The influence of air pollution on older migrants’ intentions to settle in the destination cities in China

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However, previous research on older migrants’ settlement plans tends to overlook the role of air pollution. Using data from the 2017 and 2018 China Migrants Dynamic Survey (CMDS), this study especially examines whether air pollution affects the settlement intentions of older migrants, aiming to address this research gap. The research presents two groundbreaking contributions to the literature. First, it distinguishes between short-term residential choices and permanent settlement intentions, utilizing appropriate indicators to measure the intentions of migrants opting for permanent settlement. Second, it focuses on older migrants as a distinct demographic group, offering novel evidence at the intersection of air pollution and settlement intentions. Findings demonstrate that urban air pollution adversely impacts older migrants' settlement intentions, with each one-unit rise in the Air Quality Index (AQI) reducing their likelihood of permanent settlement by 0.1 percentage points. Additionally, our research reveals that older migrants with agricultural hukou status, higher educational attainment, lower income, longer migration duration, and less exposure to air pollution in their hometowns are more vulnerable to air pollution's negative impacts. Air pollution Older population Migration Settlement intention China 1 Introduction Air pollution has consistently been ranked among the most critical environmental issues in China (Zhang et al., 2022 ). Earlier studies have indicated that prolonged exposure to air pollution elevates the likelihood of various illnesses (Yang et al., 2025 ), including cardiovascular conditions (Perera, 2018 ), hypertension (Chan et al., 2015 ), diabetes (Paul et al., 2020 ) and lung cancer (Balmain, 2023 ). Particularly, Air pollution significantly diminishes the quality of life for older adults, posing a significant risk to their well-being (Bentayeb et al., 2012 ). Studies have identified stronger correlations between air pollution and mortality from various diseases among older adults (Di et al., 2017 ; Wong et al., 2015 ), which may be attributed to factors such as a depressed immune system, progressive deterioration of organ function, pre-existing diseases and the accumulation of harmful substances within the body (Balfour & Kaplan, 2002 ). To mitigate these risks, people tend to take various protective measures to against ambient air pollution (Strine et al., 2008 ). One measure of protection is to migrate from heavily polluted regions to regions with better air quality (Cao et al., 2015 ; Zhao, Lao, et al., 2021 ). Over recent decades, there has been a dramatic rise in migrants engaging in amenity-led migration (Chen & Bao, 2021 ; Liu et al., 2022 ), which requires a thorough investigation into the linkage between air quality and migration decisions, especially among older migrants. The global aging phenomenon has emerged as a defining demographic trend of the 21st century (Bloom & Luca, 2016 ). Over the past two decades, China’s elderly population ratio has surged from 7% in 2000 to 13.5% by 2020, reflecting a near-doubling of its aging demographic (Xu & Ma, 2024 ). This demographic trend has led to a corresponding increase in the number of older migrants. China has one of the largest population of older migrants, with the number of older migrants reaching 33.27 million, accounting for 8.9% of the total migrant population (MacLachlan & Gong, 2022 ). The settlement intentions of this substantial demographic group in their destination cities have emerged as a key topic of concern for scholars. A growing body of literature has investigated the determinants of settlement intentions among older migrants in China. Empirical studies consistently demonstrate that multilevel factors, including individual, familial, social capital and institutional infrastructure significantly shape older migrants' long-term settlement decisions (Wu & Wu, 2023 ). At the individual level, migration decisions of older migrants are primarily driven by socioeconomic constraints and life-course transitions (Chen, 2005 ). While health deterioration often motivates older adults’ relocation to access enhanced medical services (Brandhorst et al., 2021 ; Gu et al., 2022 ), financial precarity frequently compels older adults to co-residence with adult children for economic support (Liu, 2016 ). Meanwhile, retirement typically disrupts established routines and undermines economic security (Chen, 2005 ) and offspring marital events reconfigure intergenerational care obligations (Yue et al., 2010 ). Collectively, these pivotal life-course transitions further catalyse their migrations. At the familial level, Familial obligations have also been identified as critical drivers of settlement intentions (Song & Zhu, 2022 ). Surveys from the National Health Commission reveal that 63% of older adults’ relocations are primarily motivated by grandchild care or family reunification with urbanized adult children pursuing educational and employment opportunities. The residential locations of adult children exert substantial influence on older migrants’ relocation patterns (Tang et al., 2022 ). Beyond individual and familial considerations, social capital emerges as another pivotal determinant of settlement intentions among older migrants. This conceptual encompasses community networks, interpersonal relationships, and social participation that older migrants develop within host communities. For older migrants, robust social ties not only facilitate community integration and provide emotional support but also improve resource accessibility (Lu et al., 2021 ), thereby reinforcing permanent settlement preferences (Huang & Guo, 2017 ). At the institutional infrastructure level, the availability of age-friendly healthcare and community support systems significantly enhances older migrants’ willingness to settle permanently (Huang et al., 2018 ). Well-developed institutional infrastructure not only addresses the practical needs of older migrants but also contributes to their overall well-being, thereby positively influencing their settlement intentions. Nevertheless, despite the breadth of research on these traditional determinants, scant attention has been paid to the role of environmental factors in destination cities. Unlike developed countries, where older migrants prefer to leave large urban agglomerations and move to less urbanized regions and temperate areas with preferable natural amenities (Evandrou et al., 2010 ; Hogan & Steinnes, 1996 ), China's older migrants initially relocate to more-developed urban areas (Huang et al., 2023 ), which are often more polluted. However, as the retirement-related perceptions among the elderly have evolved, an increasing number of older migrants may prioritize their quality of life, urban environment and and living comfort in settlement decisions, the pursuit of a more comfortable retirement environment has emerged as a new criterion for this demographic (Chen & Bao, 2021 ). Whie a few studies have indicated that older migrants' decisions on whether and where to move are shaped by outdoor amenities such as low concentration of air pollutants (Huang et al., 2024 ), existing research fails to examine older migrants' willingness to stay permanently once they migrate to a new place. To address this knowledge gap, our research aims to examine how air pollution affects older migrants’ intentions to settle in the host city, a vulnerable demographic that has experienced rapid growth but remains underrepresented in existing urban environmental and migration studies. Specifically, we utilize probit regression models to examine the linkage between air pollution in destination cities and older migrants' settlement intentions. Then, to address the potential endogeneity issues, we introduce 10-meter wind speed as an instrumental variable (IV) and employ an IV-probit model to delve deeper into the relationship between air quality and their settlement intentions in the host city. On this basis, we use different air quality measurement indices, alternative IV and subsamples to test the robustness of the findings. Finally, we consider group heterogeneity across five critical dimensions: hukou type, educational attainment, income, migration duration and air quality disparities between respondents’ hukou locations and host cities, examining how the effects of air pollution on settlement intentions vary by individual characteristics and migration patterns. This research contributes to the literature in the following aspects. First, we provide the first systematic evidence on how air pollution shapes older migrants’ settlement decisions in host cities, challenging the conventional wisdom that prioritizes economic and familial factors in existing studies of older migrants’ relocation choices. Second, we establish that effects of air pollution on older migrants are moderated by individual characteristics and migration patterns, and we conduct an in-depth analysis of the underlying causes of these effects. These insights advance the understanding of late-life migration decisions while providing evidence for targeted policies promoting age-friendly urban development. 2 Data and Methods 2.1 Data This study investigates the impact of air pollution on older migrants' intention to settle in the host city in China through an integrated analytical framework that synthesizes individual and city determinants. We position on settlement intention of older migrants as the dependent variable and air pollution as the central explanatory variable, while rigorously controlling for two critical dimensions: (1) individual characteristics including gender, age, education level, hukou type, migration distance, health insurance condition and housing pressure. (2) city-level attributes including the economic development status of the cities and the weather condition. To address endogeneity concerns, our empirical analyses construct the wind speed of cities as an instrumental variable to disentangle pollution effects from endogenous urban amenities. Settlement intention Information regarding older migrants’ individual characteristics were sourced from the China Migrants Dynamic Survey (CMDS), conducted by the National Health Commission of China. The survey targets migrants from 31 provinces (not including Hong Kong, Macao, or Taiwan) aged over 15 years without local hukou who had resided in destination cities for more than 1 month. To maximize the sample size as possible, we use data from the most recent two waves of the survey conducted in 2017 and 2018. Following China’s statutory retirement ages (60 for males, 55 for females), we selected a total of 15,696 individuals from the dataset for analysis. Settlement intention was captured through two answers in the survey to “Would you like to continue living and working in this city in the upcoming years?” and “what is the anticipated duration of your stay in the local region if you plan to stay here?”. Respondents who responded with “I plan to settle permanently here” received a score of 1, and 0 otherwise. Air pollution The Air Quality Index (AQI) served as our tool for assessing the severity of air pollution. AQI is a widely recognized indicator of air quality, ranging from 0 to 500 where elevated figures are indicative of intensified air pollution and increased health hazards. Data on Air Quality Index (AQI) were sourced from the daily air quality index of China National Environmental Monitoring Centre. Daily AQI values were aggregated to annual city-level means. To minimize the impact of sporadic events (such as natural disasters or temporary fluctuations in pollution sources) and anomalies in individual years, the average AQI over the last three consecutive years served as a measure for air pollution levels in each city. Participants from the 2018 survey were matched with the average AQI from the latest three years of 2015 to 2017, while those from the 2017 survey were matched with the average AQI from the three years of 2014 to 2016. Individual control variables For an all-encompassing evaluation of air pollution's impact, we incorporated various control factors deemed crucial in shaping the settlement intentions of older migrants. At the individual level, we controlled for potential confounders including gender, age, education level, hukou type, migration distance, health insurance condition and housing pressure. City-level controls included GDP, medical facilities and urban green space. Individual-level data were obtained from the 2017–2018 China Migrant Dynamic Survey (CMDS), while city-level indicators were sourced from the 2016–2017 Statistical Yearbooks. To ensure temporal alignment, 2018 survey participants were linked to 2017 socioeconomic data, whereas 2017 respondents were paired with 2016 records. Finally, we control the weather condition, including the temperature difference and precipitation. The monthly temperature observations and precipitation were derived from 1-km monthly mean temperature dataset and 1-km monthly mean precipitation dataset for China of National Tibetan Plateau Data Centre. Using QGIS Zonal Statistics function, we calculated city-level monthly averages, with the annual temperature differential computed as July minus January values. Temperature and precipitation are often influenced by seasonal variations, interannual climatic fluctuations, and extreme weather events. By using a three-year average, these interannual variations can be smoothed, mitigating the impact of extreme weather or anomalous years, thereby providing more stable and reliable climatic data. Participants from the 2018 survey were matched with the average temperature difference and precipitation from the latest three consecutive years of 2015 to 2017, while those from the 2017 survey were matched with the average temperature difference and precipitation from the three consecutive years of 2014 to 2016. Instrumental variable Previous studies have identified wind speed as an effective IV for air pollution levels (Liang & Gong, 2020 ). The rationale for this selection rests on its dual alignment with IV criteria: relevance and exogeneity. First, wind speed exhibits a strong negative correlation with ambient pollution concentrations, as higher velocities enhance atmospheric dispersion and dilution of pollutants (Hu et al., 2022 ). Second, wind speed is primarily determined by atmospheric circulation and topography, operates independently of individual socioeconomic characteristics and lacks direct pathways to influence migration-related settlement intentions beyond its mediation through air quality. The 10-meter wind speed data were sourced from the National Centres for Environmental Information (NCEI) of the National Oceanic and Atmospheric Administration (NOAA). The grid of daily average wind speed was calculated based on interpolating daily meteorological station data. Finally, the annual average wind speed for each city was calculated according to daily average wind speed. To mitigate short-term variability from seasonal cycles, episodic weather events, and microscale terrain effects, we use the average wind speed over the last three years to represent the wind speed of cities. Participants from the 2018 survey were matched with the average wind speed from the latest three years of 2015 to 2017, while those from the 2017 survey were matched with the average wind speed from the three years of 2014 to 2016. Table 1 Definition of variables Variable Explanation Mean Std. dev. Dependent variable Settlement Willing to settle in their current city in the future = 1 Not willing to settle in their current city in the future = 0 0.472 0.499 Air pollution variable AQI The average AQI (air quality index) of the older migrants living in the city in recent three years 86.692 20.676 Individual control variable Gender Male = 1 Female = 0 0.453 0.498 Age The age of respondents 63.853 6.199 Education year Migrant’s years of schooling 7.679 4.190 Non-Agri hukou Non-Agri hukou = 1 Agri hukou = 0 0.364 0.481 Inter-provincial migration Inter-provincial migration = 1 Intra-provincial migration = 0 0.447 0.497 Migration time How long this migration journey lasts 8.795 8.050 Housing pressure The average monthly amount spent on housing as a percentage of monthly expenses 16.912 22.776 Health Healthy or basically healthy = 1 Unhealthy = 0 0.833 0.373 Health insurance Enrol in health insurance and be reimbursed locally = 1 Enrolled in health insurance and reimbursed in a place other than your own or not covered by health insurance = 0 0.133 0.339 City control variable GDP Logarithm of GDP per capita (10,000 yuan) 11.209 0.509 Medical treatment The number of hospital beds per 1,000 inhabitants 6.193 1.810 Greenland Green coverage rate (%) 40.358 5.239 Temperature difference The temperature difference between July and January (℃) 26.642 7.723 Precipitation Annual precipitation (mm) 9.008 0.526 Instrumental variable Wind speed 10-meter wind speed (m/s) 2.708 0.767 2.2 Methods Initially, we explore how air pollution influences the settlement intentions of older migrants. Given that the dependent variable is binary in nature, the Logit regression model is presented: $$Settlement{\text{ }}intention_{{ict}}^{*}={\alpha _1}+{\beta _1}pollutio{n_{ct}}+{\phi _1}{X_i}+{\gamma _1}{X_{ct}}+{\theta _c}+{\delta _t}+{\varepsilon _{1ct}}$$ 1 where \(Settlement{\text{ }}intention_{{ict}}^{*}\) represents the intention of research object i to settle in city c; \(pollutio{n_{ct}}\) is the variable representing air quality༛ \({X_i}\) represents individual variables༛ \({X_{ct}}\) represents city-level variables༛ \({\theta _c}\) is regional fixed effect༛ \({\delta _t}\) is year fixed effect; \({\varepsilon _{1ct}}\) is residual༛ \(\alpha\) , \(\beta\) , \(\phi\) , \(\gamma\) are the regression coefficients. However, daily air quality can be endogenous. Endogeneity may arise from unobservable factors that simultaneously affect both air pollution and settlement intentions. For example, economic activities such as industrial development and human behaviour may influence both air quality and migrants' decisions to settle. Economic development generates employment opportunities, attracting individuals to migrate and settle in areas with burgeoning industries. In addition, population concentration in urban areas also escalates the release of pollutants via transport and the consumption of energy. This relationship, in turn, exacerbates the level of air pollution, creating a reverse causality issue. To reduce possible biases due to endogeneity, we employ an IV method to discern the cause-and-effect link between air pollution and the settlement plans of older migrants. An appropriate IV must satisfy two criteria: 1) strong correlation with the endogenous variable, and 2) exclusion restriction, without directly influencing migrants' long-term settlement intentions. Based on previous research, we exploit 10-meter wind speed as an IV. Wind speed dictates the spread of air pollution across city regions (Vignati et al., 1996 ). Higher wind speed indicates greater air circulation, which dilutes pollutants and reduces air pollution levels (Linda et al., 2022 ). Meanwhile, wind speed is determined by complex meteorological systems and geographic factors, without having a direct impact on economic activities and human behaviour, thus meeting the external criteria for a reliable IV. We set up an IV-Probit model, controlling variables at both the city and individual levels, to account for the potential endogeneity and establish a robust link between air pollution and the settlement intentions of older migrants. $$pollutio{n_{ct}}=\lambda +\varphi win{d_{ct}}+\mu {X_i}+\eta {X_{ct}}+{\theta _c}+{\delta _t}+{\varepsilon _{2ct}}$$ 2 $$Settlement{\text{ }}intention_{{ict}}^{{}}={\alpha _2}+{\beta _2}\widehat {{pollutio{n_{ct}}}}+{\phi _2}{X_i}+{\gamma _2}{X_{ct}}+{\theta _c}+{\delta _t}+{\varepsilon _{2ict}}$$ 3 where \(win{d_{ct}}\) denotes 10-meter wind speed, \(\lambda\) , \(\varphi\) , \(\mu\) , \(\eta\) represent the regression coefficient of first stage and second stage regression. The other symbols have the same meanings as those from Eq. 1 . 3 Results and analysis We estimated the effect of AQI levels on the settlement intentions of older migrants, and the findings are displayed in Table 2 . Model 1 displays the results from the baseline probit model which employs AQI as an indicator for air pollution, considering both individual and city-level factors, along with regional and year fixed effects. The results indicate a statistically significant negative association between air pollution levels and the intentions of older migrants to settle permanently. Specifically, an increase of one unit in the Air Quality Index (AQI) corresponds to a 0.1 percentage point decrease in the probability of the older migrants opting for permanent settlement. Table 2 Results of empirical model Model 1 Probit Model 2 IV Probit Coefficients Marginal effects Coefficients Marginal effects AQI -0.003 *** -0.001 *** -0.039 *** -0.013 *** (0.001) (0.000) (0.009) (0.003) Gender -0.087 *** -0.029 *** -0.083 *** -0.028 *** (0.023) (0.008) (0.024) (0.008) Age 0.030 *** 0.010 *** 0.029 *** 0.010 *** (0.002) (0.001) (0.002) (0.001) Education years 0.017 *** 0.006 *** 0.018 *** 0.006 *** (0.003) (0.001) (0.003) (0.001) Non-Agri hukou 0.495 *** 0.166 *** 0.471 *** 0.158 *** (0.026) (0.009) (0.028) (0.009) Inter-provincial migration -0.289 *** -0.097 *** -0.345 *** -0.116 *** (0.027) (0.009) (0.031) (0.010) Migration time 0.034 *** 0.012 *** 0.032 *** 0.011 *** (0.002) (0.001) (0.002) (0.001) Housing pressure -0.007 *** -0.002 *** -0.007 *** -0.002 *** (0.001) (0.000) (0.001) (0.000) Health -0.223 *** -0.075 *** -0.198 *** -0.066 *** (0.030) (0.010) (0.032) (0.011) Health insurance 0.316 *** 0.106 *** 0.310 *** 0.104 *** (0.034) (0.011) (0.035) (0.012) GDP 0.025 0.008 0.140 *** 0.047 *** (0.031) (0.010) (0.044) (0.015) Medical treatment 0.002 0.001 0.106 *** 0.036 *** (0.008) (0.003) (0.028) (0.010) Greenland 0.007 ** 0.002 ** 0.008 ** 0.003 ** (0.003) (0.001) (0.003) (0.001) Temperature difference -0.015 ** -0.005 ** -0.024 *** -0.008 *** (0.007) (0.002) (0.007) (0.002) Precipitation -0.388 *** -0.130 *** -0.598 *** -0.201 *** (0.111) (0.037) (0.128) (0.043) Year fixed effect YES YES YES YES Regional fixed effect YES YES YES YES First stage results Wald test of exogeneity 16.24 *** First stage F statistic 1022.59 *** Wald chi2(47) 3289.56 *** N 15696 15696 15696 15696 Note: Standard errors are presented within parentheses. We denote significance levels by * , ** , and *** , which corresponded to 10%, 5%, and 1% The findings solely reveal associations between air quality and the settlement intentions of older migrants, which may introduce endogeneity issues that could lead to underestimation of the true impact of air pollution. To address this concern and provide a more accurate estimate of the causal effect, we utilize 10-meter wind speed as an IV. Model 2 presents the results from the IV-Probit model. The findings from the first stage show a substantial positive association of wind speed with air pollution, and the first-stage F-statistic surpasses the threshold of 10, suggesting the absence of weak IV concerns. As anticipated, the second-stage results reveal that the impact of air pollution on the settlement intentions of older migrants is more pronounced than that observed in the baseline model. Specifically, a one-unit rise in the AQI corresponds to a 1.3 percentage point reduction in the probability of older migrants opting for permanent settlement. In conclusion, the results across all models consistently demonstrate that poor air quality significantly deters older migrants from establishing residence in polluted cities. Adverse air conditions may prompt the elderly to relocate to areas with cleaner air. 4 Robustness checks 4.1 Change the measurement of air quality It is noteworthy that while residents may not consistently express concern about air quality, their level of concern tends to escalate in response to severe air pollution that directly impacts their daily lives. This heightened awareness is often manifested through behaviors such as using indoor air purifiers, wearing masks outdoors, and reducing outdoor pursuits to lessen the adverse impacts of air pollution. In this section, we discuss replacing the AQI with the frequency of days classified as heavily polluted, where the AQI exceeds 200, and severely polluted, with AQI greater than 300, over the past three years to further verify the causal connection between air pollution and the settlement intentions of older migrants. While individuals may exhibit diminished sensitivity to average air quality levels, they are more inclined to recognize specific days with significantly elevated pollution. The impacts of days with heavy and severe pollution on older migrants' willingness to settle are shown in Table 3 . Models 3 and 4 show that the number of days with high levels of pollution and older migrants' intentions to settle are significantly correlated negatively at the 1% level. Specifically, the likelihood of older migrants planning to settle decreases by 0.06 percentage point for every extra day of high pollution, suggesting that baseline findings are robust. Furthermore, the models 5 and 6 show that the number of days with severe pollution levels has a substantial detrimental impact on older migrants' long-term settlement intentions. This effect is more pronounced than that of heavily polluted days, each additional day of severe pollution leads to a 0.16 percentage point decrease in their settlement intentions. All results remain consistent with the baseline estimations. Table 3 Robustness I: Change the measurement of air quality Model 3 Probit Heavily Polluted Days (AQI > 200) Model 4 IV Probit Heavily Polluted Days (AQI > 200) Model 5 Probit Severely Polluted Days (AQI > 300) Model 6 IV-Probit Severely Polluted Days (AQI > 300) Coefficients Marginal effects Coefficients Marginal effects Coefficients Marginal effects Coefficients Marginal effects AQI > 200 -0.0017 *** -0.0006 *** -0.013 *** -0.004 *** (0.0005) (0.000) (0.003) (0.001) AQI > 300 -0.0049 *** -0.0016 *** -0.085 *** -0.040 *** (0.0012) (0.000) (0.012) (0.012) Individual control variable YES YES YES YES YES YES YES YES City control variable YES YES YES YES YES YES YES YES Year fixed effect YES YES YES YES YES YES YES YES Regional fixed effect YES YES YES YES YES YES YES YES First stage results Wald test of exogeneity 14.77 *** 17.46 *** First stage F statistic 989.55 *** 736.10 *** N 15696 15696 15696 15696 15696 15696 15696 15696 Note: Standard errors are presented within parentheses. ** and *** indicate significance at 5% and 1% level 4.2 Narrow sample analysis In the baseline model, respondents who expressed uncertainty about settling in their current location were categorized as 'not inclined to settle'. While such uncertainty does not definitively indicate a lack of intent to settle, it should not be entirely disregarded. To eliminate this potential confounding effect in the regression results, we excluded these respondents from the study. As seen in Table 4 , the findings from models 7 and 8 support the conclusion that air pollution has a detrimental impact on older migrants' intentions to settle. After addressing the endogeneity issue, the probability of settlement intention is more accurately represented. Our conclusions remain robust despite this adjustment. Table 4 Robustness Ⅱ: Narrow sample analysis Model 7 Probit Without the sample with the answer “have not decided” Model 8 IV-Probit Without the sample with the answer “have not decided” Coefficients Marginal effects Coefficients Marginal effects AQI -0.0036 *** -0.001 *** -0.046 *** -0.017 *** (0.0011) (0.0004) (0.0079) (0.0037) Individual control variable YES YES YES YES City control variable YES YES YES YES Year fixed effect YES YES YES YES Regional fixed effect YES YES YES YES First stage results Wald test of exogeneity 21.64 *** First stage F statistic 849.18 *** N 13167 13167 13167 13167 Note: Standard errors are presented within parentheses. *** p < 0.01 4.3 Remove the megacities In China, city size has a direct correlation with the degree of institutional factors, public services, and economic development (Yue et al., 2021 ). Larger cities provide migrants with better resource allocation and institutional preferences (Chan & Wan, 2017 ). Therefore, metropolitan areas demonstrate a stronger gravitational pull (Wang et al., 2023 ), migrants in large cities have higher permanent settlement intention (Liu & Wang, 2020 ). Megacities mitigate the effect of air pollution on migrants' intentions to settle due to their favourable conditions related to city size. Megacities are defined under China's 2014 city categorization standard as urban areas that host a permanent population of at least 10 million. In our sample, five megacities were identified in 2016: Shanghai, Beijing, Chongqing, Guangzhou and Shenzhen. These megacities are not included in the study to prevent city size from influencing the outcomes. The restricted sample results from model 9 and 10 demonstrate that the conclusions remain consistent, indicating that the results of the empirical analysis retain their robustness even when controlling for city size. Table 5 Robustness Ⅲ: Remove the megacities Model 9 Probit Without megalopolis Model 10 IV-Probit Without megalopolis Coefficients Marginal effects Coefficients Marginal effects AQI -0.0033 *** -0.001 *** -0.037 *** -0.013 *** (0.0010) (0.000) (0.0078) (0.003) Individual control variable YES YES YES YES City control variable YES YES YES YES Year fixed effect YES YES YES YES Regional fixed effect YES YES YES YES First stage results Wald test of exogeneity 15.39 *** First stage F statistic 719.82 *** N 13,619 13,619 13,619 13,619 Note: Standard errors are presented within parentheses. *** p < 0.01 4.4 Replace the IV Although wind speed serves as a valid IV, it is not without its limitations. Specifically, it doesn’t account for additional meteorological or geographical conditions that affect the dispersion of air pollution, such as wind direction (F. Xu et al., 2022 ). Previous research has confirmed that the ventilation coefficient fulfils the condition of an IV. Improved ventilation conditions enhance air movement, facilitating the dissipation of air pollutants, thereby fulfilling the criteria for a reliable IV (Hering & Poncet, 2014 ). Additionally, the ventilation coefficient results from the product of wind speed and the atmospheric boundary layer height, both of which are influenced by intricate weather systems and geographical features (Broner et al., 2012 ). Consequently, the ventilation coefficient is treated as an alternative IV for air pollution. Table 6 displays the findings which reveal no notable alteration in the direction or scale of the AQI’s impact. Table 6 Robustness Ⅳ: Change the IV Model 11 IV-Probit Ventilation coefficient as IV Model 12 IV-Probit Ventilation coefficient as IV Coefficients Marginal effects Coefficients Marginal effects AQI -0.014 *** -0.003 *** -0.045 *** -0.017 *** (0.0018) (0.001) (0.011) (0.005) Individual control variable YES YES YES YES City control variable YES YES YES YES Year fixed effect NO NO YES YES Regional fixed effect NO NO YES YES First stage results Wald test of exogeneity 20.04 *** 10.81 *** First stage F statistic 642.58 *** 1032.34 *** N 15696 15696 15696 15696 Note: Standard errors are presented within parentheses. *** p < 0.01 5 Heterogeneity 5.1 Individual characteristic-based heterogeneity The effect of air pollution can vary significantly depending on personal characteristics (Z. Wang et al., 2020 ). Research has established a consensus that variations in behaviour and lifestyle are shaped by different individual characteristics (Huang et al., 2018 ), leading to varied responses to air pollution. Existing research indicate varied reactions to air pollution among individuals based on their social and economic standing (Chen & Chen, 2020 ). Therefore, older migrants in this study are categorized based on their hukou type, income level and educational attainment. 1) Hukou type Earlier research has established hukou status as a key determinant in shaping migrants' intentions to settle (Li et al., 2024 ; Zhang et al., 2022 ). For a further investigation of the heterogeneous effects of air pollution, we divided the sample into two groups based on whether they held non-agricultural hukou . The results from the regression analyses reveal that the effect of air quality on settlement intentions was not notably significant in older migrants who had agricultural hukou. Since air pollution tends to be more severe in urban areas compared to rural areas, individuals with urban backgrounds may be more accustomed to air pollution or may anticipate experiencing similar conditions when returning to their hometowns. The results indicate a reduced susceptibility of these individuals to air pollution. Moreover, owning agricultural or residential land in countryside regions could affect their decision to return home when faced with intolerable air quality, allowing them to benefit from their land and associated dividends. 2) Educational attainment Earlier study suggests that well-educated migrants are more inclined to establish themselves in urban areas (Zhu & Chen, 2010 ). In this study, older migrants are grouped into two categories according to their level of education: individuals with at least a high school diploma and those with a lower level of education. The estimated results in Table 8 indicate a higher propensity for older migrants with higher educational attainment to relocate in response to air pollution hazards. Higher level education attainment enables older migrants’ better comprehension of pollution's long-term health risks and those with higher educational attainment correlates with greater financial capacity. Therefore, older migrants with higher levels of education have more motivation and greater freedom in the choice of settlement. As evidenced by the results of the IV-Probit model showing each additional unit of AQI the probability to settle down among older migrants with lower level of education will decrease by 1.3 percentage points; while the migrants with a higher education level will decrease by 1.4 percentage points. 3) Income The level of income is intricately related to the quality of life experienced by migrants in their local environments and plays a crucial role in influencing their relocation decisions (Zhu, 2007 ). For this study, we categorized older migrants based on whether their monthly income exceeds 5,000 yuan, which is approximately the average income within the sample. The main observation shows that older migrants with lower income levels have a reduced tolerance for air pollution. Compared with the younger migrants, older migrants exhibit unique income-related characteristics. All the older migrants in this study have reached China's statutory retirement age, those who did not have stable employment or failed to make timely social insurance payments before retirement are unlikely to have a reliable income source. However, cities with poor air quality often have developed economies and elevated living costs. Therefore, older migrants with lower income levels may find it challenging to afford the high cost of living, leading economic factors to be the primary impetus for their relocation, transcending the environment factors. Table 7 Heterogeneity analysis based on Individual characteristic Model 13 Non- agricultural hukou Model 14 Agricultural hukou Model 15 High school education or above Model 16 Below high school education Model 17 Income less than 5000 yuan Model 18 Income more than 5000 yuan Marginal effects Marginal effects Marginal effects Marginal effects Marginal effects Marginal effects AQI -0.007 -0.014 *** -0.014 *** -0.013 *** -0.019 *** -0.006 (0.007) (0.004) (0.005) (0.004) (0.005) (0.005) Individual control variable YES YES YES YES YES YES City control variable YES YES YES YES YES YES Year fixed effect YES YES YES YES YES YES Reginal fixed effect YES YES YES YES YES YES N 5711 9985 3626 12070 7921 7775 Note: Standard errors are presented within parentheses. *** p < 0.01 5.2 Migration pattern-based heterogeneity In addition to individual characteristics, migrants' responses to air pollution are influenced by their distinct migration patterns (Zhu, 2007 ). Migrants with shorter migration durations or those from periods with higher air quality typically have a harder time adjusting to the contaminated metropolis, potentially leading them to leave (Zhang et al., 2022 ). In light of the above discussion, this study categorizes the sample of older migrants based on migration duration and AQI disparity between the destination city and their hometown. 1) migration duration We categorize the sample based on the duration of residence, differentiating between residents with over five years and those with less than five years of residency. Results from the IV-probit regression shown in the first and second columns of Table 8 indicate that air pollution more significantly influences the settlement intentions of older migrants with shorter migration durations. The reason behind this occurrence is that older migrants who have moved to the city for an extended period have acclimatized to the lifestyle and social environment, and the perceived costs associated with relocating are higher for them than those with shorter migration durations. As a result, they exhibit diminished sensitivity to air quality issues. On the contrary, older migrants with shorter durations of residence have not established a deep connection with their current environment yet. Therefore, when the air quality deteriorates, they are more inclined to relocate to mitigate the adverse effects on their well-being. 2) Air quality disparities between migration city and location Both older migrants' adaptation to air pollution and their preferences for air quality have an impact on how air pollution affects their settlement intentions. This study compares the AQI between older migrants’ hukou location and cities of residence, calculating the AQI disparities between these two locations (Zhao et al., 2021 ). Based on these disparities, the sample is divided into two categories: those hukou locations with better air quality and those hukou locations with inferior air quality. The third and fourth columns of Table 8 summarizes the results of the regression analysis. As anticipated, older migrants originating from cities with better air quality show reduced tolerance to air pollution. Older migrants' intentions to settle in their current city decline by 1.5 percentage points for every unit of decreasing air quality compared to their hometown. Table 8 Heterogeneity analysis based on migration patterns Model 19 Migration over 5 years Model 20 Migration less than 5 years Model 21 AQI disparity between migration city and hukou location > = 0 Model 22 AQI disparity between migration city and hukou location < 0 Marginal effects Marginal effects Marginal effects Marginal effects AQI -0.012 *** -0.013 ** -0.015 *** -0.047 (0.004) (0.005) (0.005) (0.038) Individual control variable YES YES YES YES City control variable YES YES YES YES Year fixed effect YES YES YES YES Reginal fixed effect YES YES YES YES N 9801 5895 5432 2348 Note: Standard errors are presented within parentheses. *** and ** indicate significance at 1% and 5% level 6 Conclusion and Discussion As living conditions improve and health awareness increases, city dwellers have voiced an escalating demand for cleaner air. In response to air pollution, individuals may decide to migrate to regions characterized by superior air conditions (Banzhaf & Walsh, 2008 ), effectively “vote with their feet”. Therefore, after controlling for the individual and city-level factors, this study investigates how the quality of urban air influences the decision of older migrants to settle permanently. The key finding of our study indicates a markedly adverse impact of air pollution on the intention of older migrants to settle permanently. The findings demonstrate that it is essential for older migrants to take air pollution into account when making decisions regarding settlement. In addition to air quality, factors such as migration distance, housing pressure, health status, temperature difference and precipitation are also significantly negatively correlated with the settlement intention of older migrants. Conversely, years of education, migration time, living with their children, and having local medical insurance show significant positive correlations with their intention to settle down. Meanwhile, there is substantial heterogeneity within different groups regarding how air quality affects older migrants. Air pollution impacts differ based on individual characteristics and migration patterns among older adults. Specifically, those with agricultural household registration (agricultural hukou), lower education levels or income levels, shorter duration of migration and residence in cities with worse air quality than their hometowns tend to be more affected by adverse air conditions. Our findings reveal notable disparities in how air pollution impacts the decision to settle between younger and older migrants. First, despite the impact of air pollution on both demographics, prior studies have shown that every increment in the Air Quality Index (AQI) results in an approximate 0.77 percentage point reduction in the likelihood of migrants, predominantly younger migrants, choosing to settle permanently (F. Xu et al., 2022 ). Our results show that this percentage rises to 1.3 percentage points among older migrants. This finding aligns with earlier studies indicating the heightened sensitivity of the elderly to the quality of the environment (Liu et al., 2017 ). Particularly, air pollution disproportionately impacts older migrants due to both normal and pathological aging processes compared to younger migrants (Balfour & Kaplan, 2002 ). Consequently, it appears reasonable to conclude that older migrants being more vulnerable to air pollution, are less likely to remain in areas with poor air quality. Second, the evidence from existing studies implies that migrants with higher income are more susceptible to the effects of air pollution in general. This is because the cost of relocating to other cities may exceed the financial capacity of low-income families, making migration economically unviable (Zhao, Lao, et al., 2021 ). However, our findings reveal that older migrants with lower income levels are particularly susceptible to air pollution. This phenomenon is probably explained by differences in settlement motivations between younger and older migrants. For younger migrants, air pollution serves as a reference indicator in selecting a place to reside. Those with higher income tend to seek a higher quality living environment once their material needs are met, possessing better economic resources and greater flexibility in their choices. Therefore, when air pollution levels exceed their acceptable thresholds, they are likely to relocate without hesitation in search of a more suitable habitat. Conversely, due to the traditional Chinese cultural emphasis on familial ties, older adults often prioritize family benefits over personal well-being when making migration decisions. Many lack a regular source of income and depend on financial support and health care provided by their children. Thus, even amidst intense air contamination, many older adults opt to remain with their children, enduring the health risks associated with environmental pollution. The mobility of older migrants not only leads to spatial redistribution of the population but also plays a crucial role in shaping policy development. This study carries significant policy implications for local governments as it confirms that better air quality is a critical factor influencing city livability, which in turn affects the settlement intentions of older migrants. First, our study confirms that non-economic factors, such as air pollution, alongside traditional economic factors, are determinants of migrants’ settlement intentions. It is necessary for cities particularly in highly industrialized and developed regions to prioritize environmental pollution control, strengthen environmental protection and atmospheric governance. Given the heightened susceptibility of older migrants from lower socio-economic statuses to air pollution, tailored policies for each group are essential to lessen the detrimental effects of air pollution. In terms of economic support, highly effective protective masks (e.g., N95) and air purifiers should be distributed free of charge to eligible older migrants, while subsidies could be provided to support the purchase of air quality monitoring or protective equipment for low-income older migrants. From a healthcare perspective, cities should design easy-to-understand publicity material and disseminate air pollution prevention knowledge through dialects or formats familiar to older adults. Additionally, health lectures should be organized in communities, markets and other gathering places for older migrants, with medical professionals invited to educate and raise awareness about air pollution prevention. Regarding environmental improvement and monitoring, efforts should include promoting the installation of air purification facilities and delivering accurate air pollution warning information to older migrants through SMS, phone calls and other direct communication channels. Second, assessing how air pollution affects migration holds immense importance for local authorities in developing strategies to improve social service infrastructure. As a result of China's three-child policy, an increasing number of families will require the assistance of grandparents in caring for newborns, leading to a continuous expansion of older migrants and exerting additional pressure on social and public services in cities with better air quality. Moving forward, beyond controlling urban air pollution, it will be vital for the government to enhance elderly care services and create better settlement opportunities for older migrants within cities. Potential measures include establishing health service stations in areas frequented by older migrants, offering free air pollution awareness and health check-ups, developing unified health records to monitor the risks and protection measures associated with air pollution-related illnesses, providing temporary shelters for older migrants during periods of severe air pollution. To align with future developmental trends, our study focuses on a specific subgroup of migrants represented by elderly individuals and places greater emphasis on enhancing urban livability rather than solely concentrating on economic factors, providing new evidence for migration research. However, this paper has several limitations that require further investigation: Firstly, the cross-sectional nature of CMDS data limits our ability to track whether settlement intentions change alongside variations in air pollution levels. Additionally, due to regional differences in topography and climate across China, the 10-meter wind speed may not be the most appropriate IV in certain areas. Future research should explore better-suited IVs tailored to the specific characteristics of each geographical region. Declarations Ethics declarations Ethics approval and consent to participate : The data utilized in this study were secondary data obtained from the China Migrant Dynamic Survey (CMDS) conducted by the National Health Commission of the People’s Republic of China. This dataset had received prior ethical approval from the appropriate institutional review board. Prior to participation, written informed consent was obtained from all participants after they were fully informed about the study’s purpose and procedures. The investigation was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and other applicable international ethical guidelines. All research procedures strictly adhered to relevant guidelines and regulatory standards. Consent to Participate declarations: Not Applicable. Conflicts of interest/Competing interests declarations: The authors have no relevant financial or non-financial interests to disclose. The authors have no conflicts of interest to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article. Funding : This work was supported by the National Natural Science Foundation of China (Grant Nos. 42471264 and 42171196), Guangzhou Municipal Science and Technology Bureau (SL2023A04J00959) Author Contribution Jiarong Zheng: Conceptualization, Methodology, Data curation, Writing- original draft, Writing- Reviewing and Editing;Cuiying Huang: Conceptualization, Methodology, Writing- Reviewing and Editing;Ye Liu: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Validation, Writing- Reviewing and Editing; References Balfour, J. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6191639","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444277567,"identity":"941285bf-0cfa-44c9-8532-70929551f0e8","order_by":0,"name":"Jiarong Zheng","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Jiarong","middleName":"","lastName":"Zheng","suffix":""},{"id":444277568,"identity":"05f58d74-2ffd-48c7-ba75-cd531eb43045","order_by":1,"name":"Cuiying Huang","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Cuiying","middleName":"","lastName":"Huang","suffix":""},{"id":444277569,"identity":"66a1e81f-6bec-4910-ad35-ce87e340ba3c","order_by":2,"name":"Ye Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACPmYQWfFPjoGBsfEATFQCnxY2sJYzB4yBWhqI1AIiGNsOJDYAaSK1sPOYSXxgu5O+tv0w0JY/h+0NDjAfvM3DYJeH22E8ZpIzeJ7lbjuT2HCAse1w4oYDbMnWPAzJxfi0SPNIMOduOwDS0nA4weAASIQB4lScWv4YMKebnX8Icxj/N8JaGBIOJ5jdANrCwHaYccMBHjYCWtiKLXsOpBluuwG0JbEtPXHmYTZjyzkGyTi18PMf3njj5z8bebPz6Q8ffPhjbc93vPnhjTcVdji1AAELIhYSGJoZGMCRa4BbPRAwf0Di1OFVOgpGwSgYBSMTAABhSlj6Ou1XtAAAAABJRU5ErkJggg==","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Ye","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-03-10 04:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6191639/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6191639/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12061-025-09719-6","type":"published","date":"2025-09-27T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92430780,"identity":"28bd2778-8e1f-467a-a9b0-393fdb7c4568","added_by":"auto","created_at":"2025-09-29 16:07:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1597914,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6191639/v1/0157cdab-c66e-40ba-b87f-b141fe1c6f9b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The influence of air pollution on older migrants’ intentions to settle in the destination cities in China","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAir pollution has consistently been ranked among the most critical environmental issues in China (Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Earlier studies have indicated that prolonged exposure to air pollution elevates the likelihood of various illnesses (Yang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), including cardiovascular conditions (Perera, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), hypertension (Chan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), diabetes (Paul et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and lung cancer (Balmain, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Particularly, Air pollution significantly diminishes the quality of life for older adults, posing a significant risk to their well-being (Bentayeb et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Studies have identified stronger correlations between air pollution and mortality from various diseases among older adults (Di et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wong et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which may be attributed to factors such as a depressed immune system, progressive deterioration of organ function, pre-existing diseases and the accumulation of harmful substances within the body (Balfour \u0026amp; Kaplan, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). To mitigate these risks, people tend to take various protective measures to against ambient air pollution (Strine et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). One measure of protection is to migrate from heavily polluted regions to regions with better air quality (Cao et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhao, Lao, et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Over recent decades, there has been a dramatic rise in migrants engaging in amenity-led migration (Chen \u0026amp; Bao, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which requires a thorough investigation into the linkage between air quality and migration decisions, especially among older migrants.\u003c/p\u003e \u003cp\u003eThe global aging phenomenon has emerged as a defining demographic trend of the 21st century (Bloom \u0026amp; Luca, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Over the past two decades, China\u0026rsquo;s elderly population ratio has surged from 7% in 2000 to 13.5% by 2020, reflecting a near-doubling of its aging demographic (Xu \u0026amp; Ma, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This demographic trend has led to a corresponding increase in the number of older migrants. China has one of the largest population of older migrants, with the number of older migrants reaching 33.27\u0026nbsp;million, accounting for 8.9% of the total migrant population (MacLachlan \u0026amp; Gong, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The settlement intentions of this substantial demographic group in their destination cities have emerged as a key topic of concern for scholars.\u003c/p\u003e \u003cp\u003eA growing body of literature has investigated the determinants of settlement intentions among older migrants in China. Empirical studies consistently demonstrate that multilevel factors, including individual, familial, social capital and institutional infrastructure significantly shape older migrants' long-term settlement decisions (Wu \u0026amp; Wu, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At the individual level, migration decisions of older migrants are primarily driven by socioeconomic constraints and life-course transitions (Chen, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). While health deterioration often motivates older adults\u0026rsquo; relocation to access enhanced medical services (Brandhorst et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), financial precarity frequently compels older adults to co-residence with adult children for economic support (Liu, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Meanwhile, retirement typically disrupts established routines and undermines economic security (Chen, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and offspring marital events reconfigure intergenerational care obligations (Yue et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Collectively, these pivotal life-course transitions further catalyse their migrations. At the familial level, Familial obligations have also been identified as critical drivers of settlement intentions (Song \u0026amp; Zhu, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Surveys from the National Health Commission reveal that 63% of older adults\u0026rsquo; relocations are primarily motivated by grandchild care or family reunification with urbanized adult children pursuing educational and employment opportunities. The residential locations of adult children exert substantial influence on older migrants\u0026rsquo; relocation patterns (Tang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Beyond individual and familial considerations, social capital emerges as another pivotal determinant of settlement intentions among older migrants. This conceptual encompasses community networks, interpersonal relationships, and social participation that older migrants develop within host communities. For older migrants, robust social ties not only facilitate community integration and provide emotional support but also improve resource accessibility (Lu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), thereby reinforcing permanent settlement preferences (Huang \u0026amp; Guo, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). At the institutional infrastructure level, the availability of age-friendly healthcare and community support systems significantly enhances older migrants\u0026rsquo; willingness to settle permanently (Huang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Well-developed institutional infrastructure not only addresses the practical needs of older migrants but also contributes to their overall well-being, thereby positively influencing their settlement intentions.\u003c/p\u003e \u003cp\u003eNevertheless, despite the breadth of research on these traditional determinants, scant attention has been paid to the role of environmental factors in destination cities. Unlike developed countries, where older migrants prefer to leave large urban agglomerations and move to less urbanized regions and temperate areas with preferable natural amenities (Evandrou et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hogan \u0026amp; Steinnes, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), China's older migrants initially relocate to more-developed urban areas (Huang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which are often more polluted. However, as the retirement-related perceptions among the elderly have evolved, an increasing number of older migrants may prioritize their quality of life, urban environment and and living comfort in settlement decisions, the pursuit of a more comfortable retirement environment has emerged as a new criterion for this demographic (Chen \u0026amp; Bao, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Whie a few studies have indicated that older migrants' decisions on whether and where to move are shaped by outdoor amenities such as low concentration of air pollutants (Huang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), existing research fails to examine older migrants' willingness to stay permanently once they migrate to a new place.\u003c/p\u003e \u003cp\u003eTo address this knowledge gap, our research aims to examine how air pollution affects older migrants\u0026rsquo; intentions to settle in the host city, a vulnerable demographic that has experienced rapid growth but remains underrepresented in existing urban environmental and migration studies. Specifically, we utilize probit regression models to examine the linkage between air pollution in destination cities and older migrants' settlement intentions. Then, to address the potential endogeneity issues, we introduce 10-meter wind speed as an instrumental variable (IV) and employ an IV-probit model to delve deeper into the relationship between air quality and their settlement intentions in the host city. On this basis, we use different air quality measurement indices, alternative IV and subsamples to test the robustness of the findings. Finally, we consider group heterogeneity across five critical dimensions: hukou type, educational attainment, income, migration duration and air quality disparities between respondents\u0026rsquo; hukou locations and host cities, examining how the effects of air pollution on settlement intentions vary by individual characteristics and migration patterns.\u003c/p\u003e \u003cp\u003eThis research contributes to the literature in the following aspects. First, we provide the first systematic evidence on how air pollution shapes older migrants\u0026rsquo; settlement decisions in host cities, challenging the conventional wisdom that prioritizes economic and familial factors in existing studies of older migrants\u0026rsquo; relocation choices. Second, we establish that effects of air pollution on older migrants are moderated by individual characteristics and migration patterns, and we conduct an in-depth analysis of the underlying causes of these effects. These insights advance the understanding of late-life migration decisions while providing evidence for targeted policies promoting age-friendly urban development.\u003c/p\u003e"},{"header":"2 Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data\u003c/h2\u003e \u003cp\u003eThis study investigates the impact of air pollution on older migrants' intention to settle in the host city in China through an integrated analytical framework that synthesizes individual and city determinants. We position on settlement intention of older migrants as the dependent variable and air pollution as the central explanatory variable, while rigorously controlling for two critical dimensions: (1) individual characteristics including gender, age, education level, \u003cem\u003ehukou\u003c/em\u003e type, migration distance, health insurance condition and housing pressure. (2) city-level attributes including the economic development status of the cities and the weather condition. To address endogeneity concerns, our empirical analyses construct the wind speed of cities as an instrumental variable to disentangle pollution effects from endogenous urban amenities.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSettlement intention\u003c/b\u003e \u003c/p\u003e \u003cp\u003eInformation regarding older migrants\u0026rsquo; individual characteristics were sourced from the China Migrants Dynamic Survey (CMDS), conducted by the National Health Commission of China. The survey targets migrants from 31 provinces (not including Hong Kong, Macao, or Taiwan) aged over 15 years without local hukou who had resided in destination cities for more than 1 month. To maximize the sample size as possible, we use data from the most recent two waves of the survey conducted in 2017 and 2018. Following China\u0026rsquo;s statutory retirement ages (60 for males, 55 for females), we selected a total of 15,696 individuals from the dataset for analysis. Settlement intention was captured through two answers in the survey to \u0026ldquo;Would you like to continue living and working in this city in the upcoming years?\u0026rdquo; and \u0026ldquo;what is the anticipated duration of your stay in the local region if you plan to stay here?\u0026rdquo;. Respondents who responded with \u0026ldquo;I plan to settle permanently here\u0026rdquo; received a score of 1, and 0 otherwise.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAir pollution\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Air Quality Index (AQI) served as our tool for assessing the severity of air pollution. AQI is a widely recognized indicator of air quality, ranging from 0 to 500 where elevated figures are indicative of intensified air pollution and increased health hazards. Data on Air Quality Index (AQI) were sourced from the daily air quality index of China National Environmental Monitoring Centre. Daily AQI values were aggregated to annual city-level means. To minimize the impact of sporadic events (such as natural disasters or temporary fluctuations in pollution sources) and anomalies in individual years, the average AQI over the last three consecutive years served as a measure for air pollution levels in each city. Participants from the 2018 survey were matched with the average AQI from the latest three years of 2015 to 2017, while those from the 2017 survey were matched with the average AQI from the three years of 2014 to 2016.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIndividual control variables\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor an all-encompassing evaluation of air pollution's impact, we incorporated various control factors deemed crucial in shaping the settlement intentions of older migrants. At the individual level, we controlled for potential confounders including gender, age, education level, \u003cem\u003ehukou\u003c/em\u003e type, migration distance, health insurance condition and housing pressure. City-level controls included GDP, medical facilities and urban green space. Individual-level data were obtained from the 2017\u0026ndash;2018 China Migrant Dynamic Survey (CMDS), while city-level indicators were sourced from the 2016\u0026ndash;2017 Statistical Yearbooks. To ensure temporal alignment, 2018 survey participants were linked to 2017 socioeconomic data, whereas 2017 respondents were paired with 2016 records. Finally, we control the weather condition, including the temperature difference and precipitation. The monthly temperature observations and precipitation were derived from 1-km monthly mean temperature dataset and 1-km monthly mean precipitation dataset for China of National Tibetan Plateau Data Centre. Using QGIS Zonal Statistics function, we calculated city-level monthly averages, with the annual temperature differential computed as July minus January values. Temperature and precipitation are often influenced by seasonal variations, interannual climatic fluctuations, and extreme weather events. By using a three-year average, these interannual variations can be smoothed, mitigating the impact of extreme weather or anomalous years, thereby providing more stable and reliable climatic data. Participants from the 2018 survey were matched with the average temperature difference and precipitation from the latest three consecutive years of 2015 to 2017, while those from the 2017 survey were matched with the average temperature difference and precipitation from the three consecutive years of 2014 to 2016.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInstrumental variable\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePrevious studies have identified wind speed as an effective IV for air pollution levels (Liang \u0026amp; Gong, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The rationale for this selection rests on its dual alignment with IV criteria: relevance and exogeneity. First, wind speed exhibits a strong negative correlation with ambient pollution concentrations, as higher velocities enhance atmospheric dispersion and dilution of pollutants (Hu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Second, wind speed is primarily determined by atmospheric circulation and topography, operates independently of individual socioeconomic characteristics and lacks direct pathways to influence migration-related settlement intentions beyond its mediation through air quality. The 10-meter wind speed data were sourced from the National Centres for Environmental Information (NCEI) of the National Oceanic and Atmospheric Administration (NOAA). The grid of daily average wind speed was calculated based on interpolating daily meteorological station data. Finally, the annual average wind speed for each city was calculated according to daily average wind speed. To mitigate short-term variability from seasonal cycles, episodic weather events, and microscale terrain effects, we use the average wind speed over the last three years to represent the wind speed of cities. Participants from the 2018 survey were matched with the average wind speed from the latest three years of 2015 to 2017, while those from the 2017 survey were matched with the average wind speed from the three years of 2014 to 2016.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDefinition of variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplanation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. dev.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDependent variable\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSettlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWilling to settle in their current city in the future\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003eNot willing to settle in their current city in the future\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAir pollution variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe average AQI (air quality index) of the older migrants living in the city in recent three years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndividual control variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003eFemale\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe age of respondents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMigrant\u0026rsquo;s years of schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Agri hukou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Agri hukou\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003eAgri hukou\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInter-provincial migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInter-provincial migration\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003eIntra-provincial migration\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMigration time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow long this migration journey lasts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousing pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe average monthly amount spent on housing as a percentage of monthly expenses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy or basically healthy\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003eUnhealthy\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnrol in health insurance and be reimbursed locally\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003eEnrolled in health insurance and reimbursed in a place other than your own or not covered by health insurance\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCity control variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogarithm of GDP per capita (10,000 yuan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe number of hospital beds per 1,000 inhabitants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreenland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreen coverage rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe temperature difference between July and January (℃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual precipitation (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInstrumental variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWind speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10-meter wind speed (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Methods\u003c/h2\u003e \u003cp\u003eInitially, we explore how air pollution influences the settlement intentions of older migrants. Given that the dependent variable is binary in nature, the Logit regression model is presented:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$Settlement{\\text{ }}intention_{{ict}}^{*}={\\alpha _1}+{\\beta _1}pollutio{n_{ct}}+{\\phi _1}{X_i}+{\\gamma _1}{X_{ct}}+{\\theta _c}+{\\delta _t}+{\\varepsilon _{1ct}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Settlement{\\text{ }}intention_{{ict}}^{*}\\)\u003c/span\u003e\u003c/span\u003e represents the intention of research object i to settle in city c;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(pollutio{n_{ct}}\\)\u003c/span\u003e\u003c/span\u003e is the variable representing air quality༛\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X_i}\\)\u003c/span\u003e\u003c/span\u003erepresents individual variables༛\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X_{ct}}\\)\u003c/span\u003e\u003c/span\u003erepresents city-level variables༛\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\theta _c}\\)\u003c/span\u003e\u003c/span\u003eis regional fixed effect༛\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\delta _t}\\)\u003c/span\u003e\u003c/span\u003eis year fixed effect;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varepsilon _{1ct}}\\)\u003c/span\u003e\u003c/span\u003eis residual༛ \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\phi\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\gamma\\)\u003c/span\u003e\u003c/span\u003e are the regression coefficients.\u003c/p\u003e \u003cp\u003eHowever, daily air quality can be endogenous. Endogeneity may arise from unobservable factors that simultaneously affect both air pollution and settlement intentions. For example, economic activities such as industrial development and human behaviour may influence both air quality and migrants' decisions to settle. Economic development generates employment opportunities, attracting individuals to migrate and settle in areas with burgeoning industries. In addition, population concentration in urban areas also escalates the release of pollutants via transport and the consumption of energy. This relationship, in turn, exacerbates the level of air pollution, creating a reverse causality issue. To reduce possible biases due to endogeneity, we employ an IV method to discern the cause-and-effect link between air pollution and the settlement plans of older migrants. An appropriate IV must satisfy two criteria: 1) strong correlation with the endogenous variable, and 2) exclusion restriction, without directly influencing migrants' long-term settlement intentions. Based on previous research, we exploit 10-meter wind speed as an IV. Wind speed dictates the spread of air pollution across city regions (Vignati et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Higher wind speed indicates greater air circulation, which dilutes pollutants and reduces air pollution levels (Linda et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Meanwhile, wind speed is determined by complex meteorological systems and geographic factors, without having a direct impact on economic activities and human behaviour, thus meeting the external criteria for a reliable IV. We set up an IV-Probit model, controlling variables at both the city and individual levels, to account for the potential endogeneity and establish a robust link between air pollution and the settlement intentions of older migrants.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$pollutio{n_{ct}}=\\lambda +\\varphi win{d_{ct}}+\\mu {X_i}+\\eta {X_{ct}}+{\\theta _c}+{\\delta _t}+{\\varepsilon _{2ct}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$Settlement{\\text{ }}intention_{{ict}}^{{}}={\\alpha _2}+{\\beta _2}\\widehat {{pollutio{n_{ct}}}}+{\\phi _2}{X_i}+{\\gamma _2}{X_{ct}}+{\\theta _c}+{\\delta _t}+{\\varepsilon _{2ict}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(win{d_{ct}}\\)\u003c/span\u003e\u003c/span\u003e denotes 10-meter wind speed, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\lambda\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varphi\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mu\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\eta\\)\u003c/span\u003e\u003c/span\u003e represent the regression coefficient of first stage and second stage regression. The other symbols have the same meanings as those from Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and analysis","content":"\u003cp\u003eWe estimated the effect of AQI levels on the settlement intentions of older migrants, and the findings are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Model 1 displays the results from the baseline probit model which employs AQI as an indicator for air pollution, considering both individual and city-level factors, along with regional and year fixed effects. The results indicate a statistically significant negative association between air pollution levels and the intentions of older migrants to settle permanently. Specifically, an increase of one unit in the Air Quality Index (AQI) corresponds to a 0.1 percentage point decrease in the probability of the older migrants opting for permanent settlement.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of empirical model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003eProbit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003cp\u003eIV Probit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.003\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.039\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.013\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.087\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.029\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.083\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.028\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.030\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.017\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Agri hukou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.495\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.166\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.471\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.158\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInter-provincial migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.289\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.097\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.345\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.116\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMigration time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.034\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousing pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.007\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.002\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.007\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.002\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.223\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.075\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.198\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.066\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.316\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.106\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.310\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.140\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreenland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.015\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.005\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.024\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.008\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.388\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.130\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.598\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.201\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.043)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear fixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional fixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eFirst stage results\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald test of exogeneity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.24\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst stage F statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1022.59\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald chi2(47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3289.56\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Standard errors are presented within parentheses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe denote significance levels by \u003csup\u003e*\u003c/sup\u003e, \u003csup\u003e**\u003c/sup\u003e, and \u003csup\u003e***\u003c/sup\u003e, which corresponded to 10%, 5%, and 1%\u003c/p\u003e \u003cp\u003eThe findings solely reveal associations between air quality and the settlement intentions of older migrants, which may introduce endogeneity issues that could lead to underestimation of the true impact of air pollution. To address this concern and provide a more accurate estimate of the causal effect, we utilize 10-meter wind speed as an IV. Model 2 presents the results from the IV-Probit model. The findings from the first stage show a substantial positive association of wind speed with air pollution, and the first-stage F-statistic surpasses the threshold of 10, suggesting the absence of weak IV concerns. As anticipated, the second-stage results reveal that the impact of air pollution on the settlement intentions of older migrants is more pronounced than that observed in the baseline model. Specifically, a one-unit rise in the AQI corresponds to a 1.3 percentage point reduction in the probability of older migrants opting for permanent settlement. In conclusion, the results across all models consistently demonstrate that poor air quality significantly deters older migrants from establishing residence in polluted cities. Adverse air conditions may prompt the elderly to relocate to areas with cleaner air.\u003c/p\u003e"},{"header":"4 Robustness checks","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Change the measurement of air quality\u003c/h2\u003e \u003cp\u003eIt is noteworthy that while residents may not consistently express concern about air quality, their level of concern tends to escalate in response to severe air pollution that directly impacts their daily lives. This heightened awareness is often manifested through behaviors such as using indoor air purifiers, wearing masks outdoors, and reducing outdoor pursuits to lessen the adverse impacts of air pollution. In this section, we discuss replacing the AQI with the frequency of days classified as heavily polluted, where the AQI exceeds 200, and severely polluted, with AQI greater than 300, over the past three years to further verify the causal connection between air pollution and the settlement intentions of older migrants. While individuals may exhibit diminished sensitivity to average air quality levels, they are more inclined to recognize specific days with significantly elevated pollution.\u003c/p\u003e \u003cp\u003eThe impacts of days with heavy and severe pollution on older migrants' willingness to settle are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Models 3 and 4 show that the number of days with high levels of pollution and older migrants' intentions to settle are significantly correlated negatively at the 1% level. Specifically, the likelihood of older migrants planning to settle decreases by 0.06 percentage point for every extra day of high pollution, suggesting that baseline findings are robust. Furthermore, the models 5 and 6 show that the number of days with severe pollution levels has a substantial detrimental impact on older migrants' long-term settlement intentions. This effect is more pronounced than that of heavily polluted days, each additional day of severe pollution leads to a 0.16 percentage point decrease in their settlement intentions. All results remain consistent with the baseline estimations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness I: Change the measurement of air quality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003cp\u003eProbit\u003c/p\u003e \u003cp\u003eHeavily Polluted Days (AQI\u0026thinsp;\u0026gt;\u0026thinsp;200)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003cp\u003eIV Probit\u003c/p\u003e \u003cp\u003eHeavily Polluted Days (AQI\u0026thinsp;\u0026gt;\u0026thinsp;200)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003cp\u003eProbit\u003c/p\u003e \u003cp\u003eSeverely Polluted Days (AQI\u0026thinsp;\u0026gt;\u0026thinsp;300)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 6\u003c/p\u003e \u003cp\u003eIV-Probit\u003c/p\u003e \u003cp\u003eSeverely Polluted Days (AQI\u0026thinsp;\u0026gt;\u0026thinsp;300)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQI\u0026thinsp;\u0026gt;\u0026thinsp;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0017\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0006\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.013\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.004\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQI\u0026thinsp;\u0026gt;\u0026thinsp;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0049\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0016\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.085\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.040\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual control variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity control variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear fixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional fixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eFirst stage results\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald test of exogeneity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.77\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.46\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst stage F statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e989.55\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e736.10\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: Standard errors are presented within parentheses.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e**\u003c/sup\u003e and \u003csup\u003e***\u003c/sup\u003e indicate significance at 5% and 1% level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Narrow sample analysis\u003c/h2\u003e \u003cp\u003eIn the baseline model, respondents who expressed uncertainty about settling in their current location were categorized as 'not inclined to settle'. While such uncertainty does not definitively indicate a lack of intent to settle, it should not be entirely disregarded. To eliminate this potential confounding effect in the regression results, we excluded these respondents from the study. As seen in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the findings from models 7 and 8 support the conclusion that air pollution has a detrimental impact on older migrants' intentions to settle. After addressing the endogeneity issue, the probability of settlement intention is more accurately represented. Our conclusions remain robust despite this adjustment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness Ⅱ: Narrow sample analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 7\u003c/p\u003e \u003cp\u003eProbit\u003c/p\u003e \u003cp\u003eWithout the sample with the answer \u0026ldquo;have not decided\u0026rdquo;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 8\u003c/p\u003e \u003cp\u003eIV-Probit\u003c/p\u003e \u003cp\u003eWithout the sample with the answer \u0026ldquo;have not decided\u0026rdquo;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0036\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.046\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.017\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0037)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual control variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity control variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear fixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional fixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eFirst stage results\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald test of exogeneity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.64\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst stage F statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e849.18\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Standard errors are presented within parentheses.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e***\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Remove the megacities\u003c/h2\u003e \u003cp\u003eIn China, city size has a direct correlation with the degree of institutional factors, public services, and economic development (Yue et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Larger cities provide migrants with better resource allocation and institutional preferences (Chan \u0026amp; Wan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, metropolitan areas demonstrate a stronger gravitational pull (Wang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), migrants in large cities have higher permanent settlement intention (Liu \u0026amp; Wang, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Megacities mitigate the effect of air pollution on migrants' intentions to settle due to their favourable conditions related to city size. Megacities are defined under China's 2014 city categorization standard as urban areas that host a permanent population of at least 10\u0026nbsp;million. In our sample, five megacities were identified in 2016: Shanghai, Beijing, Chongqing, Guangzhou and Shenzhen. These megacities are not included in the study to prevent city size from influencing the outcomes. The restricted sample results from model 9 and 10 demonstrate that the conclusions remain consistent, indicating that the results of the empirical analysis retain their robustness even when controlling for city size.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness Ⅲ: Remove the megacities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 9\u003c/p\u003e \u003cp\u003eProbit\u003c/p\u003e \u003cp\u003eWithout megalopolis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 10\u003c/p\u003e \u003cp\u003eIV-Probit\u003c/p\u003e \u003cp\u003eWithout megalopolis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0033\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.037\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.013\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual control variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity control variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear fixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional fixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eFirst stage results\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald test of exogeneity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.39\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst stage F statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e719.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13,619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13,619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Standard errors are presented within parentheses.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e***\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Replace the IV\u003c/h2\u003e \u003cp\u003eAlthough wind speed serves as a valid IV, it is not without its limitations. Specifically, it doesn\u0026rsquo;t account for additional meteorological or geographical conditions that affect the dispersion of air pollution, such as wind direction (F. Xu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous research has confirmed that the ventilation coefficient fulfils the condition of an IV. Improved ventilation conditions enhance air movement, facilitating the dissipation of air pollutants, thereby fulfilling the criteria for a reliable IV (Hering \u0026amp; Poncet, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Additionally, the ventilation coefficient results from the product of wind speed and the atmospheric boundary layer height, both of which are influenced by intricate weather systems and geographical features (Broner et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Consequently, the ventilation coefficient is treated as an alternative IV for air pollution. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e displays the findings which reveal no notable alteration in the direction or scale of the AQI\u0026rsquo;s impact.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness Ⅳ: Change the IV\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 11\u003c/p\u003e \u003cp\u003eIV-Probit\u003c/p\u003e \u003cp\u003eVentilation coefficient as IV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 12\u003c/p\u003e \u003cp\u003eIV-Probit\u003c/p\u003e \u003cp\u003eVentilation coefficient as IV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.014\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.003\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.045\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.017\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual control variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity control variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear fixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional fixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eFirst stage results\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald test of exogeneity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.04\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.81\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst stage F statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e642.58\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1032.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Standard errors are presented within parentheses.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e***\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5 Heterogeneity","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Individual characteristic-based heterogeneity\u003c/h2\u003e \u003cp\u003eThe effect of air pollution can vary significantly depending on personal characteristics (Z. Wang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Research has established a consensus that variations in behaviour and lifestyle are shaped by different individual characteristics (Huang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), leading to varied responses to air pollution. Existing research indicate varied reactions to air pollution among individuals based on their social and economic standing (Chen \u0026amp; Chen, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, older migrants in this study are categorized based on their \u003cem\u003ehukou\u003c/em\u003e type, income level and educational attainment.\u003c/p\u003e \u003c/div\u003e\n\n\u003cdiv class=\"Heading\"\u003e1) \u003cem\u003eHukou\u003c/em\u003e type\u003c/div\u003e \u003cp\u003eEarlier research has established hukou status as a key determinant in shaping migrants' intentions to settle (Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For a further investigation of the heterogeneous effects of air pollution, we divided the sample into two groups based on whether they held non-agricultural \u003cem\u003ehukou\u003c/em\u003e. The results from the regression analyses reveal that the effect of air quality on settlement intentions was not notably significant in older migrants who had agricultural hukou. Since air pollution tends to be more severe in urban areas compared to rural areas, individuals with urban backgrounds may be more accustomed to air pollution or may anticipate experiencing similar conditions when returning to their hometowns. The results indicate a reduced susceptibility of these individuals to air pollution. Moreover, owning agricultural or residential land in countryside regions could affect their decision to return home when faced with intolerable air quality, allowing them to benefit from their land and associated dividends.\u003c/p\u003e\n\u003ch3\u003e2) Educational attainment\u003c/h3\u003e\n\u003cp\u003eEarlier study suggests that well-educated migrants are more inclined to establish themselves in urban areas (Zhu \u0026amp; Chen, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In this study, older migrants are grouped into two categories according to their level of education: individuals with at least a high school diploma and those with a lower level of education. The estimated results in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e indicate a higher propensity for older migrants with higher educational attainment to relocate in response to air pollution hazards. Higher level education attainment enables older migrants\u0026rsquo; better comprehension of pollution's long-term health risks and those with higher educational attainment correlates with greater financial capacity. Therefore, older migrants with higher levels of education have more motivation and greater freedom in the choice of settlement. As evidenced by the results of the IV-Probit model showing each additional unit of AQI the probability to settle down among older migrants with lower level of education will decrease by 1.3 percentage points; while the migrants with a higher education level will decrease by 1.4 percentage points.\u003c/p\u003e\n\u003ch3\u003e3) Income\u003c/h3\u003e\n\u003cp\u003eThe level of income is intricately related to the quality of life experienced by migrants in their local environments and plays a crucial role in influencing their relocation decisions (Zhu, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). For this study, we categorized older migrants based on whether their monthly income exceeds 5,000 yuan, which is approximately the average income within the sample. The main observation shows that older migrants with lower income levels have a reduced tolerance for air pollution. Compared with the younger migrants, older migrants exhibit unique income-related characteristics. All the older migrants in this study have reached China's statutory retirement age, those who did not have stable employment or failed to make timely social insurance payments before retirement are unlikely to have a reliable income source. However, cities with poor air quality often have developed economies and elevated living costs. Therefore, older migrants with lower income levels may find it challenging to afford the high cost of living, leading economic factors to be the primary impetus for their relocation, transcending the environment factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity analysis based on Individual characteristic\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 13\u003c/p\u003e \u003cp\u003eNon- agricultural \u003cem\u003ehukou\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 14\u003c/p\u003e \u003cp\u003eAgricultural \u003cem\u003ehukou\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 15\u003c/p\u003e \u003cp\u003eHigh school education or above\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 16\u003c/p\u003e \u003cp\u003eBelow high school education\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 17\u003c/p\u003e \u003cp\u003eIncome less than 5000\u003cem\u003eyuan\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 18\u003c/p\u003e \u003cp\u003eIncome more than 5000\u003cem\u003eyuan\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.014\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.014\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.013\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual control variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity control variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear fixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReginal fixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Standard errors are presented within parentheses.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e***\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Migration pattern-based heterogeneity\u003c/h2\u003e \u003cp\u003eIn addition to individual characteristics, migrants' responses to air pollution are influenced by their distinct migration patterns (Zhu, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Migrants with shorter migration durations or those from periods with higher air quality typically have a harder time adjusting to the contaminated metropolis, potentially leading them to leave (Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In light of the above discussion, this study categorizes the sample of older migrants based on migration duration and AQI disparity between the destination city and their hometown.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1) migration duration\u003c/h3\u003e\n\u003cp\u003eWe categorize the sample based on the duration of residence, differentiating between residents with over five years and those with less than five years of residency. Results from the IV-probit regression shown in the first and second columns of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e indicate that air pollution more significantly influences the settlement intentions of older migrants with shorter migration durations. The reason behind this occurrence is that older migrants who have moved to the city for an extended period have acclimatized to the lifestyle and social environment, and the perceived costs associated with relocating are higher for them than those with shorter migration durations. As a result, they exhibit diminished sensitivity to air quality issues. On the contrary, older migrants with shorter durations of residence have not established a deep connection with their current environment yet. Therefore, when the air quality deteriorates, they are more inclined to relocate to mitigate the adverse effects on their well-being.\u003c/p\u003e\n\u003ch3\u003e2) Air quality disparities between migration city and location\u003c/h3\u003e \u003cp\u003eBoth older migrants' adaptation to air pollution and their preferences for air quality have an impact on how air pollution affects their settlement intentions. This study compares the AQI between older migrants\u0026rsquo; \u003cem\u003ehukou\u003c/em\u003e location and cities of residence, calculating the AQI disparities between these two locations (Zhao et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Based on these disparities, the sample is divided into two categories: those \u003cem\u003ehukou\u003c/em\u003e locations with better air quality and those \u003cem\u003ehukou\u003c/em\u003e locations with inferior air quality. The third and fourth columns of Table \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e summarizes the results of the regression analysis. As anticipated, older migrants originating from cities with better air quality show reduced tolerance to air pollution. Older migrants' intentions to settle in their current city decline by 1.5 percentage points for every unit of decreasing air quality compared to their hometown.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity analysis based on migration patterns\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 19\u003c/p\u003e \u003cp\u003eMigration over 5 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 20\u003c/p\u003e \u003cp\u003eMigration less than 5 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 21\u003c/p\u003e \u003cp\u003eAQI disparity between migration city and \u003cem\u003ehukou\u003c/em\u003e location\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 22\u003c/p\u003e \u003cp\u003eAQI disparity between migration city and \u003cem\u003ehukou\u003c/em\u003e location\u0026thinsp;\u0026lt;\u0026thinsp;0\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarginal effects\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.012\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.013\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.015\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.038)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual control variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity control variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear fixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReginal fixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Standard errors are presented within parentheses.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e***\u003c/sup\u003e and \u003csup\u003e**\u003c/sup\u003e indicate significance at 1% and 5% level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"6 Conclusion and Discussion","content":"\u003cp\u003eAs living conditions improve and health awareness increases, city dwellers have voiced an escalating demand for cleaner air. In response to air pollution, individuals may decide to migrate to regions characterized by superior air conditions (Banzhaf \u0026amp; Walsh, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), effectively \u0026ldquo;vote with their feet\u0026rdquo;. Therefore, after controlling for the individual and city-level factors, this study investigates how the quality of urban air influences the decision of older migrants to settle permanently.\u003c/p\u003e \u003cp\u003eThe key finding of our study indicates a markedly adverse impact of air pollution on the intention of older migrants to settle permanently. The findings demonstrate that it is essential for older migrants to take air pollution into account when making decisions regarding settlement. In addition to air quality, factors such as migration distance, housing pressure, health status, temperature difference and precipitation are also significantly negatively correlated with the settlement intention of older migrants. Conversely, years of education, migration time, living with their children, and having local medical insurance show significant positive correlations with their intention to settle down. Meanwhile, there is substantial heterogeneity within different groups regarding how air quality affects older migrants. Air pollution impacts differ based on individual characteristics and migration patterns among older adults. Specifically, those with agricultural household registration (agricultural hukou), lower education levels or income levels, shorter duration of migration and residence in cities with worse air quality than their hometowns tend to be more affected by adverse air conditions.\u003c/p\u003e \u003cp\u003eOur findings reveal notable disparities in how air pollution impacts the decision to settle between younger and older migrants. First, despite the impact of air pollution on both demographics, prior studies have shown that every increment in the Air Quality Index (AQI) results in an approximate 0.77 percentage point reduction in the likelihood of migrants, predominantly younger migrants, choosing to settle permanently (F. Xu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our results show that this percentage rises to 1.3 percentage points among older migrants. This finding aligns with earlier studies indicating the heightened sensitivity of the elderly to the quality of the environment (Liu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Particularly, air pollution disproportionately impacts older migrants due to both normal and pathological aging processes compared to younger migrants (Balfour \u0026amp; Kaplan, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Consequently, it appears reasonable to conclude that older migrants being more vulnerable to air pollution, are less likely to remain in areas with poor air quality.\u003c/p\u003e \u003cp\u003eSecond, the evidence from existing studies implies that migrants with higher income are more susceptible to the effects of air pollution in general. This is because the cost of relocating to other cities may exceed the financial capacity of low-income families, making migration economically unviable (Zhao, Lao, et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, our findings reveal that older migrants with lower income levels are particularly susceptible to air pollution. This phenomenon is probably explained by differences in settlement motivations between younger and older migrants. For younger migrants, air pollution serves as a reference indicator in selecting a place to reside. Those with higher income tend to seek a higher quality living environment once their material needs are met, possessing better economic resources and greater flexibility in their choices. Therefore, when air pollution levels exceed their acceptable thresholds, they are likely to relocate without hesitation in search of a more suitable habitat. Conversely, due to the traditional Chinese cultural emphasis on familial ties, older adults often prioritize family benefits over personal well-being when making migration decisions. Many lack a regular source of income and depend on financial support and health care provided by their children. Thus, even amidst intense air contamination, many older adults opt to remain with their children, enduring the health risks associated with environmental pollution.\u003c/p\u003e \u003cp\u003eThe mobility of older migrants not only leads to spatial redistribution of the population but also plays a crucial role in shaping policy development. This study carries significant policy implications for local governments as it confirms that better air quality is a critical factor influencing city livability, which in turn affects the settlement intentions of older migrants. First, our study confirms that non-economic factors, such as air pollution, alongside traditional economic factors, are determinants of migrants\u0026rsquo; settlement intentions. It is necessary for cities particularly in highly industrialized and developed regions to prioritize environmental pollution control, strengthen environmental protection and atmospheric governance. Given the heightened susceptibility of older migrants from lower socio-economic statuses to air pollution, tailored policies for each group are essential to lessen the detrimental effects of air pollution. In terms of economic support, highly effective protective masks (e.g., N95) and air purifiers should be distributed free of charge to eligible older migrants, while subsidies could be provided to support the purchase of air quality monitoring or protective equipment for low-income older migrants. From a healthcare perspective, cities should design easy-to-understand publicity material and disseminate air pollution prevention knowledge through dialects or formats familiar to older adults. Additionally, health lectures should be organized in communities, markets and other gathering places for older migrants, with medical professionals invited to educate and raise awareness about air pollution prevention. Regarding environmental improvement and monitoring, efforts should include promoting the installation of air purification facilities and delivering accurate air pollution warning information to older migrants through SMS, phone calls and other direct communication channels. Second, assessing how air pollution affects migration holds immense importance for local authorities in developing strategies to improve social service infrastructure. As a result of China's three-child policy, an increasing number of families will require the assistance of grandparents in caring for newborns, leading to a continuous expansion of older migrants and exerting additional pressure on social and public services in cities with better air quality. Moving forward, beyond controlling urban air pollution, it will be vital for the government to enhance elderly care services and create better settlement opportunities for older migrants within cities. Potential measures include establishing health service stations in areas frequented by older migrants, offering free air pollution awareness and health check-ups, developing unified health records to monitor the risks and protection measures associated with air pollution-related illnesses, providing temporary shelters for older migrants during periods of severe air pollution.\u003c/p\u003e \u003cp\u003eTo align with future developmental trends, our study focuses on a specific subgroup of migrants represented by elderly individuals and places greater emphasis on enhancing urban livability rather than solely concentrating on economic factors, providing new evidence for migration research. However, this paper has several limitations that require further investigation: Firstly, the cross-sectional nature of CMDS data limits our ability to track whether settlement intentions change alongside variations in air pollution levels. Additionally, due to regional differences in topography and climate across China, the 10-meter wind speed may not be the most appropriate IV in certain areas. Future research should explore better-suited IVs tailored to the specific characteristics of each geographical region.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data utilized in this study were secondary data obtained from the China Migrant Dynamic Survey (CMDS) conducted by the National Health Commission of the People\u0026rsquo;s Republic of China. This dataset had received prior ethical approval from the appropriate institutional review board. Prior to participation, written informed consent was obtained from all participants after they were fully informed about the study\u0026rsquo;s purpose and procedures. The investigation was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and other applicable international ethical guidelines. All research procedures strictly adhered to relevant guidelines and regulatory standards.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declarations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;declarations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003eThe authors have no financial or proprietary interests in any material discussed in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant Nos. 42471264 and 42171196), Guangzhou Municipal Science and Technology Bureau (SL2023A04J00959)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJiarong Zheng: Conceptualization, Methodology, Data curation, Writing- original draft, Writing- Reviewing and Editing;Cuiying Huang: Conceptualization, Methodology, Writing- Reviewing and Editing;Ye Liu: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Validation, Writing- Reviewing and Editing;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBalfour, J. 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The settlement intention of China\u0026rsquo;s floating population in the cities: Recent changes and multifaceted individual-level determinants. \u003cem\u003ePopulation, Space and Place\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(4), 253\u0026ndash;267. https://doi.org/10.1002/psp.544\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Air pollution, Older population, Migration, Settlement intention, China","lastPublishedDoi":"10.21203/rs.3.rs-6191639/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6191639/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid increase in the number of older migrants in China has highlighted the need for a nuanced and comprehensive understanding of the factors influencing their settlement intentions. However, previous research on older migrants\u0026rsquo; settlement plans tends to overlook the role of air pollution. Using data from the 2017 and 2018 China Migrants Dynamic Survey (CMDS), this study especially examines whether air pollution affects the settlement intentions of older migrants, aiming to address this research gap. The research presents two groundbreaking contributions to the literature. First, it distinguishes between short-term residential choices and permanent settlement intentions, utilizing appropriate indicators to measure the intentions of migrants opting for permanent settlement. Second, it focuses on older migrants as a distinct demographic group, offering novel evidence at the intersection of air pollution and settlement intentions. Findings demonstrate that urban air pollution adversely impacts older migrants' settlement intentions, with each one-unit rise in the Air Quality Index (AQI) reducing their likelihood of permanent settlement by 0.1 percentage points. Additionally, our research reveals that older migrants with agricultural \u003cem\u003ehukou\u003c/em\u003e status, higher educational attainment, lower income, longer migration duration, and less exposure to air pollution in their hometowns are more vulnerable to air pollution's negative impacts.\u003c/p\u003e","manuscriptTitle":"The influence of air pollution on older migrants’ intentions to settle in the destination cities in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-23 07:22:23","doi":"10.21203/rs.3.rs-6191639/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"62a3c9ef-bf9f-4512-a7bd-1cb3dc2c4dde","owner":[],"postedDate":"April 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T16:05:37+00:00","versionOfRecord":{"articleIdentity":"rs-6191639","link":"https://doi.org/10.1007/s12061-025-09719-6","journal":{"identity":"applied-spatial-analysis-and-policy","isVorOnly":false,"title":"Applied Spatial Analysis and Policy"},"publishedOn":"2025-09-27 15:57:47","publishedOnDateReadable":"September 27th, 2025"},"versionCreatedAt":"2025-04-23 07:22:23","video":"","vorDoi":"10.1007/s12061-025-09719-6","vorDoiUrl":"https://doi.org/10.1007/s12061-025-09719-6","workflowStages":[]},"version":"v1","identity":"rs-6191639","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6191639","identity":"rs-6191639","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}