark and Withers, 2007; Geist and McManus, 2008; Simini et al., 2012). With the development of urbanization, some developing countries have displayed population migration in the middle and late stages of urbanization proposed by Zelinsky’s theory. The most critical point is that urban-to-urban population migration has become active. What are the main characteristics and formation mechanisms of urban-to-urban population migration in these developing countries? Our study takes China as an example to explore the migration pattern and influencing factors of urban-to-urban migration in contemporary context.
Since the reform and opening up in 1978, China has undergone large-scale and high-intensity migration. This process has prompted large rural populations to move to cities and towns and has profoundly affected China’s urbanization. The 7th Census revealed that the urbanization rate in China was 63.89% in 2020, which indicated that China had entered the middle and later stages of urbanization (Northam, 1975; Zhu, 2018; NBSC, 2021a). According to the Hypothesis of Mobility Transition (Zelinsky, 1971), the structure of urban and rural migration in China will transition from rural to urban migration to urban-to-urban migration. As urbanization has increased in China, we have witnessed large-scale urban-to-urban migration. Critical migrants, who do not possess the hukou certification at the destination due to the household registration system, are the floating population in China. Therefore, they usually cannot enjoy the same public services as locals. According to the 7th Census, the floating population in China’s urban areas was 331 million in 2020, accounting for 88.12% of the entire floating population of the country. This number increased by 3.85 percentage points compared with that in 2010. Of this increase, the rural-to-urban floating population (RUFP) accounted for 249 million (an increase of 106 million over 2010), and the urban-to-urban floating population (UUFP) was 82 million, representing an increase of 39 million over 2010 and nearly four times that in 2000 (NBSC, 2021b). The RUFP thus remained the main driving force of population mobility; however, the scale of the UUFP was also significantly increasing. Currently, the origins of population migration are rural areas, cities, and towns. This change means that the driver of mechanical growth of the urban population will change from a rural origin to an urban origin in the future. China has long adhered to the red line of cultivated land and agricultural protection to remain self-sufficient in its food supply. Although rural migrants work in the urban area, they still have land rights and interests in the rural area. However, there is no guarantee of arable land and homesteads for people migrating from urban areas. In addition, similar to rural-to-urban migration, urban-to-urban migrants also face obstacles such as the household registration system. With the development of the 2014 New-type Urbanization in China, the migrants have encountered a new obstacle: the varying settlement policies in different cities. It is not easy to get the hukou certification in larger cities; however, in the middle- and small-sized cities and towns, it is more accessible. In the future, China’s rate of urbanization will continue to rise, and the UUFP will most likely become an important driving force. The UUFP will become critical to the high-quality development of China’s urbanization. However, migration in China is different from that in developed countries. As mentioned above, most migrants in China are part of the floating population who cannot enjoy all the public services of locals because of the particular household registration system. Therefore, research on urban-to-urban migration in a Chinese context should focus on the UUFP.

2 Literature review

The urbanization process of developed countries is faster than China’s, and the research on urban-to-urban migration started earlier. Since the second half of the 20th century, developed countries have produced a series of essential theories and methods to understand the time course of urban-to-urban migration (Zhu and Lin, 2017). Potter and Rossi (1956) first proposed the Life Cycle Theory in their study of residential migration in Philadelphia communities, which suggested that family members migrate due to changes in housing demand caused by orderly events at different stages of life. These life-ordering events included education, first job, marriage, childbirth, divorce, death, and retirement. Some UK and Canadian studies have also reached similar conclusions (Li, 2004a; 2004b). However, the strict assumption of the theory that life cycle changes were ordered and age progression represented such changes has been called into question. The Life Course Approach suggests that age was not the only variable impacting personal life events. For instance, the divorce rate in developed countries was rising, more people were delaying the age of marriage, and the relationship between age, marriage, and childbirth was decreasing. Additional variables were the diversity of family forms leading to various events throughout adult life and codetermining residence migration with age (Clark and Withers, 2007; Geist and McManus, 2008). The theory also states that different groups of people had specific living conditions and experiences. Thus, they formed different life trajectories, emphasizing the impact of social background on migration (Odland and Shumway, 1993). Therefore, the Life Course Approach is more comprehensive and in-depth for understanding urban-to-urban migration. In addition, scholars are also committed to revealing the laws of migration-related to life processes. Many family migrations occurred over long distances for employment or other reasons. A lack of knowledge about the destinations could lead to a failed first migration, leading to another long-distance or short-distance migration (William et al., 2004). Recent evidence suggests that urban-to-urban migration in developed countries comprised predominantly downward mobility along urban hierarchies (Plane et al., 2005). There were highly differentiated patterns at specific stages of individual and family life courses, such as upward mobility in young people. In contrast, older people were more likely to move downward (Newbold, 2011). Most international scholars regard migration and settlement as two processes. This kind of temporary circular migration means that migrants are “permanent” migrants. Therefore, there have been few studies on settlement intentions (Zhu and Lin, 2016). Although the above research examined industrial and post-industrial societies in developed countries, it still proves helpful in studying urban-to-urban migration, which refers specifically to UUFP in China.
Since China’s reform and opening up, urban development has required a large labor force, but the rural job market has lagged. Under the influence of the pull and push effects, a large population flowed from the rural to the urban areas. Yet, a considerable part of the population has not obtained urban household registration at its destination, thus becoming part of the RUFP, representing the rural-to-urban floating population (Shen, 2002). The migrants have generally migrated from central and western inland areas to the eastern coastal provinces of China. As a result, the main destinations included the Pearl River Delta, Yangtze River Delta, and Beijing-Tianjin-Hebei region. The primary origin was located in the central and western areas (Fan, 2005b; Liang and Ma, 2004). Such a pattern has remained stable for years with specific changes within the urban agglomeration areas (Liu et al., 2015). The migration of the RUFP has dramatically affected the urbanization levels in both the destinations and origins. For this reason, most of the existing studies on population migration in China have focused on the flow from rural to urban areas. In this study, we summarized two aspects of these studies on the migration of the RUFP.
The first aspect concerned the spatial-temporal patterns and the influencing mechanism based on census data. In the 1980s, a macroscopic pattern of migration from west to east or from economically backward area to economically developed area was gradually formed (Chen, Zhang, 1999; He, 2002). In the 1990s, the scale and intensity of internal migration continued to increase. At the same time, migration from the western region to the central region became more attractive (Shen, 1996; 1999; 2012; Li, 2004; Fan, 2005a). At the beginning of the 21st century, the scale and intensity of population migration have increased further. In addition, the attractiveness of inland areas, especially the provincial capital cities, has also continued to grow (Cai, Wang, 2003; Ding et al., 2005; Liu et al., 2015). However, the basic migration pattern from inland to the coast did not undergo fundamental changes (Qi et al., 2016). The spatial-temporal pattern of the RUFP was affected by individual factors and macroscopic regional factors. These factors included age, gender, education, mobility characteristics, social network, and family characteristics. The macroscopic regional factors covered economic development, income, employment, environment, and public services. The economic factor influences were stronger while the distance-related factor influences declined (Liang and White, 1997; Wei, 1997; Shen, 1999; Gu et al., 2019; Cao et al., 2021).
The second aspect of RUFP migration involved the intention to settle based on questionnaire surveys. Due to the lack of, or difficulty obtaining, individual-level data on migration, most research on migration in China has applied census data and focused on macroscopic-pattern studies. In 2009, the National Health Commission released the annual data of the dynamic monitoring of the national floating population. The data provided significant support for the microscopic study of the RUFP migration process and has yielded valuable results on the RUFP intention to settle in a given area (Zhu et al., 2017). Scholars have long regarded rural-to-urban migration as a function of the household registration system and generally believed that household registration hindered mobility (Chan and Zhang, 1999; Guo and Iredale, 2004; Zhang, 2010). However, due to the limitations of its hypothetical premises and the accelerated liberalization of household registration, using the framework of household registration to explain migrant intention to settle down was weak. Rural-to-urban migration can be viewed as a strategy for making full use of family resources by obtaining high income at the destination with low-cost consumption at the origin. The intention to settle was seen as a trade-off between benefits and costs. Moreover, personal factors, including age, gender, marital status, education, employment, and family income, also affected the decision to settle (Yue et al., 2010; Zhu and Chen, 2010; Fan et al., 2011; Hu et al., 2011; Liu and Xu, 2017). Farmers’ ownership of farmland, homesteads, and other agricultural hukou-related benefits weakened their intentions to settle in cities and towns (Hao and Tang, 2015; Chen and Fan, 2016). However, these factors ignored the heterogeneity of individuals and regions. The intention to settle varied according to the given group’s area. The groups, including young people, single individuals, women with high academic qualifications, engaged in unproductive work with higher family incomes, and migrants with better housing conditions, were more willing to settle in the urban areas they live. Large cities have better public services than small cities, and thus the hukou status in these cities is more valuable. Nevertheless, large cities have higher living costs. Therefore, people moved to and settled in cities of different sizes depending on their particular situations (Zhang and Tao, 2012; Liu and Wang, 2019).
Presently, the structure of migration has gradually changed due to urbanization development. From 2010 to 2020, the scale of the UUFP increased substantially, with the rural-to-urban and urban-to-urban floating population rising by 74.13% and 90.70%, respectively. The UUFP increased at a higher rate (Lin et al., 2020; Cheng and Duan, 2021; Zhou, 2021). The rapid growth of the UUFP reflected the imbalance in development among cities and the obstacles of household registration systems between different city sizes. The 14th Five-Year Plan in China and the Outline of the 2035 Long-Term Goals both emphasize the continuing reform of the household registration system. For example, there has been an abolishment of settlement restrictions in cities with a permanent population of less than 3 million, relaxing policies for settlement in large cities with a permanent population of 3 to 5 million, and restrictions for settlement in mega-cities with a permanent population of more than 5 million. With the new-type urbanization strategy, China has been increasingly concerned about migration and the welfare of the floating population centered on cities. In this new situation, the scale of the UUFP was bound to continue to increase. Some scholars have focused on the UUFP (Duan et al., 2008) and have compared it with the RUFP in terms of social security, labor intensity, and intention to stay. In general, the UUFP has an excellent economic foundation and adaptability to urban life, which highlights the disadvantaged position of the RUFP in terms of urban integration, social security, and labor remuneration under the household registration system (Li et al., 2010; Guo and Zhang, 2012; Ma et al., 2014; Yang, 2015). However, these studies mainly focused on analyzing the sociological characteristics of the UUFP with insufficient attention to spatial flows. Ma et al. (2019) calculated the total UUFP (excluding town populations) based on the samples of the floating population with an urban household registration type. They found that the UUFP tended to move to developed regions and large cities. The gaps in economic development, employment opportunities, high-level education, and medical facilities between areas formed the internal driving force, and the proximity of space was an essential factor. Zhuo et al. (2021) emphasized the proximity effect for the migration of UUFP. The above research provides a good perspective for understanding the UUFP. However, assuming that the migration of this population relies only on the type of household registration is biased. Therefore, we used the dual interactions between urban and rural areas in household registration and current residence to define the UUFP and extract its sample data.
In short, most studies on migration in China have focused on the RUFP. In the middle and late stages of urbanization, the UUFP has gradually attracted more attention. However, research on the UUFP is still in its infancy. Few studies have examined the spatial patterns and factors influencing UUFP intention to settle down. The migration of UUFP is a key factor in shaping China’s pattern and model of urbanization. The intention to settle indicates an expectation of the residential space in the future. It is a comprehensive judgment based on subjective intention and objective constraints. The analysis of the desire to settle can help understand the spatial distribution of the UUFP and the changes in patterns of urbanization in the approaching decades. Additionally, the UUFP can reflect the attractiveness of different cities and regions and provide a reference for decision-making on the relevant policies of “attracting people” and “retaining people,” especially for the rational talents competition among different urban areas.

3 Research methods and data

3.1 Research methods

A two-category logistic regression model was applied to detect the main drivers of urban-to-urban migrants’ settlement intentions.
$\operatorname{logit}(P)=\ln \left(\frac{P}{1-P}\right)=\beta_{0}+\beta_{1} X_{1}+\cdots+\beta_{n} X_{n}$
where P is the intention of the UUFP in China to settle (the probability of intending to settle in the current residence), P/(1 - P) is the ratio of the probability that the UUFP in China intends to settle in its current residence to that whereby it does not intend to settle in this residence, Xn is the independent variable, and exp(βn) is the odds ratio.

3.2 Data sources

We obtained the research data from the 2017 survey for the dynamic monitoring of the national floating population organized by the China Health Commission. The surveyed objects covered the floating population that had lived in the local area for 1 month or longer, were not registered with the district (county, prefecture), and were 15 years of age or older. The population was sampled through the stratified, multistage, and scale-proportionate PPS sampling method. Questionnaires were distributed to selected households and filled out by any family member. Table 101 in the questionnaire solicited the basic information of the family members. The questionnaire’s cover stated, “Sample Site Type” (1. Residential Committee, 2. Village Committee). “Residential Committee” indicated that the residence was a city or town. Question 301 stated, “The geographical location of your hometown (place of household registration)? 1. Rural area. 2. Township. 3. County town. 4. Prefecture-level city. 5. Provincial capital city. 6. Municipality directly under the central government.” According to the definition of the urban functional area in China’s 2008 edition of the “Statistics on the Classification of Urban and Rural Areas,” “urban” referred to municipal districts, cities without districts, neighborhood committees, and other areas connected to the construction of districts and municipal government resident sites. “Town” referred to residential committees and other areas where the structure of government residences was connected to the county’s government station and other towns outside the urban area. It also included the housing of independent residents unrelated to the construction of government residences. Additionally, housing in areas that had a permanent population of more than 3000 people, including particular areas such as industrial and mining areas, development zones, scientific research units, colleges, universities, farms, and forest farms, were included. “Village” was defined as the area outside the designated towns. Thus, the urban area was considered county-level cities, counties, prefecture-level cities, provincial capitals, and municipalities with household registration. The group that met the requirements of both current residence and place of household registration in the physical area of the urban area or town was recognized as the UUFP. In addition, for consistency with the time scale of the census year, the sample with a duration of 6 months or longer was selected as the final sample for the urban floating population. After the above screening steps, 31,017 samples were obtained, accounting for 18.25% of all samples collected. In addition, floating population samples that were not included in the prefecture-level administrative units in the “China Urban Statistical Yearbook (2017)” were excluded. Finally, a sample of the urban-to-urban floating population of 26961 individuals was obtained.
The household registration attribute was the dual urban structure that divided the population into the “local population” and “out-of-town population.” The floating population usually could not obtain social welfare, public housing, and the educational resources available to the local population. Thus, it was difficult for the floating population to integrate with the city and achieve full citizenship (Cao et al., 2021). If the UUFP was entirely civic, it was necessary to go through the two-stage decision-making process of “local residence” and “local registration.” We used the monitoring question “If you meet the requirements for local registration, are you willing to move your household registration to the local area?” to represent the intention of the UUFP to settle in its destination. The choice “willing” was recorded as 1: “unwilling,” or 0: “not sure,” and the percentage of surveyed individuals in the area who chose “1” was calculated. The higher the percentage was, the higher was the intention to settle in the given area. The data on influential individual factors were derived from the 2017 National Floating Population Dynamics Monitoring Survey (CMDS), and data on regional factors were taken from the 2016 China City Statistical Yearbook. Regional factors like urban social amenities and settlement intentions may present issues of reciprocal causation, thereby resulting in endogeneity problems. Although urban social amenities were positively correlated with settlement decisions and location choices, it was likely that increasing the numbers of UUFP workers improved the urban social amenities and promoted continuous urban economic development. Therefore, urban variables lagged 1 year in the model to solve the potential endogenous problem.

3.3 Variable design

In general, the intention of the UUFP to settle was affected by both individual factors and regional factors. Individual factors included gender, age, education, marital status, mobility characteristics (range and duration of mobility), the ratio of monthly average housing expenditure to total expenditure, average monthly income, occupation type, housing pressure, and social integration. The scope of mobility was closely related to the cost of mobility, and a more extensive range of mobility often meant higher cost. The longer the mobility lasted, the higher the degree of social integration and the higher the intention to settle. Drawing on past research (Lin and Zhu, 2016; Liu and Wang, 2020), we divided the types of workers into white-collar workers and other personnel that included business service personnel, production, and transportation equipment operators, those involved in agriculture, forestry, animal husbandry, fishery, and water conservancy, and personnel with no fixed occupation and no employment. White-collar workers were the heads of state agencies, party organizations, enterprises and institutions, professional and technical personnel, civil servants, clerks, and related personnel. Housing pressure included the ratio of housing expenditure to total expenditure and the nature of housing. The greater the housing expenditure ratio was, the greater was the economic pressure faced by locals, which weakened their intention to settle. On the other hand, if an individual owned real estate, this increased their desire to settle. The status of social integration of destination included three indicators: whether to participate in medical insurance for urban residents or employees, the degree of local social recognition, and the degree of participation in local activities. Urban medical insurance was related to the employment status of the floating population. The indicator for the involvement in medical insurance was focused on integrating the floating population at the economic level. Those with social identity and activity involvement concentrate on integrating the floating population at the socio-psychological level. We used research by Lin and Zhu (2016) to construct an index of social identity and participation in local activities.
The push-pull theory holds that the factors, including regional factors in origin and destination, individual factors, and intermediate barriers, affect migration decisions (Heberle, 1938). Empirical studies in China have also found that regional factors act as external factors in population migration in China (Goodkind and West, 2002; Hu et al., 2011). In recent years, the characteristics of destinations have been more incorporated into the analysis framework of settlement intention (Lin and Zhu, 2016; Gu et al., 2018). Regional factors included regional economic factors and amenity factors. Regional economic factors provided economic opportunities for the labor market, while amenity factors referred to non-economic elements unique to the locality (Cao et al., 2021). Migration responds to differences in utility caused by uneven development and economic differences between regions. Migration seeks to find high income, low unemployment, and better job opportunities. When the primary motivation is satisfied, amenity becomes the major motivator of migration (Liao and Wang, 2019). As China’s urbanization entered the middle and late stages, industrialization and people’s living standards have continually improved. The impact of regional amenity on China’s migration has gradually appeared and strengthened. Therefore, the destination-related factors included two types of economic factors and amenity factors. Economic factors included economic development, per capita wages, employment structure, and housing prices, which reflect the regional income, salary package, employment opportunities, and economic cost of settlement, respectively. GDP can reflect the level of economic development of a given place. The higher the level of economic growth is, the stronger is the intention to settle down. The higher the per capita income is, the stronger is the intention to settle. The ratios of employees in the secondary and tertiary industries represented employment opportunities provided by the region. Non-agricultural industries absorb more labor, especially the tertiary sector (Huang et al., 2018a). High housing prices mean a high cost of settlement, which weakens the intention to settle. Amenity factors included air quality, educational resources, medical facilities, and cultural environment. The AQI reflected air quality. The teacher-pupil ratio in junior and high schools and the number of doctors per 10,000 each reflected the status of educational resources and basic medical facilities. The collection of books per 100 people in public libraries demonstrated the level of social and cultural development. Distance increased the cost of migration. We estimated that the migration distance was inversely proportional to the intention to settle down and was included in verifying the driving distance between the origin and the destination. The descriptive statistics of various indicators are shown in Table 1.
Table 1 Description of variables representing factors influencing the settlement intention of the urban-to-urban floating population
Influential factor Variable name Variable description Unit
Male Gender, male=1, female=0 -
Age Numerical variables Years
Primary, junior, high school, college, undergraduate, postgraduate Education level, primary, junior, high school, college, undergraduate, postgraduate =1, illiterate as the reference group -
Married Marital status, married=1, not married=0 -
Migration feature Interprovincial, interprefecture Flow range, interprovince, interprefecture=1, intercounty as the reference group -
Flow time Current flow time, numerical variables Month
Economic revenue and expenditure Housingexp Housing expenditure as a percentage of total expenditure, numerical variables %
Self Housing nature, self-purchased house=1, others=0 -
Income Average monthly income, numerical variables yuan
Type of job White-collar Types of jobs: White collar=1, others=0 -
Medical Economic integration, participation in urban medical insurance=1, others=0 -
Identity Psychological integration, social identity, numerical variables -
Participation Psychological integration, participation in local events, numerical variables -
PGDP Per capita GDP, numerical variables yuan/person
Psalary Per capita salary, numerical variables yuan/person
House price Numerical variables yuan/km2
Tertiary The proportion of employees in the tertiary industry, numerical variables %
Air quality Numerical variables -
Tea-stu ratio Teacher-pupil ratio in junior and high schools, numerical variables -
Doctors Number of doctors per 10,000 people, numerical variables People (104)
Library Public library collections per 100 people, numerical variables Book
Distance factor Distance Driving distance, numerical variables km

3.4 Descriptive statistical analysis

After removing missing variables, a total of 26,961 samples were retained. Table 2 shows that the ratio of males in the UUFP was 47.8%. The average age was about 38 years. The illiteracy rate was close to 0%, and the percentage of college graduates was about 40% of the total, indicating a highly educated population. About 79.6% of the respondents were married. Long-distance migrations (interprovincial and interprefecture) were the most critical migrations, and the total ratio of the two sets of samples was approximately 84.3%. The duration flow at any given time was long, and the average duration was approximately 86 months, indicating that the population had increased mobility. Nearly half (44.2%) of the UUFP had purchased their own houses. The average monthly income was higher than the national average (approximately 6000 yuan/month) at about 9242 yuan, the high-income group. In general, most members of the UUFP were older, highly educated, and married. They usually had higher income and a broader migration experience; they migrated long distances.
Table 2 Results of statistical descriptions of the variable
Variable Sample size Mean Standard deviation Minimum Maximum
Male 26961 0.478 0.499 0 1
Age 26961 37.88 12.212 15 96
Illiteracy 26961 0.006 0.081 0 1
Primary 26961 0.052 0.219 0 1
Junior 26961 0.274 0.446 0 1
High 26961 0.277 0.448 0 1
College 26961 0.197 0.398 0 1
Undergraduate 26961 0.175 0.380 0 1
Postgraduate 26961 0.019 0.138 0 1
Married 26961 0.796 0.403 0 1
Interprovince 26961 0.484 0.500 0 1
Interprefecture 26961 0.359 0.480 0 1
Intercounty 26961 0.156 0.363 0 1
Flow time 26961 86.380 70.889 13 784
Housingexp 26961 0.247 0.236 0 1
Self 26961 0.442 0.497 0 1
Income 26961 9242.406 8130.039 1000 180000
White-collar 26961 0.180 0.385 0 1
Medical 26961 0.251 0.433 0 1
Identity 26961 25.801 3.258 11 32
Participation 26961 1.092 1.129 6 12
PGDP 26961 78893.57 4493.31 10974.58 597888.9
Psalary 26961 69294.78 19219.54 35229 113037
House price 26961 9799.527 6811.969 2245.45 33942.34
Tertiary 26961 56.256 13.544 17.8 86.37
Air quality 26961 81.465 17.998 38 132
Tea-stu ratio 26961 0.090 0.022 0.044 0.197
Doctors 26961 28.661 13.232 7.925 183.892
Library 26961 241.704 459.770 5.68 7347
Distance 26961 617.897 729.183 0 5150.937

4 Spatial differences of urban-to-urban floating population’s intention to settle

4.1 Settlement intention of UUFP for interprovince, interprefecture, and intercounty gradually weakened

The interprovincial floating population showed the highest intention to settle down (58.86%), which was higher than the national average (52.39%), followed by the interprefecture (49.65%) and intercounty (39.53%) (Figure 1). The intercounty, interprefecture, and interprovincial migration distances increased, and the settlement intention maintained the above sequential relationship. These increased distances meant that the settlement intention of UUFP was positively related to distance, and a destination was more attractive if it was farther away from the origin.
Figure 1 Settlement intention of the urban-to-urban floating population at different scales in China in 2017

4.2 Concentration of intention of the urban-to-urban floating population to settle in Beijing, Tianjin, Shanghai, and Zhuhai

The spatial results of the intention to settle in areas of specific destinations are shown in Figure 2. As the nation’s political and economic centers, Beijing and Shanghai ranked first and second in the country for attractiveness to settle down. There was no significant sign of UUFP groups fleeing Beijing and Shanghai. However, because of the economic downturn and loss of labor in the northeastern region, the intention to settle in cities there was generally low. The central cities of the three major urban agglomerations that include Beijing (0.875), Tianjin (0.712), Shanghai (0.872), Suzhou (0.612), Zhuhai (0.803), Shenzhen (0.679), and Guangzhou (0.622) were all cities where migrant populations were very willing to settle down. These cities have developed economies and offer many job opportunities. Qingdao (0.669), Linyi (0.619), Jinan (0.611), Longyan (0.706), Xiamen (0.705), Sanya (0.751), and Haikou (0.632) as well as cities with an excellent coastal environment, also had high values in terms of desirable destinations. Certain cities in the central and western regions were similarly attractive, such as Chengdu in Sichuan Province (0.512) and Wuhan in Hubei Province (0.609). In general, regions where migrants were more willing to settle down at the prefecture-level had developed economies, coastal areas, good environmental conditions, and many amenities.
Figure 2 Settlement intention of the urban-to-urban floating population for destinations in China in 2017

5 Factors influencing the UUFP intention to settle

5.1 Questionnaire-based explanatory factors

The top three reasons for UUFP at a nationwide scale or an interprovincial level were: broad space available for personal development, good educational opportunities for children, and ease of becoming familiar with local life. The difference between the intention of interprefecture migrants to settle within a province and intercounty migrants to settle within a prefecture was caused by the same three factors. There was a significant gap in the “high-income level” among the top three reasons. The economic motivation for the UUFP to settle was weaker than those owing to personal career development and children’s education (Figure 3). The difference in employment opportunities and educational services between provinces was more remarkable than between prefectures and counties. The interprovincial UUFP could obtain better employment and education services from their current residence than those requiring household registration. The interprovincial floating population could get better public services than intercounty and interprefecture floating populations, resulting in the interprovincial floating population’s intention to settle down to be higher than that of intraprovince. System friction is the reason for this. Intercounty mobility within a prefecture allowed for the same level of public services without needing to register and settle down. However, after crossing the boundaries of administrative divisions, certain thresholds and obstacles need to be overcome to settle down, especially in huge cities and mega-cities. Economic factors such as income were not major factors in the settlement of the UUFP. Personal and family development was the first consideration. For interprefecture and intercounty migrants within a province, the status of urban transportation and daily conveniences were more important than income. In summary, personal development and children’s education were the dominant factors that influenced the decision of UUFP to settle down, and income was not a dominant factor. Members of this population who chose to settle down were focused on personal and family development and life amenities.
Figure 3 Ratios of the main reasons for the urban-to-urban floating population intending to remain local in China in 2017

5.2 Model-based explanatory factors

Because the explained variable is a discrete binary variable, the linear probability model (LPM) and Logit model have advantages and disadvantages for estimation per relevant studies (Liao and Wang, 2019; Liu and Wei, 2019). First, LPM and Logit were used for estimation, and the two results were compared. Models 1 and 3 did not include regional variables, while Models 2 and 4 included regional variables. Models 1 and 2 were the regression results of LPM, Models 3 and 4 were the regression results of Logit, and the coefficients shown in the table were marginal effects (Table 3). Comparison of the results of the two models shows that the coefficients and significance levels of the explanatory variables were similar, and the regression results of the two models were consistent. These results show the robustness of the basic regression results. In addition, the Logit model’s ordinary standard error and robust standard error estimation were very close. Thus, there was no need to focus on the model setting. In this paper, we analyzed only the robust Logit model standard error estimation results, and the regression results were marginal effects. After adding regional variables, the R2 of both the LPM and Logit models increased in detail, indicating that individual and regional variables gave more substantial explanatory power to the model and proving the need for considering regional factors. The variance inflation factor (VIF) of each variable was less than four, and there was no severe multicollinearity.
Table 3 Results of logistic regression of the urban-to-urban floating population settlement intention (N=26961)
Variable LPM LPM Logit Logit
Model Model 1 Model 2 Model 3 Model 4
Individual factors
Male -0.027***(0.000) -0.010*(0.068) -0.026***(0.000) -0.011**(0.047)
Age -0.0004*(0.086) -0.0014***(0.000) -0.0004*(0.088) -0.0013***(0.000)
Illiteracy (Reference group)
Primary 0.013(0.735) 0.018(0.634) 0.014(0.714) 0.022(0.565)
Junior 0.035(0.329) 0.025(0.478) 0.036(0.332) 0.028(0.446)
High school 0.082**(0.022) 0.058(0.106) 0.079**(0.031) 0.056(0.127)
College 0.141***(0.000) 0.098***(0.007) 0.134***(0.000) 0.093**(0.012)
Undergraduate 0.173***(0.000) 0.107***(0.003) 0.170***(0.000) 0.109***(0.004)
Postgraduate 0.149***(0.000) 0.064***(0.008) 0.146***(0.001) 0.067**(0.021)
Married 0.017**(0.025) 0.035***(0.000) 0.014*(0.077) 0.034***(0.000)
Interprovince 0.191***(0.000) 0.067**(0.015) 0.183***(0.000) 0.014***(0.006)
Interprefecture 0.102***(0.000) 0.058***(0.000) 0.098***(0.000) 0.048***(0.000)
Intercounty (Reference group)
Flow time 0.0003***(0.000) 0.0001***(0.001) 0.0003***(0.000) 0.0002***(0.000)
Housingexp 0.125***(0.000) 0.087***(0.000) 0.125***(0.000) 0.084***(0.000)
Self 0.014**(0.024) 0.029***(0.000) 0.013**(0.037) 0.026***(0.000)
Income 0.008***(0.000) 0.011***0.005 0.007***(0.000) 0.009***(0.007)
White collar 0.010**(0.030) 0.014*(0.063) 0.008**(0.052) 0.012***(0.024)
Medical 0.116***(0.000) 0.103***(0.000) 0.111***(0.000) 0.097***(0.000)
Identity 0.027***(0.000) 0.028***(0.000) 0.026***(0.000) 0.027***(0.000)
Participation 0.010***(0.000) 0.014***(0.000) 0.011***(0.000) 0.014***(0.000)
Destination factors
PGDP 0.0001*(0.086) 0.0001*(0.057)
Psalary 0.0002***(0.000) 0.0001***(0.000)
House price 0.001***(0.000) 0.0001***(0.000)
Tertiary 0.009**(0.04) 0.0010***(0.000)
Air quality 0.008***(0.000) 0.009***(0.000)
Tea-stu ratio 0.202**(0.035) 0.609***(0.000)
Doctors 0.051**(0.031) 0.042*(0.094)
Library 0.0016**(0.037) 0.0019**(0.040)
Distance 0.0019***(0.000) 0.0020***(0.001)
F/Chi2 266.2 369.61 3066.87 4147.06
R2/Pseudo R2 0.1275 0.1902 0.0991 0.1563

*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively; the data in parentheses are robust standard errors; the LPM model reports R2 and F, and the Logit model reports Pseudo R2 and Chi2.

For the demographic characteristics of individual factors, the male coefficient was negative, and a significance test was obtained, indicating that females influenced the UUFP intention to settle. For many years, Chinese married life has been dominated by the idea of men controlling the outside factors of the house and women influencing that inside. This means that the cost-related pressure for men to settle was greater than that for women. Furthermore, the destination could provide better conditions for females to find suitable male partners. Therefore, women were more willing to covert hukou than men. The coefficient of age was negative and significant, indicating that younger members of UUFP were more eager to settle down in cities. As age increased, the intention to settle down became weaker. Young urbanites are generally more competitive in employment than older people. They are more willing to work and settle down as part of the labor force, while older people prefer to stay in their original city. Illiteracy, primary school-, junior school-, and high school-educated status had no significant influence on the UUFP intention to settle down. College-undergraduate-, postgraduate-educated status had a significant positive impact on this willingness. The level of education strongly enhanced the intention to settle down by improving the members’ competitiveness for employment. Married people were significantly more willing to settle down, and those who had children prioritized education. The coefficients of demographic characteristics found in our study were consistent with results reported in the mainstream rural-to-urban migration literature on this issue (Connelly et al., 2009; Tang and Feng, 2015; Liu and Wang, 2019).
For mobility characteristics, relative to intercounty, interprovincial, and interprefecture migration, impacts on settlement intention were positive. The regression coefficients showed that the interprovincial effect was stronger than the interprefecture. This result shows that distance was not an obstacle in the intention to settle. Modern modes of transportation such as high-speed rail have shortened travel times. In contrast, the needs of the UUFP related to their children’s education and personal development were stronger than economic motivations. The greater the geographical distance between the place of origin and destination, the more substantial the heterogeneity of such factors as employment and education. The destination was often better than the origin in this instance. Therefore, the greater the distance was from the origin, the stronger the intention to settle down was. Institutional friction was the primary reason for this and verified the above analysis. The positive and significant coefficient of flow time suggests that the longer the migration had been to a given place, the closer the connection to local life and the more socially integrated. However, Liu et al.’s study (2018) shows that the interprefecture rural-to-urban migration within the province had the highest settlement intention, followed by the interprovincial. Gu et al. (2020) also found that distance was inversely proportional to the intention of migrant workers to settle down. This fact meant that the ability of the UUFP to overcome the cost of long-distance migration was more potent than that of the RUFB. Strong ability strengthened intention while poor ability weakened it.
For personal economic income and expenditure, the increase in average monthly income significantly increased the intention to settle down and reduced the pressure on the destinations to improve quality of life. Owning a house had a significant effect on the intention to settle. For most of the population, the cost of a house was the majority of its economic expenditures. Owning real estate in an area helped establish a closer relationship with the local economy and promoted the intention of settlement. Housing expenditures accounted for a large ratio of the total, indicating that the UUFP was subjected to significant pressure from destination areas. Owing to economic pressure, these populations are likely to move. However, the regression results show that the ratio of housing expenditure had a significant positive impact on the intention to settle down, possibly because most people now buy houses through loans. Owners of houses generally bear a high ratio of housing expenditure. Therefore, the increase in the ratio of housing expenditures has forced people to settle down. In addition, the primary UUFP consideration in deciding to settle was children’s education; modern education in China is often tied to housing (school district housing). Therefore, owning a house and high housing expenditures facilitated the settlement of the UUFP. Generally speaking, rural-to-urban migrants with higher incomes and lower expenses were more willing to settle down in destinations (Zhu, 2007; Huang et al., 2018b), as were migrants willing to buy a house (Gu et al., 2020).
Compared with other types of workers, white-collar workers have significantly promoted the settlement of UUFP. White-collar workers’ social status and competitiveness are higher than other workers, enhancing their ability and intention to settle. Participation in urban medical insurance and social activities significantly increased the intention to settle. The correlation coefficients were higher than per capita income, indicating that social integration had a more vital role in promoting the intention to settle than economic income. Rural-to-urban migrants with high employment status and those with insurance also tended to stay in destinations (Huang et al., 2018a).
For the economic factors in destination areas, the per capita GDP and per capita wage had a significant positive impact on the intention to settle. Both reflect the UUFP’s pursuit of income. A high level of economic development means more opportunities for personal development at the destination. A higher per capita salary can significantly improve the ability to settle, increasing the intention to settle. Housing prices play a significant role in hindering settlement. The higher the housing prices are, the greater the living pressure at the destination, which forces migrants to flee from areas with a high cost of living. The tertiary industry plays an essential role in absorbing employment. A high ratio of employees in the tertiary industry signifies the job market is promising and social vitality is high. Therefore, the intention to settle down is significantly increased. In addition to economic factors, amenities are the most crucial consideration for decisions related to settling down. Air quality, teacher-pupil ratio in junior and high schools, and public library collections per 100 people have significantly increased the intention to settle. In contrast, the number of doctors per 10,000 people has had no significant impact. With the improvement in people’s incomes and living standards, a green natural environment, fresh air, high-quality primary education, and an excellent cultural environment has become increasingly important in the decisions to settle down. The correlation coefficient of the teacher-pupil ratio in junior and high schools was much higher than those of the per capita GDP and per capita wages, indicating that children’s education was considered more important than economic income in the intention to settle down. Both individual and regional factors show that social factors had a more substantial influence on settling down than economic factors. The UUFP focused more attention on social integration at the destination. Distance had a significant positive impact on the intention of the UUFP to settle down, which is consistent with previous analyses. China presents enormous regional differences, and rural-to-urban migrants have moved to larger cities with more job opportunities, higher wages, and better public services (Zhu and Chen, 2010). However, larger cities have controlled the scale of in-migrants with strict implementation of the residence permit system called hukou. It has become increasingly difficult for the RUFP to settle down in larger cities and obtain local hukous. As a result, the settlement intentions of the floating population were weakened (Liu et al., 2018; Liu and Wang, 2019).

6 Discussion and conclusion

6.1 Discussion

In the middle and late stages of urbanization in developed countries (China), the positive effects of the level of education, income, and employment in the tertiary industry on migration was significant, which is in line with theories of urban population agglomeration, such as New Employment Geography. On the other hand, in some highly developed western countries (such as Switzerland, Germany, and France), people frequently moving for life and work has been increasingly common. This fact is related to many people in developed countries owning second houses and using them as leisure places. Also, many people have to adopt a multi-dwelling livelihood strategy due to life- and work-related needs (part-time jobs in two locations, taking care of family in other places) (Cédric et al., 2016). However, against China’s unique household registration system, children’s education and housing have emerged as driving factors different from those in other countries and regions.
From the analysis of the regression results, the determinants of the urban-to-urban and rural-to-urban floating population’s intentions to settle down were generally the same. Both were affected by individual and regional factors, with some differences. First, the educational level and migration ability of the UUFP were higher than those of the RUFP. Therefore, their intention to settle at long distances was more substantial than that of the RUFP. Long-distance was an obstacle for RUFP to settle down, but it did not hinder the UUFP. Second, the UUFP has always been engaged in urban economic activities. In addition to considering basic income, this population valued career promotion, children’s education, and family development. The movement of the RUFP from rural to urban areas needs to undergo a transition from a rural economy to an urban economy, and the problem of increasing income needs to be solved. The main contradictions of migration between urban-to-urban and rural-to-urban floating populations varied. The main factors affecting urban-to-urban and rural-to-urban floating population were not different, but the prioritization of these factors was (Liu and Wang, 2019; Gu et al., 2020). Public services had a more substantial impact on the UUFP’s intention to settle down (Liu and Wei, 2019). Third, most RUFP had rights and interests regarding household registration, such as homestead and land (Huang et al., 2018b). These special rights and magnets attached to the villages of origin inevitably affected the settlement of the RUFP. The UUFP was disturbed by rights and interests such as real estate. Fourth, although the urban household registration has been fully opened, there were no restrictions to the flow of household registration in small and medium-sized cities. However, there were still certain thresholds for entering super-large cities. The differences in the levels of urban scale and the potential threshold for public social services were common obstacles of the UUFP and the RUFP. For institutional barriers, the RUFP was hindered by the dual urban-rural system, while the UUFP was restricted by the various settlement policies in the cities with different population scales.
We discussed the spatial patterns and factors influencing China’s UUFP intention to settle down. Some areas need to be further researched. First, China has a vast territory, and regional differences are the core tool of geography. The results of spatial differentiation here showed that the intention to settle in different places had different manifestations. Of course, the influential factors were also regionally heterogeneous. Future research should focus on the spatial heterogeneity of these factors related to the intention to settle down. Moreover, the shift from rural-to-urban mobility to urban-to-urban mobility manifested as the transition of origin from rural-to-urban areas. We discussed only the influence of inflow-related factors and ignored outflow-related factors. In the future, a model should be developed to focus on local aspects in the areas of origin of migrant populations. Furthermore, we used a literature analysis to compare the differences in factors affecting urban-to-urban and rural-to-urban mobilities. Due to the difference in the data structures used in the article, the credibility of the results was affected. A unified model and data format should be constructed to overcome this issue in the future.

6.2 Conclusion

China has undergone large-scale rural-to-urban migration in recent decades. From 2010 to 2015, the ratio of China’s rural-to-urban migrant population decreased significantly, while the urban-to-urban population continued to proliferate, accounting for 37.9% of the total migrants by 2015 (Duan et al., 2019). The educational and occupational structures of the UUFP are relatively advanced, and they are different from other forms of floating populations in terms of reasons for mobility, distance traveled, and distribution of main destinations (Ma et al., 2014). Compared with the RUFP, the UUFP enjoys such social benefits as housing, employment, education, medical care, and insurance owing to living in urban areas (Ma et al., 2019).
Using data from the nationwide survey in 2017, we investigated the settlement intentions of the UUFP in China, focusing on the spatial differences and driving factors. This study supplies an innovative contribution to the literature in two ways. First, this paper provides a method of obtaining the UUFP data in a setting where it is difficult to get data. Second, we discovered the spatial pattern and driving factors of settlement intention for an emerging population that can be contrasted with domestic RUFP and foreign urban-to-urban migration. Our analysis indicates that most UUFP were older, better educated, and had a higher income than average. The UUFP members were generally married, had rich migration experience, and had traveled long distances across provinces and prefectures. A strong UUFP intention to settle down was concentrated in areas with developed economies, coasts, better environmental conditions, and higher amenities. Examples of these areas were Beijing, Tianjin, Shanghai, and Zhuhai.
The UUFP choice to settle was affected by microscopic individual and macroscopic regional factors. First, as the head of the family, men who settled down in urban areas experienced more pressure to buy a house than women. Women who settled in these areas could find high-quality male partners. Therefore, women were more willing to settle down than men. Young people and those with higher education levels were more competitive in the labor market than the elderly and those with lower levels of education. Therefore, young people with higher levels of education were more likely to settle down in urban areas. Married people were more willing to settle in urban areas to create a better education for their children. Finally, a high average monthly income could reduce the pressure of the cost of living in a city.
Loans are the primary means of buying a house in modern times. Owning a house also requires a certain amount of loans. Therefore, our finding that owning a house and having a high housing expenditure ratio promoted settlement was valid. The UUFP with high employment status and insurance tended to change household registration. The longer the migration time, the higher the social integration at the destination, and the stronger the intention to settle down.
The per capita GDP, per capita wages, the ratio of employees in the tertiary industry, air quality, educational resources, and cultural environment at the destination had a significant positive impact on the intention to settle. In contrast, rising housing prices had a significant negative effect. Participation in urban medical insurance, social recognition, and other activities have significantly increased the intention to settle down. The correlation coefficients of these three factors were higher than that of per capita income. The correlation coefficient of the teacher-pupil ratio in junior and high schools was more significant than those of the per capita GDP and the per capita salary. This result shows that social factors had a more substantial influence on the settlement intention than economic factors. The UUFP focused more on social integration and amenities, such as the quality of education for children at the destination. The UUFP generally migrates across provinces and prefecture, and its intention to settle down here within the province was higher than that across the county within the prefecture. The empirical model of factors influencing this intention showed that distance was not only a factor that did not hinder UUFP migration, it also had a significant positive impact on the intention to settle.
China’s new urbanization policy emphasizes the differentiated intention to settle in cities of different scales. The rural population in cities and towns has chosen other cities to settle in. Generally, the floating population of cities and towns migrated to the upper socio-economic class. In terms of geographical distribution, this population gathered in coastal prefecture clusters, the capitals of central and western provinces, and other economically developed and comfortable large cities. Unlike the RUFP, the UUFP does not depend on the welfare of rural collective land. The government should attend to this process of migration. At the same time, it should meet the needs of the UUFP’s intention to settle down, especially regarding the driving factors unique to China, such as children’s education and housing. The government can improve public services and enhance urban governance to increase urban capacity. Simply controlling the growth of urban areas works against the law of migration and is not conducive to urban development. On the contrary, the UUFP intention to move from one prefecture to another to settle within a province was relatively strong. Through industrial transfer and wisely using public resources, the government can create job opportunities and improve education and other public services to encourage urban migrants to settle down and ease the pressure on large cities.
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