1. Introduction
In recent years, China has experienced rapid urbanization, with the urbanization level increased from 49.68% in 2010 to 63.89% in 2020 (China National Bureau of Statistics, 2021). In 2020, the number of floating population in the mainland reached 375.82 million, which rose from 221.43 million in 2010, an increase of 69.73% (National Bureau of Statistics of China, 2021). At the same time, with a series of traditional barriers restricting population mobility have been broken, attracting and retaining floating population has become an important way to improve regional competitiveness. As an important contributor to China’s rapid urbanization and economic development, how to design and implement appropriate policy measures to help the floating population settle down in cities is an urgent issue faced by policymakers and scholars.
For more than a decade now, the research results on the floating population’s settlement intention have increased rapidly. To the best of our knowledge, Zhu (2007) was one of the first scholars to pay attention to this subject. In a questionnaire survey based on Fujian Province in 2002, he found that only about 20.6% of floating population plan to permanently settle in cities [
1]. Four years later, this figure increased to 35.8% in Fujian Province [
2]. Prior research mainly takes the institutional, demographic, culture, social and economic factors as the influencing factors in floating population’s settlement intention [
3,
4,
5,
6,
7]. However, air quality, as an important factor of urban livability characteristics, is often neglected in previous studies.
Dramatic economic development over the past few decades in China has also brought about serious environmental problems, especially air pollution. Serious air pollution has brought great pressure on people’s life and health [
8,
9]. On one hand, air pollution is directly harmful to physical health, easily causing respiratory diseases such as pneumonia. At the same time, it will also increase the death rate of hypertension, cardiovascular diseases [
10,
11]. Air pollution will also affect the weight of newborn babies. If pregnant women are overexposed to air pollution, they may face problems such as premature delivery and low birth weight [
12,
13]. On the other hand, air pollution will bring adverse effects on people’s mental health. Air pollution increases psychological pressure and makes people more prone to depression resulting in a lower level of life satisfaction and mental well-being [
14,
15]. The literature also shows that air pollution has a negative impact on fertility intention in China [
16]. Concerns about the health of their own and their families may affect the settlement intention of migrants to settle in cities, and make them move to cities with better air quality. Therefore, air pollution has become an important factor in determining their intention to settle down.
With the improvement of people’s demand for environmental quality, taking environmental factors into account in the analysis has important theoretical significance for accurately understanding the behavior of floating population’s settlement intention. So, does air pollution affect the floating population’s settlement intention in cities? Furthermore, are there group differences in this effect? How does air pollution affect the settlement intention of floating population in different types cities? In order to answer the above questions, this study, based on the data from the 2017 China Migration Dynamics Survey (CMDS), investigated the impact of air quality on the settlement intention of the floating population’s, especially individual and urban characteristics.
Compared with previous studies, the contributions of this study are as follow, first, in terms of data, PM2.5 data is obtained by matching remote sensing satellite data and county-level maps, which is more accurate and objective. Second, in terms of the research design, this paper analyzes the impact of air pollution on the settlement intention of the floating population, and uses instrumental variables to verify the research results. Third, this paper also examines and discusses the differential impacts of different individuals and different types of cities. The effect of air pollution on the settlement intention of the floating population will be influenced by individual health status, education level, occupation type, and other factors, especially individual adaptability, which previous studies did not include. In addition, the settlement intention of the floating population is also affected by regional characteristics of place of destination. Such as the city types, economic development level and public services of the destination areas, which have not received enough attention by previous studies.
3. Data and Methods
3.1. Data Source
Based on three datasets from multiple sources, this study formulated a ‘air quality-migrant population matching dataset’ to answer the core question of how air quality influence the resident intention of the floating population. The first dataset is the 2017 China Migrants Dynamic Survey (CMDS) data. The survey is a large-scale survey conducted by the China Population and Development Research Centre under the direction of the China National Health and Family Planning Commission. So far, the CMDS is the most detailed micro-level survey data about China’s floating population. The migrants in the survey were 15–85 years old migrants who live in the inflow area for one month or longer and have no registered permanent residence in the area. The sample was selected from the Chinese mainland’s 31 provinces (except for Hong Kong, Macau, and Taiwan) and the annual report data of the floating population in the first year of the Xinjiang production and Construction Corps as the basic sampling framework, and then stratified. The survey applied a multistage, cluster, stratified, probability--proportional-to-size (PPS) sampling technique to select migrant respondents. The survey data were carried out in 1325 county-level administrative units (including Xinjiang production and Construction Corps), while the source of floating population was extensive, including all county-level administrative units in China.
The second source provides PM2.5 concentration data, which comes from the Atmospheric Composition Analysis Group at Washington University (
https://sites.wustl.edu/acag/datasets/surface-pm2-5/ (accessed on 7 September 2021)). Compared with other data, the geographic coverage of satellite observation data is wide, and can match our floating population data. At the same time, the satellite monitoring data is more objective and accurate, which can avoid the measurement errors caused by human factors. Third, some macro statistical data are also used, which are mainly from China’s Urban Statistical Yearbook and China’s County Statistical Yearbook for 2017, including indicators such as per capita GDP, the share of tertiary industry, and the number of doctors per 100 people. In addition, we extracted the mean wind speed at the county level in 2017 as instrumental variable to overcome the endogeneity of air pollution. The data came from the European Center for medium range weather forecasts (
https://apps.ecmwf.int/datasets/data/interim-full-moda/levtype%3Dsfc/ (accessed on 15 December 2021)).
3.2. Variable Selection
3.2.1. Dependent Variable
We examined respondents’ settlement intention based on the question “In the future, do you intend to live in the current city? (Yes or No)”. This question was used to identify those who have settlement intentions. Specifically, respondents who answered “Yes” could be considered to have settlement intentions, and the answer was coded “1”, “0” otherwise. The study does not consider respondents who are still considering or have no answers.
3.2.2. Independent Variables
The core explanatory variable of this study is air quality, which is the PM2.5 concentration value calculated based on raster data format. There are several reasons for choosing PM2.5 to represent air pollution. First, PM2.5 data obtained from county-level administrative units are more objective and accurate. Second, after the nationwide smog in 2013, residents paid more attention to air pollution, especially PM2.5. Third, the study of the relationship between air quality and economic and social development has been widely recognized in the academic community by taking PM2.5 concentration as a proxy variable for air pollution. After downloading the global PM2.5 map of raster data format, we matched the vector map of China’s county-level administrative divisions in 2015 (The data comes from the resource and environment science data center of the Chinese Academy of Sciences,
http://www.resdc.cn/data.aspx?DATAID=202 (accessed on 30 June 2022)) with the administrative codes of China Migrants Dynamic Survey (CMDS) data in 2017. With the help of ArcGIS (East China Normal University, Shanghai, China), we calculated the PM2.5 concentration values of each respondent’s outflow area (
hukou registration area or area of origin) and inflow area (Destination area) at the county level in 2017.
3.2.3. Control Variables
Following some previous studies [
53,
54,
55,
56], we included a set of covariates in the models, including age (continuous variable), gender ( female = 1, male = 0), hukou (agricultural hukou = 0, nonagricultural hukou = 1), marital status (single, divorced and widowed = 0, married = 1), education (categorical variable, primary school or below = 1, junior high school = 2, senior high school = 3, college or above = 4), general self-rated health (categorical variable, not good = 0, good = 1), income (continuous variable measured in yuan), flow range (categorical variable, inter-province migration = 1, inter-city migration = 2, Inter-county migration = 3), occupation type (categorical variable, employee = 1, employer = 2, own business = 3, other = 4), housing situation (categorical variable, renting = 1, commercial housing = 2, self built house = 3). To control the impact of different city types, according to the administrative level, the cities where the migrants were located are divided into four categories: provincial capital city (including municipalities directly under the central government), prefecture level city, county-level city and county. Additionally, it has been demonstrated that economic development differences between inflow city (destination city) and outflow area (
hukou registration area or area of origin) will affect the settlement intention of the floating population. According to the response, we obtained the county-level administrative unit of the outflow area and the inflow city of the respondent. By matching the data of the statistical yearbook, the variable of economic gap is obtained, which is composed of the ratio of the per capita GDP of the inflow city and the outflow area of the floating population. In addition, we also selected the share of the tertiary industry representing the city industrial structure, and the number of medical doctors per 100 people, which represents the level of public services in the city. The results show the definitions and descriptive statistics of variables involved in the empirical analysis(
Table 1).
3.3. Empirical Strategy
In order to verify our research hypothesis, we use probit models to evaluate the impact of air pollution on floating population’s settlement intentions. The model expression is shown in Equation (1).
In the model, a migrant’s settlement intention is a binary choice variable, defined as either 1 or 0. refers to air pollution, expressed by the average concentration of PM2.5 in the place where migrant resided. is a vector coefficients for the a set of control variables , i.e., age, gender, hukou, marital status, education, personal income, health status, flow time, flow range, occupation, housing conditions, economic gap, city types, the number of medical doctors per 100 people, proportion of tertiary industry in GDP. is a vector of coefficients for the city’s characteristics, i.e., city types, economic development level, industrial structure, and medical resources.
In the analysis, we included an instrumental variable(IV) to control the endogeneity of air pollution, and focus on the heterogeneity of air pollution in different individuals and cities. In addition, we discussed the limitation of research and summarized the results.
5. Discussion
Researchers has conducted a large number of comprehensive studies on the factors that affect the floating population’s settlement intention, including economic factors (e.g., income level) [
58], personal factors (e.g., age, gender, education, occupation, etc.) [
59], public services (such as educational resources, medical resources, cultural facilities) [
60,
61], policy (hukou system) [
62], social factors (e.g., social network and social environment) [
63], and environmental factors (air) [
64]. In the existing literature, there are few empirical studies on the impact of air pollution on the heterogeneity of the floating population’s settlement intentions. In this study, based on the 2017 China Migrants Dynamic Survey (CMDS) data plus two other data sources, more than 100 thousand Chinese floating population were selected for the analysis the impact of air pollution on the floating population’s settlement intentions, especially the exploration of individual heterogeneity and differences in city characteristics. We constructed a probit analysis, and used the instrumental variable method to deal with the potential endogeneity problem. China is not only the largest developing country in the world, but also has the largest number of migrant workers. Therefore, the results from the current study have strong theoretical and practical significance.
Our study found that air pollution has a statistically significant and negative effect on the settlement intention of floating population. This is consistent with the results of Yue et al. [
65], who found that the concentration of PM2.5 increases by 1 unit, the probability of migrants settling down in the city in which they currently resided for work or business will significantly decrease. The results of heterogeneity analysis in the current study show that older, higher education levels and poor health are more sensitive to air pollution. The effect of air pollution on the settlement intentions of the floating population is also affected by the migrants’ adaptability to air pollution. That is, the intensification of air pollution will reduce the attraction of destination cities to those migrants from areas with better air quality. In first-tier cities, air pollution has not weakened their attraction to the floating population. While in the higher administrative level of the city, the negative impact of air pollution on the floating population’s settlement intention is smaller, and the role of air quality also varies among different regions. Our findings are also consistent with those Wang et al. [
47], who found that city administrative level and air quality play an important role in shaping the willingness of
hukou conversion for migrants with settlement intention. The findings of this paper provide new empirical evidence for research on the settlement intention of floating population.
This study has several limitations that can be addressed in future studies. First, as a cross-sectional study, the study time span was limited only to 2017, unable to establish a temporal relationship between the air pollution and the migrants’ settlement intention. Because, the impact of air pollution on the settlement intention of floating population is a dynamic process. future research should use panel data, when available, to further expand and verify this relationship in more detail. Second, air pollution indicators also include NO
2, SO
2, etc., and PM2.5 is just one of them. Individual sensitivity to different air pollution may be different. Thus, it will be necessary to use multiple indicators for a more comprehensive analysis. Finally, the study of the relationship between air pollution and the floating population’s settlement intention also involves many omitted variables, such as urban population size, urban climate and natural environment [
52,
61,
66] Due to data availability, an analysis including these variables is beyond the scope of the current article.
6. Conclusions
Based on the China Migrants Dynamic Survey Data from 2017 the satellite grid data of global PM2.5 concentration, and additional area-level data, this study investigated the influences of air quality on China’s domestic migrants’ settlement intention of the floating population, and analyzed the individual heterogeneity and city characteristics and their effects on the relation between air pollution and migrants’ settlement intention. The main findings are summarized as follows:
Air pollution could significantly decrease the settlement intention of Chinese floating population, when concentration of PM2.5 increases by 1 μg/m3, the probability of settlement intention will fall by 1.2%. Our individual heterogeneity analysis shows that the influences of air pollution on different groups of migrants has significant heterogeneity. Those migrants who were older, better educatated levels and with poorer health are more sensitive to air pollution when it came to settlement intention. It is also worth mentioning that the influences of air pollution on settlement intention is influenced by the adaptability of individual to air pollution, that is, respondents with better air quality in their hometown were more sensitive to air pollution. Furthermore, the study found that there were significant city differences in the impact of air pollution on settlement intention. The higher the administrative level of a city, the smaller the negative impact of air pollution. And there are also regional differences in the effects ofair pollution: Its effect on settlement intention in the western provincial capital cities was negative, though it did not reduce settlement intention in the eastern and central provincial capital cities. Unsurprisingly, air pollution has not weakened the attractiveness of Tier-1 cities to the floating population. It is clear that air quality is not a priority in those cities, which can provide more employment opportunities, higher salaries and better public services for the floating population. Based on the conclusions above, we suggest the following policy recommendations,. First, local government should pay more attention to the role of environmental factors in forming their talent attraction strategies. The results of this study show that environmental quality indicated by air quality became a significant influencing factor of floating population’s settlement intention, and the groups with high human capital are more sensitive to air pollution. Therefore, in the context of the "talent competition", new policies for attracting and retaining talents should highlight the advantages of environmental quality, strengthen the synergy between environmental policies and talent policies. Second, local governments must strengthen the research on their impact of environmental pollution on health and increase public awareness in this respect. While this study found that air pollution did not weaken the attraction of large cities to migrants, and the potential increase in income could offset the negative impact of environmental pollution on settlement intention, this temporary solution at the cost of health is not sustainable in the long run. It will be a much more viable solution to reduce the research results of environmental pollution in all localities and improve local citizens’ understanding of the importance of air quality its impact on their lived environment.