Migration and inequality in rental housing: Affordability stress in the Chinese cities

Applied Geography 115 (2020) 102138

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Migration and inequality in rental housing: Affordability stress in the Chinese cities Ran Liu a, Tingzhu Li a, Richard Greene b, * a b

College of Resource Environment and Tourism, Capital Normal University, No.105, West 3 Rd Ring Road North, Beijing, 100048, China College of Geospatial Information Science and Technology, Capital Normal University, No.105, West 3 Rd Ring Road North, Beijing, 100048, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Rental stress Regional inequality Spatial heterogeneity Spatial regression model Local buzz China’s internal migration

Housing affordability is a widely accepted notion used to assess the housing poverty problem in the Global North and South. China is experiencing an unprecedented urban revolution, with two-thirds of its 250 million migrants now being sheltered in the private rental housing sector of the receiving cities. The discriminatory hukou and an exclusive public housing system with appalling living conditions for migrant housing have been a huge challenge in contemporary China. In this paper, we aim to examine the state of housing affordability inequality in the current rental market for all of China and provide policy recommendations for a more accessible and equitable migrant housing provision system. On the basis of China’s Migrant Dynamics Monitoring Survey (MDMS) con­ ducted in 2011 and 2016, this paper analyzes the dynamic spatial inequalities of the rent affordability stress (rent-to-income ratio) among migrants from 2011 to 2016 across China’s prefecture-level cities and above. We use CV, Gini and Theil indices to investigate the interregional, interprovincial and inter-prefectural inequalities of migrant rent stress, and then adopt spatial autocorrelation to examine the regional variance for these urban units. Our study reveals the convergence of rent stress inequality at different geographical scales and an increasingly apparent north-south divide in housing affordability inequality in the rental market accessible to the migrant workers of China. The agglomeration of the high rent-stress migrants in the “local buzz” in China is found, too, which is closely associated with policy and economic factors, as well as being embedded in the hi­ erarchical structure of the Chinese urban administrative system and the contrasting urbanization paths of the north and south, and of the rustbelt and sunbelt.

1. Introduction Over the past decades, the advanced and the newly industrialized economies, as well as the developing and transitional countries, have been accumulating wealth faster through the housing market than in any other sectors (Smith & Searle, 2010). Since the mid-1970s, neoliberal housing policy has shrunk the supply of social rental housing and has increasingly relied upon private investment and commercial property management (see Gruis, Tsenkova, & Nieboer, 2009; Wu, 2018). The discussion of neo-liberal housing policy has tended to be dominated by three perspectives: a) unaffordability and lack of access to housing among low-income groups; b) an on-going residential segregation faced by minority communities; and c) property rights, civic activism, and the politics of “accumulation by dispossession” in the neoliberal era (Bun­ ting, Walks, & Filion, 2004; Harvey, 2008). These studies also referred to the significance of institutional legacies and path dependency in shaping

the varied outcomes of neo-liberal policy between and within societies (Forrest & Hirayama, 2009). As a specific case where neo-liberalism has operated alongside authoritarian centralized control, China has contributed to a wider literature that critically considers the migration—neo-liberalism rela­ tion, involving migrants’ capacities to access and afford housing and navigate the everyday life milieu and social relationships in the host cities (see Logan, Fang, & Zhang, 2009). From the 1980s, with a great structural transformation from an urban-restrictive period to a metropolitan-led economic base, China has seen an unprecedented rise in urbanization rates driven by rural to urban migration. Whilst the migrants are welcome in the city building process, their permanent residency rights are withheld. After four decades of pro-market reform in an incremental and progressive manner, the hukou legacy and dual-track land and housing systems have not been suspended, but instead become a hotbed of urban inequality and institutional

* Corresponding author. E-mail addresses: [email protected] (R. Liu), [email protected] (R. Greene). https://doi.org/10.1016/j.apgeog.2019.102138 Received 2 May 2019; Received in revised form 6 September 2019; Accepted 28 December 2019 Available online 2 January 2020 0143-6228/© 2020 Elsevier Ltd. All rights reserved.

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of the paper firstly gives a brief review on the uneven geography of rent affordability stress, including that of Chinese migrants who can hardly access the affordable housing coverage. Then, this paper clarifies the definitions, data, model, and research area. In the empirical parts, we employ the CV, Gini and Theil indices and spatial autocorrelation to test the spatial inequalities of migrants’ rent affordability stress at interre­ gional, interprovincial and inter-prefectural scales, and the spatiality of high-stress groups in China. Finally, a pair of spatial regression models (spatial error model and spatial lag model) are conducted to investigate the factors that influence the spatial heterogeneity of high rent-stress migrant tenants and their implications for further study.

discrimination that pushes the migrants into a disadvantaged stratum without access to decent housing in the receiving cities (Liu, 2015). In its hukou and dual-track systems, the housing privatization, the affordable housing and public rental housing schemes, and monetary subsidies are mainly available for the locals and danwei employees as insiders, but inaccessible to those without an official permanent residency permit (hukou) as outsiders. The problems with migrants’ inaccessibility of affordable housing and their exposure to the speculative rental market are worthy of greater scrutiny. In 2017 and 2018 the major rental companies (including Ziroom) fueled a spike in rental prices in Beijing by using procured funds from bank or other financial channels to capi­ talize rental businesses and grab rental listings (Reuters, 2018). The Beijing Bureau has since launched a joint inspection with the financial system on such “vicious competition” in the rental market. While studies of development and inequality have focused on the phenomena of divergence and convergence of economic/income indices, we broaden the spatial inequality research to urban housing inequalities in Chinese cities. The unequal distribution of affordable housing combined with rapidly spiking housing expenses have been the main contributor to increasing wealth inequality and coastal-interior gaps in China in the past two decades (Li & Wan, 2015; Wei, 2017). Empirically, overwhelming evidence was found that housing inequality has very strong structural forces and geographical foundations that are hard to change. The multi-hierarchical and multi-scalar dimensions of spatial inequalities, and the spatial dependence and spatial processes in shaping the uneven socio-economic landscape, are all important for a better understanding of the complexity of urban housing inequality in Chinese cities (Li, Wei, Liao, & Huang, 2015; Wei, 2015; Wei, Li, & Yue, 2017). More specifically, the global restructuring and economic reforms have simulated neo-liberal housing policy in China today. The state, the locality and households interact with each other to shape urban housing inequality at interregional, interprovincial and inter-prefectural scales. However, the spatiality and inequalities of urban housing have received much less attention at inter-prefectural scales. The official statistical report and empirical evidence prove that twothirds of 250 million migrants in China are sheltered in the private rental housing sector of the receiving cities in 2016 (Sohu, 2018). In Beijing itself, rental housing is playing an increasingly significant role in settling its eight million migrants. It was reported that 39.6 percent of the mi­ grants were tenants in 1997, and grew fast to 51.3 percent in 2003, and then to 62.3 percent in 2016 (see National Health and Family Planning Commission in China, 1997, 2003 and 2016). The inflation of a specu­ lative bubble in the leasehold market has been another unexpected by-product of the migrant explosion (Liu, 2019; Yang & Chen, 2014). While there is a large body of theoretical and empirical research on the migrants’ residence problems with this large-scale migration, little has referred to the spatial inequality in paying rent in the host cities that vary between and within regions. The uneven geography of rent affordability stress can tell us about the heterogeneous housing provi­ sion accessible to migrants in the different regions of China. It also represents migrants’ trade-off between their different forms of expen­ diture that can predict their long-term welfare entitlement and the set­ tlement intention in the host cities. What then is the spatial extent and policy implication through which housing policy can take effect on the affordability among migrant tenants? What immigrant areas render rents affordable, while others don’t? The answers can contribute to the on-going debates on the migrants’ inclusiveness in public housing pol­ icies but also hinge on their right to the city. In sum, this study looks into the migrants’ affordability stress in the rental housing markets of their host cities within China, so as to reveal how the considerable variation in affordability stress can predict the migrants’ contested citizenship in Chinese cities but to also show how they varied considerably by prefecture-level characteristics. The nonhukou status is linked to an “incomplete” internal migration and hence the inaccessibility to a full legal status of “residency” including public housing assistance in their destinations (see Fan, 2007). The remainder

2. Literature review and contextual issues 2.1. Uneven geography of rent affordability stress: a global perspective The unevenness of rent affordability stress and its spatial aspects have attracted much attention in recent years. First of all, the uneven geography of rent affordability stress was explored in Western postindustrial metropolitan regions, especially with high rates of immigra­ tion and thus huge numbers of low-waged immigrants in the unskilled and semi-skilled services sector needing shelter. Quite a lot of empirical studies have been conducted to identify the inter- and intrametropolitan rates of rent affordability stress and where the rent stress is greatest in the major Canadian Census Metropolitan Areas (CMAs), the U.S. metropolitan areas, the UK, and Australia (Bunting et al., 2004; Dong, 2017; Moore, 2017). They traced the different housing policies in each country and the labour-market dynamics, where rents had soared in the over-heated markets of the fast-growing metropolitan areas. It is now well recognized that the relative contribution of incomes versus that of housing costs in rent stress (rent-to-income ratio) is a vibrant indi­ cator of the process of institutional and socioeconomic restructuring at different scales. Implication of the uneven distribution of affordable rentals with respect to demographic, income and locational structure of the metro­ politan areas in North America, Europe, and Australia is discussed. Dong’s (2017) longitudinal analyses confirmed the worsening rental affordability for low-income tenants in large American metropolitan areas along with the rising income inequality since the 2008 U.S. financial crisis. Fields and Uffer (2016) noticed a financialization pro­ cess in North American and European metropolises, which had height­ ened existing inequalities in housing affordability and gentrified affordable housing into a new global asset class. Moore (2017) and McKee et al., (2017) elaborated on the necessity of regulation on UK’s private rental sector and the need for greater geographical sensitivity, since a devolution of housing policy responsibilities to UK jurisdictions with geographical variances. The political and welfare agendas and the ongoing financialization process have contributed to urban inequality with a spatial divergence in the global urban process. Rent affordability stress is therefore not simply a payment capacity issue at household level, but also subject to the globalization process, institutional reforms, and local agents at multiple levels. Second, the political and economic analysis can give a clearer explanation as to why this affordability crisis persists, far beyond the above mapping of who is at risk and where. As early as 1974, Harvey (1974) proposed a concept of “class-monopoly rent” to explain the power and interests between the landlord and low-income tenant, and between the speculator-developer and higher-income consumer in the real estate market. Consequently, the examination of how rent changes with labour-market dynamics cannot proceed without looking into the supportive institutions. If so, how have these immigrants defined their “class power” and “class interest” in claiming an affordable shelter? The Marxist analysis of labour, capital, and housing reveals the very nature behind the emerging patterns of housing inequality and affordability crisis in advanced capitalist societies. In the past several years, new findings stressed the emergence of “private rental gentrification” 2

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phenomena in UK, Switzerland, Swiss, Brussels, Canada and USA, taking the various forms of buy-to-let gentrification, new-build gentrification, reinvestment and financialization fueled by the private equity investors and real estate investment trusts (see August & Walks, 2018; Paccoud, €derstro €m, & Piguet, 2010). These 2016; Fields & Uffer, 2016; R�erat, So studies have been rephrased in recent years into “planetary gentrifica­ tion” to elaborate on today’s rhythm of unevenness and housing unaf­ fordability issue at the urban scale (Slater, 2017). The political and economic analysis gives some clues to explain the public housing scar­ city, land financing, urban village demolition and gentrification phe­ nomena in China today. It is apparent that migrants’ power and claim in access to affordable housing and its spatial parameter are rooted in the transitional background in contemporary China.

series of statistical measurement of price-to-income ratio that is believed to be beyond the reach of the average citizen; but affordability stress in the rental housing market is rarely hinted at. How to access affordable rental housing is the primary concern of migrant workers in host cities, and then those who are economically better off would be able to climb further up the housing ladder for homeownership attainment. By exploring the rent affordability stress, it would be easier to interrogate what does the neoliberal policy mean in satisfying the varying housing needs of migrant workers since that the governments fail to provide affordable public housing to these hikers. Thirdly, the empirical studies on Beijing, Shanghai, Guangzhou and Shenzhen show that the residential mobility of disadvantaged migrant tenants is part of the “circuits of capital” over city space (Liu, Geertman, Van Oort, & Lin, 2018). In the post-modernist theoretical system, the dialectical relation and mutual effect between capital accumulation (exchange value) and migrants’ necessity for everyday life (use value) were thought to be a key to the urban phenomena (Harvey, 1978, 2008). Henri Lefebvre’s slogan of “right to the city” and David Harvey’s discourse on “accumulation by dispossession” were cited repeatedly to reveal: a) housing unaffordability and inaccessibility against low-wage migrants; and b) power underlying the city-marketing, redevelopment and gentrification process that would raise the housing costs or even force out the migrant workers.

2.2. Migration and rent affordability in the transitional China Commencing in 1979, China introduced market forces interdigitated with its centralized controls by imitating the high-speed economic growth model of the “Four Asian Dragons” to some extent. Since the gradual pro-market reforms, the city-centered and export-oriented ur­ banism mode has witnessed the emergence of several intersecting stratification systems in the urban housing allocation that failed to equalize the local-nonlocal (hukou) and rural-urban divide in access to decent housing. The allocation of government housing assistance (including Economic Housing, Cheap Rented Housing, Public Rented Housing, Limited Priced Housing, Common Rights Housing, Housing Purchase Subsidies and other types), made accessible only to local hukou holders, is often cited as an example of the distorted housing allocation system (Huang, 2012; Logan et al., 2009). As stated earlier, neoliberal housing policies serve a multiple purpose but there exists a lack of the common aspirations between immigrants and city government. The conflict of interests between the disadvan­ taged migrants’ needs for an affordable housing and local “growth coalition” has attracted much attention. First of all, the stratified hukou pyramid, especially in China’s top-tier cities namely Beijing, Shanghai, Guangzhou and Shenzhen, confers different local welfare provisions to different social groups in order to control the “unnecessary” increases in environmental burden and economic expenditure on unskilled migrants (Fan, 2007). Recent developments have shown that policy-making be­ comes “people-oriented” and inclusive in its approach towards the migrant population. In 2014, the Chinese central government pro­ claimed a new-type of urbanization blueprint 2014–2020 with an emphasis on human aspects, including the granting of 100 million urban hukous and to provide urban social benefits such as social housing, ed­ ucation and health for the migrant households during this period. Ob­ servers however were quick to point out that more work was required to reform the fiscal system and related institutions to enable cities to pro­ vide for millions of newcomers on a more sustainable fiscal basis (Chan, 2014; Wang, Hui, Choguill, & Jia, 2015). Huang (2012) analyzed the low-income housing polices in Chinese cities and argued that, while policy changes since 2010 gave cause for some encouragement in settling migrants, the outcome is still uncertain, because of a systematic exclusion of migrants from accessing low-income housing and the local public finance not committing to low-income housing. Shi et al., (2016) explored the new developments of affordable housing policy in China under the “new urbanization strategy” launched in 2014 and found that, its recent PRH (public rental housing) serves as a propelling engine to promote accommodating the migrant workers. However, provision of public rental housing becomes a selective tool by policymakers to sub­ sidize the specific target groups including highly-educated and skilled migrants that they want to attract and retain (Wang & Murie, 2011). Second, while the liberalization of housing provision has facilitated internal migration, the rent upsurge also makes decent housing ac­ commodation increasingly unaffordable for migrants. A housing affordability crisis has been extensively documented in the transitional China (Yang & Chen, 2014). Empirical knowledge is augmented by a

2.3. Conceptualizing migrants’ rent stress in China: a complex spatial inequality Interestingly, China and the other developing countries share the predicament of budget scarcity and the inability to provide adequate support to their low-wage immigrants. Non-hukou migrants are not only excluded from the benefit of the public housing scheme and made vic­ tims of the speculative real estate market, but also classified under “urban informalities” including urban villages for “Ant Tribes”, under­ ground accommodations for “Rat Tribes”, and “capsule apartments” dispersed widely in the old municipal and work-unit housing areas and newly created residential compounds (see Huang & Yi, 2015; Liu, 2015). What is more serious is that housing inequality creates a conflictual relationship between what is authorized/legal and what is unauthor­ ized/illegal, in which the authority has aimed to economize public spending and prevent physical slum formation. The recent studies show that the state-initiated redevelopment of these informal habitats in Guangzhou, Shenzhen and Beijing is in line with the city marketing strategies and gentrification process (see Liu et al., 2018; Liu & Wong, 2018). The rent unaffordability is thus a growing problem with migrants in Chinese metropolises. The market-led movement and its neoliberal housing policies, which sets off the internal migration of labour force and their residential mobility in different ways, have formed the focal point of this paper. In the past several decades, hukou is noted as a main institutional constraint against the migrants’ equitable access to affordable housing and other forms of welfare. In Beijing for instance, home occupiers took up 16.2 per cent of migrants in 2016, and this portion was recorded as high as 78.6 per cent in local urban hukou-holders. Rental housing settled 62.3 per cent of migrants in 2016, but this ratio was just 8.2 per cent in the urban Beijing hukou-holders. CRH (cheap rented housing) and PRH (public rental housing) are confined to hukou holders (only 7.9 per cent in urban Beijing hukou-holders) but rarely accessible to mi­ grants (less than one per cent in migrants, see National Health and Family Planning Commission in China, 2016). This has been an inevi­ table result of the neoliberal housing policies which dissolved its social housing into a residual form, and directed the social surplus into the built environment in pursuit of higher profits and quicker returns. In a word, rent stress is a growing problem with Chinese metropol­ ises, especially among its non-hukou migrant workforce, thus distinct from the situation in the West and the other developing countries (see Fig. 1). This paper tries to address several as-yet unanswered spatial 3

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informality is rooted in the partial reforms in China that have resulted in a liberalized urban real estate market and an unreformed thus inalien­ able rural real estate sector. As seen in Zhang and Zhao’s (2018) first-hand investigation, the pricing in informal markets was dependent on the tenure security on rural plots, which varied with the village power and ability to seek political patronage and regulate the market transaction to secure the tenure. Transactions and rent levels are full of uncertainties, presumably not comparable from one to another. For this reason, this study would not consider the urban village phenomenon. Secondly, we adopt the straightforward definition and computation of rent affordability as a ratio of the housing expenditure to household income. The residual income approach modified by Stone (2006), he­ donic price estimates used by Thalmann (1999), or other more comprehensive measures of housing affordability (Kutty, 2005) are not applied here, due to the unavailability of data on non-housing con­ sumption or housing attributes. Even if this definition of rent stress cannot address whether the migrants have adequate income to pay for non-housing necessities in the host cities, such a rent-to-income ratio is still a satisfactory indication about the challenges each household faces in affording the housing costs within the constraints of its income and within the housing provision framework of the host cities. Therefore, the indicator of rent stress or rent affordability is operationally measured as the ratio of housing cost to income, as widely accepted and used in the numerous reports and research on housing affordability issues. Thirdly, the “rule of thumb” ratio standard (housing cost to income) was normally fixed at 30 percent for assessing the housing unafford­ ability problem. This ratio paradigm was posed earlier at 25 percent of income until the 1980s in the United States, and then set at 30 percent. Drawing on the drastically uneven regional development in China, we would set this threshold ratio at a lower level than that of the Western countries. In this research, rent is considered unaffordable if it accounts for over 25 percent of income, despite the considerable critical discus­ sions on this approach and ratio standard. In reality, this ratio can vary across the more developed and less developed regions, and wealthier and poorer households. The 25 percent or 30 percent threshold ratio is still widely acknowledged in the literature as a comparable baseline across the different regions and households.

issues concerned with the migrants’ rent unaffordability in the rapidly urbanizing China: where do the high rent-stress cities reside in the mainland China? And, where do the high rent-stress migrants congre­ gate? The spatiality of migrants’ rent stress is also in its dynamic change, straddling the year 2011 as one marked by its old GDP-centered growth paradigm and the year 2016 when its “New Urbanization Strategy” just started. And thus, what do these spatial inequalities imply about China’s affordable housing policies intended to promote the “New Urbanization Strategy” launched in 2014, which is a reorientation of urbanization with an emphasis on the human aspects? In the next section, we follow the multi-hierarchical and multi-scalar framework, which has been widely applied for a better understanding of the complexity of regional inequality phenomena (Li et al., 2015; Wei et al., 2017). Coefficient of variation (CV), Gini coefficient and the Theil index are used to measure the interregional, interprovincial and inter-prefectural inequality of migrants’ rent stress. And then spatial autocorrelation is used to examine the regional variance of high rent-stress migrants for the urban units. Our research at the inter-prefectural scale would contribute to the academic inquiries of multi-scalar regional inequalities, since the private rental housing market studies were conducted mostly at the intra-metropolitan levels (see Li, 2012; Wu, 2004). From the policy and theoretical perspective, this paper demonstrates the greater attention needs to be paid to the spatial aspects of migrants’ rent stress and to the related, rent-unaffordability-induced risk of the social exclusion against migrants. 3. Definitions, study area, data and methods 3.1. Definitions The definitional issue is clarified first before quantifying the spatial pattern of migrants’ rent stress in the residential market of mainland China. First, the rent stress or the rent affordability that is used to gauge the relation between internal migration and residential mobility (i.e. affordability, accessibility and preference) refers to the private rental housing market in the urban neighborhood communities of the prefecture-level cities and above in transitional China. This does not include urban village (chengzhongcun) housing for several reasons. First of all, a more comprehensive measurement of rent affordability stress needs to account for the migrants’ ability to acquire both the formalities and informalities in a dual market in the Global South. Urban villages are a specific informality phenomenon in the Chinese urbanization where local land-losing peasants have built “informal habitats” to house migrant workers and other low-wage earners (see Liu, 2015). This

3.2. Study area, data and variables The data on migrants’ rent-stress in the prefecture-level host cities and above is derived from the 2011 and 2016 Migrant Dynamics Monitoring Survey (MDMS), conducted by the National Health and Family Planning Commission of China. The eligible migrants in this �n) or city (Shì) national survey are those who moved across a county (Xia

Fig. 1. Conceptual interpretation of spatial inequality and dynamics of migrants’ rent stress in China. 4

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boundary from their registered household, and have stayed in their current destination for more than one month. Specifically, data collec­ tion on the residential committee units is based on the probability proportional to size (PPS) sampling schemes from each sub-district or town in the city unit. Till now, MDMS data has been widely used in urban studies and public policy research in recent years (Hou & Zhang, 2017; Huang, Liu, Xue, Li, & Shi, 2018). The study area covers mainland China without Hongkong, Macao and Taiwan regions. The incomes and rent expenses of migrant house­ holds in China’s prefecture-level cities or above are the raw data sets for the rent-stress index calculation. Average income and rent expenses of migrant households per month in each city unit in 2016 are mapped in Fig. 2. In 2016, the migrants’ income and rent expense is an uneven phenomenon, geographically different in China’s eastern, central, western and north-eastern regions. The migrants’ household income was on average higher in southern China than that found in China’s central, northern, western and northeastern regions (Fig. 2a). In eastern China, migrants’ high rent expense was concentrated in the regional centers (e.g. Beijing, Tianjin, Shanghai, Nanjing, Fuzhou, Shenzhen, Zhuhai, etc.) and their sur­ rounding cities, which are the “local buzz” and growth engine that eventually pushed up rentals to a new height and beefed up the regional inequality in rent expense (Fig. 2b). Meanwhile, in central China, high rent expense events reside in several provincial capital cities like Zhengzhou, Hefei, Changsha, Wuhan etc., with a strong economic base and a privileged administrative status. In western China, however, the high rent expense events are scattered mainly in those cities with a favorable industry, a high administrative status, together with a new developmental opportunity from the “One Belt, One Road” initiative, such as Dali, Linzhi, Guiyang, Zunyi, Jiuquan, Xining etc. (see Fig. 2b). China’s 329 prefecture-level cities and above are compared spatially, based on related rent-stress indices as follows: a) the migrants’ rent stress (rent-to-income ratio) in each city unit; b) the high rent-stress migrants (rent-to-income ratio above 25%) in each city unit. In exploring the influencing mechanism behind the uneven geography of the high rent-stress migrants, ten administrative and socioeconomic indicators entered the regression models (Table 1). The nine adminis­ trative and socioeconomic indicators used in this study——city ranks (prov and sub-prov as dummy variables), GDP, Non-agri, FDI, APS, Wage, Edu, FAI and Residence, were derived from the China City Sta­ tistical Yearbook (National Bureau of Statistics, 2017); and Tenants was derived from the Migrant Dynamics Monitoring Survey (National Health and Family Planning Commission in China, 2016). The reasons for independent variables selection are given as follows.

Table 1 Independent variables and their definitions. Categories

Variables

Definition

Symbol

City ranks

Provincial level

Prov

Economic factors

Sub-provincial level GDP Non-agri share in GDP FDI

Centrally administrated municipality (dummy) Designated in the state plan, subprovincial capitals (dummy) GDP in each city (billion Yuan) Secondary and tertiary industries as share in GDP (%) Foreign direct investment from overseas (billion USD) Share of advanced producer services in urban jobs (%) Average wage of urban employees (Yuan) Per capita public spending on education (Yuan) Investment on the physical assets (infrastructure, real estate, machinery etc.) in each city (billion Yuan) The proportion of tenants in migrants (%) Per capita residential building investment (Yuan)

Social factors

Housing supply

Producer services level Wage level Educational service level Fixed asset investment Tenants share in migrants Residential development

Sub-prov GDP Non-agri FDI APS Wage Edu FAI

Tenants Residence

Firstly, in the city rank group, the administrative level of a city repre­ sents the power, territory and autonomy of local governance, and also the attractiveness to capital flow and labour migration (Li et al., 2015; Ma, 2005). The association between city rank and housing unafford­ ability tends to be self-evident, too (Li, 2012; Yang & Chen, 2014). On the one hand, the megacities like Beijing, Shanghai, Guangzhou and Shenzhen are facing continuous talent agglomeration, despite the fact that the first-tier cities are implementing a strict control on population inflow and housing purchase. On the other hand, the “war for talents” has heated up in China’s second-tier cities like Wuhan, Chengdu, Nanjing, Xi’an, Dalian, Zhengzhou and Shenyang Since 2017, where local governments are offering housing subsidies to college graduates (Ye, 2019). Apparently, rent stress inequality is stratified to some extent under the tiered political-economic power structure in city ranks. The variable for centrally administrated municipality (Prov) and sub-provincial cities (Sub-prov) is the dummy variable. For Prov, 0 means that the city is not a centrally administrated municipality; 1 means yes. For Sub-prov, 0 means that the city is not a sub-provincial level city; 1 means yes.

Fig. 2. Spatial distribution of migrants’ income and rent expense in China at prefecture level and above. 5

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Second, in the four economic indicators, GDP and its nonagricultural industries as share in GDP are the most comprehensive and widely used measures of a city’s economic capacity and attrac­ tiveness to migrant workers. FDI is the important determinant and widely accepted measurement of a city’s economic openness and attractiveness to foreign investors. Lin and Zhang (2014) elaborated on the spatial variation of the practices of land commodification in transi­ tional China, and its dependence upon the economic growth levels as well as degree of openness. We can expect that the speculative bubble in leasehold market, as a byproduct of the practice of urban land commodification and the “land-based municipal finance” (tudi caizheng), would be more severe in the more developed regions with more FDI inflow. We also added the share of advanced producer services in urban jobs as the 4th economic indicator to explain the spatial variance of high rent-stress migrants. According to existing literature, the high-tech cityregions and foreign-affiliated economies in China are more capital intensive and would have stronger impetus to re-territorialize and recreate urban space for the creative class (Wei, Zhou, Sun, & Lin, 2012; Zhou, Sun, Wei, & Lin, 2011). The gentrification variable constitutes a vital part of the “local buzz” to compete in a global market, and rent inflation and high rent-stress is inevitable as a result of the neoliberalism and uneven development process. Third, we selected three social indicators to explain the spatial un­ evenness of high rent-stress migrants. Conceptually, the migration and housing choice are the processes through which the households or in­ dividuals realize their desires for higher wage, improved public services (education and health care for example) and more satisfactory living environment. As listed in Table 1, the wage level, education service level and fixed asset investment are the three representative social indicators related to the “pull factors” of internal migration in China today (Fan, 2007). For the better endowed cities in this aspect, high rental housing demand and high rent level may put the low-wage migrants at a serious disadvantage compared with those inside the “growth coalition” and public housing schemes. The existing studies have proved that the higher housing expenses in high-wage areas, especially in the catchment area of good state schools (so-called Jiaoyufication phenomenon), and also in the places with new investments in the public infrastructure and real estate schemes (Wu, Zhang, & Waley, 2015; Yang & Chen, 2014). Fourth, in the perspective of housing supply structure and its policy making, we use two indicators: the tenants share in migrants and the per capita residential building investment in each city. Normally, one would expect that a high ratio of tenants in migrant population may feature prominently in a higher market demand and a greater policy influence on rental housing supply, thus possibly a higher rent stress in the cities. This demand, pricing and regulation relationship would be apparent because of the migrant workers’ exposure to the speculative rental markets due to hukou legacies in China. Accordingly, taking Beijing as an example, rent inflation (almost doubled from 2011 to 2016) has been an unexpected by-product of the recent “urban village” redevelopment and urban entrepreneurialism movements (Liu, 2019). One may predict a mitigated effect of residential investment on housing stress. However, migrant tenants as hikers or sojourners in the urbanization and growth process tend to be excluded from bargaining with the “growth coalition” in access to affordable housing. For this reason, the residential devel­ opment indicator may be positively correlated with the migrants’ rent stress.

inequality. These indices have both strengths and limitations. CV is known as a standardized measure of dispersion of a probability distri­ bution or frequency distribution, and is usually defined as the ratio of the standard deviation σ to the mean μ. The Gini coefficient was pro­ posed as early as 1912 to represent how far a country’s wealth/income distribution deviated from a totally equal distribution. The Gini coeffi­ cient has been the most widely used tool to plot and measure the inequality among values of a frequency distribution in the fields of economics, sociology, ecology, health science and other social sciences (e.g. Gini Coefficient of Education). The Gini coefficient has advantages as a measure of inequality, such as the value scale independence and population size independence——it does not matter how large the size of the economy or population of the country is, and it is comparable between different units with different population sizes. The Theil index is adopted to decompose the sources of regional inequality into different scales (Fan & Sun, 2008; Wei et al., 2017). The Theil index can identify the share which is attributable to the between-region component. In the geographical and regional science literature, the Theil index is commonly used to decompose the relative importance of the spatial dimension of inequality (see Liao & Wei, 2012; Novotny, 2007). But the Theil index’s result cannot directly compare populations with different sizes or structures (Wei et al., 2017). 3.3.2. Spatial autocorrelation Tobler (1970) in his First Law of Geography pointed out that geographical data is not independent but related to each other due to spatial interaction and spatial diffusion effects. The spatial autocorre­ lation, involving global and local spatial autocorrelation, is employed to explore the spatial clustering patterns of rent stress. The global spatial autocorrelation and the Global Moran’s I index were raised by the statistician Patrick Moran in 1948, to evaluate the similarity degree of the socio-economic indices of the neighboring or adjacent regions. Global Moran’s I index can merely identify the spatial pattern in general, but fails to show the specific location where the clustering is going on. The local indicator of spatial association (LISA) introduced by Anselin (1995) was adopted in this article to identify the specific location where the rent stress is taking on a certain spatial pattern. 3.3.3. Spatial regression models In order to find the sources of inequality in rent stress, it is necessary to understand the mechanisms behind the spatial differentiation of mi­ grants’ rental stress. Studies of regional inequality often adopt varieties of conventional regression methods, such as ordinary least squares (OLS), generalized method of moments (GMM), or maximum likelihood (ML). But in the event of which the spatial autocorrelation exists and unable to be eliminated, spatial regression models (SRM) would then be conducted. Two types of SRM can be produced, which is the spatial lag model and spatial error model. This research used both sets of the complementary regressions, in order to investigate directions of impact factors influencing the rent stress at the Chinese prefecture-level cities and above. The spatial lag model (SLM) was used to reflect the impact of spatial units on other near units in the whole region. The spatial lag model can be expressed in an equation as follows: yi ¼ σ

n X

Wi yj þ βxik þ μk þ εi ; εi � k:k:d 0; δ2

�

(1)

j¼1

where i ¼ 1, …, 329 denotes the spatial location of cities; yi is the dependent variable; xik (k ¼ 1, …, 10) is the ten independent variables, including City ranks, GDP, Non-agri, FDI, APS, Wage, Edu, FAI, Tenants and Residence; and β is the local regression parameters to be estimated. Wi is a diagonal weighting matrix. Spatial autocorrelation may exist among the variables. Given that the independent error term may impact on the spatial spillover effects that exist between geographic units, spatial autocorrelation with no

3.3. Methods 3.3.1. Regional inequality indices We first follow the multi-hierarchical and multi-scalar framework (Li et al., 2015; Wei et al., 2017) to investigate the regional inequality of migrants’ rent stress. Regional inequality indices such as coefficient of variation (CV), Gini and Theil coefficients are used to examine spatial 6

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independent error term may produce biased conclusions. The spatial error model (SEM) can solve this problem. SEM is generally based on the following model: yi ¼ βxik þ μk þ ϕi ; ϕi ¼ ρ

n X

�� � Wi ϕi þ εi ; εi � k:k:d 0; δ2

augment savings, rather than a place to take root. Fan (2011) drew attention to this utility-maximizing shelter consumption in the tempo­ rary and circular migration in China, including migrants’ slip house­ holds and under-consumption in housing markets. For this reason, unaffordable rents are not purely a low-income issue in China, but also institutionalized in the state-migrant relations today. China’s planned and formal city development with a limited tolerance towards infor­ mality is identified as a major housing challenge arising in the post-Mao era, especially in big cities with high incidences of city-branding movements (Liu, 2015, 2019). Table 2 presents the details and baseline information of spatial variance of migrants’ rent expenses, rent stress and high rent-stress ratio in three different city ranks, in four regions and three urban clusters. Migrants’ rent stress jumped from 14.5% in 2011 to 16.4% in 2016, with an annual growth rate of 2.5% (Table 2). Spatially, migrants’ rent stress was reported higher in northeastern China than it was in southeastern China. Until 2016, the rent-stress “hills” were growing ever more agglomerated in north and northeastern China such as Beijing, Chang­ chun and Dalian, while the “valleys” have mostly persisted in the south. Considering the higher wage level and rent affordability in south China, southeastern coastal areas would be more habitable and attractive to the workforce than North and northeastern China. A similar conclusion can be proved from the observed spatial variance between urban clusters, which indicates a higher rent-to-income ratio in the Bei­ jing–Tianjin–Hebei regions than that found in the Pearl and Yangtze River Deltas. This north-south divide in rent stress comes with no sur­ prise given that the high levels of rental unaffordability tend to be found in the cities and regions of north and northeastern China, particularly in the northern coastal regions like Liaoning and Hebei provinces and the hinterland of Shandong Peninsula, who have lagged behind their southern peers in terms of their market-oriented reform, stateownership transformation, and industrial efficiency upgrading (He & Wang, 2012; Yu & Wei, 2008). The above north-south gap in rent stress has been gradually strengthening in the current development trajectory of deindustrializa­ tion, alleviating excess capacity, and enhancing capital allocation effi­ ciency, due to the north and northeastern China’s lagging behind in this new trend. On the one hand, in the aftermath of the 2008 U.S. financial crisis and especially since the GDP growth slowdown in 2015, local governments’ push for investment for the sake of GDP growth and the subsequent overinvestment have worsened the overall efficiency of capital allocation (see Bai, Hsieh, & Song, 2016). Manufacturing and real estate accounted for the top two of economy-wide investment, above 30% and 20% respectively during the 2010s. Outdated industries, relatively low wages in north and northeastern China, together with real

(2)

i¼1

where ρ is the spatial autocorrelation coefficient of error term; and ϕ is the error term of the spatial autocorrelation. 4. Research result 4.1. Spatial inequality of migrants’ rent stress and its changes: 2011 vs. 2016 The migrants’ rent stress (rent-to-income ratio) across Chinese prefecture-level cities and above in 2011 and 2016 are demonstrated in Fig. 3. The striking contrast between Figs. 2b and 3b shows that the high rent-expense area does not overlap with high rent-stress ones. The eastern coast, surrounding Shanghai, Shenzhen and Zhuhai, does not exhibit the spatial concentration of high rent-stress indices any more from 2011 to 2016 (see Fig. 3). But in the northeast, north and west of China, high rent-stress has been clustered in the leading cities together with their surrounding areas. The complexity of rental housing supply and a greater variation of migrants’ consumption preference suggest why the first-tier cities in the coastal South of China (e.g. Shanghai, Guangzhou, Shenzhen) are not the tallest rent-stress peaks. First, mi­ grants’ average data masks the diversities in rent affordability stress when their incomes, skills, and higher- and lower-end rental sectors are taken into account. The Chinese metropolises have capacity to attract migrants and provide them with hybrid shelters, whereas Beijing, Shanghai, Guangzhou and Shenzhen are representative of this hybridity — basement renting (Rat Tribes), qunzu (co-renting) and penghuqu (shantytowns). Huang and Yi (2015) documented the popularity of basement renting, where hundreds of thousands of migrants lived in crowded and dark basements, as an invisible enclave underneath the Chinese modern city. Shen (2017) introduced another type of crowding for migrants in Shanghai, known as qunzu, whereby each bedroom of the apartment would be divided and leased to several migrant families separately. Migrants also turned to some other uninhabitable spaces such as self-help and informal housing that is built in crowded penghuqu dated back to an agglomeration of refugee or migrants in 19th century in Shanghai (Chan & Li, 2016). Migrants’ housing choices are diversified but insecure, and there is thus a levelling-off or decline in migrants’ rent stress, especially when migrants see the city as a place to earn wages and

Fig. 3. Spatial distribution of migrants’ rent stress in China at prefecture level and above. 7

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Table 2 Spatial variance of migrants’ rent stress in city ranks, main regions and urban clusters (2011 & 2016). Migrants’ rent expenses (yuan)

Migrants’ rent-to-income ratio (%)

High rent-stress ratio in migrants (%)

2011

2016

Change

2011

2016

Change

2011

2016

Change

City Ranks Centrally administrated Beijing Shanghai Tianjin Chongqing Vice provincial-level cities Changchun Nanjing Shenyang Hangzhou Jinan Wuhan Guangzhou Chengdu Xi’an Ningbo Xiamen Qingdao Shenzhen Dalian Prefectural level cities

904 1358 1062 948 554 686 699 636 505 636 470 672 718 630 734 716 523 616 809 679 478

1729 2635 1874 1258 845 1155 1123 1316 955 1114 1184 1390 1078 753 1154 1117 905 952 1318 938 882.4

13.8% 14.2% 12.0% 5.8% 8.8% 11.0% 9.9% 15.7% 13.6% 11.9% 20.3% 15.6% 8.5% 3.6% 9.5% 9.3% 11.6% 9.1% 10.3% 6.7% 13.0%

20.3 24.2 19.9 23.6 17.0 16.5 20.4 15.1 18.5 14.6 14.7 18.2 16.3 17.6 19.3 14.4 11.4 16.9 15.5 19.9 13.7

20.2 25.0 19.0 19.5 15.5 16.3 22.7 17.9 18.4 15.1 17.5 19.3 15.3 13.6 19.0 13.9 10.9 14.4 14.8 20.4 15.8

0.1% 0.7% 0.9% 3.7% 1.8% 0.2% 2.2% 3.5% 0.1% 0.7% 3.5% 1.2% 1.3% 5.0% 0.3% 0.7% 0.9% 3.2% 0.9% 0.5% 2.9%

31.1 37.8 29.7 38.1 20.0 20.3 28.4 17.9 25.4 13.6 13.5 22.4 19.6 22.0 27.7 19.5 7.4 22.2 16.0 30.4 15.1

31.2 48.4 27.6 27.2 15.2 19.6 37.4 24.5 20.5 16.5 23.8 28.5 16.5 11.5 25.8 11.9 8.7 12.8 15.0 29.5 18.5

0.1% 5.1% 1.5% 6.5% 5.3% 0.7% 5.7% 6.5% 4.2% 3.9% 12.0% 4.9% 3.4% 12.2% 1.4% 9.4% 3.3% 10.4% 1.3% 0.6% 4.1%

Zone Division Eastern China Central China Western China North-East China

557 566 492 516

1206 959 889 903

16.7% 11.1% 12.6% 11.8%

13.6 15.7 14.8 15.7

15.8 16.2 16.6 18.8

3.0% 0.6% 2.3% 3.7%

13.4 17.2 15.9 19.4

19.3 20.0 19.5 24.8

7.6% 3.1% 4.2% 5.0%

Urban Clusters Pearl River Delta Yangtze River Delta Beijing–Tianjin–Hebei

634 508 697

983 1235 1753

9.2% 19.4% 20.3%

13.7 11.8 17.2

12.6 15.6 21.7

1.7% 5.7% 4.8%

12.7 10.3 21.6

11.6 18.3 35.5

1.8% 12.2% 10.4%

National Average

533

1022

13.9%

14.5

16.4

2.5%

15.4

18.9

4.2%

Note: The annual growth rate is calculated in this way: Geometric growth rate ¼ exp [ln (Last Year Value/First Year Value)/5]

estate fever, have led to the negative consequence of high rent stress in the rustbelt. On the other hand, in Lin and Zhang’s (2014) theorizing and modeling on the emerging spaces of neoliberal urbanism in China, it is found that the importance of land sales as a means of municipal finance would wither as the level of urban economic growth improves and its tax base augments. It offers more explanations about the reason why the rent stress is more severe in the rustbelt, than those that have witnessed an increasing role of the private sector in the regional economy rather than a receding share of it. What’s more, village co-ops without full legal titles are playing a significant role in providing affordable shelters to migrants. According to Hsing (2010), in the southern metropolitan areas of Guangdong, village housing made up over 20% of Guangzhou’s and 60% of Shenzhen’s planned areas, sheltering 80% of migrant workers who had flocked to these cities since the 1980s. The coverage of informal property in Beijing is apparently lower than that in South China. Their different tolerance to the “informalities” can interpret the 10-floor buildings in the urban village of Guangdong versus the two- or three-story at most in Beijing. For this reason, rent stress in South China would be levelled down by these urban villages. This explains why the rent stress and high rent stress ratio are kept at a low level, or even a slight declining trend in the Pearl River Delta in recent years (see Table 2). As shown in Table 2, the rent stress is apparently higher in the centrally administrated municipalities than that in the vice provincialand prefectural-level cities. Two findings have attracted our attention. First, several cities above the prefectural level such as Tianjin, Chongqing, Chengdu, Qingdao etc. have witnessed a conspicuous decline of rent stress between 2011 and 2016, owing to the pilot projects

1.

of rural homestead land transfers and the state-sponsored “talent apartments” as a new source of affordable rental housing: a) exchanging a homestead for an apartment in Tianjin (zhaijidi huanfang); b) the land ticket in Chongqing (dipiao); c) double abandonment in Chengdu (shuangfangqi); and d) various public rental housing or talent apartment schemes in these cities (Bandao, 2013; Huang, 2011; Kong, Liu, Jiang, Tian, & Zou, 2018). Second, Beijing was the peak of all the rent stress indices. A slight declining trend was observable in Shanghai between 2011 and 2016. Table 3 further compares the differentiated composition of migrants’ housing choice in urban neighborhoods in the top two megacities. Bei­ jing and Shanghai migrants have a similarly high ratio of renting private housing in the urban market (46.4% and 46.9% respectively), but Bei­ jing migrants lag behind in access to homeownership than Shanghai peers (29.5% and 42.1% respectively). This variance is clear: the greater barrier to own homes in Beijing pushes up the rental market. This pattern also holds for college graduates, since they have greater incen­ tive and power to pay for a quality life but only 41.6% of collegeeducated migrants in Beijing can own homes. Among college-educated migrants in Shanghai, 57.4% are homeowners, clearly doing better than their Beijing peers in the up-ward mobility and integration. The high homeownership ratio in Shanghai migrants from MDMS data is consistent with another data source from Fudan Yangtze River Delta Social Transformation Survey (FYRST), which was conducted by Fudan University in 2013 and published by Qian in 2019. As reported, the mild increasing of rentals in Shanghai was attributable to being far away from the financialization trends (Sina, 2018). Besides, as shown in Table 2, the spike in rent stress in Shanghai’s contiguous cities (Nanjing, Hang­ zhou, Suzhou, Kunshan etc.) can be seen as the spatial spillover effect 8

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Table 3 Migrants’ tenure choices in the urban neighborhoods in Beijing and Shanghai in 2016. City

Educational level

Beijing

Junior secondary and below Senior/technical secondary College and above Total Junior secondary and below Senior/technical secondary College and above Total

Shanghai

Housing tenure Private rental housing

Occupied housing

Provided by employers

Provided by governments

Others

Total

14.3% 10.2% 21.8% 46.4% 20.1% 11.2% 15.5% 46.9%

4.3% 5.0% 20.2% 29.5% 8.7% 7.8% 25.6% 42.1%

9.0% 4.6% 4.4% 18.0% 2.2% 1.0% 1.2% 4.4%

0.3% 0.0% 0.1% 0.4% 0.3% 0.3% 0.3% 0.9%

2.1% 1.5% 2.0% 5.7% 2.1% 1.7% 2.0% 5.8%

30.1% 21.4% 48.5% 100.0% 33.4% 22.0% 44.6% 100.0%

Note: This data refers merely to migrants living in the urban neighborhoods. If all the migrants are included, their homeownership ratio is only 25.9% in Shanghai, and 16.2% in Beijing.

that can cool down the overheated real estate market in Shanghai to a certain extent. Next, we follow the multi-scalar framework and employ CV, Gini and Theil coefficients to analyze the regional inequality of migrants’ rent stress in 2011 and 2016. Table 4 lists the indices for spatial inequalities of migrants’ rents stress at interregional, interprovincial and interprefectural scales. A trend of convergence at three geographical levels is observable between 2011 and 2016, implying a shrinking inequality of migrants’ rent stress across the different regions in the past several years. As listed in Table 2, migrants’ rent stress in the centrally administrated municipalities and vice provincial level cities kept at a relatively high and quite stable level (20.3%–20.2%, and 16.5%–16.3%, respectively). So did the index of high rent stress ratio (see Table 2). But the rent stress indices of the prefectural level cities had a substantial increase from 2011 to 2016, with an annual growth rate of 2.9% for migrants’ rent stress, and even higher at 4.1% for high rent stress ratio. Conceptually, the convergence of rent stress between the higherranked cities and the prefectural-level cities is rooted in the great restructuring away from traditional industrial economies to a more services-based post-industrial one. Although debate in the literature as to whether this economic shift causes regional income to converge or polarize, based on our empirical study, the great economic trans­ formation in China has reduced regional inequality of migrants’ rent stress across the multiple scales. First, it has increased the rent stress of migrant workers of lower-tier cities, which would question the old thesis arguing that the migrants pay less for necessities, especially housing, in the less developed regions. Second, it is also possible that a better skilled workforce has led to a higher affordability in more advanced areas. This convergence is worthy of greater scrutiny in future studies.

is used to explain the similarity and correlation between high rent-stress migrants of each city unit and its adjacent space units. As seen in Fig. 4, in 2011, HH-type distributions of high rent-stress migrants emerge in Beijing–Tianjin and Haikou, with an obvious agglomeration pattern in China’s top innovative hub and the inflationary real estate markets in the early 2010s. These areas show a large number of high rent-stress migrants, including their surrounding areas, thus forming a cluster pattern in the LISA map (Fig. 4b). In 2011, the prominent areas in HLtype were mainly distributed around other first-tiered cities and pro­ vincial capital cities like Shanghai, Shenzhen, Chongqing, Chengdu in Sichuan Province, Wuhan in Hubei Province, Zhengzhou in Henan Province, Urumchi in Xinjiang Uygur Autonomous Region, Xi’an in Shanxi Province, and Lanzhou in Gansu Province. When it came to 2016, high rent-stress migrants have become increasingly agglomerated in the “local buzz” with the favorable coastal locations and institutional advantages, such as Beijing, Yangtze River Delta (Shanghai, Suzhou and Hangzhou), and Pearl River Delta (Guangzhou, Shenzhen and Dongguan). On the one hand, these large coastal cities provide favorable conditions to develop technologyintensive industries, and have been the leaders of high-tech industry overall in China, representing its global city regions of Jing-Jin-Ji Re­ gion, the YRD and the PRD. According to the elaborations by Wei (2010 and 2012) and Zhou et al., (2011) on the regional technological dyna­ mism and its spatial organization in China, the above three global city regions are now playing a crucial role of extra-local connections and global production networks. What we found here is that China’s most important high-tech regions have witnessed the greatest agglomeration of high rent-stress migrant tenants. This is an inevitable result of the rapid spatial-sectoral upgrading in these high-tech hubs. The high rent-stress phenomenon has attested Harvey’s (1978) discourses on the “sectoral/geographical switching crisis”, — on one hand, through a switching of capital allocation from the heavy industrial sector to the tertiary sector, especially high-tech and real estate industries; and on the other hand, through the switching of capital flow to more strategic central locations like the three most elite high-tech hubs. Urban space has been reproduced by their respective positions in the global hierarchy

4.2. Tempo-spatiality of high rent-stress migrants and impact factors: 2011 vs. 2016 Moreover, we narrow down the target group from all migrant la­ borers in urban China to the high-stress ones who spend one-quarter of their income on housing. The local indicator of spatial association (LISA) Table 4 Multi-scalar regional inequalities of migrants’ rent stress (2011 vs. 2016). CV Mean (SD)

Gini Range Min

Interregional level 2011 0.997 (0.035) 2016 0.756 (0.086) Interprovincial level 2011 0.972 (0.238) 2016 0.760 (0.123) Inter-prefectural level 2011 0.880 (0.339) 2016 0.734 (0.231)

Theil

Mean (SD)

Range

Max

Mean (SD)

Min

Max

Range Min

Max

0.959 0.623

1.041 0.865

0.410 (0.015) 0.368 (0.028)

0.390 0.320

0.425 0.388

0.305 (0.022) 0.232 (0.034)

0.271 0.175

0.326 0.265

0.591 0.562

1.615 1.093

0.405 (0.048) 0.366 (0.036)

0.319 0.300

0.524 0.428

0.300 (0.082) 0.232 (0.048)

0.167 0.153

0.537 0.316

0.259 0.190

3.583 1.864

0.392 (0.089) 0.352 (0.079)

0.141 0.090

0.679 0.563

0.294 (0.162) 0.229 (0.107)

0.031 0.014

1.513 0.702

9

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Fig. 4. LISA cluster map of the high rent-stress migrants in China at prefecture level and above.

and by their local capacities to mediate the multiple needs of land (Zhou et al., 2011). Rent uplift is thus unavoidable in this context. On the other hand, the industrial upgrading also represents a move away from the labor-intensive era and a shift towards new spatial practices, including a) industrial automation and “replacing humans with machines” initiatives in Dongguan; b) “easing of non-capital function” and key urban village redevelopment in Beijing; c) Master Plan of Urban Village in Shenzhen and its emerging green urbanism et al. (Liu, 2019; Ng, 2017; Sharif & Huang, 2019). These spatial projects can boost land use efficiency and productivity, but they also raise problems with the displacement of migrant tenants. It is thus further proved that the internal migration and rent stress of migrants are actually a part of the “circuits of capital”, and also a spatial manifesta­ tion of uneven wealth distribution among different groups and regions. Next, we will look into the sources of spatial inequality of the high rent-stress migrants in 2016. The global Moran’s I index of 0.293 is highly significant (p < 0.001), thus indicating a strong spatial autocor­ relation of the residuals. GeoDa also reports the OLS residuals as follows: a) the sum of squared residual which is also abbreviated SSR or SSE (470546); b) the residual variance (σ2 ¼ 1421.59); and c) the standard error estimate (37.704). Lagrange Multiplier (LM) test statistics are then used to choose which spatial regression (spatial lag or spatial error) would be better. Only the significance of the LM estimation of the spatial lag model is reported below 0.05 (P ¼ 0.004) indicating the presence of spatial dependence. We proceed to do the further Robust LM tests, which

are both significant for lag and error models, but the value of spatial lag model (24.059) is reported higher than that of the spatial error model (16.991). The spatial lag model would be a better choice for spatial regression analysis. As tested here, OLS regression cannot account for the spatial interaction between city units, but the spatial regression (spatial error and lag models) can reveal the spatial autocorrelation and the spatial lag model is proved a better choice. Given this advantage, spatial regression analysis is carried out, and the comparative results of three regression analyses (OLS, spatial error and spatial lag models) are reported in Table 5 to investigate the factors influencing the spatial heterogeneity of the high rent-stress migrants, and their implications for further study. It is found that the data is a better fit when using spatial regression than an OLS regression, and that the spatial lag model is more suitable than the spatial error model here (see higher Adjusted R2 and Log likelihood, lower AIC, as reported in Table 5). Results of the spatial lag model indicate that the indicators City rank (centrally administrated municipality and sub-provincial level cities as dummy variable), APS, FAI, Tenants and Residence are positively and significantly influencing the high rent-stress migrants at the prefecture level and above in China. FDI and Wage are the positive and marginally significant indicators. Non-agri is identified as a significant but an inhibitory factor on high rent-stress migrants, while GDP and Edu are the insignificant indicators here. Taken together, the above spatial regression results reveal the main impact factors on the high rent-stress migrants. 10

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better endowed cities have suffered from the heaviest rent unafford­ ability, partially due to such an affordability-based membership politics in the competition for scarce urban amenities (Liu, 2019). The indicator of education service level (Edu) is not significant in the spatial lag model, but marginally significant in the spatial error model (Table 5), indicating a relatively strong power of scarce public resources to predict the clustering of high rent-stress migrants. More specifically, the eligi­ bility to access high-quality educational public goods in the first-tier cities depends on whether one can afford the catchment area of the school or not. Third, the housing supply structure, involving the tenants share in migrants and the per capita residential building investment, also has significant power to explain the spatiality of high rent-stress migrants. The results have verified the hypothesis we originally made. The greater share of leasehold in migrants’ tenure choice would incur a higher de­ mand in the rental housing market, and therefore this is basically a seller’s market in China today. Residential investment is also proven to be a significant and positive indicator for the agglomeration of high rentstress migrants. Neoliberal housing policies, together with the local governments’ upgrading initiatives as a way of avoiding migrant ex­ plosion, have actually shrunk the migrants’ bargaining space in the rental housing market. There is still a long way ahead to inspire the housing rights movement like Latin America to restructure the tradi­ tional relationship between the immigrants and city government (see Ananya & Nezar, 2004; Huang, 2012).

Table 5 Regression results for 2016 high rent-stress migrants in China at prefecture level and above. Coefficients

Estimate

Standard error

t-value

Pr(>|t|)

OLS regression Intercept 1.04 5.40 0.19 Provincial level cities 120.96 48.89 2.47 Sub-provincial level cities 31.07 14.62 2.13 GDP 0.02 0.02 0.82 Non-agri 39.64 14.48 2.74 FDI 3.32 2.06 1.61 APS 0.42 0.19 2.15 Wage 0.0004 0.0002 1.77 Edu 0.006 0.004 1.43 FAI 0.06 0.02 2.74 Tenants 19.74 9.05 2.18 Residence 75.86 27.77 2.73 2 Adjusted R : 0.65; p value: 0.00; Log likelihood: -1725.6; AIC: 3475.2

0.85 0.01 0.03 0.41 0.01 0.11 0.03 0.08 0.15 0.01 0.03 0.01

Spatial error model Intercept 3.35 5.53 0.61 Provincial level cities 102.11 48.33 2.11 Sub-provincial level cities 27.01 14.46 1.87 GDP 0.03 0.02 1.49 Non-agri 39.29 14.61 2.69 FDI 3.35 2.05 1.63 APS 0.34 0.19 1.78 Wage 0.0004 0.0002 1.98 Edu 0.007 0.004 1.67 FAI 0.06 0.02 2.72 Tenants 16.08 9.12 1.76 Residence 67.20 27.14 2.48 2 Adjusted R : 0.66; p value: 0.00; Log likelihood: -1724.68; AIC: 3473.36

0.54 0.03 0.06 0.14 0.01 0.10 0.07 0.05 0.09 0.01 0.08 0.01

Spatial lag model Intercept 3.02 5.45 0.55 Provincial level cities 117.75 47.67 2.47 Sub-provincial level cities 27.33 14.34 1.91 GDP 0.02 0.02 0.99 Non-agri 39.95 14.07 2.84 FDI 3.32 2.00 1.66 APS 0.40 0.19 2.12 Wage 0.0004 0.0002 1.76 Edu 0.006 0.004 1.46 FAI 0.06 0.02 2.78 Tenants 19.87 8.80 2.26 Residence 79.94 26.97 2.96 Adjusted R2: 0.66; p value: 0.00; Log likelihood: -1722.25; AIC: 3470.5

0.58 0.01 0.06 0.32 0.00 0.10 0.03 0.08 0.14 0.01 0.02 0.00

5. Conclusion In the current era of housing neoliberalization, rent stress and its unevenness have been widely recognized in the Western literature (Bunting et al., 2004; Fields & Uffer, 2016). But little was mentioned about the rent stress among the 250 million migrants in China today, among whom two-thirds were sheltered in the private rental housing sector of host cities. Given the importance of this topic, our paper ana­ lyses the dynamics of tempo-spatiality of high rent-stress cities and high rent-stress migrants at prefecture level and above in China. This research straddles the year 2011 as one marked by its GDP-centered growth paradigm, and the year 2016 when its “New Urbanization Strategy” just started, as a reorientation of urbanization with an emphasis on the human aspects. We follow the multi-hierarchical and multi-scalar framework and employ the CV, Gini and Theil indices to attest the spatial inequality of migrants’ rent affordability stress at the interregional, interprovincial and inter-prefectural scales in China. It is found that rent stress is apparently higher in the centrally administrated municipalities than that found in the vice provincial- and prefectural-level cities. Our study reveals a convergence of rent stress inequality at different geographical scales. Migrants’ rent stress in centrally administrated municipalities and vice provincial level cities kept at a relatively high and quite stable level, but rent stress of prefectural level cities had a substantial increase from 2011 to 2016. This explains why the rent stress indices tended to converge in recent years. An increasingly apparent north-south divide is found, too. Such a north-south gap in rent stress has been gradually strengthening in the current deindustrialization process and “excess capacity” alleviation movements, due to North China’s lagging behind in this new trend. The real estate fever and the “land-based municipal finance” phenomena have sharpened this rent-stress inequality in China’s rustbelt to some extent. Besides, agglomeration of high rentstress migrants in the “local buzz” in China (such as Beijing, Shanghai, Suzhou, Hangzhou, Guangzhou, Shenzhen and Dongguan) tells more about the potential rent-stress disputes in China’s sunbelt. This labourhousing relations and migrants’ disadvantage in collective bargaining for a more affordable shelter in the local level may not be producing the desired effects of social harmony, as articulated in the “New Urbaniza­ tion Strategy”. Ordinary least squares (OLS) and spatial regression models are then

Firstly, the economic factors, especially the openness and creativity of city economy, are exerting a strong and positive influence on the agglomeration of high rent-stress migrants. Such a housing inequality is derived from the power and interest relations among different social groups in the transitional China, quite similar to Sassen’s (2000) inter­ pretation upon the bifurcated labour markets in global cities, namely the labour-intensive, lowly-paid and unstable jobs at one end, and a growing sector of knowledge-intensive and well-paid jobs at the other. The housing unaffordability would be severe among disadvantaged migrants in the high-tech hubs and post-industrial consumption cities like Beijing, Shanghai-Suzhou, and Shenzhen-Dongguan. It is also found that, the high non-agricultural industry share in GDP would mitigate the high rent stress among migrants. The urbanization taking place at a higher level predicts a higher probability to address the housing unaffordability crisis. Second, representative social indicators involving wage level and fixed asset investment, have made the significant and positive impact on the clusters of high rent-stress migrants. The results have supported our earlier assumptions on higher rental housing demand, and thus heavier rent stress in the better endowed cities. In reality, rent stress is now getting legitimized in today’s mainstream “developmentalist” mind-set, and performing a means of stratifying the “deserving” and the “unde­ serving” groups, and the “legitimacy” and the “illegitimacy” of land-uses and amenity in the service of the local developmental interests. The 11

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employed to reveal the factors influencing the spatial heterogeneity of the high rent-stress migrants. It is found that the openness and creativity of city economy, and a better well-being (wage, education, infrastruc­ ture and real estate investment), as well as a higher tenant share and residential development, are all exerting a strong and positive influence on the agglomeration of high rent-stress migrants. These findings are particularly important for a better understanding of the migration—neoliberalism relation in China’s rustbelt and sunbelt, both witnessing a considerable urban inequality in areal development and social change (Logan et al., 2009). By exploring the rent-stress spatiality at the national level, we crit­ icize the existing property and planning system which has neglected the low-wage migrants’ rights to habitation and participation in the receiving cities. According to David Harvey’s (1978: 109) theory on the urbanization of capital, the state is playing a significant role in the or­ ganization of capital flows in its tertiary circuit, including preventing over-accumulation within its primary and secondary circuits of capital, and improving the migrant workers’ housing affordability in the host cities. Without a more profound reform, it would be difficult to address the long-lasting developmental challenges, including urban inequality and high rent-stress among migrants in the Chinese metropolises. There are several policy implications based on our study. First of all, Figs. 2b and 3 depict the rent-stress landscapes in China today. The firsttier cities which are the “peaks” in economic production, however, did not generate the tallest peaks in rent stress in a city unit. The tenurial hybridity and functional resilience in migrants’ housing supply in China can tell the reason why there is a levelling-off in migrants’ rent stress in big cities. For this reason, a limited tolerance of permanent slum for­ mation in image-building Chinese cities would be a key challenge to achieving “social harmony” in a migrant explosion era. Second, a more strategic welfare framework should be designed in service of migrants’ temporary and circular migration, including their preference for an under-consumption in shelter and an easier access to public resource in the receiving cities. What’s more, the exclusive public housing system should be open to the migrants, too. As demonstrated in Fig. 4, high rentstress migrants have become increasingly agglomerated in the “local buzz” and high-tech hubs (like Beijing, Shanghai, Suzhou, Hangzhou, Guangzhou, Shenzhen and Dongguan), where land use efficiency and productivity are boosted greatly but raise problems with the displace­ ment of migrant tenants in recent years. Our study further attests the geographical pattern of “neoliberal urbanism” in China today, which is actually a global process namely “planetary gentrification” beyond the local control.

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