How do institutions and human capital impact the homeownership in China: Skilled migrants vs labor migrants

How do institutions and human capital impact the homeownership in China: Skilled migrants vs labor migrants | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article How do institutions and human capital impact the homeownership in China: Skilled migrants vs labor migrants ya wang, bo zhang, jing cao, xiaomei cai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8270174/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract This paper examines how human capital and institutional factors shapes homeownership disparities between skilled and labor migrants in host city in China. We analyze how human capital and institutional factors ( hukou and danwei ) exert influence on skilled and labor migrants seeking homeownership. We introduce a theoretical framework that includes social demographic factors, family factors, employment factors, institutional factors, and urban factors impacting migrant homeownership. Using the pooled cross-sectional data of China Migrants Dynamic Survey (CMDS) from 2010 to 2017, we build a binary probit regression and the Shapley decomposition method to examine how the mentioned factors influence migrant homeownership. Next, we compare labor and skilled migrants and find that human capital differences are the main cause of the housing inequality for migrants, and that such inequality is intensified by the screening mechanism of institutions and markets. Last, our time trend analysis shows that, although institutional factors are weakening, their negative impacts on labor migrants remain significant. Scientific community and society/Geography Social science/Geography Social science/Social policy Social science/Sociology homeownership labor migrants skilled migrants institutions human capital Figures Figure 1 Figure 2 1. Introduction With the continuing expansion of housing inequality trends across world economies, it is observed that (Sykora, 1999 ; Z. Li & Wu, 2006 ; Li & Fan, 2020 ) marketization and institutional interventions are most often seen as fundamental triggers of homeownership inequality (Ruoppila, 2005 ; Hall & Greenman, 2013 ; He et al., 2016 ). The market-oriented resource distribution mechanism generally prioritizes elites access to greater housing resources. As a result, houseless and low-income groups are usually only able to meet their housing needs by renting; high rents may also further exhaust their wealth, exacerbating inequality (Lux et al., 2011 ). The pricing and distribution of houses is not purely determined by market supply and demand. On the contrary, the operation of housing markets is largely (at least in socialist countries) determined by the actions of society and political authorities. Institutional housing inequality includes government intervention, housing loan policy, housing reserve funds, and the participation of both public and private sector (Ronald & Doling, 2012). Much research has focused on the effects of systems and markets on individual homeownership, but as far as we are aware, in-depth analyses of the relationship between institutional and market housing mechanisms are lacking. Moreover, the differentiated market distribution mechanisms and national policies that shape diverse resource distribution modes tend to worsen housing inequality. The primary goal of this paper is to explore how housing inequality is rooted in specific government systems (e.g., hukou and danwei ) and markets, and how inequality evolves through policy adjustments and market segmentation. As housing markets in Chinese cities have transitioned from a welfare system to a market-oriented system, the increasing proportion of homeownership, coupled with rising inequality, have become prominent features of China’s real estate market (Huang & Jiang, 2009 ; Yi & Huang, 2014 ). The transition involves a relationship between the market-oriented reform of homeownership and China’s institutional hukou system.The hukou system was implemented nationwide in 1958 as a household registration system that separated the Chinese population into rural and urban and distributed resources based on the status of family hukou . Prior to the 1980s, urban housing was allocated and provided by governments as welfare, whereas rural housing was built by farmers on collectively owned land. At that time, the impacts of hukou on urban and rural housing inequality were small because they were separate entities and thus did not compete for housing. However, the more recent rapid shift of large-scale populations into cities due to and the growth of China’s market economy, has meant that these folks are seen as temporary, ‘flowing’, migrants. Compared to permanent residents with urban hukou , migrants have not yet obtained equal access to welfare and services, including housing (Zeng et al., 2019 ). Without ‘local urban’ hukou , migrants working in cities are ineligible for guaranteed housing, despite the influence of the previous welfare system with its secure public housing, and the new affordable housing program adopted by government post-housing reform (Huang, 2012 ). The discriminatory hukou system and the exclusive urban labor market segmentation has given rise to the migrants’ disadvantages in homeownership (Liu, 2019 ). Fan ( 2011 ) proposed that China’s hukou system is the key factor resulting in a two-track system of permanent and temporary migrants, each receiving different treatment based on their status in their host cities. Permanent migrants are individuals who have changed their hukou registration locations to their host cities. As elites screened by the Hukou system, they are now entitled to the social welfare, public services, and socio-economic status afforded to all local residents. Temporary migrants are individuals who have not yet obtained the hukou of their host cities; they are excluded from hukou due to low education levels or inferior socio-economic status, and they lack the right to obtain stable residences and public services (Du et al., 2017 ). Nevertheless, as a group, migrants in Chinese cities have high internal heterogeneity: not everyone is at the bottom of society or engages in ‘dirty, difficult, and dangerous’ jobs. Some have education levels and socio-economic status above that of local residents (Gong et al., 2024 ). For this reason, and following Fan ( 2011 ), we have divided temporary migrants in cities into labor migrants and skilled migrants, depending on their socio-economic status and employment type. Labor migrants mainly refer to laborers with low education who engage in physical labor, while skilled migrants have non-laborious skills and higher education attainment. Although neither type has been granted local hukou status, the current hukou government policies are biased in favor of skilled migrants for settlement. Preferential migrants are better educated and skilled. Many Chinese cities attract skilled migrants for settlement and house purchase by broadening settlement conditions, providing housing allowances, and issuing rewards for talent (Gu et al., 2019). Whereas most labor migrants continue to be excluded from housing opportunities in their host cities. The present paper has explored the factors that impact homeownership for labor migrants and skilled migrants and examined possible housing inequality trends. Using the pooled cross-sectional data of China Migrants Dynamic Survey (CMDS) from 2010 to 2017, we examined how the internal differentiation of migrants against the background of Chinese systems results in homeownership differences among migrants, as do the roles of institutions and market factors in obtaining homeownership. We have also attempted to explore the possibility of narrowing the homeownership gap between skilled and labor migrants, which may help achieve more inclusive urbanization. By analyzing the differences in homeownership between labor migrants and skilled migrants, the paper aimed to demonstrate how existing policies are not yet able to assist low-income labor migrants with housing. Furthermore, if no decisions are taken, housing inequality may intensify, thus impeding China’s housing reforms and realization of inclusive urbanization. 2. What factors impact migrant homeownership in host cities? Skilled migrants vs labor migrants Housing is considered to be central to welfare (Groves, 2016); it is a complex welfare good that supplements and mediates the flow of other welfare goods and services at household level. Housing is much more complex than the mere provision of physical shelter (Doling & Ronald, 2010). Housing policies not only reflect national welfare but also the changes in economic and social power relations. In recent decades, the proportion of homeownership has risen markedly in eastern countries due to greater focus on homeownership. Nowadays, providing households with opportunities to purchase houses has become increasingly popular with policy makers seeking to popularize homeownership as a means of allocating wealth and financial responsibility in society. Higher homeownership is understood as a way to improve communities, raise property values, and strengthen the financial conditions of the poor (Retsinas & Belsky, 2004). For governments, it symbolizes the commercialization of housing commodities, the establishment of housing markets, and the alleviation of arduous national duties in housing maintenance and supply. However, expanding homeownership means that vulnerable or marginal families also deserve homeownership, but the consequences are higher house price and market access costs. With the rise of homeownership rates and higher property values, ‘climbing the ladder of housing’ becomes increasingly problematic. Migrants in China are confronted with institutional inequality in homeownership in their host cities, with the hukou -based institutional system intensifying their already disadvantaged housing status. Housing wealth often accumulates in specific families; the mechanism of intergenerational inheritance is expected to expand intergenerational wealth transfer and consolidate the social status of subsequent generations. The wealth inheritance is especially noticeable in capital-intensive housing markets and sharply reflects housing inequality between social classes (Zhang et al., 2021). When China implemented housing reform in 1988, the socialist housing distribution system developed into the housing market. During the reform, house prices rose dramatically, reporting noteworthy price hikes at faster rates than national income growth (Glaeser et al., 2017). Housing inequality appeared then for the first time. One reason for China’s housing wealth inequality is because a high proportion of previously public rental housing was sold in the private sector along with subsidies being given to sitting tenants. Hence, those with political connections and resourceful work units initially benefited the most during the privatization process (Yi & Huang, 2014). With the further liberalization of the hukou system for migrants in the era, the focus of housing inequality has shifted from inequality between migrants and urban residents to inequality among all migrants. In this study we have defined skilled migrants and labor migrants in line with other studies. Migrants refer to people who live in places other than their original home locations (Zhang et al., 2017). ‘Skilled’ migrants are defined as migrants with higher education degrees who engage in skilled jobs and have not obtained the hukou of their host cities. Whereas ‘Labor’ migrants are defined as migrants with relatively low education levels who engage in physical labor jobs and have not obtained the hukou of their host cities (Gong et al., 2024). The National Planning on New-Type Urbanization (2014-2020) passed in 2014, states that the government will remove the limits on hukou registration in townships and small cities, relax restrictions in medium-sized cities, and set qualifications for registration in megacities. A main goal of national planning is to promote the stable settlement of migrants to work and live in cities and towns. On the one hand, middle-sized and small cities with populations below three million have lifted their restrictions for migrant workers to settle in such cities. On the other hand, megacities still have access barriers to varying degrees, although research has shown that large cities appeal the most to migrants (Fan, 2011). Local governments may determine the access requirements for migrants to obtain local hukou . In response, megacities have implemented a string of complicated regulations according to their population and urban development planning, and they adopt different policies for skilled migrants and labor migrants. Generally, megacities inhibit the increase of labor migrants by implementing various laws and regulations to raise the settlement costs for labor migrants. In the meantime, megacities are tremendously appealing to skilled migrants, and they attract and retain such migrants using local hukou and well-established social welfare (Gong et al., 2024). Therefore, homeownership and welfare distribution benefit skilled migrants with higher incomes, education, and vocational skills. The policies targeted to skilled migrants mainly impact on the settlement, relocation, and house purchase decisions of skilled migrants in cities, and partially offset the housing inequality of the hukou system. However, labor migrants are still excluded from the receipt of welfare. As the process of institutional intervention and marketization continues, gaps between skilled migrants and labor migrants in homeownership will likely widen. One effect of the current disparity for migrants means that, although they have the will to settle into their inflow cities, few can settle down and purchase homes. According to the China Migrants Dynamic Survey (CMDS) data of 2017, more than 80% of migrants had the strong will to settle, and 46.5% reported they were likely to stay for 10 years or more in their inflow location.[1] However, only 28.9% of migrants purchased houses. Apart from the above-mentioned institutional factor, social demographic characteristics also play a vital role in market-based housing. A consensus of existing studies shows that homeownership is paramount over the course of a person’s lifetime. With the shift to market-based housing in Chinese cities, it is more likely that housing results are driven by family lifecycles (e.g., age, marital status, birth, family size, and family affordability) (Chen, 2015; Li & Li, 2006) and personal employment options (e.g., education, occupation) (Logan & Bian, 1993). In China’s real estate sector, in addition to market factors and traditional redistributed economic factors, employment factors such as danwei characteristics and occupation types play a vital role; skilled migrants working in primary labor markets are likely to own houses. Nevertheless, most labor migrants work in secondary labor markets on a short-term no-contract basis and receive low salaries (Zhao & Jin, 2019). Most low-income migrants will therefore have to live in low-cost urban rental accommodation in unfavorable conditions. However, it is difficult to obtain a deeper understanding of the migrant/housing picture in the absence of an explicit theoretical framework of contributions and impact trends. Homeownership is influenced by social demographic factors, family factors, employment factors, institutional factors, and urban factors, but each factor’s degree of influence is different. This paper has attempted to build a framework of homeownership impact factors for migrants, distinguishing between labor migrants and skilled migrants, and exploring the differences and relationships of homeownership impact factors for the two groups. We expect to observe a gap in the homeownership rate between labor migrants and skilled migrants because skilled migrants are likely to earn higher salaries and have more opportunities to purchase houses (given certain welfare allowances); other skilled migrants may also have a great deal of intergenerational transmission of wealth due to hukou . Conversely, most labor migrants leave their hometowns for a livelihood, and considering their mobility and employment instability, many labor migrants are reluctant to spend salaries on housing in their host cities. Others also lack the ability and opportunities, even if they strongly wish to settle and purchase a house (Deng et al., 2016). [1] Source of data: http://www.chinaldrk.org.cn/wjw/#/data/classify/population 3. Data and method 3.1 Data source Data for the present study derive from the China Migrants Dynamic Survey (CMDS). The survey used the Probability Proportional to Size Sampling (PPS Sampling), covering China’s 31 provinces (autonomous regions, municipalities) and Xinjiang Production and Construction Corps. Eight rounds of surveys were conducted between 2011 and 2018. Respondents were migrant populations aged 15–59 without local hukou who stayed more than six months in host cities. The survey comprised personal information, mobility, scope and tendency, employment and social security, income and living, basic public health service, and education of the migrant population and family members. The data of six years, 2011, 2012, 2013, 2014, 2016, and 2017 were pooled for the study. A total of 829,356 final valid samples were obtained. The data for 2015 and 2018 were not pooled as these years lacked housing information on migrant populations. 3.2 Variable setting The dependent variable is migrant homeownership in host cities. House purchase is not only an economic behavior but is also a process for migrants to gradually integrate into their host cities and establish extensive social relations (Wu & Logan, 2016). Homeownership for migrants not only indicates strong economic dependence on their host cities but also reflects their commitment to remain in their location long term (Liu et al., 2017); such actions play an important role in their social integration. In our study, “Yes” to the question, “whether you have a house in your host city” is set as 1, and “No” as 0. In line with the theoretical analysis, we set out the factors impacting migrant homeownership into five categories. The first category, human capital , includes age, sex, education, and marital status. In an analysis of UK housing, health and education were the most prominent factors driving housing demand (Eichholtz & Lindenthal, 2014). In studies, family life cycle circumstances such as marriage and childbirth, also often give rise to changes, namely, a shift from renting to house purchase (Clark & Huang, 2004). The descriptive analysis results in Table 1 show that, on average, labor migrants in China attain homeownership at age 35.64. Whereas skilled migrant homeownership is achieved approximately five years sooner, at age 30.69. The second category, employment factors , includes occupation type and employment status of migrants; these two factors jointly determine their position in the labor market. The economic transition of Chinese cities has resulted in noteworthy impacts on the return of labor markets. In Table 1 below, 34.51% of skilled migrants engage in white-collar jobs, while the proportion of participation by labor migrants is 4.51%. The third category, family factors , encompasses family migration strategy and family income. According to Mulder (2006), the more integrated the family structure in the host city, the higher the probability that a migrant has his or her own house, i.e., family-based migration accelerates the process of owning a house to a certain extent. Also, family income is fundamental in the house purchase decision. According to research by Haurin (1991), if the variable coefficient of family income increases by 20%, the probability of owning a house will decrease by 1.5%. The fourth category, institutional factors , includes the hukou system and socialist danwei system in particular; the category is reflected through the types of hukou and danwei . Hukou s are divided into agricultural and non-agricultural hukou , while danwei is divided into stated-owned sector and private sector. Although the continuous Hukou system reform in recent years has gradually loosened the restrictions on population mobility and migrant life (Tao et al., 2015), local hukou remains the key factor determining whether migrants will receive full access to public services in their inflow cities. Not only is local hukou reflected in restrictions in house purchase qualifications, but it also involves irrational policies including policies for purchasing house and settling (Zhang & Su, 2010). As an institutional design peculiar to China, the socialist danwei system and the capitalist economic system employment model have significant differences. In China, ‘state-owned sector’ and ‘private sector’ have tremendous differences in employment opportunities, remuneration, and welfare. State-owned sectors provide stable salaries as well as a string of generous welfare, such as housing distribution, medical security, job security, and long-term career development paths (Fan, 2001). The fifth category, urban factors , uses the measurement standard ‘level of host cities.’ Based on the Adjustment of Classification Standards on Urban Sizes , published by China’s State Council in 2014, and the actual demands of the study, the inflow cities of migrants were classified in three levels, namely ‘megacities’, ‘very large-sized cities and type-I large-sized cities,’ and ‘other cities.’ Megacities attract the inflow of the most migrants because of their developed economic and unique urban appeal. However, population pressures and resource restrictions in megacities have resulted in their imposing strict guidelines in their population policies and hukou system. The permanent resident population of ‘very large-sized cities and type-I large-sized cities’ is above three million, making these cities the emerging powers of regional economic development. ‘Other cities’ refers to middle-sized and small cities with a permanent resident population below three million. China’s hukou reform has explicitly specified that the hukou restrictions of cities with a population below three million should be cancelled, and cities with a population above three million should be relaxed, resulting in an easier transition for migrants. Table 1 provides the descriptive analysis and between-group differences of all variables. The between-group differences of these samples indicate that skilled migrants and labor migrants differ significantly in homeownership. At 23.74%, the homeownership of skilled migrants is higher than that of labor migrants (12.69%), a noteworthy difference. We further investigated the differences of the two groups in personal characteristics, employment characteristics, family characteristics, and host cities characteristics. Findings show that these characteristics all passed the T-Test of at least 5% which indicated the significance of between-group differences. [Table 1 is here] 3.3 Research method We set up a Probit model to investigate factors impacting on the probability for migrants to buy their own houses, as shown below: $$\:{\text{H}\text{o}\text{m}\text{e}\text{o}\text{w}\text{n}\text{e}\text{r}}^{\ast\:}={{\beta\:}}_{1}{\text{X}}_{\text{p}\text{e}\text{r}\text{s}\text{o}\text{n}}+{{\beta\:}}_{2}{\text{X}}_{\text{e}\text{m}\text{p}\text{l}\text{o}\text{y}\text{m}\text{e}\text{n}\text{t}}+{{\beta\:}}_{3}{\text{X}}_{\text{f}\text{a}\text{m}\text{i}\text{l}\text{y}}+{{\beta\:}}_{4}{\text{X}}_{\text{i}\text{n}\text{s}\text{t}\text{i}\text{t}\text{u}\text{t}\text{i}\text{o}\text{n}}+{{\beta\:}}_{5}{\text{X}}_{\text{c}\text{i}\text{t}\text{y}}+{{\mu\:}}_{}$$ 1 $$\:\text{H}\text{o}\text{m}\text{e}\text{o}\text{w}\text{n}\text{e}\text{r}=\left({\text{H}\text{o}\text{m}\text{e}\text{o}\text{w}\text{n}\text{e}\text{r}}^{\ast\:}>0\right)=\left({{\beta\:}}_{\text{i}}{\text{X}}_{\text{i}}+{\mu\:}>0\right),$$ 2 Where, Homeowner * represents a proxy variable for whether a family has its own house. As shown in Eq. (2), when Homeowner * >0 and its value is 1, the migrants have their own house; otherwise, the value is 0. X i represents a string of variables influencing migrants to have their own houses; µ is an un-estimated residual. We first conducted a benchmark model regression estimation of all samples (including both skilled and labor migrants). Next, we used a seemingly unrelated regression to inspect the differences between skilled migrants and labor migrants in homeownership, and the differences of regression coefficient were compared to identify the extent to which the homeownership of the two groups is influenced by various factors. 4. Results 4.1 Benchmark regression Models (1) - (5) in Table 2 demonstrate the regression estimation of migrant homeownership by gradually adding the personal, employment, family, institutional, and urban characteristics of migrants. To guarantee the comparability of regression coefficients, we supplemented marginal effects in Model (6) (See Table 2) and used Shapley decomposition to compare the contribution values of various characteristics (See Table 3). To compare the differences of homeownership impact factors for migrants, Figure 1 shows the seemingly unrelated test for labor migrants and skilled migrants and the influential coefficients of the variables on homeownership of the two groups. The main conclusions are the following. [Table 2 is here] Human capital plays a vital role in homeownership for migrants. The overall contributions of personal and employment characteristics reached 41.16%. Specifically, as the education level of migrants increased by 1 unit, the probability of homeownership for migrants increased by 4.65%. By way of comparison, the probability of homeownership for skilled migrants was 3.45% higher than that of labor migrants since higher skilled and higher educated migrants can obtain loans and house purchase support more easily, thereby increasing the probability of homeownership. Occupational factors also significantly impact the inequality of homeownership. Among labor migrants, the homeownership rate of self-employed people and employers of laborers is higher than that of ordinary employees because self-employed people have flexibility in accumulating house purchasing capital due to their relatively stable sources of income. In contrast, ordinary employees are disadvantaged in the housing market because of their unstable employment, low salary, and lack of social welfare (D’Souza, 2019). Skilled migrants with formal labor contracts can achieve homeownership more easily because they are entitled to housing funds and house purchase allowances (Wu & Xiao, 2020). Although self-employed people have relatively high human capital, they are less able to purchase a house due to lack of welfare (Zhang, 2020). The implication here is that housing inequality not only depends on the skills of migrants, but is also affected by myriad factors, including the distribution of market resources and the welfare system. [Figure 1 is here] In our study, family characteristics impact the strongest on migrant homeownership, with a contribution rate up to 41.11%. Family migration, marital status, and income level of families jointly determine whether migrants can purchase houses in their host cities. Specifically, compared to migrating alone, the probability of homeownership for migrants in family migration is as high as 12.22%; the increase of family income makes the probability of house purchase increase by 5.60%. The probability of married migrants in house purchase is 4.36% higher than that of unmarried migrants. The implication here is that, as the minimum unit of society, families play a crucial role in the settlement decision of migrants. A study by Cui et al. (2016) shows that stable families are usually accompanied by higher economic capability, abundant social networks, and stable ways of life, helping to pave the way for migrant house purchase. This phenomenon is more evident because of marriage and family income. Institutional characteristics are the most important external factors influencing the homeownership of migrants, with a contribution rate up to 15.23% (Table 3), including the hukou system and socialist danwei system. The reform has not benefited all migrants, however. Labor migrants especially have difficulty obtaining urban welfare due to severe hukou restrictions. In terms of the allocation of housing resources in particular, labor migrants confront barriers to access public housing compared to skilled migrants. Secondly, the socialist danwei system also significantly impacts on the distribution of housing welfare. Although China’s welfare-oriented housing distribution system has ended, migrants employed by regular danwei , e.g., state-owned enterprises, still receive preferential treatment, such as discounts on house purchases, priority for loans, first choice for affordable housing, and other housing funds (Fu, 2015; Ying et al., 2013). Labor migrants do not have similar access to such housing welfare, since most work for irregular danwei . The limited job opportunities in regular danwei further intensifies housing inequality among migrants. By introducing city levels and quadratic coefficients (in Table 2), Model (5) represents that the relationship between migrant homeownership and city level presents nonlinear characteristics in an inverted U shape; the implication here is that the homeownership proportion of migrants is relatively high in ‘very large-sized cities and type-I large-sized cities,’ and low in ‘megacities’ and ‘other cities.’ Possible reasons for this are that megacities implement rather severe hukou systems and have unbalanced housing distributions, shutting out house purchase options for most migrants; megacities also feature high population densities, where demand for housing is high, and house prices exceed income increases. Labor migrants are especially affected, since their economic ability and welfare support are weak (Huang et al., 2019). Whereas middle-sized and small-sized cities have insufficient appeal for migrants compared to large cities with regard to health care, education, traffic, and other infrastructure. The inverted U shape reflects the complex interaction between city level and migrant options for house purchase. ‘Very large-sized cities and type-I large-sized cities’ have achieved a relative balance of housing affordability, social welfare, and urban living quality, and are the most likely areas for migrants to obtain homeownership. This conclusion is consistent with Song & Zhang (2019) and verified using pooled panel data to clarify the relationship between city level, housing policy, and the settlement model of migrants. [Table 3 is here] 4.2 Time-trend analysis on the impact factors of homeownership for migrants The increase in human capital has been shown to offset some institutional obstacles.Table 4 and Figure 2 demonstrate that the trends affecting migrant homeownership evolve over time. The effects of human capital keep strengthening, with emphasis on the impact of labor migrant education level on homeownership. With urban economic development and strong demand for a high-quality labor force, the competitive power of well-educated migrants continues to increase (Zou & Deng, 2020). High education not only results in stable, better-paid jobs, but also enhances migrants’ opportunities for welfare, such as housing funds and house purchase allowances (Chen et al., 2023). Enhanced human capital indicates competitive power of labor migrants in job markets as well as conveys the selective requirements for a quality labor force. Highly educated migrants can provide long-term contributions to urban development. This trend suggests that the education level of migrants has been a key factor influencing their homeownership. [Table 4 is here] Next, we found a decrease in the impact of the hukou system on homeownership rates of both labor migrants and skilled migrants, especially in large cities. The National Planning on New-Type Urbanization (2014-2020) passed in 2014, resulted in the cancellation of the division between agricultural and non-agricultural hukou , proposing that “China will steadily promote urban basic public services covering permanent resident populations” and “China will fully implement the residence permit[2] system.” The Provisional Regulations on Residence Permit, issued in 2015, and policies promoting the settlement of populations without local hukou in cities, issued in 2016, further lowered the obstacles for migrants to purchase houses in host cities. Specifically, the policies mentioned that restrictions for migrants to settle in cities with a permanent resident population below three million will be cancelled, and conditions for migrants to settle in large cities with a permanent resident population between three and five million will be fully relaxed. Some policies have emphasized that, “commercial residential building infrastructures and indemnificatory housing policies will be improved, and farmers settling in cities will be fully included in urban housing guarantee systems”. For example, the Administrative Measures of Chengdu on Residence Permit specifies that any migrant may apply for a residence permit if the migrant pays social security continuously, has a legal employment certificate, and has been living in the city for a certain period; the conditions for migrants to settle will be relaxed gradually. In accordance with the Detailed Implementation Rules of Chengdu on Housing Policies for Talents , issued in 2020, any migrant may apply for a residence permit by continuously residing in the city for a certain period and paying for social security. The residence permit holder will qualify for house purchase treatment equal to local residents after paying social security for a certain period. The Administrative Measures of Nanjing on Housing Guarantee and Houses stipulates that any migrant with a valid residence permit and paying social security or taxes for a certain number of years will have access to a relatively lower payment ratio (generally 20%-30%) and lower loan rates. Such policy reforms send positive signals to migrants, encouraging them to settle in their host cities. These relaxed measures are gradually paving the way for migrants to receive local hukou and settle, especially in large cities. However, the relaxation of the hukou system is not taking place in a one-size-fits-all approach. In small cities, the conditions for migrants to settle sooner relaxed, whereas in megacities the reform of settlement conditions has been relatively prudent and slow. The differentiated reform paths explain the varied impacts of hukou on homeownership in cities of all sizes, with the fastest decline occurring in large cities. Migrants have nevertheless been actively coping with the multiple challenges in their host cities (Zhu, 2017). As Tao et al. (2015) pointed out, against the background of relaxed policies, migrants are actively able to integrate through flexible employment and living strategies, further highlighting their long-term will to settle in their host cities. Over time, as their employment stability and residence time in host cities increases, migrants are then able to purchase houses and settle long term (Lin & Zhu, 2022). According to the 2021 Report on Residence of New First-Tier Cities , the proportion of migrants to home buyers has been rising year-on-year, surpassing 50% in seven cities, namely, Zhuhai, Shenzhen, Beijing, Dongguan, Tianjin, Shanghai, and Hangzhou. The effects of institutional reform, especially in large cities, lead to more migrants to settle. [Figure 2 is here] [2]The residence permit is a certificate for the permit holder to reside in the place of residence and have access to basic local public services and apply for the registration of permanent hukou as a permanent resident. 5. Conclusion and discussion We have analyzed the factors that impact migrant homeownership in Chinese cities by focusing on the differences between labor migrants and skilled migrants. Using binary probit regression and Shapley decomposition, we found that family migration strategies and economic support are the main factors influencing migrant homeownership in their host cities. Our findings also demonstrated that labor migrants are subject to entrenched institutional constraints, e.g., the hukou and danwei system. In contrast, due to their own backgrounds, skilled migrants can overcome institutional obstacles and access primary labor markets the same as do all local residents. In our exploration of the effects of all factors in a time-trend analysis using pooled cross-sectional data, we found a year-on-year decline in the influence of the Hukou system; however, the rise of marketization seems to be slowly atomizing the relationships among different types of ‘human capital’. Disparities in ‘human capital’ are the root cause of housing inequality between different types of migrants, and we have observed that the screening mechanism of institutions and markets further widens the inequality. By comparing skilled migrants with labor migrants, our results revealed that skilled migrants have much greater access to regular primary labor markets and therefore enjoy more stable employment and homeownership. By following the time-trend analysis of Zhou et al. ( 2021 ), we have confirmed that skilled migrants are advantaged by urban policies and enjoy housing support due to their higher education and skill levels, while labor migrants (having basic education levels) are often excluded from regular markets and restricted by institutional barriers. Policy reforms and market distributions are continuously reshaping the access conditions of housing markets, further reinforcing the dominant position of skilled migrants, and intensifying the marginalization of labor migrants. Indeed, urban economic growth requires high-caliber talent, but urban growth also needs labor migrants for fundamental industries (Chen et al., 2023). Although the hukou system is in the process of reform and its impacts on migrants have been weakening, its influence still makes homeownership highly problematic for labor migrants, depending on where they wish to settle. However, taking Shenzhen as an example, Hui et al. ( 2014 ) found that with the progress of hukou reform, migrants hoped to improve their residence conditions and reside in public rental housing, not due to their hukou identities, but instead mainly due to the high rents and poor living conditions in villages in cities. The present study has further verified Hui et al. ( 2014 ) in a more detailed way. Although many small and middle-sized cities have deregulated hukou systems earlier and more thoroughly, large cities continue to attract and retain all kinds of migrants through increasingly flexible, inclusive social welfare systems and housing allowances. The weakening of institutional factors has sent the signal that cities are taking a practical and inclusive approach to attract labor forces. The continuous optimization of systems in large cities not only promotes changes in migrant behavior but also provides role models for other cities to adopt appropriate settlement policies for migrants. Different cities have established flexible policies for the settlement of migrants in accordance with their respective development goals and characteristics to achieve the mutual development of cities and migrants together. Finally, this study has confirmed that, despite their gradual weakening, some negative impacts of institutional factors on labor migrants remain. Institutional discrimination and the market-oriented housing system interact to form a chain effect. The marketization of homeownership and the chain effects of the hukou system imply that labor migrants are still disadvantaged regarding market access, especially in housing. A market-oriented competitive environment is unfavorable to labor migrants without resources and capital, and existing policies still have deficiencies to help low-income labor migrants obtain homeownership. As the number of temporary migrants increases, the distinction between skilled and labor migrants may intensify social inequality in cities. Therefore, we suggest that targeted policy regulations are needed to address inequality. First, urban governance could focus on the actual housing demands of migrants of various types to equalize housing welfare. Diverse housing supply, such as the mixed community construction of cooperative housing, public rental housing, and commercial housing, could be provided as a guarantee that all migrants have appropriate housing choices. Second, more inclusive housing allowance policies could be implemented and differentiated support provided as per income, family conditions, and years of employment, to guarantee that labor migrants receive more housing welfare in existing policy frameworks. Finally, from the perspective of labor migrants, they will enhance their own competitive power through vocational training, and cities and enterprises will provide them with career development and skill training opportunities through cooperative programs. With the above policies and measures, labor migrants will have more housing opportunities and thus more opportunities to integrate and settle into cities in a more stable manner. Declarations Data Availability: The datasets generated during and/or analysed during the current study are available in the [Global Change Research Data Publishing & Repository] repository, [https://www.geodata.cn/data/publisher.html]. 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Cities 103, 102752. https://doi.org/10.1016/j.cities.2020.102752 Tables Table 1 Descriptive analysis and between-group differences of variables Variable Skilled migrants Labor migrants Between-group differences _Diff Variable Skilled migrants Labor migrants Between-group differences _Diff Homeownership (%) 23.74 12.69 -0.110*** Employment status (%) 0.463*** Education level (year) 15.45 9.07 -2.351** Employee 81.54 56.17 Sex (%) -0.018** Employer 7.19 11.6 Male 51.96 53.73 Self-employed 11.27 32.24 Female 48.04 46.27 Migration strategy (%) 0.398*** Age (year) 30.69 35.64 4.949** Single mobility 38.28 22.74 Marital status (%) 0.143*** Couple mobility 19.61 24.32 Not married 33.96 19.62 Family mobility 42.11 52.94 Married 66.04 80.38 Annual household income (yuan; take the logarithm) 8.76 8.45 -0.303*** hukou (%) -0.400*** City level (%) 0.522*** Agricultural hukou 46.9 86.86 Megacities 21.04 10.19 Non-agricultural hukou 53.1 13.14 Very large-sized and Type-I large-sized cities 31.48 26.93 Nature of danwei (%) -0.148*** Other cities 47.52 62.87 Private sectors 79.94 94.73 State-owned sectors 20.06 5.27 Occupation type (%) -0.299*** Blue collar 65.49 95.43 White collar 34.51 4.51 Note : * p < 0.10, ** p < 0.05, *** p < 0.01 Table 2 Benchmark regression: Impact factors of homeownership for migrants (1) (2) (3) (4) (5) Marginal Effect Personal characteristics edu 0.2583 *** 0.1873 *** 0.1988 *** 0.1859 *** 0.2152 *** 0.0465 *** (0.0015) (0.0016) (0.0019) (0.0020) (0.0021) (0.0004) sex 0.1302 *** 0.1250 *** 0.0596 *** 0.0679 *** 0.0766 *** 0.0165 *** (0.0030) (0.0030) (0.0034) (0.0035) (0.0036) (0.0008) age 0.0214 *** 0.0183 *** 0.0145 *** 0.0187 *** 0.0188 *** 0.0040 *** (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0000) marr 0.4088 *** 0.4271 *** 0.5553 *** 0.1867 *** 0.2020 *** 0.0436 *** (0.0044) (0.0044) (0.0053) (0.0063) (0.0065) (0.0014) Institutional characteristics hukou 0.3476 *** 0.3152 *** 0.3290 *** 0.3439 *** 0.0743 *** (0.0040) (0.0046) (0.0047) (0.0048) (0.0010) danwei 0.2199 *** 0.3389 *** 0.3726 *** 0.2429 *** 0.0524 *** (0.0054) (0.0058) (0.0059) (0.0065) (0.0014) Employment characteristics occu 0.1441 *** 0.1277 *** 0.1600 *** 0.0345 *** (0.0059) (0.0061) (0.0062) (0.0013) emp_iden emp_iden_employer 0.2134 *** 0.1102 *** 0.0554 *** 0.0120 *** (0.0052) (0.0054) (0.0056) (0.0012) emp_iden_Self-employed 0.1918 *** 0.0945 *** 0.0451 *** 0.0097 *** (0.0040) (0.0041) (0.0043) (0.0009) Family characteristics Migration_single migration_couple 0.1141 *** 0.0939 *** 0.0152 *** (0.0063) (0.0065) (0.0010) Migration_family 0.6060 *** 0.5814 *** 0.1222 *** (0.0056) (0.0057) (0.0011) lnfaminc 0.1992 *** 0.2593 *** 0.0560 *** (0.0030) (0.0032) (0.0007) Urban characteristics urbansize 0.1867 *** 0.0138 *** (0.0074) (0.0004) urbansize 2 -0.0164 *** (0.0011) _cons -3.0404 *** -2.7817 *** -2.8940 *** -4.7385 *** -5.8005 *** (0.0105) (0.0108) (0.0129) (0.0267) (0.0318) N 829356 829356 829356 829356 829356 Note : * p < 0.10, ** p < 0.05, *** p < 0.01, Standard errors in parentheses Table 3 Shapley value contribution rate of influence factors of homeownership for labor migrants and skilled migrants Shapley Value Contribution Rate (%) Overall Labor migrants Skilled migrants Included variables Personal characteristics 36.12 26.59 39.96 edu, sex, age, marr Institutional characteristics 15.23 12.27 7.72 hukou , danwei Employment characteristics 5.04 4.96 1.55 occu, employment Family characteristics 41.11 49.90 49.57 migration, lnfaminc Urban characteristics 2.50 6.28 1.20 urbansize Table 4 Time-trend: Impact factors of homeownership for migrants (1) (2) (3) (4) (5) (6) Seemingly unrelated test labor migrants Skilled migrants 2011 2014 2017 2011 2014 2017 edu 0.1177 *** 0.1459 *** 0.1702 *** 0.1034 *** 0.0911 *** 0.0946 *** 121.71 *** (0.0095) (0.0073) (0.0067) (0.0325) (0.0199) (0.0168) sex 0.0461 *** 0.0064 0.0775 *** 0.1470 *** 0.1098 *** 0.1235 *** 107.90 *** (0.0123) (0.0094) (0.0091) (0.0346) (0.0211) (0.0179) age 0.0224 *** 0.0173 *** 0.0160 *** 0.0258 *** 0.0225 *** 0.0090 *** 79.72 *** (0.0008) (0.0006) (0.0005) (0.0029) (0.0018) (0.0016) marr -0.0722 *** -0.1481 *** 0.3368 *** 0.1830 *** 0.0373 0.7708 *** 1380.67 *** (0.0251) (0.0178) (0.0161) (0.0533) (0.0337) (0.0251) hukou 0.4680 *** 0.3757 *** 0.2749 *** 0.3032 *** 0.2819 *** 0.2037 *** 21.50 *** (0.0167) (0.0135) (0.0119) (0.0379) (0.0219) (0.0184) danwei 0.2707 *** 0.3193 *** 0.2394 *** 0.2634 *** 0.2509 *** 0.2100 *** 18.92 *** (0.0225) (0.0191) (0.0198) (0.0409) (0.0270) (0.0232) occu 0.1279 *** 0.2543 *** 0.1936 *** 0.0838 ** 0.0290 0.0150 74.16 *** (0.0209) (0.0198) (0.0198) (0.0372) (0.0237) (0.0200) emp_iden emp_iden_employer -0.0014 0.2287 *** 0.3489 *** -0.1900 *** 0.0148 0.0672 * 438.75 *** (0.0127) (0.0152) (0.0182) (0.0510) (0.0381) (0.0374) emp_iden_Self-employed 0.0470 0.0910 *** 0.0598 *** 0.0827 -0.1884 *** -0.1340 *** 120.40 *** (0.0407) (0.0102) (0.0098) (0.1747) (0.0314) (0.0276) migration migration_couple 0.3548 *** 0.4967 *** -0.1473 *** 0.6237 *** 0.6132 *** -0.2425 *** 776.42 *** (0.0232) (0.0202) (0.0150) (0.0538) (0.0377) (0.0258) migration_family 0.8510 *** 1.0709 *** 0.3533 *** 0.9536 *** 1.0406 *** 0.1535 *** 743.58 *** (0.0208) (0.0185) (0.0133) (0.0495) (0.0336) (0.0216) lnfaminc 0.1155 *** 0.0741 *** 0.1013 *** 0.3273 *** 0.4510 *** 0.3731 *** 696.44 *** (0.0106) (0.0088) (0.0085) (0.0300) (0.0200) (0.0169) urbansize 0.2491 *** 0.1548 *** 0.0826 *** 0.3718 *** 0.2868 *** 0.2326 *** 77.47 *** (0.0262) (0.0211) (0.0201) (0.0615) (0.0379) (0.0332) urbansize 2 -0.0284 *** -0.0098 *** 0.0019 -0.0638 *** -0.0376 *** -0.0235 *** 116.27 *** (0.0039) (0.0031) (0.0030) (0.0098) (0.0060) (0.0052) _cons -4.5384 *** -4.0833 *** -3.8238 *** -6.3363 *** -7.2004 *** -5.7702 *** (0.1052) (0.0884) (0.0881) (0.3246) (0.2180) (0.1887) N 95508 145134 110096 8521 21999 24212 Note : * p < 0.10, ** p < 0.05, *** p < 0.01, Standard errors in parentheses Additional Declarations No competing interests reported. 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09:42:09","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":180335,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8270174/v1/902bf9e6fa6d7e86a1a50784.html"},{"id":100571256,"identity":"0853cfdc-47f5-4361-8245-2f6a571e4006","added_by":"auto","created_at":"2026-01-19 09:42:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":114743,"visible":true,"origin":"","legend":"\u003cp\u003eSeemingly Unrelated Test and between group coefficients comparison of labor migrants and skilled migrants\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8270174/v1/c9e5fb0c6648c9a30bd6707d.png"},{"id":100571257,"identity":"bc79279e-3363-40c9-ad6e-5033dcf65c25","added_by":"auto","created_at":"2026-01-19 09:42:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34370,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of the \u003cem\u003eHukou\u003c/em\u003e system on the homeownership of labor migrants\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8270174/v1/123089afaa099bc6470f4bf2.png"},{"id":100597439,"identity":"7dfb5d11-7f14-4b4e-a258-1783abe17385","added_by":"auto","created_at":"2026-01-19 14:17:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1701725,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8270174/v1/aebd7a7e-02ef-46c7-8b58-5161cc8626ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"How do institutions and human capital impact the homeownership in China: Skilled migrants vs labor migrants","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the continuing expansion of housing inequality trends across world economies, it is observed that (Sykora, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Z. Li \u0026amp; Wu, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Li \u0026amp; Fan, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) marketization and institutional interventions are most often seen as fundamental triggers of homeownership inequality (Ruoppila, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Hall \u0026amp; Greenman, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; He et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The market-oriented resource distribution mechanism generally prioritizes elites access to greater housing resources. As a result, houseless and low-income groups are usually only able to meet their housing needs by renting; high rents may also further exhaust their wealth, exacerbating inequality (Lux et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The pricing and distribution of houses is not purely determined by market supply and demand. On the contrary, the operation of housing markets is largely (at least in socialist countries) determined by the actions of society and political authorities. Institutional housing inequality includes government intervention, housing loan policy, housing reserve funds, and the participation of both public and private sector (Ronald \u0026amp; Doling, 2012). Much research has focused on the effects of systems and markets on individual homeownership, but as far as we are aware, in-depth analyses of the relationship between institutional and market housing mechanisms are lacking. Moreover, the differentiated market distribution mechanisms and national policies that shape diverse resource distribution modes tend to worsen housing inequality. The primary goal of this paper is to explore how housing inequality is rooted in specific government systems (e.g., \u003cem\u003ehukou\u003c/em\u003e and \u003cem\u003edanwei\u003c/em\u003e) and markets, and how inequality evolves through policy adjustments and market segmentation.\u003c/p\u003e \u003cp\u003eAs housing markets in Chinese cities have transitioned from a welfare system to a market-oriented system, the increasing proportion of homeownership, coupled with rising inequality, have become prominent features of China\u0026rsquo;s real estate market (Huang \u0026amp; Jiang, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yi \u0026amp; Huang, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The transition involves a relationship between the market-oriented reform of homeownership and China\u0026rsquo;s institutional \u003cem\u003ehukou\u003c/em\u003e system.The \u003cem\u003ehukou\u003c/em\u003e system was implemented nationwide in 1958 as a household registration system that separated the Chinese population into rural and urban and distributed resources based on the status of family \u003cem\u003ehukou\u003c/em\u003e. Prior to the 1980s, urban housing was allocated and provided by governments as welfare, whereas rural housing was built by farmers on collectively owned land. At that time, the impacts of \u003cem\u003ehukou\u003c/em\u003e on urban and rural housing inequality were small because they were separate entities and thus did not compete for housing. However, the more recent rapid shift of large-scale populations into cities due to and the growth of China\u0026rsquo;s market economy, has meant that these folks are seen as temporary, \u0026lsquo;flowing\u0026rsquo;, migrants. Compared to permanent residents with urban \u003cem\u003ehukou\u003c/em\u003e, migrants have not yet obtained equal access to welfare and services, including housing (Zeng et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Without \u0026lsquo;local urban\u0026rsquo; \u003cem\u003ehukou\u003c/em\u003e, migrants working in cities are ineligible for guaranteed housing, despite the influence of the previous welfare system with its secure public housing, and the new affordable housing program adopted by government post-housing reform (Huang, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe discriminatory \u003cem\u003ehukou\u003c/em\u003e system and the exclusive urban labor market segmentation has given rise to the migrants\u0026rsquo; disadvantages in homeownership (Liu, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Fan (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) proposed that China\u0026rsquo;s \u003cem\u003ehukou\u003c/em\u003e system is the key factor resulting in a two-track system of permanent and temporary migrants, each receiving different treatment based on their status in their host cities. \u003cem\u003ePermanent migrants\u003c/em\u003e are individuals who have changed their \u003cem\u003ehukou\u003c/em\u003e registration locations to their host cities. As elites screened by the \u003cem\u003eHukou\u003c/em\u003e system, they are now entitled to the social welfare, public services, and socio-economic status afforded to all local residents. \u003cem\u003eTemporary migrants\u003c/em\u003e are individuals who have not yet obtained the \u003cem\u003ehukou\u003c/em\u003e of their host cities; they are excluded from \u003cem\u003ehukou\u003c/em\u003e due to low education levels or inferior socio-economic status, and they lack the right to obtain stable residences and public services (Du et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Nevertheless, as a group, migrants in Chinese cities have high internal heterogeneity: not everyone is at the bottom of society or engages in \u0026lsquo;dirty, difficult, and dangerous\u0026rsquo; jobs. Some have education levels and socio-economic status above that of local residents (Gong et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For this reason, and following Fan (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), we have divided temporary migrants in cities into \u003cem\u003elabor\u003c/em\u003e migrants and \u003cem\u003eskilled\u003c/em\u003e migrants, depending on their socio-economic status and employment type.\u003c/p\u003e \u003cp\u003eLabor migrants mainly refer to laborers with low education who engage in physical labor, while skilled migrants have non-laborious skills and higher education attainment. Although neither type has been granted local \u003cem\u003ehukou\u003c/em\u003e status, the current \u003cem\u003ehukou\u003c/em\u003e government policies are biased in favor of skilled migrants for settlement. Preferential migrants are better educated and skilled. Many Chinese cities attract skilled migrants for settlement and house purchase by broadening settlement conditions, providing housing allowances, and issuing rewards for talent (Gu et al., 2019). Whereas most labor migrants continue to be excluded from housing opportunities in their host cities.\u003c/p\u003e \u003cp\u003eThe present paper has explored the factors that impact homeownership for labor migrants and skilled migrants and examined possible housing inequality trends. Using the pooled cross-sectional data of China Migrants Dynamic Survey (CMDS) from 2010 to 2017, we examined how the internal differentiation of migrants against the background of Chinese systems results in homeownership differences among migrants, as do the roles of institutions and market factors in obtaining homeownership. We have also attempted to explore the possibility of narrowing the homeownership gap between skilled and labor migrants, which may help achieve more inclusive urbanization. By analyzing the differences in homeownership between labor migrants and skilled migrants, the paper aimed to demonstrate how existing policies are not yet able to assist low-income labor migrants with housing. Furthermore, if no decisions are taken, housing inequality may intensify, thus impeding China\u0026rsquo;s housing reforms and realization of inclusive urbanization.\u003c/p\u003e"},{"header":"2. What factors impact migrant homeownership in host cities? Skilled migrants vs labor migrants ","content":"\u003cp\u003eHousing is considered to be central to welfare (Groves, 2016); it is a complex welfare good that supplements and mediates the flow of other welfare goods and services at household level. Housing is much more complex than the mere provision of physical shelter (Doling \u0026amp; Ronald, 2010).\u0026nbsp;Housing policies not only reflect national welfare but also the changes in economic and social power relations. In recent decades, the proportion of homeownership has risen markedly in eastern countries due to greater focus on homeownership. Nowadays, providing households with opportunities to purchase houses has become increasingly popular with policy makers seeking to popularize homeownership as a means of allocating wealth and financial responsibility in society. Higher homeownership is understood as a way to improve communities, raise property values, and strengthen the financial conditions of the poor\u0026nbsp;(Retsinas \u0026amp; Belsky, 2004). For governments, it symbolizes the commercialization of housing commodities, the establishment of housing markets, and the alleviation of arduous national duties in housing maintenance and supply. However, expanding homeownership means that vulnerable or marginal families also deserve homeownership, but the consequences are higher house price and market access costs. With the rise of homeownership rates and higher property values, \u0026lsquo;climbing the ladder of housing\u0026rsquo; becomes increasingly problematic.\u003c/p\u003e\n\u003cp\u003eMigrants in China are confronted with institutional inequality in homeownership in their host cities, with the \u003cem\u003ehukou\u003c/em\u003e-based institutional system intensifying their already disadvantaged housing status.\u0026nbsp;Housing wealth often accumulates in specific families; the mechanism of intergenerational inheritance is expected to expand intergenerational wealth transfer and consolidate the social status of subsequent generations. The wealth inheritance is especially noticeable in capital-intensive housing markets and sharply reflects housing inequality between social classes\u0026nbsp;(Zhang et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen China implemented housing reform in 1988, the socialist housing distribution system developed into the housing market. During the reform, house prices rose dramatically, reporting noteworthy price hikes at faster rates than national income growth (Glaeser et al., 2017). Housing inequality appeared then for the first time. One reason for China\u0026rsquo;s housing wealth inequality is because a high proportion of previously public rental housing was sold in the private sector along with subsidies being given to sitting tenants. Hence, those with political connections and resourceful work units initially benefited the most during the privatization process (Yi \u0026amp; Huang, 2014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith the further liberalization of the \u003cem\u003ehukou\u003c/em\u003e system for migrants in the era, the focus of housing inequality has shifted from inequality between migrants and urban residents to inequality among all migrants. In this study we have defined skilled migrants and labor migrants in line with other studies. Migrants refer to people who live in places other than their original home locations (Zhang et al., 2017). \u0026lsquo;Skilled\u0026rsquo; migrants are defined as migrants with higher education degrees who engage in skilled jobs and have not obtained the \u003cem\u003ehukou\u003c/em\u003e of their host cities. Whereas \u0026lsquo;Labor\u0026rsquo; migrants are defined as migrants with relatively low education levels who engage in physical labor jobs and have not obtained the \u003cem\u003ehukou\u003c/em\u003e of their host cities (Gong et al., 2024). The \u003cem\u003eNational Planning on New-Type Urbanization (2014-2020)\u003c/em\u003e passed in 2014, states that the government will remove the limits on \u003cem\u003ehukou\u003c/em\u003e registration in townships and small cities, relax restrictions in medium-sized cities, and set qualifications for registration in megacities. A main goal of national planning is to promote the stable settlement of migrants to work and live in cities and towns. On the one hand, middle-sized and small cities with populations below three million have lifted their restrictions for migrant workers to settle in such cities. On the other hand, megacities still have access barriers to varying degrees, although research has shown that large cities appeal the most to migrants (Fan, 2011). Local governments may determine the access requirements for migrants to obtain local \u003cem\u003ehukou\u003c/em\u003e. In response, megacities have implemented a string of complicated regulations according to their population and urban development planning, and they adopt different policies for skilled migrants and labor migrants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenerally, megacities inhibit the increase of labor migrants by implementing various laws and regulations to raise the settlement costs for labor migrants. In the meantime, megacities are tremendously appealing to skilled migrants, and they attract and retain such migrants using local \u003cem\u003ehukou\u003c/em\u003e and well-established social welfare (Gong et al., 2024). Therefore, homeownership and welfare distribution benefit skilled migrants with higher incomes, education, and vocational skills. The policies targeted to skilled migrants mainly impact on the settlement, relocation, and house purchase decisions of skilled migrants in cities, and partially offset the housing inequality of the \u003cem\u003ehukou\u003c/em\u003e system. However, labor migrants are still excluded from the receipt of welfare. As the process of institutional intervention and marketization continues, gaps between skilled migrants and labor migrants in homeownership will likely widen.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne effect of the current disparity for migrants means that, although they have the will to settle into their inflow cities, few can settle down and purchase homes. According to the China Migrants Dynamic Survey (CMDS) data of 2017, more than 80% of migrants had the strong will to settle, and 46.5% reported they were likely to stay for 10 years or more in their inflow location.[1] However, only 28.9% of migrants purchased houses. Apart from the above-mentioned institutional factor, social demographic characteristics also play a vital role in market-based housing. A consensus of existing studies shows that homeownership is paramount over the course of a person\u0026rsquo;s lifetime. With the shift to market-based housing in Chinese cities, it is more likely that housing results are driven by family lifecycles (e.g., age, marital status, birth, family size, and family affordability) (Chen, 2015; Li \u0026amp; Li, 2006) and personal employment options (e.g., education, occupation) (Logan \u0026amp; Bian, 1993).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn China\u0026rsquo;s real estate sector, in addition to market factors and traditional redistributed economic factors, employment factors such as\u0026nbsp;\u003cem\u003edanwei\u003c/em\u003e characteristics and occupation types play a vital role; skilled migrants working in primary labor markets are likely to own houses. Nevertheless, most labor migrants work in secondary labor markets on a short-term no-contract basis and receive low salaries (Zhao \u0026amp; Jin, 2019).\u0026nbsp;Most low-income migrants will therefore have to live in low-cost urban rental accommodation in unfavorable conditions. However, it is difficult to obtain a deeper understanding of the migrant/housing picture in the absence of an explicit theoretical framework of contributions and impact trends. Homeownership is influenced by social demographic factors, family factors, employment factors, institutional factors, and urban factors, but each factor\u0026rsquo;s degree of influence is different. This paper has attempted to build a framework of homeownership impact factors for migrants, distinguishing between labor migrants and skilled migrants, and exploring the differences and relationships of homeownership impact factors for the two groups. We expect to observe a gap in the homeownership rate between labor migrants and skilled migrants because skilled migrants are likely to earn higher salaries and have more opportunities to purchase houses (given certain welfare allowances); other skilled migrants may also have a great deal of intergenerational transmission of wealth due to \u003cem\u003ehukou\u003c/em\u003e. Conversely, most labor migrants leave their hometowns for a livelihood, and considering their mobility and employment instability, many labor migrants are reluctant to spend salaries on housing in their host cities. Others also lack the ability and opportunities, even if they strongly wish to settle and purchase a house\u0026nbsp;(Deng et al., 2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[1] Source of data: http://www.chinaldrk.org.cn/wjw/#/data/classify/population\u003c/p\u003e"},{"header":"3. Data and method","content":"\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e3.1 Data source\u003c/h2\u003e\n \u003cp\u003eData for the present study derive from the China Migrants Dynamic Survey (CMDS). The survey used the Probability Proportional to Size Sampling (PPS Sampling), covering China\u0026rsquo;s 31 provinces (autonomous regions, municipalities) and Xinjiang Production and Construction Corps. Eight rounds of surveys were conducted between 2011 and 2018. Respondents were migrant populations aged 15\u0026ndash;59 without local \u003cem\u003ehukou\u003c/em\u003e who stayed more than six months in host cities. The survey comprised personal information, mobility, scope and tendency, employment and social security, income and living, basic public health service, and education of the migrant population and family members. The data of six years, 2011, 2012, 2013, 2014, 2016, and 2017 were pooled for the study. A total of 829,356 final valid samples were obtained. The data for 2015 and 2018 were not pooled as these years lacked housing information on migrant populations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e3.2 Variable setting\u003c/h2\u003e\n \u003cp\u003eThe dependent variable is migrant homeownership in host cities. House purchase is not only an economic behavior but is also a process for migrants to gradually integrate into their host cities and establish extensive social relations (Wu \u0026amp; Logan, 2016). Homeownership for migrants not only indicates strong economic dependence on their host cities but also reflects their commitment to remain in their location long term (Liu et al., 2017); such actions play an important role in their social integration. In our study, \u0026ldquo;Yes\u0026rdquo; to the question, \u0026ldquo;whether you have a house in your host city\u0026rdquo; is set as 1, and \u0026ldquo;No\u0026rdquo; as 0.\u003c/p\u003e\n \u003cp\u003eIn line with the theoretical analysis, we set out the factors impacting migrant homeownership into five categories.\u003c/p\u003e\n \u003cp\u003eThe first category, \u003cem\u003ehuman capital\u003c/em\u003e, includes age, sex, education, and marital status. In an analysis of UK housing, health and education were the most prominent factors driving housing demand (Eichholtz \u0026amp; Lindenthal, 2014). In studies, family life cycle circumstances such as marriage and childbirth, also often give rise to changes, namely, a shift from renting to house purchase (Clark \u0026amp; Huang, 2004). The descriptive analysis results in Table 1 show that, on average, labor migrants in China attain homeownership at age 35.64. Whereas skilled migrant homeownership is achieved approximately five years sooner, at age 30.69.\u003c/p\u003e\n \u003cp\u003eThe second category, \u003cem\u003eemployment factors\u003c/em\u003e, includes occupation type and employment status of migrants; these two factors jointly determine their position in the labor market. The economic transition of Chinese cities has resulted in noteworthy impacts on the return of labor markets. In Table\u0026nbsp;1 below, 34.51% of skilled migrants engage in white-collar jobs, while the proportion of participation by labor migrants is 4.51%.\u003c/p\u003e\n \u003cp\u003eThe third category, \u003cem\u003efamily factors\u003c/em\u003e, encompasses family migration strategy and family income. According to Mulder (2006), the more integrated the family structure in the host city, the higher the probability that a migrant has his or her own house, i.e., family-based migration accelerates the process of owning a house to a certain extent. Also, family income is fundamental in the house purchase decision. According to research by Haurin (1991), if the variable coefficient of family income increases by 20%, the probability of owning a house will decrease by 1.5%.\u003c/p\u003e\n \u003cp\u003eThe fourth category, \u003cem\u003einstitutional factors\u003c/em\u003e, includes the \u003cem\u003ehukou\u003c/em\u003e system and socialist \u003cem\u003edanwei\u003c/em\u003e system in particular; the category is reflected through the types of \u003cem\u003ehukou\u003c/em\u003e and \u003cem\u003edanwei\u003c/em\u003e. \u003cem\u003eHukou\u003c/em\u003es are divided into agricultural and non-agricultural \u003cem\u003ehukou\u003c/em\u003e, while \u003cem\u003edanwei\u003c/em\u003e is divided into stated-owned sector and private sector. Although the continuous \u003cem\u003eHukou\u003c/em\u003e system reform in recent years has gradually loosened the restrictions on population mobility and migrant life (Tao et al., 2015), local \u003cem\u003ehukou\u003c/em\u003e remains the key factor determining whether migrants will receive full access to public services in their inflow cities. Not only is local \u003cem\u003ehukou\u003c/em\u003e reflected in restrictions in house purchase qualifications, but it also involves irrational policies including policies for purchasing house and settling (Zhang \u0026amp; Su, 2010). As an institutional design peculiar to China, the socialist \u003cem\u003edanwei\u003c/em\u003e system and the capitalist economic system employment model have significant differences. In China, \u0026lsquo;state-owned sector\u0026rsquo; and \u0026lsquo;private sector\u0026rsquo; have tremendous differences in employment opportunities, remuneration, and welfare. State-owned sectors provide stable salaries as well as a string of generous welfare, such as housing distribution, medical security, job security, and long-term career development paths (Fan, 2001).\u003c/p\u003e\n \u003cp\u003eThe fifth category, \u003cem\u003eurban factors\u003c/em\u003e, uses the measurement standard \u0026lsquo;level of host cities.\u0026rsquo; Based on the \u003cem\u003eAdjustment of Classification Standards on Urban Sizes\u003c/em\u003e, published by China\u0026rsquo;s State Council in 2014, and the actual demands of the study, the inflow cities of migrants were classified in three levels, namely \u0026lsquo;megacities\u0026rsquo;, \u0026lsquo;very large-sized cities and type-I large-sized cities,\u0026rsquo; and \u0026lsquo;other cities.\u0026rsquo; Megacities attract the inflow of the most migrants because of their developed economic and unique urban appeal. However, population pressures and resource restrictions in megacities have resulted in their imposing strict guidelines in their population policies and \u003cem\u003ehukou\u003c/em\u003e system. The permanent resident population of \u0026lsquo;very large-sized cities and type-I large-sized cities\u0026rsquo; is above three million, making these cities the emerging powers of regional economic development. \u0026lsquo;Other cities\u0026rsquo; refers to middle-sized and small cities with a permanent resident population below three million. China\u0026rsquo;s \u003cem\u003ehukou\u003c/em\u003e reform has explicitly specified that the \u003cem\u003ehukou\u003c/em\u003e restrictions of cities with a population below three million should be cancelled, and cities with a population above three million should be relaxed, resulting in an easier transition for migrants.\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;1 provides the descriptive analysis and between-group differences of all variables. The between-group differences of these samples indicate that skilled migrants and labor migrants differ significantly in homeownership. At 23.74%, the homeownership of skilled migrants is higher than that of labor migrants (12.69%), a noteworthy difference. We further investigated the differences of the two groups in personal characteristics, employment characteristics, family characteristics, and host cities characteristics. Findings show that these characteristics all passed the T-Test of at least 5% which indicated the significance of between-group differences.\u003c/p\u003e\n \u003cp\u003e[Table\u0026nbsp;1 is here]\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e3.3 Research method\u003c/h2\u003e\n \u003cp\u003eWe set up a Probit model to investigate factors impacting on the probability for migrants to buy their own houses, as shown below:\u003c/p\u003e\n \u003cdiv id=\"Equ1\"\u003e\n \u003cdiv id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{\\text{H}\\text{o}\\text{m}\\text{e}\\text{o}\\text{w}\\text{n}\\text{e}\\text{r}}^{\\ast\\:}={{\\beta\\:}}_{1}{\\text{X}}_{\\text{p}\\text{e}\\text{r}\\text{s}\\text{o}\\text{n}}+{{\\beta\\:}}_{2}{\\text{X}}_{\\text{e}\\text{m}\\text{p}\\text{l}\\text{o}\\text{y}\\text{m}\\text{e}\\text{n}\\text{t}}+{{\\beta\\:}}_{3}{\\text{X}}_{\\text{f}\\text{a}\\text{m}\\text{i}\\text{l}\\text{y}}+{{\\beta\\:}}_{4}{\\text{X}}_{\\text{i}\\text{n}\\text{s}\\text{t}\\text{i}\\text{t}\\text{u}\\text{t}\\text{i}\\text{o}\\text{n}}+{{\\beta\\:}}_{5}{\\text{X}}_{\\text{c}\\text{i}\\text{t}\\text{y}}+{{\\mu\\:}}_{}$$\u003c/div\u003e\n \u003cdiv\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ2\"\u003e\n \u003cdiv id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:\\text{H}\\text{o}\\text{m}\\text{e}\\text{o}\\text{w}\\text{n}\\text{e}\\text{r}=\\left({\\text{H}\\text{o}\\text{m}\\text{e}\\text{o}\\text{w}\\text{n}\\text{e}\\text{r}}^{\\ast\\:}\u0026gt;0\\right)=\\left({{\\beta\\:}}_{\\text{i}}{\\text{X}}_{\\text{i}}+{\\mu\\:}\u0026gt;0\\right),$$\u003c/div\u003e\n \u003cdiv\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eWhere, Homeowner\u003csup\u003e*\u003c/sup\u003e represents a proxy variable for whether a family has its own house. As shown in Eq.\u0026nbsp;(2), when Homeowner\u003csup\u003e*\u003c/sup\u003e\u0026gt;0 and its value is 1, the migrants have their own house; otherwise, the value is 0. X\u003csub\u003ei\u003c/sub\u003e represents a string of variables influencing migrants to have their own houses; \u0026micro; is an un-estimated residual. We first conducted a benchmark model regression estimation of all samples (including both skilled and labor migrants). Next, we used a seemingly unrelated regression to inspect the differences between skilled migrants and labor migrants in homeownership, and the differences of regression coefficient were compared to identify the extent to which the homeownership of the two groups is influenced by various factors.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003e\u003cstrong\u003e4.1 Benchmark regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModels (1) - (5) in Table 2 demonstrate the regression estimation of migrant homeownership by gradually adding the personal, employment, family, institutional, and urban characteristics of migrants. To guarantee the comparability of regression coefficients, we supplemented marginal effects in Model (6) (See Table 2) and used Shapley decomposition to compare the contribution values of various characteristics (See Table 3). To compare the differences of homeownership impact factors for migrants, Figure 1 shows the seemingly unrelated test for labor migrants and skilled migrants and the influential coefficients of the variables on homeownership of the two groups. The main conclusions are the following.\u003c/p\u003e\n\u003cp\u003e[Table 2 is here]\u003c/p\u003e\n\u003cp\u003eHuman capital plays a vital role in homeownership for migrants. The overall contributions of personal and employment characteristics reached 41.16%. Specifically, as the education level of migrants increased by 1 unit, the probability of homeownership for migrants increased by 4.65%. By way of comparison, the probability of homeownership for skilled migrants was 3.45% higher than that of labor migrants since higher skilled and higher educated migrants can obtain loans and house purchase support more easily, thereby increasing the probability of homeownership. Occupational factors also significantly impact the inequality of homeownership. Among labor migrants, the homeownership rate of self-employed people and employers of laborers is higher than that of ordinary employees because self-employed people have flexibility in accumulating house purchasing capital due to their relatively stable sources of income. In contrast, ordinary employees are disadvantaged in the housing market because of their unstable employment, low salary, and lack of social welfare\u0026nbsp;(D\u0026rsquo;Souza, 2019). Skilled migrants with formal labor contracts can achieve homeownership more easily because they are entitled to housing funds and house purchase allowances\u0026nbsp;(Wu \u0026amp; Xiao, 2020). Although self-employed people have relatively high human capital, they are less able to purchase a house due to lack of welfare\u0026nbsp;(Zhang, 2020). The implication here is that housing inequality not only depends on the skills of migrants, but is also affected by\u0026nbsp;myriad\u0026nbsp;factors, including the distribution of market resources and the welfare system.\u003c/p\u003e\n\u003cp\u003e[Figure 1 is here]\u003c/p\u003e\n\u003cp\u003eIn our study, family characteristics impact the strongest on migrant homeownership, with a contribution rate up to 41.11%. Family migration, marital status, and income level of families jointly determine whether migrants can purchase houses in their host cities. Specifically, compared to migrating alone, the probability of homeownership for migrants in family migration is as high as 12.22%; the increase of family income makes the probability of house purchase increase by 5.60%. The probability of married migrants in house purchase is 4.36% higher than that of unmarried migrants. The implication here is that, as the minimum unit of society, families play a crucial role in the settlement decision of migrants. A study by Cui et al. (2016) shows that stable families are usually accompanied by higher economic capability, abundant social networks, and stable ways of life, helping to pave the way for migrant house purchase. This phenomenon is more evident because of marriage and family income.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInstitutional characteristics are the most important external factors influencing the homeownership of migrants, with a contribution rate up to 15.23% (Table 3), including the \u003cem\u003ehukou\u003c/em\u003e system and socialist \u003cem\u003edanwei\u003c/em\u003e system. The reform has not benefited all migrants, however. Labor migrants especially have difficulty obtaining urban welfare due to severe \u003cem\u003ehukou\u003c/em\u003e restrictions. In terms of the allocation of housing resources in particular, labor migrants confront barriers to access public housing compared to skilled migrants. Secondly, the socialist \u003cem\u003edanwei\u0026nbsp;\u003c/em\u003esystem also significantly impacts on the distribution of housing welfare. Although China\u0026rsquo;s welfare-oriented housing distribution system has ended, migrants employed by regular \u003cem\u003edanwei\u003c/em\u003e, e.g., state-owned enterprises, still receive preferential treatment, such as discounts on house purchases, priority for loans, first choice for affordable housing, and other housing funds\u0026nbsp;(Fu, 2015; Ying et al., 2013). Labor migrants do not have similar access to such housing welfare, since most work for irregular \u003cem\u003edanwei\u003c/em\u003e. The limited job opportunities in regular \u003cem\u003edanwei\u003c/em\u003e further intensifies housing inequality among migrants.\u003c/p\u003e\n\u003cp\u003eBy introducing city levels and quadratic coefficients (in Table 2), Model (5) represents that the relationship between migrant homeownership and city level presents nonlinear characteristics in an inverted U shape; the implication here is that the homeownership proportion of migrants is relatively high in \u0026lsquo;very large-sized cities and type-I large-sized cities,\u0026rsquo; and low in \u0026lsquo;megacities\u0026rsquo; and \u0026lsquo;other cities.\u0026rsquo; Possible reasons for this are that megacities implement rather severe \u003cem\u003ehukou\u003c/em\u003e systems and have unbalanced housing distributions, shutting out house purchase options for most migrants; megacities also feature high population densities, where demand for housing is high, and house prices exceed income increases. Labor migrants are especially affected, since their economic ability and welfare support are weak\u0026nbsp;(Huang et al., 2019). Whereas middle-sized and small-sized cities have insufficient appeal for migrants compared to large cities with regard to health care, education, traffic, and other infrastructure. The inverted U shape reflects the complex interaction between city level and migrant options for house purchase. \u0026lsquo;Very large-sized cities and type-I large-sized cities\u0026rsquo; have achieved a relative balance of housing affordability, social welfare, and urban living quality, and are the most likely areas for migrants to obtain homeownership. This conclusion is consistent with Song \u0026amp; Zhang (2019) and verified using pooled panel data to clarify the relationship between city level, housing policy, and the settlement model of migrants.\u003c/p\u003e\n\u003cp\u003e[Table 3 is here]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Time-trend analysis on the impact factors of homeownership for migrants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe increase in human capital has been shown to offset some institutional obstacles.Table 4 and Figure 2 demonstrate that the trends affecting migrant homeownership evolve over time. The effects of human capital keep strengthening, with emphasis on the impact of labor migrant education level on homeownership. With urban economic development and strong demand for a high-quality labor force, the competitive power of well-educated migrants continues to increase (Zou \u0026amp; Deng, 2020). High education not only results in stable, better-paid jobs, but also enhances migrants\u0026rsquo; opportunities for welfare, such as housing funds and house purchase allowances (Chen et al., 2023). Enhanced human capital indicates competitive power of labor migrants in job markets as well as conveys the selective requirements for a quality labor force. Highly educated migrants can provide long-term contributions to urban development. This trend suggests that the education level of migrants has been a key factor influencing their homeownership.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Table 4 is here]\u003c/p\u003e\n\u003cp\u003eNext, we found a decrease in the impact of the \u003cem\u003ehukou\u003c/em\u003e system on homeownership rates of both labor migrants and skilled migrants, especially in large cities. The \u003cem\u003eNational Planning on New-Type Urbanization (2014-2020)\u0026nbsp;\u003c/em\u003epassed in 2014, resulted in the cancellation of the division between agricultural and non-agricultural \u003cem\u003ehukou\u003c/em\u003e, proposing that \u0026ldquo;China will steadily promote urban basic public services covering permanent resident populations\u0026rdquo; and \u0026ldquo;China will fully implement the residence permit[2] system.\u0026rdquo; The Provisional Regulations on Residence Permit, issued in 2015, and policies promoting the settlement of populations without local \u003cem\u003ehukou\u003c/em\u003e in cities, issued in 2016, further lowered the obstacles for migrants to purchase houses in host cities. Specifically, the policies mentioned that restrictions for migrants to settle in cities with a permanent resident population below three million will be cancelled, and conditions for migrants to settle in large cities with a permanent resident population between three and five million will be fully relaxed. Some policies have emphasized that, \u0026ldquo;commercial residential building infrastructures and indemnificatory housing policies will be improved, and farmers settling in cities will be fully included in urban housing guarantee systems\u0026rdquo;. For example, the \u003cem\u003eAdministrative Measures of Chengdu on Residence Permit\u003c/em\u003e specifies that any migrant may apply for a residence permit if the migrant pays social security continuously, has a legal employment certificate, and has been living in the city for a certain period; the conditions for migrants to settle will be relaxed gradually. In accordance with the \u003cem\u003eDetailed Implementation Rules of Chengdu on Housing Policies for Talents\u003c/em\u003e, issued in 2020, any migrant may apply for a residence permit by continuously residing in the city for a certain period and paying for social security. The residence permit holder will qualify for house purchase treatment equal to local residents after paying social security for a certain period. The \u003cem\u003eAdministrative Measures of Nanjing on Housing Guarantee and Houses\u003c/em\u003e stipulates that any migrant with a valid residence permit and paying social security or taxes for a certain number of years will have access to a relatively lower payment ratio (generally 20%-30%) and lower loan rates. Such policy reforms send positive signals to migrants, encouraging them to settle in their host cities. These relaxed measures are gradually paving the way for migrants to receive local \u003cem\u003ehukou\u003c/em\u003e and settle, especially in large cities.\u003c/p\u003e\n\u003cp\u003eHowever, the relaxation of the \u003cem\u003ehukou\u003c/em\u003e system is not taking place in a one-size-fits-all approach. In small cities, the conditions for migrants to settle sooner relaxed, whereas in megacities the reform of settlement conditions has been relatively prudent and slow. The differentiated reform paths explain the varied impacts of \u003cem\u003ehukou\u003c/em\u003e on homeownership in cities of all sizes, with the fastest decline occurring in large cities.\u003c/p\u003e\n\u003cp\u003eMigrants have nevertheless been actively coping with the multiple challenges in their host cities (Zhu, 2017). As Tao et al. (2015) pointed out, against the background of relaxed policies, migrants are actively able to integrate through flexible employment and living strategies, further highlighting their long-term will to settle in their host cities. Over time, as their employment stability and residence time in host cities increases, migrants are then able to purchase houses and settle long term\u0026nbsp;(Lin \u0026amp; Zhu, 2022). According to the\u003cem\u003e\u0026nbsp;2021 Report on Residence of New First-Tier Cities\u003c/em\u003e, the proportion of migrants to home buyers has been rising year-on-year, surpassing 50% in seven cities, namely, Zhuhai, Shenzhen, Beijing, Dongguan, Tianjin, Shanghai, and Hangzhou. The effects of institutional reform, especially in large cities, lead to more migrants to settle.\u003c/p\u003e\n\u003cp\u003e[Figure 2 is here]\u003c/p\u003e\n\u003cp\u003e[2]The residence permit is a certificate for the permit holder to reside in the place of residence and have access to basic local public services and apply for the registration of permanent \u003cem\u003ehukou\u003c/em\u003e as a permanent resident.\u003c/p\u003e"},{"header":"5. Conclusion and discussion","content":"\u003cp\u003eWe have analyzed the factors that impact migrant homeownership in Chinese cities by focusing on the differences between labor migrants and skilled migrants. Using binary probit regression and Shapley decomposition, we found that family migration strategies and economic support are the main factors influencing migrant homeownership in their host cities. Our findings also demonstrated that labor migrants are subject to entrenched institutional constraints, e.g., the \u003cem\u003ehukou\u003c/em\u003e and \u003cem\u003edanwei\u003c/em\u003e system. In contrast, due to their own backgrounds, skilled migrants can overcome institutional obstacles and access primary labor markets the same as do all local residents. In our exploration of the effects of all factors in a time-trend analysis using pooled cross-sectional data, we found a year-on-year decline in the influence of the \u003cem\u003eHukou\u003c/em\u003e system; however, the rise of marketization seems to be slowly atomizing the relationships among different types of \u0026lsquo;human capital\u0026rsquo;.\u003c/p\u003e \u003cp\u003eDisparities in \u0026lsquo;human capital\u0026rsquo; are the root cause of housing inequality between different types of migrants, and we have observed that the screening mechanism of institutions and markets further widens the inequality. By comparing skilled migrants with labor migrants, our results revealed that skilled migrants have much greater access to regular primary labor markets and therefore enjoy more stable employment and homeownership. By following the time-trend analysis of Zhou et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we have confirmed that skilled migrants are advantaged by urban policies and enjoy housing support due to their higher education and skill levels, while labor migrants (having basic education levels) are often excluded from regular markets and restricted by institutional barriers. Policy reforms and market distributions are continuously reshaping the access conditions of housing markets, further reinforcing the dominant position of skilled migrants, and intensifying the marginalization of labor migrants. Indeed, urban economic growth requires high-caliber talent, but urban growth also needs labor migrants for fundamental industries (Chen et al., 2023).\u003c/p\u003e \u003cp\u003eAlthough the \u003cem\u003ehukou\u003c/em\u003e system is in the process of reform and its impacts on migrants have been weakening, its influence still makes homeownership highly problematic for labor migrants, depending on where they wish to settle. However, taking Shenzhen as an example, Hui et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that with the progress of \u003cem\u003ehukou\u003c/em\u003e reform, migrants hoped to improve their residence conditions and reside in public rental housing, not due to their \u003cem\u003ehukou\u003c/em\u003e identities, but instead mainly due to the high rents and poor living conditions in villages in cities. The present study has further verified Hui et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) in a more detailed way. Although many small and middle-sized cities have deregulated \u003cem\u003ehukou\u003c/em\u003e systems earlier and more thoroughly, large cities continue to attract and retain all kinds of migrants through increasingly flexible, inclusive social welfare systems and housing allowances. The weakening of institutional factors has sent the signal that cities are taking a practical and inclusive approach to attract labor forces. The continuous optimization of systems in large cities not only promotes changes in migrant behavior but also provides role models for other cities to adopt appropriate settlement policies for migrants. Different cities have established flexible policies for the settlement of migrants in accordance with their respective development goals and characteristics to achieve the mutual development of cities and migrants together.\u003c/p\u003e \u003cp\u003eFinally, this study has confirmed that, despite their gradual weakening, some negative impacts of institutional factors on labor migrants remain. Institutional discrimination and the market-oriented housing system interact to form a chain effect. The marketization of homeownership and the chain effects of the \u003cem\u003ehukou\u003c/em\u003e system imply that labor migrants are still disadvantaged regarding market access, especially in housing. A market-oriented competitive environment is unfavorable to labor migrants without resources and capital, and existing policies still have deficiencies to help low-income labor migrants obtain homeownership.\u003c/p\u003e \u003cp\u003eAs the number of temporary migrants increases, the distinction between skilled and labor migrants may intensify social inequality in cities. Therefore, we suggest that targeted policy regulations are needed to address inequality. First, urban governance could focus on the actual housing demands of migrants of various types to equalize housing welfare. Diverse housing supply, such as the mixed community construction of cooperative housing, public rental housing, and commercial housing, could be provided as a guarantee that all migrants have appropriate housing choices. Second, more inclusive housing allowance policies could be implemented and differentiated support provided as per income, family conditions, and years of employment, to guarantee that labor migrants receive more housing welfare in existing policy frameworks. Finally, from the perspective of labor migrants, they will enhance their own competitive power through vocational training, and cities and enterprises will provide them with career development and skill training opportunities through cooperative programs. With the above policies and measures, labor migrants will have more housing opportunities and thus more opportunities to integrate and settle into cities in a more stable manner.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData Availability: The datasets generated during and/or analysed during the current study are available in the [Global Change Research Data Publishing \u0026amp; Repository] repository, [https://www.geodata.cn/data/publisher.html].\u003c/p\u003e\n\u003cp\u003eEthical approval: This article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eChen, G., 2015.\u003c/strong\u003e The heterogeneity of housing-tenure choice in urban China: a case study based in Guangzhou. 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Urban Geography 38(5), 729\u0026ndash;751. https://doi.org/10.1080/02723638.2016.1139406\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHuang, X., Dijst, M., van Weesep, J., 2019.\u003c/strong\u003e Tenure choice in China\u0026rsquo;s medium-sized cities after Hukou reform: a case study of rural\u0026ndash;urban migrants\u0026rsquo; housing careers in Yangzhou. Journal of Housing and the Built Environment 35(1), 353\u0026ndash;373. https://doi.org/10.1007/s10901-019-09686-8\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHuang, Y., 2012.\u003c/strong\u003e Low-income housing in Chinese cities: policies and practices. The China Quarterly 212, 941\u0026ndash;964. https://doi.org/10.1017/s0305741012001270\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHuang, Y., Jiang, L., 2009.\u003c/strong\u003e Housing inequality in transitional Beijing. 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Housing Studies 21(5), 653\u0026ndash;670. https://doi.org/10.1080/02673030600807159\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLi, Z., Wu, F., 2006.\u003c/strong\u003e Socio-spatial differentiation and residential inequalities in Shanghai: a case study of three neighbourhoods. Housing Studies 21(5), 695\u0026ndash;717. https://doi.org/10.1080/02673030600807365\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLin, L., Zhu, Y., 2022.\u003c/strong\u003e Types and determinants of migrants\u0026rsquo; settlement intention in China\u0026rsquo;s new phase of urbanization: a multi-dimensional perspective. Cities 124, 103622. https://doi.org/10.1016/j.cities.2022.103622\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLiu, L., Huang, Y., Zhang, W., 2017.\u003c/strong\u003e Residential segregation and perceptions of social integration in Shanghai, China. Urban Studies 55(7), 1484\u0026ndash;1503. https://doi.org/10.1177/0042098016689012\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLiu, R., 2019.\u003c/strong\u003e Hybrid tenure structure, stratified rights to the city: an examination of migrants\u0026rsquo; tenure choice in Beijing. Habitat International 85, 41\u0026ndash;52. https://doi.org/10.1016/j.habitatint.2019.02.002\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLogan, J., Bian, Y., 1993.\u003c/strong\u003e Inequalities in access to community resources in a Chinese city. Social Forces 72(2), 555\u0026ndash;576. https://doi.org/10.1093/sf/72.2.555\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLux, M., Sunega, P., Katrnak, T., 2011.\u003c/strong\u003e Classes and castles: impact of social stratification on housing inequality in post-socialist states. European Sociological Review 29(2), 274\u0026ndash;288. https://doi.org/10.1093/esr/jcr060\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMulder, C., 2006.\u003c/strong\u003e Home-ownership and family formation. Journal of Housing and the Built Environment 21, 281\u0026ndash;298. https://doi.org/10.1007/s10901-006-9050-9\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eRetsinas, N., Belsky, E., 2004.\u003c/strong\u003e Low-income homeownership: examining the unexamined goal. Brookings Institution Press, Washington.\u003cbr\u003e\u003cstrong\u003eRonald, R., Doling, J., 2012.\u003c/strong\u003e Testing home ownership as the cornerstone of welfare: lessons from East Asia for the West. Housing Studies 27(7), 940\u0026ndash;961. https://doi.org/10.1080/02673037.2012.725830\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eRuoppila, S., 2005.\u003c/strong\u003e Housing policy and residential differentiation in post-socialist Tallinn. European Journal of Housing Policy 5(3), 279\u0026ndash;300. https://doi.org/10.1080/14616710500342176\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSong, Y., Zhang, C., 2019.\u003c/strong\u003e City size and housing purchase intention: evidence from rural\u0026ndash;urban migrants in China. Urban Studies 57(9), 1866\u0026ndash;1886. https://doi.org/10.1177/0042098019856822\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSykora, L., 1999.\u003c/strong\u003e Processes of socio-spatial differentiation in post-communist Prague. Housing Studies 14(5), 679\u0026ndash;701. https://doi.org/10.1080/02673039982678\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTao, L., Hui, E., Wong, F., Chen, T., 2015.\u003c/strong\u003e Housing choices of migrant workers in China: beyond the Hukou perspective. Habitat International 49, 474\u0026ndash;483. https://doi.org/10.1016/j.habitatint.2015.06.018\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWu, F., Logan, J., 2016.\u003c/strong\u003e Do rural migrants \u0026lsquo;float\u0026rsquo; in urban China? Neighbouring and neighbourhood sentiment in Beijing. Urban Studies 53(14), 2973\u0026ndash;2990. https://doi.org/10.1177/0042098015598745\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWu, Y., Xiao, H., 2020.\u003c/strong\u003e The labor contract law and the economic integration of rural migrants in urban China. Asian and Pacific Migration Journal 29(4), 532\u0026ndash;552. https://doi.org/10.1177/0117196820984580\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eYi, C., Huang, Y., 2014.\u003c/strong\u003e Housing consumption and housing inequality in Chinese cities during the first decade of the twenty-first century. Housing Studies 29(2), 291\u0026ndash;311. https://doi.org/10.1080/02673037.2014.851179\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eYing, Q., Luo, D., Chen, J., 2013.\u003c/strong\u003e The determinants of homeownership affordability among the \u0026lsquo;sandwich class\u0026rsquo;: empirical findings from Guangzhou, China. Urban Studies 50(9), 1870\u0026ndash;1888. https://doi.org/10.1177/0042098012470398\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eZeng, H., Yu, X., Zhang, J., 2019.\u003c/strong\u003e Urban village demolition, migrant workers\u0026rsquo; rental costs and housing choices: evidence from Hangzhou, China. Cities 94, 70\u0026ndash;79. https://doi.org/10.1016/j.cities.2019.05.029\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eZhang, B., Druijven, P., Strijker, D., 2017.\u003c/strong\u003e Does ethnic identity influence migrants\u0026rsquo; settlement intentions? Evidence from three cities in Gansu Province, Northwest China. Habitat International 69, 94\u0026ndash;103. https://doi.org/10.1016/j.habitatint.2017.09.003\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eZhang, L., Su, T., 2010.\u003c/strong\u003e On the injustice of the policy for house purchasing and settling\u0026mdash;caution: two secessions of 2010 lowering the limitations of house purchasing and settling. Asian Social Science 6(12). https://doi.org/10.5539/ass.v6n12p195\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eZhang, P., Sun, L., Zhang, C., 2021.\u003c/strong\u003e Understanding the role of homeownership in wealth inequality: evidence from urban China (1995\u0026ndash;2018). China Economic Review 69, 101657. https://doi.org/10.1016/j.chieco.2021.101657\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eZhang, Y., 2020.\u003c/strong\u003e Rethinking the global governance of migrant domestic workers: the heterodox case of informal Filipina workers in China. Geo. Immigr. LJ 36, 963. https://doi.org/10.2139/ssrn.3750241\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eZhao, M., Jin, Y., 2019.\u003c/strong\u003e Migrant workers in Beijing: how hometown ties affect economic outcomes. Work, Employment and Society 34(5), 789\u0026ndash;808. https://doi.org/10.1177/0950017019870754\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eZhou, J., Song, J., Huang, X., 2021.\u003c/strong\u003e Human capital, well-being and growth rate of rural\u0026ndash;urban migration in China. The Singapore Economic Review 1\u0026ndash;34. https://doi.org/10.1142/s0217590821500776\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eZhu, Y., 2017.\u003c/strong\u003e Advancing research on internal migration in Asia: the mobility transition hypothesis revisited. Asian Population Studies 14(1), 1\u0026ndash;4. https://doi.org/10.1080/17441730.2017.1328862\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eZou, J., Deng, X., 2020.\u003c/strong\u003e Residential neighbourhood choices, capital investment and economic integration of migrants in Chinese cities. Cities 103, 102752. https://doi.org/10.1016/j.cities.2020.102752\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u0026nbsp;\u003c/strong\u003eDescriptive analysis and between-group differences of variables\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkilled migrants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLabor migrants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBetween-group differences\u003c/strong\u003e_Diff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkilled migrants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLabor migrants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBetween-group differences\u003c/strong\u003e _Diff\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eHomeownership (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e23.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.110***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eEmployment status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.463***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eEducation level (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e15.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e9.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-2.351**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eEmployee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e81.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e56.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eSex\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.018**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eEmployer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e51.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e53.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eSelf-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e32.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e48.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e46.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eMigration strategy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.398***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e30.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e35.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e4.949**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eSingle mobility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e38.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e22.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eMarital status\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.143***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCouple mobility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e19.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e24.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNot married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e33.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e19.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eFamily mobility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e42.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e52.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e66.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e80.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eAnnual household income (yuan; take the logarithm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e8.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e8.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.303***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cem\u003ehukou\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.400***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCity level\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.522***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eAgricultural \u003cem\u003ehukou\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e46.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e86.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eMegacities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e21.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e10.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNon-agricultural \u003cem\u003ehukou\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e53.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e13.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eVery large-sized and Type-I large-sized cities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e31.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e26.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eNature of \u003cem\u003edanwei\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.148***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eOther cities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e47.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e62.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003ePrivate sectors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e79.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e94.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eState-owned sectors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e20.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eOccupation type (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.299***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eBlue collar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e65.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e95.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eWhite collar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e34.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eBenchmark regression: Impact factors of homeownership for migrants\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"112%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eMarginal Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePersonal characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eedu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.2583\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1873\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1988\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1859\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.2152\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0465\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003esex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1302\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1250\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0596\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0679\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0766\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0165\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0008)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0214\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0183\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0145\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0187\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0188\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0040\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003emarr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.4088\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.4271\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.5553\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1867\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.2020\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0436\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0063)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstitutional characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cem\u003ehukou\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.3476\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.3152\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.3290\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.3439\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0743\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cem\u003edanwei\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.2199\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.3389\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.3726\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.2429\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0524\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eoccu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1441\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1277\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1600\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0345\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0061)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0062)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eemp_iden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eemp_iden_employer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.2134\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1102\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0554\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0120\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0012)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eemp_iden_Self-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1918\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0945\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0451\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0097\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0009)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eMigration_single\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003emigration_couple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1141\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0939\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0152\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0063)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eMigration_family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.6060\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.5814\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.1222\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003elnfaminc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1992\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.2593\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0560\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eurbansize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1867\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.0138\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.0004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eurbansize\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.0164\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-3.0404\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-2.7817\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-2.8940\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-4.7385\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-5.8005\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0267)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0318)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e829356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e829356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e829356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e829356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e829356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, Standard errors in parentheses\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eShapley value contribution rate of influence factors of homeownership for labor migrants and skilled migrants\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eShapley Value Contribution Rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eLabor\u003c/p\u003e\n \u003cp\u003emigrants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eSkilled migrants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eIncluded variables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003ePersonal characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e36.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e26.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e39.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eedu, sex, age, marr\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eInstitutional characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e15.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e12.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e7.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003ehukou\u003c/em\u003e, danwei\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eEmployment characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e5.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e4.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eoccu, employment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eFamily characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e41.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e49.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e49.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003emigration, lnfaminc\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eUrban characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e6.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eurbansize\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eTime-trend: Impact factors of homeownership for migrants\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"110%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eSeemingly unrelated test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003elabor migrants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eSkilled migrants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eedu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1177\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.1459\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.1702\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1034\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0911\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0946\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e121.71\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0073)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0325)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0168)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003esex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0461\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0775\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1470\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.1098\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1235\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e107.90\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0346)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0179)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0224\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0173\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0160\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0258\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0225\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0090\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e79.72\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003emarr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.0722\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.1481\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.3368\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1830\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.7708\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1380.67\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0178)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0533)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0337)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0251)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cem\u003ehukou\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.4680\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.3757\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.2749\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.3032\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.2819\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.2037\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e21.50\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0167)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0379)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0219)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0184)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cem\u003edanwei\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.2707\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.3193\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.2394\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.2634\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.2509\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.2100\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e18.92\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0225)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0191)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0198)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0409)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0270)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0232)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eoccu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1279\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.2543\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.1936\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0838\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e74.16\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0209)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0198)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0198)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0372)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0237)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eemp_iden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eemp_iden_employer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.2287\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.3489\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.1900\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0672\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e438.75\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0510)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0381)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0374)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eemp_iden_Self-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0910\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0598\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.1884\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.1340\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e120.40\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0407)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.1747)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0314)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0276)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003emigration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003emigration_couple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.3548\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.4967\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.1473\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.6237\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.6132\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.2425\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e776.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0232)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0538)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0377)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0258)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003emigration_family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.8510\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.0709\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.3533\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.9536\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1.0406\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1535\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e743.58\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0185)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0495)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0336)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0216)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003elnfaminc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.1155\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n 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12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0169)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eurbansize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.2491\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.1548\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0826\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.3718\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.2868\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.2326\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e77.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0262)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0201)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0615)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0379)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0332)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eurbansize\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.0284\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.0098\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.0638\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.0376\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.0235\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e116.27\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.0052)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-4.5384\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-4.0833\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-3.8238\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-6.3363\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e-7.2004\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-5.7702\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.1052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0884)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.0881)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.3246)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e(0.2180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e(0.1887)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e95508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e145134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e110096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e8521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e21999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e24212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, Standard errors in parentheses\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"homeownership, labor migrants, skilled migrants, institutions, human capital","lastPublishedDoi":"10.21203/rs.3.rs-8270174/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8270174/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper examines how human capital and institutional factors shapes homeownership disparities between skilled and labor migrants in host city in China. We analyze how human capital and institutional factors (\u003cem\u003ehukou\u003c/em\u003e and \u003cem\u003edanwei\u003c/em\u003e) exert influence on skilled and labor migrants seeking homeownership. We introduce a theoretical framework that includes social demographic factors, family factors, employment factors, institutional factors, and urban factors impacting migrant homeownership. Using the pooled cross-sectional data of China Migrants Dynamic Survey (CMDS) from 2010 to 2017, we build a binary probit regression and the Shapley decomposition method to examine how the mentioned factors influence migrant homeownership. Next, we compare labor and skilled migrants and find that human capital differences are the main cause of the housing inequality for migrants, and that such inequality is intensified by the screening mechanism of institutions and markets. Last, our time trend analysis shows that, although institutional factors are weakening, their negative impacts on labor migrants remain significant.\u003c/p\u003e","manuscriptTitle":"How do institutions and human capital impact the homeownership in China: Skilled migrants vs labor migrants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 09:42:04","doi":"10.21203/rs.3.rs-8270174/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-18T07:22:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-23T16:00:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T13:12:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-07T01:16:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-05T03:18:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56920911128048582411290216336199391227","date":"2026-01-17T06:17:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327698930784471949342099757542723243307","date":"2026-01-15T06:55:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115240694637045851251964399190911722747","date":"2026-01-15T02:17:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12500289374806012905762064405928889573","date":"2026-01-15T00:19:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-14T15:40:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-06T07:14:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-31T14:22:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-22T03:21:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-12-22T03:15:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4f614807-0c42-4950-ab98-86ce6b194b15","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61282334,"name":"Scientific community and society/Geography"},{"id":61282335,"name":"Social science/Geography"},{"id":61282336,"name":"Social science/Social policy"},{"id":61282337,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2026-05-13T15:07:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 09:42:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8270174","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8270174","identity":"rs-8270174","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}