Effect of hometown housing investment on the homeownership of rural migrants in urban destinations: Evidence from China

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Effect of hometown housing investment on the homeownership of rural migrants in urban destinations: Evidence from China Zicheng Wanga, Murong Guob, Juan Mingc,

⁎

a

School of public management, Jinan University, China School of labor and human resources, Renmin University, China c School of Economics and Commerce, Guangdong University of Technology, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Rural migrants Hometown housing investment Homeownership Urban destination Heterogeneous effects

The relationship between transnational housing investment and homeownership status in destinations has received considerable attention. However, the direct causal link between both activities remains scant. This study introduces a new conceptual and empirical framework, quasi-transnationalism and integration nexus, to discuss the causal effects of hometown housing investment on the homeownership of rural migrants in urban destinations. The research also applies a national representative data in China, the National Migrants Population Dynamic Monitoring Survey (NMPDMS), to explore such causal effect. The results show that hometown housing investment has a significantly negative effect on homeownership in urban destinations, whereas the substitution effect of hometown housing investment exceeds the complementary. The conclusions are consistent when IVLPM regression is applied to correct the potential endogeneity of migrants' hometown housing investment. There are some notable differentials across life cycle groups and origin-destination groups, those are, hometown housing investment produces a larger negative effect for the old generation than the new generation, while it also plays a more important role for migrants who moved within the same regions, but the effect on the migrants from Central and Western China to East China is the weakest among the four origin-destination groups. The possible mechanism is that hometown housing investment influences the housing tenure choice through affecting the return migration plan and housing purchase intent in urban destinations.

1. Introduction Homeownership is viewed as a sign of migrants' economic success. Owning a house is often portrayed as a long-term commitment to the host society and an important step toward the migration assimilation process (Alba and Logan, 1992; DeSilva and Elmelech, 2012; Painter and Yu, 2010). In housing markets of many host countries, immigrants are predominantly tenants and less likely to be homeowners (Constant et al., 2009; DeSilva and Elmelech, 2012; Sinning, 2010; Zorlu et al., 2014). The low homeownership rate of immigrants is usually ascribed to their weak socio-economic position (Magnusson Turner and Hedman, 2014; Painter et al., 2001; Zorlu et al., 2014). Moreover, institutional barriers, such as unobservable ethnic preferences and discriminations in labor and housing markets, are also viewed as important determinants (Coulson and Dalton, 2010; DeSilva and Elmelech, 2012; Haan, 2007; Tesfai, 2016). However, these empirical studies assume migration is a one-way

flow and immigrants are likely to settle permanently in the host countries, whereas less attention has been given to non-permanent groups (temporary residents or undocumented immigrants). As for the non-permanent immigrants, some of them migrate only to earn enough money to accumulate capital for investment in their hometown or supplement their family budgets (Owusu, 1998). They usually keep close economic and social relationships with their hometown, and most of them may send a large proportion of their money back to their families in their origin countries (Osili, 2004; Schill et al., 1998), while investing a house in their origin country seems to be a common practice among immigrants (Smith and Mazzucato, 2009; Zorlu et al., 2014). Transnational housing investments may be linked with future return plans. Such investments may also be related to the need to maintain ties with the community of origin, which affects the integration process and housing careers in host countries. Previous empirical studies suggest that migrants who remit to their country are less likely to be homeowners in destinations (Bradley et al., 2007; Kuuire et al., 2016a).

⁎ Corresponding author at: School of Economics and Commerce, Guangdong University of Technology, 161 Yinglong Blvd, Tianhe District, Guangzhou, Gungdong Province 510521, China. E-mail address: [email protected] (J. Ming).

https://doi.org/10.1016/j.cities.2020.102619 Received 7 July 2018; Received in revised form 5 December 2019; Accepted 20 January 2020 0264-2751/ © 2020 Elsevier Ltd. All rights reserved.

Please cite this article as: Zicheng Wang, Murong Guo and Juan Ming, Cities, https://doi.org/10.1016/j.cities.2020.102619

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2012; Gobillon and Solignac, 2015; Kauppinen et al., 2015; Painter et al., 2001). With regard to the causes of the gap, empirical studies based on spatial assimilation theory emphasize socioeconomic differences. Evidences show that observable socioeconomic characteristics can largely explain the homeownership gap (Fesselmeyer et al., 2012; Painter et al., 2001; Zorlu et al., 2014). However, this gap is explained incompletely by observable socioeconomic characteristics (Kauppinen et al., 2015; Kauppinen and Vilkama, 2016). Under the perspective of place stratification theory, structural barriers may cause this gap. Considerable research has indicated that unobserved factors, such as ethnic preferences and discrimination in credit and housing markets, may play an important role (Constant et al., 2009; Coulson and Dalton, 2010; DeSilva and Elmelech, 2012). However, these studies pay attention to permanent migrants in developed markets. They assume that immigration is a one-way flow, while immigrants can integrate completely within the host society and consequently sever their homeland ties (Levitt, 2001; Portes et al., 1999). Previous studies appear to ignore the potential impact of transnational engagements on immigrant housing outcomes in destinations (Kuuire et al., 2016b). Most empirical studies on non-permanent immigrants' housing trajectories in host countries reveal that immigrants are predominantly tenants and less likely to be homeowners (Constant et al., 2009; DeSilva and Elmelech, 2012; Sinning, 2010; Zorlu et al., 2014). Non-permanent immigrants who still consider themselves as a member of their original community may experience different housing pathways from permanent migrants (Solinger, 1995). Non-permanent immigrants also minimize costs in their destination and prioritize other housing considerations, such as habitability, privacy, or safety (Massey, 1986). In addition, they remit most of their earnings to complement their original family's expenditures (W. Wu, 2004a). Thus, investing in housing assets in their original community usually becomes one of the most frequent methods for migrants to reallocate their resources and maintain economic and social ties with their origin community (Osili, 2004). The relationship between transnational housing investment and homeownership status in destination countries has received considerable attention (Kuuire et al., 2016c). Owusu (1998) first discussed homeownership investment back home and its relationship with homeownership status in Toronto. Owusu (1998) argued that the desire for homeownership back home is critical in shaping migrants' decisions with respect to homeownership in Canada. However, they only intend to investigate their considered transnational activities, the direct effect of actual participation in housing investment on the housing attainment in destinations is still underexplored. Bradley et al. (2007) explored the contributors of attachments to Mexico to the housing tenure decision of Mexican Americans living in the metropolitan areas of Los Angeles, Houston, and Atlanta. They confirmed that the direct effect is that migrants who send remittances to Mexico are less likely to be homeowners compared with those who do not (Bradley et al., 2007). Kuuire et al. (2016a) got the same conclusion. They used a longitudinal survey of immigrants in Canada to examine the influence of remittances on immigrant homeownership in the country. Their results indicate that participation in remittance has negative consequences for homeownership. However, Kuuire et al. (2016b) obtained contrary results after using another dataset. They used survey data collected among Ghanaian immigrant residents in the Greater Toronto Area to investigate the relationship, which shows homeownership levels of immigrants in Canada are correlated positively with transnational housing investment. No consensus is found on the causal relationships between transnational housing investment and homeownership status. Both factors sometimes substitute each other, other times, they mutually reinforce (Engbersen et al., 2013).This is because of the potential contemporaneous relationship between factors of successful housing attainment and transnational housing investment (Kuuire et al., 2016b). Therefore, the transnational behavior of housing investment among

However, such studies only use remittance as a proxy for transnational housing investment and only estimate the simply effect in a multiple linear regression analysis, which cannot explore the causal link between transnational housing investment and homeownership status in destination countries. Hometown housing investment has become a common practice in rural China, a few rural migrants choose to achieve homeownership dream in their hometown, whereas purchasing a new commodity house in the origin cities or towns is becoming a primary access. However, most of the previous studies in China context mainly focus on the determinants of rural migrants housing trajectories in urban destinations (Fang and Zhang, 2016; Hui, 2005; Li et al., 2017; Tao et al., 2015), while most of them neglect the special fact that rural migrants may keep two concurrent attachments: ties to the hometown (quasi-transnationalism) and ties to the destination (integration), which may have an important influence on the migrant's housing attainments. Only few studies have explored the relationship between return plan and homeownership in urban destinations (Tang et al., 2017), while no related discussion has made to explore direct effect of actual hometown housing investment on house tenure in urban destinations. The quasitransnationalism and integration nexus which incorporated these two attachments should be addressed to discuss. The present study addresses this gap by discussing the causal effects of hometown housing investment on the homeownership of rural migrants in urban destinations. The main research questions in this study are as follows: Is there a causal relationship between hometown housing investment and homeownership status in urban destinations? Whether the substitution effect of hometown housing investment exceeds the complementary effect or not? What's the mechanism link the hometown housing investment and homeownership in urban destinations? This research contributes to the literature in three distinct ways. First, the conceptual framework firstly focuses on transregional activities as a key factor affecting on the homeownership of rural migrants in urban destinations. A new conceptual and empirical framework named quasi-transnationalism and integration nexus is introduced to discuss the determinants of rural migrants' homeownership. This study also emphasizes the migrants' homeownership in urban destinations is related to two attachments: ties to the hometown (quasitransnationalism) and ties to the destination (integration), while the discussion on the substitution and supplementary effects is also extended to the context of internal migration. Second, the research applies IV-LPM to correct the potential endogeneity of migrants' hometown investment, which can identify precise causal effects of hometown housing investment on homeownership status in urban destinations. By contrast, Kuuire et al. (2016a) and Firang (2011) only explore the correlation using multiple linear regression analysis. Third, this study is also unique, considering its focus on internal migrant groups. A comprehensive database in China is used to explore the direct link between hometown housing investment and homeownership status in urban destinations. This study extends the discussion of transnationalism-integration nexus to the context of internal migration. 2. Literature review Homeownership symbolizes the achievement of prosperity, stability, and success. Homeownership is also an important matter in understanding integration determinants (Amuedo‐Dorantes and Mundra, 2013; Mesch and Mano, 2006). Studies on immigrant integration often focus on the gap of homeownership between natives and immigrants (Erdal and Oeppen, 2013; Fesselmeyer et al., 2012). Existing literatures from several countries consistently conclude that a significant homeownership gap is still evident between natives and immigrants (Amuedo‐Dorantes and Mundra, 2013; Fesselmeyer et al., 2

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important housing choice for rural migrants in China. They usually transfer limited financial resources from urban destinations to origin areas. According to the monitoring report of migrant workers in 2015, the calculated homeownership rate of rural migrants in the local cities is only 1.3%. However, approximately 15.7% migrants have purchased housing in their hometown cities or towns. A new survey released by Anjuke, the “2017–2018 Report on Returning Home to Purchase Real Estate,” indicates that 59% of rural workers have expressed hopes of returning to their place of origin to buy houses. A growing interest is to understand the impact of hometown housing investment on the housing trajectories of rural migrants in urban destinations, especially in cases of rapid increase trends in immigrant hometown investment in China. Evidence from previous studies has confirmed that the return plan to their hometown has a significantly negative effect on migrants' homeownership in urban destinations (Tang et al., 2017). However, research on the direct effect of actual hometown housing investment on house tenure in urban destinations remains scant.

immigrants and its effect on housing outcomes in destinations require further exploration. A specific method must be considered to correct the potential endogeneity and explore the transmission mechanism. 3. Background and theoretical perspectives 3.1. Hukou system and hometown housing investment in China Since 1978, China has experienced an unprecedented wave of urbanization and industrialization, which has resulted in large-scale population migration from rural to urban areas (W. Wu, 2004a). According to the monitoring report of migrant workers in 2016 conducted by the National Bureau of Statistics, > 281.7 million rural migrants work in cities, making up approximately 35% of China's total workforce of approximately 806.9 million. Rural migrants in China have become an important and indispensable part of industrial workers. Rural migrants have also made a significant contribution to Chinese economic development (Yu and Cai, 2013). However, most rural migrants in destination cities still suffer from poor living conditions and experience low homeownership rate (Huang, 2003; Li et al., 2017; Logan et al., 2009; Ma and Xiang, 1998; Niu and Zhao, 2017; Tang et al., 2017). Urban institutional restrictions (Hukou) and other discriminatory treatments have been viewed as the main factors causing the low homeownership level and poor living conditions of rural migrants in urban destinations (Fang and Zhang, 2016; Hui, 2005; Li et al., 2017; Tang et al., 2017; Tao et al., 2015). Hukou system remains a fundamental institutional constraint in the process of migration integration in China. Without local urban Hukou, rural migrants can move to a new place but cannot obtain the same public services and welfare as local urban citizens (Wang, 2011; F. Wu, 2004b). Thus, rural migrants are often disadvantaged in urban labor markets, dealing with low-paid and unstable employment (Lu and Song, 2006; Wong et al., 2007). Hukou system largely restricts the housing choices of rural migrants in urban destinations (Wu and Zhang, 2018). Migrants live in poor quality housing not simply because most of them cannot afford the high price of housing but also because they are not treated as ‘official’ residents with full rights in the housing market (Liu et al., 2013). They have few opportunities to join the housing provident fund Scheme, which can provide lower mortgage interest rates to encourage households to obtain good housing. Migrants also have limited access to share the public rental housing (Huang, 2012; Logan et al., 2009). Another essential feature of rural migrants in China is that most of them are temporary. Their initial motive for migrating is to earn money to supplement the demand of the original household general consumption and certain investments, such as housing. These workers usually move similar to a pendulum between the host city and their hometown to maximize their earnings and minimize their expenditures in the host city (Zheng et al., 2009). Therefore, not all rural migrants intend to stay and live in destination cities permanently (Lin and Zhu, 2010; Zhang and Chen, 2014). Similar to non-permanent international immigrants, cost is more important than preference or need, considering the goal of earning in a short period to supplement their family budget at home. Therefore, minimizing costs in the destination city is the priority over other housing considerations, such as habitability, privacy, or safety (Burgers, 1998; Massey, 1986). Specifically, renting and living in poor conditions appear to be the preferred forms of housing tenure, which accumulates capital for investment back home (Osili, 2004; Owusu, 1998). In this scenario, rural migrants in China mostly consider locality as their workplace instead of their home (Tao et al., 2015). They keep close ties with their place of origin. Many forms of transregional activities, including entrepreneurship, sending remittances, and housing investment, have become common practices accompanied with migration process (De Brauw and Rozelle, 2008). Transregional housing engagements, such as hometown housing investment, have become an

3.2. Conceptual framework This study focuses on the causal relationship between rural migrants' homeownership in urban destinations and their hometown housing investment behaviors. Following Erdal and Oeppen (2013), Carling and Pettersen (2014), we introduce a conceptual framework called “quasi-transnationalism and integration nexus.” The framework facilitates the analysis of rural migrants' housing trajectories between the origin and the destination (see Fig. 1). Under the institutional constraints of Hukou, rural migrants can move to a new place but do not possess local public welfare (Wang, 2011; F. Wu, 2004b). The separation of movement and citizenship also makes integration difficult. The non-permanent movement encourages migrants to maintain two sets of attachments: ties to the urban destination and ties to the hometown. Both factors represent the two aspects of quasi-transnationalism and integration nexus. On the integration side, homeownership status usually represents a direct measure of migrant integration (Alba and Logan, 1992; DeSilva and Elmelech, 2012; Painter and Yu, 2010). Previous researches also find three groups of determinants of housing tenure: socio-economic status, demographic factors and life-cycle variables, and migration-specific variables (Bradley et al., 2007; Magnusson Turner and Hedman, 2014; Zorlu et al., 2014). Moreover, hometown housing investment represents “quasi-transnationalism,” which refers to the trans-regional housing activities of rural migrants between hometown and destination. Transregional housing investment is similar to the transnational housing activities of non-permanent international immigrants, which also transfer most of the limited financial resources to their origin countries. Furthermore, trans-regional housing investment can be viewed as voting with their feet from the inequalities and segregations encountered in the destination society (Wong and Satzewich, 2006). Transnationalism and integration are multifaceted concepts. They may influence each other in the process of migrants' housing attainments (Firang, 2011; Kuuire et al., 2016b). The positive effect suggests that transnationalism and integration are complementary, engagement in transnational activities and integration have mutually supportive functions (Erdal and Oeppen, 2013), which can be regarded as a situation of dual loyalty that the immigrants can fully integrated in destinations and maintain ties with the community of origin. Transnational houses symbolize the presence of migrants in their hometown, and can maintain their membership rights to put down roots (Osili, 2004). Some evidences also have confirmed that immigrants who integrate in destination areas also tend to be actively engaged in transnational housing investment activities (Kuuire et al., 2016b; Levitt, 2003; Portes, 2003). A more popular argument is that the relationship between integration and transnationalism amounts to a zero sum game in which 3

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Ties to the hometown (Quasi-Transnationalism)

Institutional constraints (Hukou System)

Transregional activities

Hometown housing investment

Ties to the destination (Integration )

Socio-economic status

Housing tenure in destination

Demographic and lifecycle Immigration-specific

Fig. 1. Conceptual framework for the relationship between hometown housing investment and housing tenure is destination.

This survey also provides the housing tenure of the household in hometowns and in local urban destinations. Large sample size with enough housing items is appropriate to discuss in our research problem. We define rural migrants as migrated workers who have a non-local agricultural Hukou and have been living in local cities for more than one month. Table 1 shows the measures used to operationalize homeownership, hometown housing investment, socioeconomic status, demographic and life cycle characteristics, immigration-specific variables, institutional status, housing price, and geographic location. According to the definition of migrants and after dropping missing variable data, we obtain 135,911 valid household samples. Urban destinations and hometowns are defined at administrative boundaries of the county level,1 the county-level administrative divisions includes counties, autonomous counties, county-level cities, districts, banners, autonomous banners. Housing attainment in urban destinations only includes commercial housing purchase behaviors in local counties. On the contrary, hometown housing investment represents trans-regional housing activities from the destination counties to rural migrants' original counties where their Hukou are registered, while the transregional housing investment only refer to the activities of purchasing a commercial house (the self-built housing was excluded) . Homeownership probability is the outcome variable, which is measured using a dummy variable. If migrants own commodity housing in urban destinations, then a value of 1 is given. Otherwise, a value of 0 is provided. The main explanatory variable “hometown housing investment” is also specified as a dummy variable, which is coded as follows: 1 = have purchased a commercial house in their hometown, and 0 = have no hometown housing investment. In keeping with existing studies on homeownership determinants, control variables are categorized as socioeconomic status (i.e., household income, education attainment, and occupation), demographic and life cycle (i.e., age and sex of household head and household migration status), immigration-specific variable (i.e., migration experience and migration type), institutional status and housing price (i.e., social security, housing purchases restriction and local house price), geographic location (32 provincial dummy variables).

stronger transnationalism implies weaker integration and vice versa (Carling and Pettersen, 2014). The transnationalism–integration relationship is shaped by the same family budget constraint, where hometown housing ownership would reduce the likelihood of migrants to own homes at destinations, all else equal. Furthermore, the transnational housing investment requires contracting of loans and use of life-long savings to finance, which compromise the progress of their housing careers in destination (Kuuire et al., 2016a, 2016b, 2016c). No consensus has been found among researchers on the direct causal relationships between transnationalism and integration. They sometimes interact with one another but other times do not. The interaction may depend on experiences that vary according to context and migration histories (Engbersen et al., 2013; Erdal and Oeppen, 2013). We therefore employ a theoretical framework that sees the two as intersecting dimensions in what we call the integration–transnationalism matrix which allows for different empirical possibilities: substitution effect and complementary effect.

4. Dataset and sample description We use data from the 2016 National Migrants Population Dynamic Monitoring Survey (NMPDMS) conducted by the National Health and Family Planning Commission of China. NMPDMS-2016 is open access and a national representative cross-sectional survey of internal migrants aged 15–69 years. These migrants do not have the local “Hukou” and have been living in local cities for more than one month. The survey was conducted in 32 provincial units which covered all 31 provinces and XPCC (Xinjiang Production and Construction Corps) of China, 348 cities and 8450 communities or villages. The distribution of households surveyed across provincial units ranges from 2000 to 10,000, including seven sampling units, 2000, 4000, 5000, 6000, 7000, 8000 and 10,000.2000 households were sampled from the least populous unit XPCC, and 10,000 households were sampled from the largest units such as Zhejiang province and Guangdong province. Those samples were drawn using the stratified multistage random sampling method with the probability proportional to size approach (PPS). From each of the 8450 communities/villages drawn from the sampling framework, 20 migrant households were chosen randomly, approximately 169,000 households were sampled. NMPDMS-2016 survey provides a representative sample for research because it collects a wide variety of data related to the demography, employment traits, and migration status of all family members.

1

The administrative divisions of China have consisted of several levels, which include Provincial level (1st), Prefectural level (2nd), County level (3rd), Township level (4th), and Basic level autonomy (5th). In this study we only consider the trans-regional activities at the county-level administrative boundaries. 4

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Table 1 Definition of variables. Variables

Operationalization

Homeownership

1 = owned commodity housing in destination counties, 0 = renter or others

Hometown housing investment Hometown housing tenure

Dummy: 1 = buy commercial house in the original counties, 0 = others

Socioeconomic status Household income(logarithm) Elementary school Senior middle school Junior middle school College or above Skilled occupation

Continuous: natural log of household income Dummy: 1 = elementary school or below,0 = others Dummy: 1 = senior middle school, 0 = others Dummy: 1 = junior middle school, 0 = others Dummy: 1 = college or above, 0 = others Dummy: 1 = highly skilled occupation, 0 = others

Demographic and life-cycle Age Sex Household migration Co-migrated family members

Continuous: age of respondent, aged 15 and over Dummy:1 = male, 0 = female Dummy:1 = whole family members migrated together, 0 = others Continuous: numbers of co-migrated family members in the same destination

Immigration-specific variable Local duration Cumulative duration (year1) Cumulative duration (year1–2) Cumulative duration (year3–4) Cumulative duration (year5–9) Cumulative duration (year10–14) Cumulative duration (year15) Inter-provincial migration Inter-prefectural level migration Inter-county migration

Continuous: years working in the current city Dummy:1 = years of cumulative migration experiences < 1, 0 = others Dummy:1 = years of cumulative migration experiences≥ 1& ≤2, 0 = others Dummy:1 = years of cumulative migration experiences≥ 3& ≤4, 0 = others Dummy:1 = years of cumulative migration experiences≥ 5& ≤9, 0 = others Dummy:1 = years of cumulative migration experiences≥ 10& ≤14, 0 = others Dummy:1 = years of cumulative migration experiences≥ 15, 0 = others Dummy:1 = migrated across the provinces,0 = migrated within the province Dummy:1 = migrated across prefectural level cities within province, 0 = others Dummy:1 = migrated across county level within prefectural level city, 0 = others

Institutional status & housing price Pension Medical Restrictions Price

Dummy:1 = enrolled in urban pension system in destination city Dummy:1 = enrolled in urban health insurance system in destination city Dummy:1 = restricted housing purchases in the destination city Average commercial house price in the destination city (10000RMB/m2)

Geographic location Province (32 dummy variables)

Dummy: 1 = the destination in the specified province, 0 = other province

where vector Hi measures immigrant homeownership (set equal to 1 if migrants are homeowners in a local city, and 0 otherwise), focal variable investi is a set of dummies indicating whether respondents have purchased a commercial house in their original county, and xi controls for various socio-demographic characteristics, institutional status, local housing price and geographic location variables that may affect housing ownership.

We conduct a two-sample t-test to understand trait difference between homeowners and non-homeowners. Table 2 reveals significant differences. The proportion of migrants who have invested housing in their hometown is higher among non-homeowners than homeowners. In terms of socioeconomic status, homeowners have higher income than comparable non-homeowners. Moreover, homeowners have higher education and skills attainment than counterparts. With regard to the demographic and life cycle characteristics, the proportion of old males and household migration is higher among homeowners than among non-homeowners. Moreover, homeowners have more family members migrating together than counterparts. Migrants with stable jobs are more likely to become homeowners. The proportion of inter-prefectural and inter-county migration is higher among non-homeowners than among homeowners. Conversely, the proportion of interprovincial migration is lower among non-homeowners than among homeowners. As for the institutional status and housing price, the rural migrants with enrollment in the urban social insurance schemes are more likely to be homeowner, and rural migrants will have more chance to own a house in the cities with low housing price.

5.2. Probit and IV-LPM estimation We estimate a total of six logistic regression model specifications of Eq. (1). Model I is the benchmark model, with tenure choice as a probit function of hometown housing investment, socioeconomic status and geographic locations. Demographic and life cycle variables are added to Model II, and immigration-specific variables are added to Model III. Additional controls in Model IV are institutional status and housing price. Table 3 shows hometown housing investment has a significantly negative effect on homeownership in urban destinations. The coefficients are significantly negative from Models I to IV when we add control variables to the benchmark equation step-by-step. Model IV also provides a substantially good specification because it exhibits large pseudo-R2 and log-likelihood values. However, the effect of hometown housing investment on homeownership may be biased in probit analysis. Reverse causality may be observed in the estimation that homeownership also affects hometown housing investment. Therefore, the estimated coefficients may also be biased. Instrumental variable methods are employed to estimate the equation to address potential endogeneity of migrants' hometown investment. The endogenous variable –“hometown housing investment” in Eq. (1) is a dummy variable. IV-LPM may be a good option to use. We

5. Estimation and analysis 5.1. Empirical model Binary logistic regression is applied to estimate the effects of hometown housing investment on the homeownership of migrants in urban destinations. The following reduced form equation serves as a benchmark model for assessing the role of hometown housing investment:

Hi = α + investi φ + βx i + εi,

(1) 5

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Table 2 Variables means by homeownership. Variables

Entire

Homeownership

0.1923

Hometown housing investment Hometown housing tenure

0.0891

Table 3 Determinants of homeownership: probit estimation. Owners

0.0319

No-owners

0.1027

Differences

−0.0708

8.60 0.1608 0.5240 0.2147 0.1005 0.0555

8.72 0.1306 0.4661 0.2396 0.1637 0.0727

8.57 0.1679 0.5378 0.2088 0.0855 0.0514

0.153 −0.0373⁎⁎⁎ −0.0717⁎⁎⁎ 0.0308⁎⁎⁎ 0.0783⁎⁎⁎ 0.0213⁎⁎⁎

Demographic and life-cycle Age Sex Household migration Co-migrated family members

34.50 0.5221 0.4792 2.61

35.94 0.4991 0.6139 2.94

34.16 0.5276 0.4471 2.53

1.78⁎⁎⁎ −0.0285⁎⁎⁎ 0.167⁎⁎⁎ 0.41⁎⁎⁎

Immigration-specific variable Local duration Cumulative duration (year1) Cumulative duration (year1–2) Cumulative duration (year3–4) Cumulative duration (year5–9) Cumulative duration (year10–14) Cumulative duration (year15) Inter- provincial migration Inter- prefectural level migration Inter-county migration

5.53 0.0935 0.1822 0.1816 0.2822 0.1398 0.1206 0.4887 0.3341 0.1772

7.29 0.0391 0.1335 0.1785 0.3149 0.1648 0.1693 0.3366 0.3973 0.2660

5.11 0.1065 0.1938 0.1824 0.2744 0.1338 0.1090 0.5249 0.3190 0.1561

2.18⁎⁎⁎ −0.0674⁎⁎⁎ −0.0604⁎⁎⁎ −0.0039 0.0404⁎⁎⁎ 0.0310⁎⁎⁎ 0.0603⁎⁎⁎ −0.188⁎⁎⁎ 0.0783⁎⁎⁎ 0.110⁎⁎⁎

0.2236 0.1569 0.4405 0.7542

0.1605 0.1041 0.5438 1.0237

N

26,136

109,775

135,911

Model I

Hometown housing investment Hometown housing −0.6627⁎⁎⁎ (0.0201) tenure

⁎⁎⁎

Socioeconomic status Household income (logarithm) Elementary school Senior middle school Junior middle school College or above Skilled occupation

Institutional status & housing price Pension 0.1726 Medical 0.1142 Restrictions 0.5239 Price 0.9719

Variables

Socioeconomic status Household income (logarithm) Elementary school

⁎⁎⁎

Senior middle school Junior middle school Skilled occupation

0.5401⁎⁎⁎ (0.0090) −0.3952⁎⁎⁎ (0.0173) −0.4154⁎⁎⁎ (0.0141) −0.2458⁎⁎⁎ (0.0153) 0.0477⁎⁎⁎ (0.0183)

Demographic and life-cycle Age Sex Household migration Co-migrated family members

Model II

Model III

Model IV

−0.6641⁎⁎⁎ (0.0203)

−0.6471⁎⁎⁎ (0.0208)

−0.6560⁎⁎⁎ (0.0210)

0.4595⁎⁎⁎ (0.0095) −0.7834⁎⁎⁎ (0.0194) −0.6190⁎⁎⁎ (0.0150) −0.3393⁎⁎⁎ (0.0157) 0.1115⁎⁎⁎ (0.0186)

0.5154⁎⁎⁎ (0.0099) −0.7841⁎⁎⁎ (0.0199) −0.6224⁎⁎⁎ (0.0154) −0.3477⁎⁎⁎ (0.0160) 0.1089⁎⁎⁎ (0.0189)

0.5395⁎⁎⁎ (0.0101) −0.7332⁎⁎⁎ (0.0205) −0.5720⁎⁎⁎ (0.0158) −0.3061⁎⁎⁎ (0.0162) 0.0076 (0.0195)

0.0197⁎⁎⁎ (0.0005) −0.1262⁎⁎⁎ (0.0088) 0.0301⁎⁎⁎ (0.0108) 0.1321⁎⁎⁎ (0.0049)

0.0132⁎⁎⁎ (0.0005) −0.1248⁎⁎⁎ (0.0090) 0.0966⁎⁎⁎ (0.0112) 0.0768⁎⁎⁎ (0.0051)

0.0135⁎⁎⁎ (0.0005) −0.1313⁎⁎⁎ (0.0091) 0.1055⁎⁎⁎ (0.0112) 0.0784⁎⁎⁎ (0.0052)

0.0444⁎⁎⁎ (0.0013) 0.1886⁎⁎⁎ (0.0218) 0.2819⁎⁎⁎ (0.0217) 0.2800⁎⁎⁎ (0.0217) 0.2500⁎⁎⁎ (0.0252) 0.1887⁎⁎⁎ (0.0296) −0.5390⁎⁎⁎ (0.0137) −0.2888⁎⁎⁎ (0.0122)

0.0434⁎⁎⁎ (0.0013) 0.1878⁎⁎⁎ (0.0220) 0.2766⁎⁎⁎ (0.0219) 0.2693⁎⁎⁎ (0.0218) 0.2337⁎⁎⁎ (0.0254) 0.1775⁎⁎⁎ (0.0298) −0.4973⁎⁎⁎ (0.0138) −0.2318⁎⁎⁎ (0.0125)

Immigration-specific variable Local duration Cumulative duration (year1–2) Cumulative duration (year3–4) Cumulative duration (year5–9) Cumulative duration (year10–14) Cumulative duration (year15) Inter-provincial migration Inter-prefectural level migration

⁎⁎⁎

0.0631 0.0528⁎⁎⁎ −0.103⁎⁎⁎ −0.2695⁎⁎⁎

Notes: ⁎ p < 0.1. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01.

choose two equivalent instrument to correct the endogeneity respectively: One is the rate of commodity housing purchase in the origin province, this index was calculated from the NMPDMS-2016 survey. We aggregate the individual-level housing purchase in the hometown at the provincial-level with sample weights to construct the rate of commodity housing purchase in the origin province. Another one is the hometownhousing-investment index, which is calculated from the ANJUKE's annual report on migrant's hometown-housing-investment. The annual report gives a list of the target cities where the migrant want to purchase house if they return. We construct the hometown-housing-investment index by counted the number of popular cites at the provincial-level in recent two years. Those two instruments are selected because they represent specific customs in the original regions, the housing purchase activities can be viewed as a proxy to measure the feelings of homesickness at the provincial level, more hometown-housing-investment in the original regions from the migrants, more closed ties the migrants from this original area will maintain more.Moreover, in some original regions, owning a home in the origin city has been an popular phenomenon of going with the trend, competing with others, or achieving the homeownership dream. There is a bandwagon effect that more and more migrants seek to engage in housing purchase activity, where it's even difficult for a bachelor to find a woman to marry without a house in the origin counties or towns. Those two instruments are chose at the provincial level as a proxy for the specific custom or the feelings of homesickness, thus they are correlated significantly to hometown housing investment, but only have indirect effect through the

Institutional status & housing price Pension

_cons

−4.5011⁎⁎⁎ (0.0829)

−4.6361⁎⁎⁎ (0.0857)

−5.0345⁎⁎⁎ (0.0902)

0.2144⁎⁎⁎ (0.0178) 0.1112⁎⁎⁎ (0.0200) −0.2012⁎⁎⁎ (0.0119) −0.1118⁎⁎⁎ (0.0156) −5.2032⁎⁎⁎ (0.0915)

Geographic location Log pseudolikelihood Pseudo R2 Sample

YES −56,229.5 0.1549 135,911

YES −54,824.9 0.1760 135,911

YES −52,533.5 0.2104 135,911

YES −52,006.5 0.2184 135,911

Medical Restrictions Price

Notes: robust Std. Err. in parentheses; The reference category is college or above, un-skilled occupation, non-household migration, female, cumulative duration (year1), inter-county migration. ⁎ p < 0.1. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01

hometown-housing-investment on rural migrant's house tenure choice at the household level. They are orthorhombic with migrants' homeownership in urban destinations and uncorrelated to the error term in the main regression. Therefore, the instrument does not affect migrants’ homeownership in urban destinations other than through its potential link to hometown housing investment. As shown by the first-stage F statistic at the bottom of Table 4, our instruments are strong and 6

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Table 4 Determinants of homeownership: IV-LPM. Variables

Probit (marginal)

IV-LPM(1) First

Hometown housing investment Hometown housing tenure Socioeconomic status Household income (logarithm) Elementary school Senior middle school Junior middle school Skilled occupation Demographic and life-cycle Age Sex Household migration Co-migrated family members Immigration-specific variable Local duration Cumulative duration (year1–2) Cumulative duration (year3–4) Cumulative duration (year5–9) Cumulative duration (year10–14) Cumulative duration (year15) Inter-provincial migration Inter-prefectural level migration Institutional status & housing price Pension Medical Restrictions Price

IV-LPM(2) Second

−0.1398⁎⁎⁎ (0.0044)

First

−0.2150⁎⁎⁎ (0.0585)

Second

−0.2465⁎⁎ (0.1042)

0.1150⁎⁎⁎ (0.0021) −0.1563⁎⁎⁎ (0.0043) −0.1219⁎⁎⁎ (0.0033) −0.0652⁎⁎⁎ (0.0034) 0.0016 (0.0041)

0.0425⁎⁎⁎ 0.0017 −0.0239⁎⁎⁎ 0.0037 −0.0121⁎⁎⁎ 0.0031 0.0036 0.0032 0.0048 0.0039

0.1138⁎⁎⁎ (0.0034) −0.1701⁎⁎⁎ (0.0052) −0.1344⁎⁎⁎ (0.0042) −0.0771⁎⁎⁎ (0.0044) 0.0040 (0.0047)

0.0440⁎⁎⁎ 0.0017 −0.0267⁎⁎⁎ 0.0037 −0.0134⁎⁎⁎ 0.0031 0.0031 0.0032 0.0046 0.0039

0.1152⁎⁎⁎ (0.0052) −0.1710⁎⁎⁎ (0.0057) −0.1348⁎⁎⁎ (0.0044) −0.0770⁎⁎⁎ (0.0044) 0.0041 (0.0048)

0.0029⁎⁎⁎ (0.0001) −0.0280⁎⁎⁎ (0.0019) 0.0225⁎⁎⁎ (0.0024) 0.0167⁎⁎⁎ (0.0011)

0.0006⁎⁎⁎ 0.0001 0.0008 0.0015 −0.0141⁎⁎⁎ 0.0020 −0.0056⁎⁎⁎ 0.0009

0.0022⁎⁎⁎ (0.0001) −0.0242⁎⁎⁎ (0.0020) 0.0209⁎⁎⁎ (0.0028) 0.0120⁎⁎⁎ (0.0013)

0.0007⁎⁎⁎ 0.0001 0.0006 0.0016 −0.0143⁎⁎⁎ 0.0020 −0.0059⁎⁎⁎ 0.0009

0.0023⁎⁎⁎ (0.0001) −0.0241⁎⁎⁎ (0.0020) 0.0205⁎⁎⁎ (0.0030) 0.0119⁎⁎⁎ (0.0014)

0.0092⁎⁎⁎ (0.0003) 0.0400⁎⁎⁎ (0.0047) 0.0589⁎⁎⁎ (0.0047) 0.0574⁎⁎⁎ (0.0046) 0.0498⁎⁎⁎ (0.0054) 0.0378⁎⁎⁎ (0.0063) −0.1060⁎⁎⁎ (0.0029) −0.0494⁎⁎⁎ (0.0027)

−0.0010⁎⁎⁎ 0.0002 0.0010 0.0030 0.0023 0.0031 0.0063⁎⁎ 0.0031 0.0125⁎⁎⁎ 0.0039 0.0207⁎⁎⁎ 0.0048 0.0225⁎⁎⁎ 0.0023 0.0082⁎⁎⁎ 0.0020

0.0101⁎⁎⁎ (0.0003) 0.0117⁎⁎⁎ (0.0032) 0.0266⁎⁎⁎ (0.0034) 0.0251⁎⁎⁎ (0.0035) 0.0183⁎⁎⁎ (0.0045) 0.0160⁎⁎⁎ (0.0054) −0.1098⁎⁎⁎ (0.0038) −0.0638⁎⁎⁎ (0.0034)

−0.0010⁎⁎⁎ 0.0002 0.0016 0.0030 0.0027 0.0031 0.0068⁎⁎ 0.0031 0.0133⁎⁎⁎ 0.0039 0.0224⁎⁎⁎ 0.0048 0.0267⁎⁎⁎ 0.0023 0.0073⁎⁎⁎ 0.0020

0.0100⁎⁎⁎ (0.0003) 0.0118⁎⁎⁎ (0.0032) 0.0267⁎⁎⁎ (0.0034) 0.0253⁎⁎⁎ (0.0035) 0.0188⁎⁎⁎ (0.0046) 0.0167⁎⁎⁎ (0.0058) −0.1088⁎⁎⁎ (0.0047) −0.0635⁎⁎⁎ (0.0035)

0.0457⁎⁎⁎ (0.0038) 0.0237⁎⁎⁎ (0.0043) −0.0429⁎⁎⁎ (0.0025) −0.0238⁎⁎⁎ (0.0033)

0.0109⁎⁎⁎ 0.0034 −0.0078⁎ 0.0040 −0.0163⁎⁎⁎ 0.0019 0.0120⁎⁎⁎ 0.0022 0.5207⁎⁎⁎ 0.0281 −0.3786 0.0156

0.0481⁎⁎⁎ (0.0041) 0.0243⁎⁎⁎ (0.0047) −0.0439⁎⁎⁎ (0.0027) −0.0188⁎⁎⁎ (0.0021)

0.0107⁎⁎⁎ 0.0034 −0.0080⁎⁎ 0.0040 −0.0162⁎⁎⁎ 0.0019 0.0120⁎⁎⁎ 0.0022 0.0058⁎⁎⁎ 0.0006 −0.3555⁎⁎⁎ 0.0156

0.0484⁎⁎⁎ (0.0042) 0.0241⁎⁎⁎ (0.0048) −0.0444⁎⁎⁎ (0.0031) −0.0184⁎⁎⁎ (0.0023)

IV _cons

— —

Geographic location R-squared (Pseudo R2) F statistic-first stage N

Yes 0.2184 — 135,911

Yes 0.1803 73.32 135,911

−0.5427⁎⁎⁎ (0.0282)

−0.5533⁎⁎⁎ (0.0406)

Yes 0.1794 69.53 135,911

Notes: robust Std. Err. in parentheses; The reference category is college or above, un-skilled occupation, non-household migration, female, cumulative duration (year1), inter-county migration. ⁎ p < 0.1. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01

estimation. Respondents who have bought a house in their hometown are less likely to own a house in their urban destination compared with those who are not engaged in hometown investment. These results also imply that the substitution effect of hometown housing investment exceeds the complementary effect in China. This finding is in line with

significantly correlated to hometown housing investment. The results shown in Table 4 show that hometown housing investment still has a significantly negative effect on homeownership even when we correct the potential endogeneity by IV-LPM regression, while the negative effect from IV-PLM estimation is slightly larger than probit 7

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approximately 1%. Tang et al. (2017) had a similar conclusion, that is, the long duration of residence positively influences rural migrants' purchase decisions. The reason is that the length of residence may reduce certain floating costs as a part of housing costs and elevate residents' social resources and sense of belonging (Tang et al., 2017). The relationship between cumulative duration and homeownership shows an inverted U shape, illustrating an initial increase in homeownership probability, in the beginning, reaching the summit, and showing a subsequent decrease. Differentiated migration has different influences on homeownership. Interprovincial- and inter-prefectural-level migrants are less likely to own houses, unlike comparable inter-county migrants. Moreover, the homeownership probability of interprovinciallevel migrants is lower than that of inter-prefectural-level migrants. The results also demonstrate that urban housing was likely to be owned by the migrants who have enrolled in the local pension and health insurance. The local housing price and house purchase restriction also have significantly negative effect on the homeownership, in the urban destinations with high housing price and strictly house purchase restriction, the migrants will have less opportunity to be homeowner.

the conclusion of previous studies on the effect of remittances on homeownership in destinations (Bradley et al., 2007; Kuuire et al., 2016a), but show a little different from the researches on homeownership effect of international migrants, as some of them find positive relations between actual hometown housing investment and homeownership in the destinations (Kuuire et al., 2016b). The potential explanations that hometown housing investment may crowd out house buying in urban destinations are as follows. First, in the context of urban China, the Hukou household registration system still plays an important role on housing attainment of rural migrants in urban destinations, especially large cities. Hukou limits people's access to public services on their citizen status, where only a small part of rural migrants who meet a rigorous set of criteria, such as educational and skills attainment, social security or tax record, and residency status, can be awarded local Hukou (Zhang and Tao, 2012). Most rural migrants encounter difficulties in obtaining a local Hukou in large cities. Such difficulties may be considered disadvantageous for them in integrating into labor and housing markets. Given the soaring housing prices in large cities, rural migrants with lower wage cannot afford housing. Moreover, temporary migrants remain excluded from the urban public housing system without local Hukou in large cities. However, China has been reforming its household registration system and relaxing its restrictions on permanent residence registration in towns and small- and medium-sized cities since 2010. Rural migrants can easily obtain urban Hukou if they buy houses in towns or small cities of their hometown, where they can share equal public goods as urban residents including children's education and healthcare. Housing prices in towns or small cities are within the affordable range of migrants. The relative index of refraction between house prices and annual income in their hometown is lower than that in destination cities. Second, the new generation rural migrants are young, ambitious, educated, and skilled but know little about farming. They do not like to live in rural areas and instead intend to secure jobs in cities (Yue et al., 2010). When they return, they still want to live a lifestyle similar to that of city folks. Therefore, many rural migrants purchase houses in origin towns or counties instead of building new houses in origin villages. Finally, the Chinese government implemented a policy called “reduce inventory” in 2016. The policy aims to improve tax and credit policies that encourage rational housing consumption, focus on meeting rigid demands, and reduce housing inventory. Under this policy, rural migrants are viewed as important potential purchasers in small cities. Eased taxes and down payment requirements are granted to rural migrants. Local governments also subsidize housing purchases and provide them with easy access to obtain urban Hukou. The results from probit model clearly demonstrate that the effects of controls are consistent with economic theory and similar to existing empirical results (see Table 4 Column 1). The socioeconomic repressors have the expected effect. An 1% increase in family income results in approximately an 11.50% increase in the likelihood of homeownership attainment. Human capital in the form of educational attainment significantly affects the likelihood of owning houses. The higher the educational attainment, the more likely rural migrants become homeowners. Skilled migrants have advantages in the housing market. Individuals with a skilled job more likely attain homeownership than unskilled ones. Consistent with the findings of Huang et al. (2014), households with old male household heads likely attain homeownership. Migrating with the entire family also significantly improves homeownership probability by approximately 2.25%. Family size in urban destinations is also important. An increase in family members migrating together results in approximately 1.7% increase in the likelihood of homeownership. In addition, migration duration has a significantly effect on homeownership probability. Each additional year of stay in local cities increases the possibility for migrant workers to own a residence by

5.3. Heterogeneous effects We divide our sample into several subgroups to explore the heterogeneous effects to assess the difference effect in various life cycle stages and origin-destination groups. In life cycle stages, we split the sample into old and new generation groups. Individuals born before 1980 belong to the old generation group. Otherwise, they belong to the young generation group. As for the origin-destination differentials, we propose four migrant groups: migration from Central and Western China to East China (CW-E), migration within Central and Western China (CW-CW), migration from East to Central and Western China (ECW), and migration within East China (E-E). The estimation in Table 5 reveals that hometown housing investment has a negative effect on homeownership in urban destinations regardless of its generation, the homeownership of new generations and old generations are both crowded out and substituted by the hometown housing investment. Meanwhile, the negative effect for the new generation is lower than the old generation, that is, hometown housing investment may produce a larger negative effect for the old generations on their homeownership than the new generation. This suggests that new-generation migrants are more likely and adaptable to settle permanently in comparison with the old generations, even if they made hometown housing investment, they also have stronger aspiration for housing integration than the old generation. Table 6 presents estimations from different origin-destination groups, which show a robust and consistent conclusion with the benchmark estimation. This finding confirms that rural migrants who invest in housing in their hometown are less prone to buy houses in urban destinations, regardless of the origin-destination differences. But there are some notable differentials across origin-destination groups. The impact of hometown housing investment is more important for migrants who moved within the same regions (CW-CW and E-E) when compared with other groups, while the homeownership effects of hometown housing investment on migrants from Central and Western China to East China (CW-E) is weakest among the four origin-destination groups. Those results imply that the migration distance plays a sizeable role on the homeownership effects of hometown housing investment, migrants moved within the close regions will maintain closer relationship with their hometown, which will crowd out more integration activities than other groups. Moreover, for migrants who moved from developing regions (Central and Western China) to developed regions (East China), even if they bought a house near their hometown, they have no chance to live in because the local economy have no ability to provide enough decent jobs for them, they have to migrate to the developed regions to work and leave their house vacant. 8

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destinations. Another is that hometown housing investment may cause substantial financial burden to rural migrants, which may postpone the accumulation of a down payment, further weaken the willing and ability to become homeowner. We explore the possible mechanism by estimating the effect of hometown housing investment on the return migration intention and house purchase intention. Binary logistic regressions are employed to estimate. The results as showed in Table 7 reveal that hometown housing investment has a positive effect on the return migration intention, while it also produce a negative effect on the house purchase intention in destinations. Those imply that hometown housing investment not only weak the willing and ability to purchase house in the destinations, but also strengthen the ties to their homelands and encourage the return migration. Both of those activities will weaken the housing attainment of rural migrants in the destinations. Given these finding, I conclude that the hometown housing investment activities influence the housing tenure choice in urban destinations through affecting the return migration plans and housing purchase intent in urban destinations.

Table 5 Heterogeneous effects of life cycle: determinants of homeownership (Marginal effect).⁎ Variables

Entire sample

New generation

Old generation

−0.1387⁎⁎⁎ (0.0058)

−0.1403⁎⁎⁎ (0.0069)

0.1150⁎⁎⁎ (0.0021) −0.1563⁎⁎⁎ (0.0043) −0.1219⁎⁎⁎ (0.0033) −0.0652⁎⁎⁎ (0.0034) 0.0016 (0.0041)

0.1152⁎⁎⁎ (0.0026) −0.1421⁎⁎⁎ (0.0064) −0.1116⁎⁎⁎ (0.0035) −0.0574⁎⁎⁎ (0.0036) 0.0019 (0.0045)

0.1101⁎⁎⁎ (0.0035) −0.1914⁎⁎⁎ (0.0104) −0.1561⁎⁎⁎ (0.0099) −0.0973⁎⁎⁎ (0.0104) 0.0033 (0.0092)

0.0029⁎⁎⁎ (0.0001) −0.0280⁎⁎⁎ (0.0019) 0.0225⁎⁎⁎ (0.0024) 0.0167⁎⁎⁎ (0.0011)

0.0042⁎⁎⁎ (0.0003) −0.0320⁎⁎⁎ (0.0024) 0.0120⁎⁎⁎ (0.0031) 0.0137⁎⁎⁎ (0.0014)

0.0029⁎⁎⁎ (0.0003) −0.0236⁎⁎⁎ (0.0032) 0.0399⁎⁎⁎ (0.0039) 0.0181⁎⁎⁎ (0.0018)

0.0092⁎⁎⁎ (0.0003) 0.0400⁎⁎⁎ (0.0047) 0.0589⁎⁎⁎ (0.0047) 0.0574⁎⁎⁎ (0.0046) 0.0498⁎⁎⁎ (0.0054) 0.0378⁎⁎⁎ (0.0063) −0.1060⁎⁎⁎ (0.0029) −0.0494⁎⁎⁎ (0.0027)

0.0109⁎⁎⁎ (0.0005) 0.0392⁎⁎⁎ (0.0052) 0.0518⁎⁎⁎ (0.0053) 0.0467⁎⁎⁎ (0.0055) 0.0416⁎⁎⁎ (0.0069) 0.0192⁎⁎ (0.0093) −0.0991⁎⁎⁎ (0.0037) −0.0496⁎⁎⁎ (0.0033)

0.0092⁎⁎⁎ (0.0004) 0.0295⁎⁎⁎ (0.0094) 0.0500⁎⁎⁎ (0.0091) 0.0440⁎⁎⁎ (0.0088) 0.0286⁎⁎⁎ (0.0095) 0.0310⁎⁎⁎ (0.0102) −0.1131⁎⁎⁎ (0.0047) −0.0472⁎⁎⁎ (0.0044)

Institutional status & housing price Pension 0.0457⁎⁎⁎ (0.0038) Medical 0.0237⁎⁎⁎ (0.0043) Restrictions −0.0429⁎⁎⁎ (0.0025) Price −0.0238⁎⁎⁎ (0.0033)

0.0319⁎⁎⁎ (0.0047) 0.0264⁎⁎⁎ (0.0051) −0.0448⁎⁎⁎ (0.0032) −0.0294⁎⁎⁎ (0.0042)

0.0629⁎⁎⁎ (0.0063) 0.0208⁎⁎⁎ (0.0076) −0.0415⁎⁎⁎ (0.0042) −0.0130⁎⁎ (0.0056)

Geographic location Log pseudolikelihood Pseudo R2 N

Yes −28,199.8 0.2411 79,608

Yes −23,616.7 0.1920 56,303

Hometown housing investment Hometown housing tenure −0.1398⁎⁎⁎ (0.0044) Socioeconomic status Household income (logarithm) Elementary school Senior middle school Junior middle school Skilled occupation Demographic and life-cycle Age Sex Household migration Co-migrated family members Immigration-specific variable Local duration Cumulative duration (year1–2) Cumulative duration (year3–4) Cumulative duration (year5–9) Cumulative duration (year10–14) Cumulative duration (year15) Inter-provincial migration Inter-prefectural level migration

Yes −52,006.5 0.2184 135,911

6. Conclusion Purchasing a new commodity house in origin cities or towns has become a common practice in rural China, a few rural migrants choose to achieve homeownership dream in their hometown. But the studies on the direct effect of hometown housing investment on the homeownership in urban destinations in China remain scant. This study address three main issues to fulfill this gap: the casual relationship between transregional housing investment activities and homeownership in urban destinations; the potential effect between substitution and complementary; the mechanism link hometown housing investment and homeownership in urban destinations. A new conceptual framework named quasi-transnationalism and integration nexus is firstly introduced to discuss the three main issues in China context, where two attachments “ties to their origin” and “ties to the urban destinations” are emphasized in shaping the housing trajectories of rural migrants between urban destinations and hometown. The present study applies a unique dataset named NMPDMS and IV-LPM to explore the casual relationship, while the results confirm that hometown housing engagements have a significantly negative effect on rural migrants' homeownership in urban destinations. More importantly, this finding implies that hometown housing investment plays a substituted role on housing integration for rural migrants in the urban destinations, the activities of hometown housing investment would crowd out housing purchases in urban destinations. This negative association between transnationalism and integration in China context is in contrast with the results of Kuuire et al. (2016b), who find a complementary relationship between hometown housing investment and homeownership status for Ghanaian immigrants in Canada. The potential explanation on these two different results may be that internal rural migrants in China keep closer relationships with their hometowns, most of them only treat the urban destinations as workplace instead of home, and they have less motivation to keep the dual loyalty with hometown and urban destinations. This finding contributes to a broader debate on the transnationalism-integration nexus in the context of China, which emphasized the temporary traits of internal migration and substituted effect of quasi-transnational engagements on housing integration. In addition, there are some notable differences across subgroups according to different life cycle stages and origin-destination groups. We also find that hometown housing investment may produce a weaker negative effect on new generation's housing attainment in urban destinations due to their stronger willingness of the integration, compared with the older generation with more attachment to their home villages. Besides, migrants who moved within the same regions, the impact of hometown housing investment was more important, while the substituted effects are weakest among migrants from Central and Western

Notes: Robust Std. Err. in parentheses; The reference category is college or above, un-skilled occupation, non-household migration, female, cumulative duration (year1), inter-county migration. ⁎ p < 0.1. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01

5.4. Testing the mechanism Hometown housing investment of rural migrants has negative effects on their housing tenure choice in urban destinations. What's the mechanism behind this trait? One may be that hometown housing investment can strengthen the economic and social ties of migrants with their origin community (Bradley et al., 2007), which may bring return plans into a routine and further weakening the housing demand in 9

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Table 6 Heterogeneous effects of origin-destination: determinants of homeownership (marginal effect). Variables Hometown housing investment Hometown housing tenure Socioeconomic status Household income (logarithm) Elementary school Senior middle school Junior middle school Skilled occupation Demographic and life-cycle Age Sex Household migration Co-migrated family members Immigration-specific variable Local duration Cumulative duration (year1–2) Cumulative duration (year3–4) Cumulative duration (year5–9) Cumulative duration (year10–14) Cumulative duration (year15) Institutional status & housing price Pension Medical Restrictions Price Geographic location Log pseudolikelihood Pseudo R2 N

Entire

CW-E

CW-CW

E-CW

E-E

−0.1674⁎⁎⁎ (0.0046)

−0.0885⁎⁎⁎ (0.0057)

−0.2068⁎⁎⁎ (0.0074)

−0.1314⁎⁎⁎ (0.0230)

−0.1703⁎⁎⁎ (0.0111)

0.0874⁎⁎⁎ (0.0021) −0.2038⁎⁎⁎ (0.0044) −0.1507⁎⁎⁎ (0.0035) −0.0880⁎⁎⁎ (0.0037) 0.0136⁎⁎⁎ (0.0044)

0.0596⁎⁎⁎ (0.0033) −0.1751⁎⁎⁎ (0.0063) −0.1247⁎⁎⁎ (0.0047) −0.0681⁎⁎⁎ (0.0048) 0.0023 (0.0054)

0.0951⁎⁎⁎ (0.0030) −0.1780⁎⁎⁎ (0.0067) −0.1254⁎⁎⁎ (0.0056) −0.0799⁎⁎⁎ (0.0058) 0.0151⁎⁎ (0.0074)

0.0537⁎⁎⁎ (0.0118) −0.1636⁎⁎⁎ (0.0294) −0.1198⁎⁎⁎ (0.0230) −0.0791⁎⁎⁎ (0.0237) −0.0176 (0.0370)

0.1418⁎⁎⁎ (0.0056) −0.2187⁎⁎⁎ (0.0122) −0.1643⁎⁎⁎ (0.0079) −0.0779⁎⁎⁎ (0.0079) 0.0107 (0.0094)

0.0035⁎⁎⁎ (0.0001) −0.0344⁎⁎⁎ (0.0020) 0.0642⁎⁎⁎ (0.0025) 0.0149⁎⁎⁎ (0.0011)

0.0025⁎⁎⁎ (0.0002) −0.0356⁎⁎⁎ (0.0028) 0.0462⁎⁎⁎ (0.0034) 0.0009 (0.0016)

0.0032⁎⁎⁎ (0.0002) −0.0289⁎⁎⁎ (0.0031) 0.0628⁎⁎⁎ (0.0038) 0.0198⁎⁎⁎ (0.0017)

0.0032⁎⁎⁎ (0.0007) −0.0253⁎⁎ (0.0126) 0.0378⁎⁎ (0.0150) 0.0195⁎⁎⁎ (0.0068)

0.0049⁎⁎⁎ (0.0004) −0.0434⁎⁎⁎ (0.0052) 0.0553⁎⁎⁎ (0.0062) 0.0093⁎⁎⁎ (0.0031)

0.0117⁎⁎⁎ (0.0003) 0.0531⁎⁎⁎ (0.0048) 0.0732⁎⁎⁎ (0.0048) 0.0652⁎⁎⁎ (0.0048) 0.0412⁎⁎⁎ (0.0056) 0.0234⁎⁎⁎ (0.0065)

0.0092⁎⁎⁎ (0.0004) 0.0484⁎⁎⁎ (0.0077) 0.0468⁎⁎⁎ (0.0077) 0.0358⁎⁎⁎ (0.0076) 0.0210⁎⁎ (0.0085) −0.0020 (0.0098)

0.0108⁎⁎⁎ (0.0004) 0.0536⁎⁎⁎ (0.0069) 0.0863⁎⁎⁎ (0.0069) 0.0873⁎⁎⁎ (0.0069) 0.0685⁎⁎⁎ (0.0082) 0.0635⁎⁎⁎ (0.0096)

0.0130⁎⁎⁎ (0.0018) −0.0009 (0.0316) 0.0245 (0.0309) 0.0324 (0.0309) 0.0316 (0.0351) 0.0072 (0.0417)

0.0136⁎⁎⁎ (0.0008) 0.0742⁎⁎⁎ (0.0127) 0.0891⁎⁎⁎ (0.0126) 0.0743⁎⁎⁎ (0.0126) 0.0446⁎⁎⁎ (0.0145) 0.0117 (0.0174)

0.0436⁎⁎⁎ (0.0039) 0.0351⁎⁎⁎ (0.0045) −0.0331⁎⁎⁎ (0.0023) −0.0730⁎⁎⁎ (0.0017)

0.0079⁎ (0.0046) 0.0216⁎⁎⁎ (0.0050) 0.0015 (0.0033) −0.0308⁎⁎⁎ (0.0015)

0.0749⁎⁎⁎ (0.0068) 0.0458⁎⁎⁎ (0.0084) −0.0532⁎⁎⁎ (0.0039) 0.0124 (0.0094)

0.1442⁎⁎⁎ (0.0282) 0.0739⁎ (0.0405) 0.0073 (0.0172) −0.0568 (0.0470)

0.0319⁎⁎⁎ (0.0086) 0.0372⁎⁎⁎ (0.0090) −0.0321⁎⁎⁎ (0.0062) −0.0741⁎⁎⁎ (0.0034)

Yes −57,951.2 0.1290 135,911

Yes −9193.0 0.1794 37,322

Yes −36,091.3 0.0914 72,525

Yes −1839.2 0.0942 3948

Yes −9697.2 0.1608 22,116

Notes: Robust Std. Err. in parentheses; The reference category are Migrations within East China, college or above, un-skilled occupation, non-household migration, female, cumulative duration (year1). ⁎ p < 0.1. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01.

China. It should be highly highlighted to relax the Hukou restriction differentiated across the scale of urban cities to ensure rural migrants share equal public services with urban citizens, while the correlative supporting measures must be enhanced to cut off the linkage between Hukou status and the public welfare. It should be clearly noted that rural migrants who have invested housing or intend to make housing investment in their hometown may return home for settlement, that is, in the smaller cities, breaking the Hukou barrier should be targeted continuously in the implementation of “active destocking” to encourage hometown housing investment for rural immigrants with greater likelihood of return migration as well as hometown housing investment, which will not only stimulate the local economic development, but it will also alleviate the overcrowding pressure on larger cities.

China to East China in terms of the role of migration distance on both interaction and integration activities. This study also contributes to the discussion on the potential mechanism linking the hometown housing investment and urban homeownership. Our findings confirmed that hometown housing investment might maintain more positive economic and social ties to their hometown than the attachments to the urban destinations. For one hand, the participation in transregional housing engagements might strengthen the closer attachment to the hometown by positively enhancing rural migrants' return migration plans; for another, it could produce an additional financial pressure on housing purchase intentions, which weaken the integration of homeownership in urban destinations. This study also sheds insight into current housing policy in urban 10

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migrants, and the new pilot reform “zu shou tong quan” aiming to ensure the tenants enjoy the same rights as home buyers should be extended to nationwide. Secondly, the full implement of housing provident fund system as well as the rental subsidy system for rural migrant workers in urban cities should be enhanced to encourage them to buy houses and settle down in destinations. The labor contract should be enhanced to ensure the employer pay housing provident fund contributions for rural migrants in full, while a flexible housing provident fund payment standard with low base and universal coverage should be set up to alleviate the financial burden and maintain the long-term operation. Thirdly, encouraging the employers and NGOs participate in housing supply. The local government can apply a series of fiscal preferential policies to encourage employers to provide houses including building dormitory by its self-owned land or taking part in public rent housing construction. The NGOs also are encouraged to provide selfoperated public housing based on the cost rent principal. Fourthly, it's requested to address the fact that there is the substitution between hometown housing investment and housing tenure and those with the stronger intention of permanent settlement but no adequate budget for housing purchase in urban destinations have to return home to rebuild in villages or purchase a house in town. Therefore, establish a unified land trading mechanism between urban and rural areas and introduce the permission system of the exit of rural homesteads, which could make greater contributions on revitalizing the land and real estate for rural migrants to support the housing consumption in urban cities. Meanwhile, rural migrants who give up the ownership of homestead or lands and choose to settle down should be compensated by s set of the urban pension, medical scheme and so on in the integration of new urbanization process. This study also has certain limitations. First, the applied NMPDMS is a cross-sectional data, such that we cannot obtain exact information while housing investment is generated. The only situation in which we can obtain information is whether housing investment in urban destinations (or in hometowns) has been generated. Even when we correct for potential endogeneity, the estimated values still exhibit bias. Memory retrieval or longitudinal data should be used with the time-toevent analysis to address this problem. Besides, this study only considers one form of hometown housing investments, which is purchasing houses in origin counties. Another well-known form, self-built housing, is not covered. Hence, the effect of migrants' original self-built housing investment on house tenure in urban destinations should be discussed in future research.

Table 7 Mechanism link the hometown housing investment and homeownership in urban destinations (marginal effect). Variables

Hometown housing investment Hometown housing tenure Socioeconomic status Household income(logarithm) Elementary school Senior middle school Junior middle school Skilled occupation Demographic and life-cycle Age Sex Household migration Co-migrated family members Immigration-specific variable Local duration Cumulative duration (year1–2) Cumulative duration (year3–4) Cumulative duration (year5–9) Cumulative duration (year10–14) Cumulative duration (year15) Inter-provincial migration Inter-prefectural level migration Institutional status & housing price Pension Medical Restrictions Price Geographic location Log pseudolikelihood Pseudo R2 N

Return migration intention

House purchase intention

0.0265⁎⁎⁎ (0.0021)

−0.0145⁎⁎⁎ (0.0040)

−0.0129⁎⁎⁎ (0.0014) 0.0350⁎⁎⁎ (0.0034) 0.0279⁎⁎⁎ (0.0029) 0.0197⁎⁎⁎ (0.0030) −0.0029 (0.0033)

0.0743⁎⁎⁎ (0.0023) −0.1183⁎⁎⁎ (0.0050) −0.0865⁎⁎⁎ (0.0039) −0.0527⁎⁎⁎ (0.0041) 0.0112⁎⁎ (0.0049)

0.0015⁎⁎⁎ (0.0001) 0.0006 (0.0013) −0.0119⁎⁎⁎ (0.0018) −0.0135⁎⁎⁎ (0.0008)

−0.0013⁎⁎⁎ (0.0001) 0.0040⁎ (0.0022) 0.0354⁎⁎⁎ (0.0028) 0.0176⁎⁎⁎ (0.0013)

−0.0032⁎⁎⁎ (0.0002) −0.0082⁎⁎⁎ (0.0024) −0.0137⁎⁎⁎ (0.0025) −0.0174⁎⁎⁎ (0.0025) −0.0189⁎⁎⁎ (0.0031) −0.0215⁎⁎⁎ (0.0036) 0.0395⁎⁎⁎ (0.0023) 0.0155⁎⁎⁎ (0.0023)

0.0045⁎⁎⁎ (0.0003) 0.0251⁎⁎⁎ (0.0046) 0.0208⁎⁎⁎ (0.0047) 0.0294⁎⁎⁎ (0.0047) 0.0280⁎⁎⁎ (0.0056) 0.0081 (0.0067) −0.0838⁎⁎⁎ (0.0034) −0.0199⁎⁎⁎ (0.0032)

−0.0101⁎⁎⁎ (0.0028) −0.0065⁎⁎ (0.0033) −0.0055⁎⁎⁎ (0.0018) −0.0018 (0.0018)

0.0468⁎⁎⁎ (0.0043) 0.0030 (0.0050) 0.0307⁎⁎⁎ (0.0028) −0.0159⁎⁎⁎ (0.0029)

Yes −30,637.9 0.0712 135,911

Yes −66,946.2 0.0694 135,911

CRediT authorship contribution statement Zicheng Wang: Conceptualization, Methodology, Funding acquisition.Murong Guo: Writing - original draft, Software.Juan Ming: Formal analysis, Writing - review & editing, Supervision. Acknowledgements

Notes: robust Std. Err. in parentheses; The reference category are Migrations within East China, college or above, un-skilled occupation, non-household migration, female, cumulative duration (year1). ⁎ p < 0.1. ⁎⁎ p < 0.05. ⁎⁎⁎ p < 0.01

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