Economic and government driving forces of migration

The selection of migration destinations by migrants result in the spatial pattern of migrant population distribution. Thus, analysing the determinants of migrants’ destination decisions may indicate the migrant population distribution pattern. According to push and pull migration theory, the determinants of migration can be classified as push and pull forces. Push forces are negative factors that promote migrants to leave their previous residence, and pull forces are positive factors that attract migrants to new regions [6,10]. However, previous studies based on the gravity model have found that pull forces, rather than push forces, dominate China’s internal migration [14–16]. Therefore, this study focused on pull factors.

As per neoclassical migration theory, many earlier Western studies have considered migration as an economic behaviour, namely a human capital investment based on rational cost–benefit calculation, and economic opportunity as the sole driver of migration. In particular, migrants move in search of increased income and employment opportunities [6,14]. Economic growth brought about job opportunities and increased income, and redistributed the Chinese population under the processes of marketisation, globalisation, and industrialisation (Fig 1) [17–19]. Marketisation laid the foundation for the economic development of China. The nonstate-owned economy transformed the market from egalitarianism to comparative advantage, which caused the massive migration of labour forces [20]. Marketisation also provided a foundation for globalisation because it attracted massive foreign investments. Foreign capital was initially invested in the eastern coastal areas of China. With the deepening of reforms, investments spread to inland areas [21]. The influx of foreign capital promoted the manufacturing industry and created substantial jobs, which attracted a substantial labour force. Foreign investments transformed China into a global factory and enabled financial and technical support to China’s industrialisation [18]. Industrialisation was one of the core drivers of urbanisation in China because it created employment opportunities and offered increased wages [22,23], which attracted a massive surplus of labour and reshaped the population distribution pattern.

Fig 1

Conceptual framework of economic and government driving forces of migration in China.

Apart from the aforementioned neoclassical theory which regards economic opportunities as the sole driver if migration, migrants were also found to pay attention to urban living qualities [24]. The quality of location-specific public service amenities, such as medical facilities, schools and museums, determine urban living qualities. Restrictions on household registration (Hukou) in China have been gradually lifted [6], and migrants without local Hukou are partially included in these local social welfare system. The location-specific public service amenities in China are mainly managed by the government [6,25]. Therefore, location-specific amenities can be considered as a government force to attract and accommodate migrants. Moreover, the government can regulate economic development to a certain extent through the administrative ability, thus affecting population migration. After the economic reform, China transformed into socialist market economy in which economy mechanisms operated with government intervention [4,17]. The government played a vital role in distributing socio-economic resources, such as construction land quotas and public financial investments. The administrative hierarchy level refers to the governmental administrative capacity. Cities with a high administrative hierarchy level have privileged access to the acquisition of land quotas, key industrial projects, and major infrastructure projects, which can create numerous job opportunities, improve the quality of public service [26], and increase the level of migrant attraction.

Internal migration is common worldwide [27], such as in the United States [28], Russia [29], and Latin America [30]. The migration intensity in Western countries is higher than that in China [27]. In 1981, internal migration rates in North America has reached about 17%-19% [29], and in 2018, internal migration rates in China is 17.27%. Although migration in Western countries is also influenced by socioeconomic factors, such as income, housing price and the quality of public service [28,29], government intervention in population migration is less. Unlike Western market-orientated capitalist economies, China adapts market and central planning mechanisms simultaneously. Remnants of the socialist period, such as the Hukou system and government-oriented infrastructure investments, continue to shape the patterns of economic and social development and population migration [14]. For example, there are many high-level private hospitals and private schools in Western countries, while most of China’s high-level hospitals and schools are public. Therefore, migration in Western countries is considered as a market behaviour and is mainly affected by economic factors. However, as the aforementioned discussion, economic and government driving forces jointly determine internal migration in China (Fig 1), which distinguishes China’s migration from that of Western countries [4,16,20,31]. As crucial supports of regional economic development, urban agglomerations are main accumulation areas for migrants. However, few studies have compared differences in the driving mechanisms of economic and government factors on migrant population growth within and outside urban agglomerations as well as in different urban agglomerations.

Study area and data sources

The prefecture-level city is usually the basic unit of implementation of many policies in China. Therefore, this paper analysed migrant population distribution at the prefecture-level city scale. The accurate national city-level demographic data can only be obtained from national population censuses every ten years. The 1% National Population Sample Survey in 2015 only issues estimated demographic data at provincial level. Therefore, city-level demographic data were obtained from the 5th and 6th population censuses in 2000 and 2010. In this study, people who had resided in one city for more than 6 months but had their household registration (Hukou) in another city at the time of the survey were identified as the migrant population. People whose household registration (Hukou) and residence are in the same prefecture-level city, but not in the same district, county or county-level city are excluded from migrant population. Because in China, the prefecture-level city is usually the basic unit of implementation of many policies, and they are included the same public services system and considered as locals. The migrant population was divided into intraprovincial and interprovincial migrants, which refer to migrants whose current residence and household registration were in the same province and different provinces, respectively.

The Hu Huanyong Line splits China into two approximately equal parts. But nearly 94% of the Chinese population lives on the eastern side of the Hu Huanyong Line [7]. The vector data of administrative boundaries of prefecture-level city was provided by the Resource and Environment Data Centre (http://www.resdc.cn/). According to the administrative divisions at the prefecture-city level in 2010, 362 cities were included in this study. Hong Kong, Macao, and Taiwan were excluded from this study due to the lack of data. The 5th population censuse in 2000 was adjust to the administrative division in 2010. A total of 19 urban agglomerations were selected according to the 13th Five-Year Plan (2016–2020) [1]. The boundaries of these urban agglomerations were defined according to their master plans, and mapped in Fig 2 based on the administrative boundaries in 2010. Moreover, socioeconomic data were extracted from the China City Statistical Yearbook 2011 and China Statistical Yearbook for Regional Economy 2011.

Fig 2

Study area and urban agglomerations (the administrative boundaries of prefecture-level city are republished from [32] under a CC BY license, with permission from the resource and environment data centre, original copyright 2015).

Development level of urban agglomerations

The development levels of 19 urban agglomerations were identified by calculating their development degree indices [1,33]. The development degree indices of urban agglomerations were composed of 14 sub-indexes, covering overall level of economic development, city size, infrastructure conditions, urbanization level, industrial superiority, etc. The weight coefficients of the 14 indexes were consistent with Fang et al. [33]. Socioeconomic data were extracted from the China City Statistical Yearbook 2011 and China Statistical Yearbook for Regional Economy 2011.

The development levels of urban agglomerations were grouped into three main grades: high level, middle level, and low level. The middle level was further divided into lower- and upper-middle levels (Table 1). Urban agglomerations with high and upper-middle levels of development are mainly located in coastal regions and along the Yangtze River. Moreover, high-development-level urban agglomerations are surrounded by urban agglomerations with middle level of development. Urban agglomerations with low level of development are located in the inland midwest region and are far away from high-development-level urban agglomerations (Fig 2).

Table 1

Development level of urban agglomerations in China in 2010.

Development level		Total amount	urban agglomeration
Low level		4	Central Yunnan (CYN), Lanzhou-Xining (LZXN), Hu-Bao-E-Yu (HBEY), Central Guizhou (CGZ)
Middle level	Lower-middle level	7	Northern Tianshan Mountains (NTM), Guanzhong (GZH), Central Plains (CPL), Central Shanxi (CSX), Harbin-Changchun (HBCC), Beibu Gulf (BBG), Ningxia Yellow River (NYL),
	Upper-middle level	5	Middle Reaches of Yangtze River (MYZ), Shandong Peninsula (SDP), Chengdu-Chongqing (CDCQ), Western Coast of Taiwan Straits (WCTS), Mid-southern Liaoning (MSLN),
High level		3	Yangtze River Delta (YRD), Pearl River Delta (PRD), Beijing-Tianjin-Hebei (BTH)

Theil index

The Theil index can measure total inequality as well as within-group and between-group inequality. This index was first used to examine the income gap among different countries [34,35]. In this study, the Theil index measured the inequality of migrant population distribution for reflecting the influence of urban agglomerations on the migration pattern. The overall Theil index and the Theil indices of urban agglomerations at different development levels were calculated. Cities were then divided into two groups, namely cities within and outside urban agglomerations, and we decomposed the overall Theil index into within-group components and between-group components.

Empirical models and variable specification

Economic and government forces shaped the observed population redistribution patterns. Explanatory variables (Table 2) were selected according to aforementioned conceptual framework (Fig 2) and following previous studies. With regard to economic factors, marketisation paved the road for globalisation [19]. These two processes brought capital and technology for industrialisation, which created a massive number of jobs and was the core driver of economic growth in China [17,18]. Hence, the numbers of employees in private enterprises and self-employed individuals (PIV) were used to determine the marketisation force. The total amount of foreign capital actually utilised (FC) represents the globalisation force [18]. The average number of employees in secondary and tertiary industries (EMP) was used to measure the industrialisation force [18]. The average wage of employees (WAGE) was considered the engine of migration and denotes the level of economic development [9,36].

Table 2

Description of considered variables.

	Variables	Description
Dependent variable	MIGPOP	Natural logarithm of migrant population
Economic determinants	EMP	Natural logarithm of average number of employees in secondary and tertiary industry
	WAGE	Natural logarithm of average wage of employees
	FC	Natural logarithm of total amount of foreign capital actually utilized (2001–2010)
	PIV	Natural logarithm of employees in private enterprises and self-employed individuals
Administrative determinants	EDU	Natural logarithm of expenditure for education
	SS	Natural logarithm of expenditure for social safety and employment effort
	HLTH	Natural logarithm of expenditure for medical and health care
	GBX	Natural logarithm of local government other general budget expenditure
	LVL	Administrative hierarchy level; prefecture-level cities = 1; provincial capitals = 2; Cities specifically designated in the state plan = 3; Sub-provincial cities = 4; Municipalities directly under the central government = 5

Although the Hukou system restricts population settlement, population migration is not restricted [6]. Local governments can improve urban living quality and vary the allocation of economic resources to attract migrant population [6,25,26]. Therefore. expenditure for education (EDU), expenditure for social safety and employment effort (SS), expenditure for medical and health care (HLTH), and local government other general budget expenditure (GBX) were selected to measure different types of public services separately [37]. The administrative hierarchy level (LVL) of cities was used to capture the governmental administrative capacity [6,26]. Accordingly, the empirical model was formulated as follows:

Spatiotemporal characteristics of migrant population distribution in China from the perspective of urban agglomerations

Urban agglomerations are accumulation areas of migrant population. Urban agglomerations are the accumulation areas of migrant population. The total area of the 19 considered urban agglomerations is 3.1 × 10⁶ km², which accounts for 32.75% of the study area. The migrant population in these agglomerations accounted for 87.54% of the total Chinese migrant population in 2000 and 89.73% of the total Chinese migrant population in 2010 (Table 3). Cities with a large amount of the migrant population were mainly distributed to the east of the Hu Huanyong Line, especially in the eastern coastal region, which is in agreement with results obtained in other studies [6,9]. Some core cities of inland urban agglomerations also had large-scale migrant population, such as Chongqing, Chengdu, Nanning (Fig 3). Some cities in inland urban agglomerations, such as Chongqing, Chengdu, and Nanning, were also home to a large migrant population (Fig 3). In 2000, the average density of the migrant population in the 19 urban agglomerations was 2290people/100 km², which was significantly higher than the national average migrant population density (861 people/100 km²) and the migrant population density outside urban agglomerations (160 people/100 km²). In 2010, the migrant population density in urban agglomerations increased to 5086 people/100 km², the national average migrant population density was 1865 people/100 km², and the migrant population density outside the urban agglomerations was 286 people/100 km². With the development of Information and Communications Technology (ICT), big data, such as mobile phone signalling data and mobile app Location-based services (LBS) data, are used to detect population mobility [38]. Because most migrants return home for family reunion during the Chinese Spring Festival, and population mobility big data during this period can reflect the spatial distribution of migrant population. The YRD, PRD and BTH were also found to be the most important sink areas for migrant population based on dataset during 2016 Chinese Spring Festival [39], which matches our results.

The attraction of urban agglomerations enlarges migration differences within and outside of urban agglomerations

The migrant population has continued to flow into urban agglomerations, and the gap between the migrant population within and outside urban agglomerations has been widening. According to the statistical data in Table 3, during 2000–2010, the annual growth rate of migrant population in urban agglomerations was 8.31%, which was higher than the national growth rate (8.04%). During the aforementioned period, the annual growth rate of the migrant population outside urban agglomerations was only 5.97%. Although the annual growth rate of migrants in northeast China was 7.11% during 2000–2010, which was lower than the national average growth rate. However, the annual growth rate of migrant population in urban agglomerations in northeast China was 8.25%, which was still higher than the national rate. The proportion of migrant population in urban agglomerations increased from 87.67% in 2000 to 89.7% in 2010 (Table 3). The aforementioned results highlight the attraction of urban agglomerations to the migrant population.

The overall Theil index of China’s migrant population distribution at city level increased from 1.091 in 2000 to 1.133 in 2010, which indicates that the polarised spatial pattern of migration strengthened during 2000–2010. This polarised pattern is consistent with the previous finding that the migrant population in the top 100 largest Chinese cities was still increasing [6,9]. Cities were divided into two groups: cities within and outside urban agglomerations. The intergroup Theil index increased during 2000–2010. Moreover, the contribution rate of the intergroup Theil index for overall Theil index increased from 14.024% in 2000 to 16.681% in 2010 (Table 4), which indicates that regional differences in the number of migrants within and outside urban agglomerations has widened. Because of rapid economic development, cities in urban agglomerations have more job opportunities, higher wages, and more comprehensive infrastructure systems than cities outside urban agglomerations. These attractions have resulted in a substantial migration of people into urban agglomerations, which has enlarged the imbalance in the distribution of the migrant population in China.

Table 4

Theil index of migrant population scale and its decomposition.

	2000		2010
	Theil index	Contribution rate (%)	Theil index	Contribution rate (%)
National	1.091	100.00	1.133	100.00
Intra-group	0.938	85.976	0.944	83.319
Inter-group	0.153	14.024	0.189	16.681
Low level	0.685	-	0.587	-
Lower-middle level	0.532	-	0.726	-
Upper-middle level	0.626	-	0.795	-
High level	0.810	-	0.718	-

Effect of urban agglomerations on migrant population distribution

According to the aforementioned statistical analyses and data in Tables 3 and 4 and Fig 3, it is found that the spatiotemporal characteristics migrant population distribution in urban agglomerations at different development level agree with classical unbalanced regional development theories, such as Friedmann’s core–periphery theory, Hirschman’s unbalanced growth theory, and Williamson’s inverted-U theory. The regional inequality and urbanisation rate vary in an inverted-U-shape with economic development (Fig 4). In the early development stage, the agglomeration of productive factors promotes the rapid growth of regional centres, which results in the formation of a mono-core structure. In this stage, the urbanisation rate increases and the regional inequality gradually widens. In the developed stage, the diffusion effect promotes the development of surrounding areas, which results in the formation of a balanced and complex multicore structure. In this stage, the urbanisation rate decreases and the regional inequality gradually narrows [11,41]. Migrant population is an index of urbanisation in China [9]. Urban agglomerations at different development levels have different urbanisation levels, and different migrant population distribution pattern. A conceptual model of China’s migrant population distribution was developed from the perspective of urban agglomerations.

Fig 4

The curve of the urbanisation rate, regional inequality, and urbanisation stage.

Urban agglomerations with low level of development level just enter the accelerated stage of development when the urbanisation level and rate are relatively low [41]. In low-development-level urban agglomerations, the population and population density of migrants were the lowest. The migrant population growth rate, proportion of the interprovincial migrant population, and Theil index of migrant population distribution were relatively low (Tables 3 and 4 as well as Fig 4). Because of the relatively low regional economic development level, various production factors gathered to individual core city. Population migrated from edge cities and cities outside urban agglomerations to core cities. The population growth rate of core cities was high, and differences in the migrant population of core and edge cities in urban agglomerations began to widen. Urban agglomerations with low level of development are located far away from those with high level of development. Therefore, the population in edge cities within urban agglomerations and cities outside urban agglomerations preferred to migrate to the core city within the same province rather than to a far-away urban agglomeration with high development level. Consequently, a predominance of the intraprovincial migrant population was observed (Fig 5A).

Fig 5

Conceptual model of migrant population distribution in urban agglomerations.

Urban agglomerations with lower-middle level of development were in the mid-acceleration stage of urbanisation, and their urbanisation rates began to accelerate [41]. In urban agglomerations with a lower-middle level of development, the population, population density, and proportion of interprovincial migrants were relatively low. However, the growth rate of migrant population and the Theil index of migrant population distribution were relatively high (Tables 3 and 4 as well as Fig 4). In the mid-acceleration stage of urbanisation, industrialisation began to dominate economic growth. With increased wages available in cities, rural surplus labourers migrated to cities and towns and populations migrated from edge cities to core cities. The size of cities and towns as well as their population increased. With the rapid flow of various production factors to core cities, gaps between core cities and other cities within urban agglomerations widened. Moreover, a sub core city began to emerge in urban agglomerations but was not completely formed. Because urban agglomerations with lower-middle level of development were located close to high-development-level urban agglomerations, their attraction to surrounding cities outside urban agglomerations was relatively weak, and some people chose to migrate to urban agglomerations with a high level of development. Therefore, the migrant population was still dominated by intraprovincial migrants. The regional inequality of migrant population distribution thus widened (Fig 5B).

Urban agglomerations with upper-middle level of development were in the final accelerated stage of urbanisation, and their urbanisation rate reached the peak value. Industrial development entered a mature stage, and the economy was growing at a high speed [41]. In urban agglomerations with upper-middle level of development, the population, population density, and proportion of interprovincial migrants were relatively high. The growth rate of migrant population and the Theil index of migrant population distribution were the highest for these urban agglomerations (Tables 3 and 4 as well as Fig 4). A substantial number of rural surplus workers migrated to cities. Labour influxes to all cities within the aforementioned urban agglomerations increased. Moreover, the migrant population of most cities within urban agglomerations with upper-middle level of development continued to increase. When the growth rate of migrants in core cities increased, a sub core city was formed. Links among cities within the aforementioned urban agglomerations were strengthened, and the regional inequality of migrant population distribution widened. However, because urban agglomerations with upper-middle level of development were located close to high-development-level urban agglomerations, the outflows of population were substantial (Fig 5C).

High-development-level urban agglomerations were in the deceleration stage of urbanisation, and their speed of urbanisation was decreasing [41]. These urban agglomerations had the highest migrant population and migrant population density, and the interprovincial migrant population was dominant. However, the growth rate of the migrant population and the Theil index of migrant population distribution were the lowest for the aforementioned urban agglomerations (Tables 3 and 4 as well as Fig 4). Their attraction to the migrant population had reached saturation. In the deceleration stage of urbanisation, the industrial structure was gradually being upgraded and the service industry gradually became a pillar industry. The education level and professional skills of the migrant population improved. The economy was still growing; however, it entered a transitional period. Two or more mega-cities emerged, and a double-centre and secondary polycentric structure was formed (e.g., Hangzhou and Nanjing in YRD, Foshan and Dongguan in PRD). The regional inequality of migrant population distribution was narrowing. The attractiveness of high-development-level urban agglomerations extended to the entire country and not only to the province of residence. The total migrant population within the aforementioned urban agglomerations was large. Moreover, core cities, large cities, and medium cities in these agglomerations exhibited a trend of migrant population growth. Interprovincial migration predominated. Although the growth of the migrant population was large, due to the limited bearing capacity, the attraction of urban agglomerations with high level of development to the migrant population reached saturation (Fig 5D).

Modelling determinants of migrant population distribution in China

Because of the lack of statistical data, the data of 285 prefecture-level and above cities were modelled using econometric models. The variance inflation factors (VIFs) of all independent variables were less than 10 and tolerance values were greater than 0.1 (Table 5), which indicates that independent variables were not collinear. An economic force model, a government force model, and an integrated model were applied for cities within and outside urban agglomerations, respectively. Cities within urban agglomerations were then categorised by the development level. Estimation results of econometric models are presented in Table 5.

Table 5

Estimation results of econometric models.

		Pooled			Outside urban agglomeration			Within urban agglomeration			Development level of urban agglomerations
	Correlation coefficient	Coefficient	VIF	Tolerance	Economy	Government	Integrated	Economy	Government	Integrated	Low level	Lower-middle level	Upper-middle level	High level
EMP	0.627***	0.199***	2.145	0.466	0.091**	/	0.557***	0.665***	/	0.619***	0.505	0.683***	0.843***	0.663***
WAGE	0.664***	1.214***	1.940	0.515	1.247***	/	0.951***	1.894***	/	1.423***	2.073*	1.282***	1.524***	1.468**
FC	0.672***	0.075***	1.858	0.538	0.057	/	0.053	0.101***	/	0.057*	-0.165*	0.119***	0.132***	-0.020
PIV	0.759***	0.260***	3.058	0.327	0.435***	/	0.078	0.237***	/	0.222***	0.129	0.231**	0.117	0.164
EDU	0.751***	0.643***	9.951	0.100	/	0.690**	0.089	/	1.180***	0.463***	-0.652	0.275	-0.257	0.977***
SS	0.608***	-0.153*	4.609	0.217	/	-0.147	-0.136	/	-0.247**	-0.166*	0.092	-.011	-0.266*	0.031
HLTH	0.624***	-0.698***	9.207	0.109	/	-0.573*	-0.337	/	-1.191***	-0.636***	-0.433	-1.067***	0.380	-1.192***
GBX	0.816***	0.555***	9.294	0.108	/	0.673**	0.588**	/	1.278***	0.397***	1.220*	0.753***	0.004	0.351
LVL	0.635***	0.148***	2.064	0.485	/	-0.245	5.801**	/	0.248***	0.049	0.768*	0.104	0.091	-0.067
α0	/	-12.642***	/	/	-7.950**	2.894	-15.586***	-19.691***	-3.352***	-15.458***	-20.593**	-15.125***	-15.200***	-16.318***
Adjusted R2	/	0.834	/	/	0.459	0.322	0.582	0.847	0.785	0.862	0.935	0.840	0.835	0.890
Significance	≤0.001	≤0.001	/	/	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001	≤0.001

Note:

* Significance level at 10%.

** Significance level at 5%.

*** Significance level at 1%.

All variables exhibited a significant correlation in the integrated model (Table 5), which indicates that economic and government forces jointly attracted migrant population. The adjusted R² values of the economic models were higher than those of government models for cities within and outside urban agglomerations (0.847 > 0.785 and 0.459 > 0.322, respectively), suggesting that the main pull force of migration was the economy rather than the government. Moreover, integrated models’ adjusted R² values were larger than government and economic models’ but smaller than the sum of adjusted R² values of government and economic models for cities within and outside urban agglomerations (Table 5). This result indicates that economic and government forces interactively influenced migrants’ destination decisions.