1. Background
Rapid industrialization and urbanization are changing the urban landscape in emerging markets [
1]. One of the most profound changes affecting human society is the massive migration to cities to establish new livelihoods. This will eventually lead to the reconstruction of urban spaces and the integration of human lifestyles, both socially and economically. According to the International Migration Report 2017, there exist 253 million international migrants around the world, which have increased by approximately 49% since 2000, accounting for 3.4% of the total global population [
2]. With the development of the global economy, most employees have gradually moved into regions with abundant economic activities and job opportunities. Furthermore, large cities offer lucrative attractions regarding the settlement intention of migrants because of the superior economic and non-economic incentives, including social, medical, educational, and municipal infrastructures that city dwellers enjoy [
3].
In the past few decades, cities around the globe have experienced unprecedented growth, providing millions of people with social mobility and economic prosperity. However, the proliferation and continuous growth of urban areas have brought about many problems and challenges for the future of sustainable urban development [
4]. One of the main consequences of urban expansion caused by rapid urbanization, population growth, and changes in consumption patterns is the gap between the rich and the poor in modern cities, the generation of large amounts of urban waste and urban pollution [
5], and the increasingly serious environmental degradation [
6]. Risk society theory emphasizes the importance of solving these ecological problems [
7], which are new challenges facing the current rapid expansion of urban immigrants and urban life.
Therefore, migrants have contributed greatly to promoting global urbanization [
8,
9,
10]. Especially in China, which is one of the most important advanced emerging economies, the accelerating transformation and upgrading of economic development and the rapidly growing agricultural productivity in recent decades have released millions of rural labor workers to pursue industrial activities in cities. Statistics show that in 2018, the number of Chinese migrants was 241 million, accounting for 17.3% of the total population [
11]. Since the early 1980s, a large number of people have migrated from rural areas to cities and towns [
12]. The emergence and growth of urban migrants have become inevitable phenomena in the process of Chinese industrial transformation and economic development, and have been one of the most important reasons for the rapid urbanization of China [
13,
14]. Therefore, issues related to urban migrants and the location choice as part of their settlement intentions become important. However, because of historical reasons, the differences in social and economic welfare between the migrants and residents in cities persist, especially in the “hukou” (household registration) system. The “hukou” system was established in 1958 to regulate the flow of rural migrants into cities [
15,
16]. The “hukou” system not only involves compulsory registration for the population but is also a formal and legal way to bind the related welfare of urban residents [
17], which has been one of the key institutional factors in the settlement decisions for the migrants into cities [
18].
Recent research on migrants’ location choices regarding settlement intentions in urban areas has investigated the demographic characteristics of migrants (such as age, marital status, educational level), family factors (such as family size), economic factors (such as income level), and social factors (such as the characteristics of employment and social security) [
19,
20,
21,
22,
23]. In addition to the above factors, air pollution has become one of the most significant health risks faced by urban residents around the world and, to a certain degree, influences the settlement intentions of migrants. In 2017, United Nations Environment Programme (UNEP) found that the number of annual premature deaths was over 7 million worldwide due to air pollution. Therefore, the air quality, income, and cost of living in cities have significant influences on the perception of urban livability and the settlement decisions of migrants [
24,
25,
26,
27]. As the largest emerging economy in the process of rapid urbanization, China is faced with increasingly serious challenges regarding the air pollution in cities, which may pose a formidable challenge to the settlement intentions of migrants and the urbanization process. Therefore, exploring the influences of both urban air quality and income on the settlement intentions of migrants and investigating the characteristics and functional rule of their interactions are of great significance for promoting both the sustainable development of urbanization and the livability of cities in emerging markets, such as China.
An increasing number of studies have put emphasis on various factors that affect the settlement intentions of migrants in urban areas. From the macrolevel perspective, the economic, institutional, and cultural considerations have been emphasized as the key factors influencing the settlement intentions of migrations. Boccagni put forward the idea that the social and economic conditions of original residential places may accelerate the moving out of such migrants to more livable destinations [
28]. Ette et al. pointed out that socio-cultural and institutional factors are among the decisive factors regarding the settlement intentions of migrants to another area, such as an urban area [
29]. From the microlevel perspective, individual characteristics, human capital, integration, and connections with the home regions of migrants are the critical influencing factors regarding settlement intentions to another region. Paparusso and Ambrosetti found that microlevel factors in Italy, such as socio-economic and work conditions, determined the migration intentions of Moroccans [
30]. Studies have been done on the influencing factors of the settlement intentions of Chinese migrants, focusing on economic, socio-cultural, institutional, individual, and family factors. Among the previous studies, the economic factors are the primary influence on location choice regarding settlement intentions, where the higher the expected or actual income, the higher the settlement intention of Chinese migrants into cities [
27,
31]. Liang proposed to improve the living conditions and increase the income of Chinese migrants, which would be helpful for the promotion of social integration into city life [
32]. Different from the early generations of Chinese migrants, who strived to achieve an optimized level of economic status for both themselves and their families through working in cities far away from hometown and family [
33], new Chinese migrants tend to take social and cultural factors into consideration when making their settlement decisions [
34]. Abundant social capital, smooth social integration, and voluntary cultural adaptation are considered as positive factors that influence the sense of belonging and the settlement intentions of Chinese migrants [
22].
In brief, the existing research has comprehensively studied both the macro- and micro-level influencing factors on the settlement intentions of migrants but the decisive roles played by ecological and environmental factors in the settlement intentions have rarely been addressed by researchers. According to the International Organization for Migration (IOM), “environmental immigration” refers to those who voluntarily leave or are forced to leave their residential places temporarily or permanently due to sudden or gradual environmental degradation [
35]. The academic community is increasingly addressing the influences of environmental conditions on the settlement intentions of migrants. In fact, environmental livability plays an important role in migrants’ decisions regarding participating in the urban labor market, thus affecting their incomes and life satisfaction [
36,
37,
38]. The study of Tiebout mainly focused on the influences of non-economic factors, such as the urban living environment quality on the settlement intentions of migrants [
39]. Similarly, other researchers have also confirmed the positive influences of urban life quality [
40,
41] and comfortability [
42] on the settlement intentions of migrants. However, many types of environmental problems, such as gradual environmental degradation, soil degradation, declining vegetation, and global warming, may also affect the settlement intentions of migrants [
43,
44]. Especially in China, with the increasing demands for a more livable environment from urban residents, urban environment-related factors, such as living conditions [
45] and environment quality [
46], are found to have greater influences on the settlement intentions of migrants into Chinese cities.
In brief, it is necessary to further investigate the influences of environmental factors on the decisions of migrants, especially the air quality and its interaction with the individual incomes of migrants.
2. Model and Data
In general, this study used the China Migrants Dynamic Survey (CMDS) data from 2017 to measure the urban air quality in terms of the annual average concentration of PM2.5 (particles with diameter ≤ 2.5 μm in the air) and design regression models. Furthermore, this study used the monthly salaries and net incomes of respondents in the CMDS 2017 as the indicator for the incomes of Chinese migrants into cities and conducted an empirical study of the influences on the settlement intentions.
2.1. Chinese Migrants’ Data
The dataset of the Chinese migrants used in this study came from the latest CMDS, published in 2017, which was released by the Migrant Population Service Center of the National Health Commission of China (
http://www.chinaldrk.org.cn). The CMDS is the most detailed microlevel survey data about Chinese migrants. The survey respondents are the residents who are 15 years old and above that are not registered in the district (county, city) and have resided in their immigratory city for more than one month. The survey covers 31 provincial areas in China, including autonomous regions and municipalities. In the questionnaire of the CMDS 2017, respondents were required to answer questions such as “If you plan to stay here, how long do you plan to stay?”. The six alternative answers for this question were “1–2 years,” “3–5 years,” “6–10 years,” “more than 10 years,” “settle down,” and “not sure.” This question was used to identify those who have settlement intentions. Specifically, respondents who answered “settle down” could be considered to have a settlement intention (
SI), where the value of variable
SI was set to 1; otherwise, 0. Meanwhile, this study used the monthly salaries or net incomes of respondents in the dataset from the CMDS 2017 to represent the incomes of Chinese migrants in the sample, which excluded missing values and the minimum 1% (income <200 yuan) and the maximum 1% (income >20 thousand yuan) outliers to finally obtain 123,338 observations.
Specifically, migrants who had a settlement intention accounted for 43.20% of the migrants with a non-agricultural “hukou,” while migrants who had a settlement intention only accounted for 23.67% of the migrants with an agricultural “hukou.” Therefore, migrants with a non-agricultural “hukou” had a higher ratio of settlement intention. The reason for this was that under the current Chinese “hukou” system, there still exist certain institutional constraints regarding the settlement of migrants with an agricultural “hukou” in cities. These constraints in turn lead to the lower settlement intentions of Chinese migrants with an agricultural “hukou.” Meanwhile, most migrants with a non-agricultural “hukou” in China are peasant workers [
12,
47]. Their income cannot support their lives in the cities. Hence, they often choose to work in cities but settle down in rural areas [
48].
Table 1 reports the ratios of migrants with different levels of education who had settlement intentions. It can be found that the group of migrants with the highest proportion having settlement intentions (58.84%) was the population with a graduate level of education. However, the group of migrants with a proportion having settlement intentions lower than 20% was the population with a junior middle school level of education or less. In other words, the higher the education levels of the migrants in the observations, the stronger their settlement intentions.
2.2. PM2.5 Data of Chinese Cities
In recent years, with the acceleration of the Chinese urbanization process, PM2.5 has become one of the most important factors affecting the air quality and has caused frequent air pollution events in Chinese cities [
49]. In recent years, hazy weather caused by multiple pollutants, especially represented by PM2.5 as the main pollutants, has affected large areas of China, lasting for a long time [
50]. With the rapid economic development, China is suffering from serious air pollution, where PM2.5 has gradually become the primary pollutant, which has attracted widespread social concern [
51,
52]. The existing studies also show that PM2.5 is an important factor affecting China’s population mobility. In China, with the development of society and the improvement of living conditions, people’s demands on the living environment are gradually increasing.
Furthermore, interregional migration in China is no longer only determined by the levels of regional economic development and social employment. Having a favorable environment in any given region also significantly influences the location decisions of Chinese migrants, which can provide sustainable human capital for economic development and increase the external benefits of a favorable environment in the region [
53]. Therefore, population mobility not only depends on the quality of the economic conditions but also on the living environment, which has become an increasingly important indicator for people to consider. In particular, air quality is gradually becoming an important indicator for people to judge the quality of a living environment [
54].
The PM2.5 concentration can be calculated using satellite remote sensing data, which exhibits higher accuracy than air quality data from other sources. Therefore, this study used the annual average concentration of PM2.5 (μg/m3) to measure the air quality in Chinese cities. Due to the inaccessibility of the 2017 PM2.5 dataset of Chinese cities from official channels, this study used the satellite-based grid data on the global PM2.5 concentrations released by the Atmospheric Composition Analysis Group at Dalhousie University and used ArcGIS 10.2 (Esri, Redlands, CA, USA) to calculate the annual data of the average PM2.5 concentrations of Chinese cities in the prefecture-level cities or above in 2017. The results showed that the average annual PM2.5 concentration in 2017 in the sample of Chinese cities was 44.80 μg/m3. Among the results, the highest annual average concentration of PM2.5 was 80.66 μg/m3 in Hengshui city of Hebei province and the lowest annual average concentration of PM2.5 was 10.02 μg/m3 in Hulunbuir city of Inner Mongolia.
The spatial distribution of the PM2.5 concentration in each Chinese city in 2017 in the sample exhibited significant differences. A city’s PM2.5 concentration was highly correlated to its economic development level, industrial structure, and natural environment [
55]. For example, almost all the cities in the developed provinces of China, including Shandong, Jiangsu, Zhejiang, Shanghai, and Beijing, had higher PM2.5 concentrations. Simultaneously, cities in provinces with higher ratios of secondary industries, including Hebei and Tianjin, also had higher PM2.5 concentrations. However, because of the humid and rainy climate, the PM2.5 concentrations in the cities of southern China were not so high.
2.3. Regression Model
According to the seminal work of Henderson [
56] and Roback [
57], when a spatial equilibrium is achieved, the levels of residents’ utilities in different regions are the same. Since migrants always tend to move to the cities with higher levels of utility, whether the migrants choose to settle down or not depends on the utility levels in cities. In addition to income, which is an important influencing factor on the utility levels of migrants, satisfactory air quality can also improve their subjective wellbeing [
58,
59]. Therefore, the higher the income and air quality of cities, the higher the levels of utility experienced by the migrants, and hence the higher the probability of settling down in those cities, with everything else being equal [
60]. However, in many cases, high income and satisfactory air quality are incompatible. The existence of an environmental Kuznets curve means that the relationship between income and air quality tends to be an inverted U shape [
61,
62,
63]. Especially in emerging countries, such as China, which is a developing country with relatively low per capita income, areas with a higher income often face more serious air pollution. Therefore, whether migrants choose to settle down in a city or not is a trade-off between higher income and poorer air quality. Although poorer air quality can significantly decrease the settlement intentions of migrants, a higher income will often compensate for the resulting loss of utility of the migrants. In order to test the influences of both air quality and income on the settlement intentions of the migrants, a regression model was built, as follows:
where
i represents the individual, and
j represents the Chinese city.
SIij stands for settlement intention of the migrant,
PM2.5ij represents air quality of the cities, and
incomeij represents the monthly income of migrant
i in city
j. Since the explained variable
SI of model (1) is a binary dummy variable, this study used a logistic regression model to estimate model (1). Note that when estimating a binary choice model, such as model (1), a logistic regression model and a probit regression model are equivalent [
64]. This study also estimated model (1) using a probit regression model in the following robustness test. A logistic regression model is a nonlinear model, where the coefficients of variables in model (1) are not the marginal effects, as in a linear regression model, but their signs are consistent with the marginal effects [
64]. By substituting the estimated coefficients in model (1) into the exponential function with log-base e, the odds ratio was obtained. According to the theoretical analyses above,
β1 < 0,
β2 > 0, and
β3 > 0 was expected and assumed.
Furthermore, X represents a vector that included all the control variables of individual characteristics that affect the settlement intention of the migrant (nation, gender, age, party, edu, hukou, marriage, time, distance1, distance2, distance3, reason). All control variables came from the CMDS 2017. Z represents a vector including other city-level variables that affected the settlement intentions of migrants (third, trade, pgdp, gdpr), which all came from the China City Statistical Yearbook 2018. In addition, the provincial fixed effect in model (1) was also controlled, and represents the residual term. Model (2) added an interaction term PM2.5 × income based on model (1).
The specific definitions and descriptive statistics for all variables are reported in
Table 2 and
Table 3.