**Abstract:** Recent policies in China have encouraged rural-urban circular migration and an "amphibious" and flexible status of settlement, reacting against the recent risks of economic fluctuation in cities. Rural land, as a form of insurance and welfare, can handle random hazards, and the new Land Management Law guarantees that rural migrants who settle in the city can maintain their rights to farmland, homesteads, and a collective income distribution. Existing studies have pointed out that homeland tenure can reduce migrants' urban settlement intentions (which is a self-reported subjective perception of city life). However, little is known about how the rural-urban circularity and rural tenure system (especially for those still holding hometown lands in the countryside) affect rural migrants' temporary urban settlements (especially for those preferring to stay in informal communities in the host city). The existing studies on the urban villages in China have focused only on the side of the receiving cities, but have rarely mentioned the other side of this process, focusing on migrants' rural land tenure issues in their hometowns. This study discusses the rationale of informality (the urban village) and attests to whether, and to what extent, rural migrants' retention of their hometown lands can affect their tenure security choices (urban village or not) in Chinese metropolises such as Beijing. Binary logistic regression was conducted and the data analysis proved that rural migrants who kept their hometown lands, compared to their land-loss counterparts, were more likely to live in a Beijing urban village. This displays the resilience and circularity of rural-urban migration in China, wherein the rural migrant households demonstrate the "micro-family economy", maintaining tenure security in their hometown and avoiding the dissipation of their family income in their destination. The Discussion and Conclusions sections of this paper refer to some policy implications related to maintaining the rural-urban dual system, protecting rural migrant land rights, and beefing up the "opportunity structure" (including maintaining the low-rent areas in metropolises such as Beijing) in the 14th Five Year Plan period.

**Keywords:** rural land tenure; resilience; circular migration; rural-urban migrants; Beijing; urban village

#### **1. Introduction**

The "urban village" has become a hot topic in relation to informal settlements in rapidly expanding metropolises in developing countries, including those in the Chinese context [1,2]. The "urban village" (namely, *chengzhongcun*) is, in the context of land use and urban planning provisos, incompatible with official norms, and its participants suffer from substandard housing, inferior quality in the built environment, and a lack of facilities and infrastructure. It is acknowledged that the "urban village" has reduced the cost of urbanization and facilitated China's rural-to-urban migration in the past four decades, by acting as a "transitory shelter", providing cheap rental housing for low-wage migrant workers and creating rental income for local landless peasants. Urban villages are rural patches of land scattered in the city area, surrounded by or adjacent to urbanized and developed landscapes [1,2]. The rapidly industrializing and urbanizing areas of developing countries, including those in China, are faced with similar challenges concerning the

**Citation:** Liu, R.; Jia, Y. Resilience and Circularity: Revisiting the Role of Urban Village in Rural-Urban Migration in Beijing, China. *Land* **2021**, *10*, 1284. https://doi.org/ 10.3390/land10121284

Academic Editor: Hualou Long

Received: 29 September 2021 Accepted: 20 November 2021 Published: 23 November 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

inadequate supply of public housing for migrants, such as a shortfalls in government budgets to fund public services and inequitable access to urban welfare. The use of informal habitats to house migrant workers and other low-wage earners is popular in these areas. The urban village has been referred to as a new way to plan/build mixed-use communities in a recent literature review [3].

However, on the national circular migration scale and from the perspective of migrant tenants, the existing interpretation of the urban village is fragmented into geographical and rural-urban relations. In China, land is state-owned and commodified in the cities. However, the rural land is collectively owned by the village committee or township government and cannot be traded in most circumstances (unless designated by the government for commercial uses). This dual rural-urban land system dates back to Mao's central planning system, when China's urbanization level lagged far behind the developing world and free labor migration was a forbidden area until the 1980s. In Mao's central planning system, the rural-urban schism was apparent, and the rural *hukou* was managed through collective ownership of rural land, run by the People's Communes and aiming at self-sufficiency. The *hukou* system is a residency permit system in China that has separated the rural population from the urban population, and the local population from the non-local population. The *hukou* system confers a wide range of welfare benefits on the urban population. Migrants maintain their non-local residency status and practically enjoy relatively few welfare benefits in the host city [4]. This rural and urban dualism has been challenged since market-oriented reforms and the gradual *hukou* reforms facilitated labor mobility. However, restrictions are still imposed on the usage and transactions regarding rural land or housing. In integrated rural-urban planning and governance processes that are top-down, the administrative redefinition of rural land to urban land through farmland expropriation and resident status conversion is the only legal way of "rural land urbanization" from a rural land status (that is, collectively owned by the village and uncommodifiable) to an urban one (that is stated-owned and able to be traded in the land market) [1]. To date, the leasing and sales of self-built housing on rural lands remain extralegal, according to the Land Management Law in China. The boundaries between rural and urban land use are the administrative, regulatory, and tenure divisions in China. China's urban-rural household registration system (*hukou*) and the inherited rural-urban schism since Mao's central planning system have artificially divided rural and urban areas throughout the country, forming the dual rural vs. urban social-economic structure and land-use management system [4–6].

Interestingly, rural migrants' choice of informal settlement (e.g., urban villages) goes beyond the boundaries between the dualistic rural-urban economic and land use structure. More specifically, rural migrants attach more importance to their hometown's tenurial security but they do not care much about whether their urban shelter is formal or informal in their host city. Urban villages (as extralegal housing clusters) are rampant in their host cities. Migrant tenants' "perceived tenurial security" is strong enough to support exchanges of informal property and has shaped a huge informal housing market in host cities. Migrant households demonstrate a "micro-family economy", keeping tenure security in their hometown and avoiding the dissipation of family income in destinations, and therefore choosing to stay in urban villages. According to Fan (2021), Asian urban-bound migration is part of the "split households" strategy to diversify the sources of family income [7,8]. We have taken the phrase "micro-level family economy" from previous studies on developing countries to highlight the role of the "family utility maximization strategy" to explain migrants' tenure choices [9,10]. Breadwinners enter cities to accumulate savings for their family, and city migrants still retain their village residency status, allowing them to continue to enjoy rights and shares to the farmland and homestead back home. The migrants are not left out in benefit shares from rural collective income distribution and compensations when their farmland is acquired and converted to other uses [11,12]. The Strategic Plan for Rural Revitalization (SPRR, 2018–2022) also encourages the amphibious status of rural-urban migration (i.e., migration between urban destinations and rural

origins, rather than settling in cities) in response to the recent risks of economic fluctuation. Rural migrants are free to go to a city or back to their hometown and thus become more adaptive to the uncertainties involved in urbanization [13]. One focus of the new Land Management Law is to improve the three-layer system of rural land rights by maintaining collectivity in ownership rights, ensuring contract rights, and making management/use rights transferrable. Rural migrants who settle in the city are also allowed to maintain their land rights to their farmland, homestead, and the collective income distribution that is attached to their rural residency status.

However, urban villages are regarded as informal habitats to house rural migrant workers and other low-wage earners. The destruction or maintenance of such habitats by the governments of migrant-receiving cities has become an issue of good or bad governance related to the city's imaging and high-tech, aesthetic, and greening pursuits [1]. This study examines the rationale behind the informality of the urban village structure and attests to whether, and to what extent, the keeping of hometown lands by rural migrants can affect their tenure security choices (urban village or not) in Chinese metropolises such as Beijing. We tentatively propose the assumption that rural migrants who keep hometown lands are more likely to choose informality in their urban destinations. The following sections examine (a) the role of urban villages in rural-urban circular migration in China; (b) empirical studies and statistical analysis on the tenure choices of rural migrants in Beijing; and (c) the policy implications from the discussion of the rural-urban continuum in a circular migration context.

#### **2. Informality or Resilience? The Linkage between Urban Villages and Migration**

#### *2.1. Urban Village as Informality from a Dualistic Rural vs. Urban Land Use Perspective*

Unlike the Western community development plan for urban villages to preserve local neighborhood atmosphere [14], the urban village in China is not built to revive the nostalgic dreams and positive aspects of traditional village life in the city. The urban village is normally interpreted as a transitory shelter for low-wage migrant workers in an urbanizing China. Given migrants' low wages and lack of access to subsidized public housing, the low-cost informal rental housing sector of the urban village has surfaced to support the large demand among these migrants [15–18]. Similarly to India and Southeast Asian countries, the urban village in China refers to informal settlements in expanding metropolises [19]. The urban village is seen as a pathology of the rapid urban expansion in an urbanizing Asia and has negative connotations, including low-quality informal tenements, poor infrastructure and facilities, overcrowding, and a lack of planning regulations and management. The city governments in Beijing, Guangzhou, and Shenzhen have low tolerance for dilapidated illegal constructions on rural land and have taken action to clear up the urban village housing that has sheltered migrant tenants.

Is the urban village a disordered and unregulated informal settlement that is foreign to the planned city? The recent comparative studies on this bottom-up urban environment by Oostrum (2021) have proved that urban villages do not develop haphazardly; instead, their transformation of density, mixing, and access is adaptive and interdependent [19]. As discussed by Liu et al. (2020) in their study on the peri-urban mosaic formation, it is fragmentary and piecemeal land acquisition that produces mosaic-like urban villages—an land process that is informal in nature through the lens of the rural-cum-urban land development process (instead of a dualistic rural-versus-urban land-use conversion process) [20]. According to Oostrum (2021), the urban village in Asia demonstrates recurring patterns in a spatial configuration that embody urban design principles and are attuned to residents' needs. The urban village is not an example of pure informality that transgresses land use regulations and building codes [19].

Existing studies have also explained the reason why urban villages are deemed informal in the land management system. Ambiguous land ownership in rural villages is the main reason for the persistence of informality in a rapidly urbanizing China [21]. Differing from other developing countries, the urban village in China grows from a distinct

rural-urban dual land system, wherein urban land is state-owned and land-use rights are transferred by city governments, whereas rural land is owned collectively by farmers while being unalienable [20]. Rural land is used exclusively within agricultural communities, and cannot be converted into urban uses without going through state-initiated rural land expropriation procedures. Tenements built on rural land for migrants' transitory lodgings are therefore illegal. It has been proven that urban villages are the outcome of grassroots land speculation and rentiers, under the impact of market forces and state regulation. Informal land uses can create tensions between governments, real estate developers, and local communities over land interests, and between intra-community neighbors and family members over issues of land ownership and distribution [22]. In urban villages under the rural-urban dual land system in China, the perceived tenurial security is, however, strong enough to support exchanges of informal property and shape a huge informal housing market for migrant tenants [23,24]. Perceived tenure security has been proven to be important, affecting the supply and pricing of urban village housing [24,25].

#### *2.2. Urban Village as Resilience from a Rural-Urban Circular Migration Perspective*

The online Oxford dictionary defines resilience as "the ability of a substance or object to spring back into shape" or "the capacity to recover quickly from difficulties". In this study, resilience is interpreted as the multiple-choice capacity to sustain rural-urban migration in the face of stress and adversity in everyday city life (such as economic risks and uncertainties, poverty, institutional barriers, housing unaffordability, and public housing inaccessibility). Low-rent urban village housing can provide more housing choices for low-wage migrants and beef up the resilience of migrants to city life (such as the migrants' ability to adapt successfully in the face of stress in the city). Informal means are devised to settle down and serve low-income rural migrants, at a low cost.

The access of migrant workers to the urban village, however, is not only restricted in terms of affordability, but also on grounds of legality, as the urban village in the great metropolises has been "cleared-up" and rebuilt into commercial areas, high-tech industrial parks, and talent apartments to curb the "urban sprawl" and intensify land-use efficiency [1]. However, can formalization address the urban village phenomenon, if the key to the issue of informal housing on rural lands is undefined property rights over rural land and houses? We have underestimated the demand side, which is the migrants' tenurial choices between village homes and city places. Existing studies on the spatiality and informality of the urban village, however, have failed to prove the feasibility of "amphibious warfare" for rural-urban migrants, straddling their family "utility maximization" rationalities of (a) hometown tenure security and (b) thrifty consumption of urban housing, taking the form of informal settlement to avoid the dissipation of the family income. The amphibious status of rural migrants and their preference for hometown tenure security cannot be underestimated.

C. Cindy Fan (2021) conducted a series of surveys on the "split households" arrangement in receiving places and sending places for rural-urban migration and proved the prevalence of circular migration in China, similar to that in Africa, Asia, and Latin America [7]. Circular migration can be a step towards permanent urban settlement, and also probably towards returning to one's hometown. Fan's surveys have given some proof of familial utility maximization strategies among rural migrants in China: (a) the couple migration may simply be a strategy to maximize household labor power devoted to their urban earnings, rather than a transition toward permanent settlement in the city; (b) migrants are not willing to spend their hard-earned income in cities (instead, settling in the crowded and cheap urban village housing), not because of unaffordability, but because they can save earnings as a remittance to rural homes; (c) young migrant workers continue to build large houses in their home villages to attract their potential brides and facilitate patrilocal residence; and (d) many migrants are unwilling to give up the rural *hukou* due to access and benefits that are tied to their rural *hukou*, including farming and housing land

and compensation for a land requisition that are considered increasingly more valuable than urban *hukou* [7,11–13,18,26].

Rather than attracting the rural population to buy urban housing, the recent policy in China has begun to guarantee that rural migrants are free to go forward to the city or back to their hometown, and is becoming more adaptive to the uncertainties in urbanization [13]. The 14th Five Year Plan has reaffirmed the importance of protecting the migrants' land rights to farmland, homestead, and a collective income distribution, which are attached to their rural residency status, even though some rural migrants have a permanent urban settlement. Return migration is indeed a component of the circular movement through which rural returnees are expected to overcome the economic risk and inefficient market in cities. Return migration is prevalent in the cities of developing countries with precarious job opportunities and insufficient social security systems [8].

We assume the urban village to be an urban issue, but it is also a part of the migration trajectory, bridging a whole rural tenure system between the village home and the city. We can hardly predict the outcome of the demolition of the urban village if we remain blind to this circular migration and "amphibious warfare" institution for rural-urban migrants. As elaborated by Wu et al. (2013), since the demolition of the urban village fails to tackle the root demand for unregulated living and working space, demolition only leads to "the replication of informality in more remote rural villages" and other kinds of neighborhoods [21]. Informality in China is not only created by the dual rural-urban land market and management system and by an under-provision of migrant housing, it has also become part of micro-migration strategies to maximize familial utility on the move. In this sense, a "transitory" stay in the urban village and circular migration is typical of many breadwinners who decide to avoid family income dissipation.

#### **3. Rural Migrants' Transitory Stays in Beijing Urban Villages**

#### *3.1. Research Area*

We tentatively proposed the assumption that rural migrants who keep their hometown lands are more likely to choose the informality of urban destinations. Data analysis on Beijing was conducted to attest to this hypothesis. Beijing was chosen for this empirical study because it is the largest destination for migrant workers in North China, and it is the city that enacts the strictest *hukou* control and bans on sales of rural land and housing in China. At the same time, Beijing has a strong desire to compete for a place in the global city roster, thus initiating city re-imaging movements and having a low tolerance for dilapidated illegal constructions, as shown in Figure 1 [27–30]. The scholars, however, regard informal housing as a model for "inclusive and pro-poor urbanization" in China [31], since it has reduced the cost of urbanization and facilitated China's rural-urban migration in the last four decades. According to Peter Ho's (2014, 2017) "credibility thesis", titling or formal registration of these informal housing systems may not matter from the perspective of migrant tenants [32–34]. Unlike De Soto's advocates of formal title, Ho and Sun (2021) pointed out that actors may feel perfectly secure without formal rights; and, on the contrary, formal rights may not improve people's access to credit [31]. Ho also pointed out some factors that can affect "perceived tenure security", such as the duration of occupation, the settlement's size, the level of service provision, the perception of past and current policies, the cohesion of community organization, and the employment opportunities. The urban village constitutes a credible and affordable alternative for rural-urban migrants, for whom urban formal rental housing is out of reach, or for whom social housing belongs to a closed-off arena due to the *hukou* system [31]. Migrant tenants' fear of eviction has been proven to be important to reduce the "perceived security of tenure" [35]. Howeven, even when faced with demolition and eviction, the urban village is still "partially credible", a better choice than "empty" institutions such as unaffordable formal rental housing or inaccessible social housing.

**Figure 1.** The 50 key urban villages identified for redevelopment in Beijing since 2010 (Source: the name list of the 50 key urban villages is from News. Focus [27]; field trip by authors).

To date, formalization has been the main top-down government approach to redeveloping urban villages and legalizing their property rights. Rural lands are either converted into state-owned lands for urban uses or become a "rural-urban integrative demonstration project" to house talented migrants on the preserved rural lands. As reflected by the demolition of the famous "Ant Tribe" Tangjialing Village (Figure 1b) for low-income IT workers' apartments in Zhongguancun Science Park, the informality and the everyday life of low-wage migrants were disrupted by the state-initiated formalization schemes, and some dislocated tenants moved to more remote villages in suburban Changping [21,35]. Beijing's urban village is being rebuilt into an urban community, but this is leading to the gentrification process, replacing low-wage migrant tenants [1,36]. As pointed out by Wu et al. (2013), the "beautification and modernization narrative" of urban village redevelopment is rarely balanced with the opportunity cost to the local economy of "removing the main source of low-cost homes" [21]. This particular contextual setting and policy application in Beijing has the potential to (a) inspire the new rural-urban nexus framework (not merely on the side of urban regeneration); (b) provide more temporary affordable housing in a transitional period of urbanization; (c) offer continuous feedback of housing needs in the loop of rural-urban circular migration; and (d) create rural-cum-urban mixed functions that are not dualistic anymore. The next section introduces the data source and analysis methods used to attest whether, and to what extent, rural migrants' keeping of their hometown lands can affect their tenure security choices (urban village or not) in Beijing.

#### *3.2. Data and Methods*

The urban village is not merely an informal urban destination, but also represents a strategy of migrant family economy based on principles of resilience and circularity. This study refers to residence status in the urban village, but does not consider the rent levels, which are full of uncertainties and vary with different tenure security on rural plots [24]. This section uses the data from the 2017 Migrant Dynamics Monitoring Survey (MDMS) to look into whether, and how, rural migrants' tenure status in their places of origin would influence their tenure security choices in their destinations. The data on migrants' stays in the urban village were derived from the 2017 Migrant Dynamics Monitoring Survey (MDMS), conducted by the National Health Commission of China. The eligible migrants

in this national survey were those who moved across a county (*xiàn*) or city (*shì*) boundary from their registered household and who had stayed in the current destination for more than one month. The data collected on the residential committee units were based on the probability proportional to size (PPS) sampling schemes from each sub-district and town in the city unit. In the PPS sampling, the probability of the selection for a sampling unit is directly proportional to a size measure.

In the total Beijing migrant samples, 2992 cases of the migrant labor force with rural origins were valid for statistical studies, and the remaining retired or unemployed data were discarded. The data demonstrate a male-dominated migration, because 55.8% were male; 37.1% of rural migrants were in the below-30 age group, 33.0% were in the 31–40 age group, and 29.9% were in the above-40 age group. Our focus was on the rural migrants' preference for the informal tenure type in the urban village, which could represent an integrative rural tenurial continuum, bridging their origins and destinations.

Logistic regression was used to analyze relationships between rural migrants' choices of the urban village in their destination or not (dichotomous dependent variable) and the explanatory variables. The explanatory variables included (a) migrant household profiles; (b) migratory status; (c) employment and housing expense stress in destinations; (d) landholding in rural origins (categorical and continuous independent variables). The logistic regression approach was used to combine these independent variables to estimate the probability that the migrants' transitory stay in the urban village would occur.

With a focus on the role of hometown rural tenurial status, we first consider the tenurial distribution of hometown farmland and homesteads: (a) holding both farmland and a homestead; (b) farmland only; (c) homestead only; and (d) land loss (neither farmland nor homestead). The migrants' tenure choices in their destinations were composed of two types: (a) urban village, and (b) others. It was hypothesized that, for migrants, the keeping of rural lands in their hometown could predict a higher odds of choosing an insecure housing tenure in the urban village of their destination. Binary logistic regression was conducted in SPSS, and the regression analysis results are listed below.

#### *3.3. Results*

According to the MDMS data, 49.5% of rural migrants in Beijing had chosen to live in the urban village, especially the inner- and outer-suburban villages. Of these, 50.5% chose to settle in the urban formal community in Beijing. As shown in Table 1, the majority of those in the urban village were tenants (99.3%), and migrant homeowners in the urban village held the long-term rent that was issued by the village committee or the township government [24]. In the urban formal community, 86.0% were tenants, 13.3% were private homeowners in the housing market, and 0.6% were homeowners of the governmentsubsidized housing. When living in the urban village, rural migrants can lower their housing expenses in Beijing but suffer from substandard housing and inferior quality in their built and living environment. When choosing the urban formal community, the land use and construction are compatible with the urban planning provisos and norms, but tenants are faced with high rent costs. According to He, even young professionals are burdened with higher housing costs and precarity in megacities such as Beijing and Shanghai in China, due to the financialization of rental housing [37].

Source: 2017 MDMS.

The binary logistic regression analysis demonstrated the significant impact of the keeping of hometown land on the likelihood that rural migrants would choose the urban village or not in their urban destinations. The full model contains four groups of predictors—household profiles, migratory status, employment and housing expense stress in destinations, and landholding in rural origins.

Table 2 shows the binary regression results. Omnibus tests of model coefficients indicate that the binary regression model is highly significant, *χ*<sup>2</sup> (43, N = 2,992) = 1019.732, *p* < 0.001, indicating that the model can distinguish between rural migrants who stay and do not stay in the Beijing urban village. The Hosmer–Lemeshow goodness of fit test (higher than 0.05) indicates that the model fits the data well, with no significant differences between the observational data and the forecast data. The Cox and Snell R-squared value (0.289) and the Nagelkerke R-squared value (0.385) indicate the amount of variation in the dependent variable explained by the model. However, these are described as pseudo-R-squared statistics, rather than the true R-squared values in the multiple regression output. The predicted correct percentage (74%) indicates how well the model can predict the correct category (choosing the urban village or not) for each case.

**Table 2.** Binary logistic regression of the migrants' choices of the Beijing urban village, 2017.

**Table 2.** *Cont.*

Note: Significant at \* 0.1; \*\* 0.05; \*\*\* 0.01 level.

Exp(*β*) values are the odds ratios (OR) for each of the explanatory variables, showing "the change in odds of being in one of the categories of the outcome when the value of a predictor increases by one unit" [38] (p. 461). The rural migrants with hometown lands, compared to their land-loss counterparts (ref), were more likely to live in the urban village (OR = 2.092 for farmland and homestead holders, 2.365 for mere homestead holders, and 1.936 for mere farmland holders), controlling for all other factors in the model. A rural homestead in a hometown is a more prominent pulling factor than farmland in regard to returning home eventually, since migrants holding homesteads (OR = 2.365) were more likely to choose a transitory stay in the urban village of their destinations than those merely holding farmland (OR = 1.936). It was evident that alleviating housing expense pressure is a strong motivation for staying in the urban village (OR = 0.003), as its regression coefficient was the strongest of all these explanatory variables (Table 2). Income level was listed in the top two of the coefficient values.

As shown in Table 2, in the explanatory factors concerning household profiles, only age, marriage, education, and income variables made a statistically significant contribution to the model. Rural migrants who were younger, married, less educated, and low earners were more likely to stay in the urban village, controlling for all other factors in the model. Migrants from the north-eastern and south-western region were 1.701 and 1.475 times more likely to stay in the Beijing urban village, compared to those from the more advanced East China (ref), when controlling for all other factors in the model. Those from south China were the least likely to choose the urban village. The place of origin matters, as south China is more advanced and affluent than most of the listed regions, and those from south China can afford to stay elsewhere. Additionally, second-generation rural migrant workers, compared to those from other backgrounds (ref), were (OR = 0.825) less likely to choose the urban village.

When it comes to employment in urban destinations, rural migrants who have other occupation types (such as freelance workers in different kinds of metropolitan industries), get involved in public services and state-owned sectors and assume the role of employers, and are less likely to stay in the urban village (compared to blue collar workers, especially those engaged in labor-intensive manufacturing and the private economy as stable employees). Migrants in capital-intensive manufacturing are more inclined to choose the urban village (OR = 1.775), compared to those in labor-intensive manufacturing (ref), due to the suburbanization of manufacturing clusters (such as electronic and equipment manufacturing) and the migrant workers' preference for the nearby villages in the suburb.

#### **4. Discussion: Revisiting a Synthesis between the Rural-Urban Dual System**

We performed exploratory regression analysis to obtain convincing statistical results about the relationship between the holding of hometown tenure and rural migrants' choices as to their housing tenure (informally residing in the urban village or not) in their destinations. Rural land status in rural origins was proven to exert a statistically significant contribution to the model. Those holding homesteads in their places of origin were most likely to choose the urban village in their destinations, displaying a hypothesized "microfamily economy", keeping tenure security in their hometown and avoiding the dissipation of their family income in their destinations. The assumed "permanent urban settlement" in the previous studies is questioned in this study, and doubts are therefore raised as to its underestimation of the "amphibious" and flexible status of rural-urban migration in transitional economies such as China. The "amphibious warfare" for rural-urban migrants includes (a) the hometown landholding in their rural places of origin, and (b) the multiplechoice of urban housing in their destinations (settlement or a transitory stay and the thrifty consumption of informal housing).

#### *4.1. Different Rural Land Institutions, Different Urbanization Paths in Developing Countries*

First, urban villages are regarded as informal settlements built on rural lands in host cities. However, they are also a manifestation of China's unique urbanization path and rural land institutions. In the 1950s, Brazil began its agricultural modernization at the expense of small-scale farming, explaining why landless rural workers had to migrate to the rapidly industrializing cities that provided the only prospects for them [39]. Such squatting is, however, discouraged or even forbidden in urban China. This migration policy difference between different countries in the developing countries is derived from their disparate urbanization paths in different contexts.

Studies on China's rural-urban divide have highlighted a series of *hukou* policies against migrants' permanent stays in cities [40]. However, these have neglected the longlasting divide in the rural vs. urban welfare distribution system, which is a kind of "complementary balance" in the use of a rural vs. urban dualist land system to overcome the economic risks, if any, in the transitional period. As though situated in a rural-cum-urban "amphibious" status, migrants can access the labor and capital market for efficiency in urban destinations, and also ensure the continuity of their family and lineage, maintaining security in their rural places of origin. The importance of small-scale farming and the strong agricultural collective entity (in both origins and destinations) cannot be understated in a mobile and urbanizing Asia. In recent years in China, some harsher residency controls were enforced on lower-skilled migrant workers and illegal village constructions, and the urban villages were ended with demolitions. The non-*hukou* migrant tenants are neglected and become victims to some extent as a result of informal tenement demolition and soaring rent in the city. The urban village as the main source of low-rent housing cannot be understated.

The statistical analysis presented in Table 2 revealed some interesting findings. Secondgeneration rural migrant workers were found to be less likely to choose the urban village, compared to other backgrounds. This implies that second-generation rural migrant workers have a great desire to become urban settlers, and they can gain access to opportunities and resources (compared with others with rural origins). This assimilation process is important for rural-urban migration, as second-generation migrants tend to settle down in formal communities and gradually integrate into the receiving society. In addition, the blue collar workers among rural migrants (especially those in capital-intensive manufacturing jobs) are more inclined to choose the urban village. The suburbanization of manufacturing in the cities in China has created the residential suburbanization of migrant workers. Some of them choose to live in the nearby villages in the suburb as a result. For instance, in the

China–Singapore Suzhou Industrial Park (SIP), low-skilled migrants live in the low-end rental housing market, including informal ones in the nearby villages [41].

#### *4.2. Maintaining the Rural-Urban Dual System and Improving the Opportunity Structure for "Amphibious" Migrant Workers*

There have been long-lasting academic debates on the "structural" power of China's rural-urban dual system on its migrant workers. Chan and Wei elaborated on the impacts of the rural-urban dual system in China on internal migration. They analyzed the mechanisms of the rural-urban dual system, and how it has helped to generate low-cost migrant labor for urban markets [4]. As argued earlier, rural migrants in China are embedded firmly in a mega-land institution: hometown lands as welfare and security, urban lands as a speculative market (if they become home-occupiers), and a consumptive use (as tenants), and the urban village as their affordable though transitory shelter. Chan and Wei's (2019) thesis on the rural-urban dual system recognized the asymmetry of the power structure, highlighting that the dual system provides the "opportunity for discrimination" against rural migrant workers [4]. On the contrary, Chen and Fan's (2016) thesis on the split household strategy for economic calculus ignored this structural asymmetry but highlighted the "opportunities for risk reduction" for microeconomic rationalities [12]. How can we interpret the divergence in the opinions of Chan and Fan?

As argued by Chan and Wei (2019), China's rural-urban dual system is a "mega dual system", different from the more limited and smaller dualistic structure in the Lewis model [4]. To fix the rural-urban gap and the plight of "split households" (such as migrant children and left-behind children), central governments in China enacted the National New Urbanization Plan (NUP, 2014–2020) and the Strategic Plan for Rural Revitalization (SPRR, 2018–2022) to grant more welfare benefits for migrants in urban destinations and more economic opportunities for returnees to their rural places of origin. However, the rural land system has not been removed from a transitional China, as reaffirmed in the new version of the 2021 Land Management Law in China.

However, policy-makers have neglected the fact that transitory stays and the need for cheap housing are still the primary concerns of migrants. The demolition of urban villages is validated according to security and public interest concerns, but this has changed the housing opportunity structure for low-wage migrants. It is an interesting question whether the peri-urban migrant enclaves will become regulatory areas, or if they will provide low-rent housing opportunities for risk reduction [35]. The more committed institutions of land use are still engaged in debate regarding how to regulate and serve these urban floaters within China's mega rural-urban dual system and development model.

#### **5. Conclusions**

In this study, we have revisited the concept of the urban village in metropolitan China, which does not represent pure informality in a destination, but it is also a part of a ruralurban circular migration trajectory, since rural migrants who keep their hometown lands are more likely to choose a transitory stay in an urban village. Migrants' straddling of their village homes and city living-places has been underestimated in previous studies and policy-making. To date, few resettlement sites have been provided for these dislocated migrant tenants.

We have also discussed the policy debates on the Chinese rural-urban dual system does the rural-urban dual system provide "opportunities for discrimination" (for investors and urban governments), or "opportunities for risk reduction" (for the migrant family economy)? Chinese governments are implementing macro policies (NUP and SPRR) that beef up the opportunity structure for "amphibious" migrant workers. However, there is still a long way ahead, regarding the questions of how to perverse low-rent housing areas for temporary migrant workers, and how to provide more public services for the new trends of the increasing "familization of migration".

Another implication in land use policy is the importance of a welfare-oriented land system. Optimal land use is deemed good as it can generate higher economic returns, but the value of resilience for social welfare and integration is underestimated. The demolition of urban villages is the result of long-term land-use conflicts between city governments, property developers, low-wage migrant workers, and other disadvantaged residents. In this study, resilience is conceived as a capacity that can develop through village-based stakeholder interactions to mediate the housing stress of migrants (e.g., the urban village). More research is needed to look into forms of community property and collective land ownership that are still robust in the 21st Century [42].

**Author Contributions:** Conceptualization and original draft preparation, R.L.; data curation, Y.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the grant from the National Natural Science Foundation of China (NSFC project number: 42071201, 41701188), and a grant from the Beijing Philosophy and Social Science Planning Program and Social Science Research Key Program of Beijing Municipal Commission of Education (project number: SZ202010028016).

**Data Availability Statement:** Data use of the 2017 Migrant Dynamics Monitoring Survey (MDMS) is supported and approved by the Migrant Population Service Center, the National Health Commission of China. https://www.chinaldrk.org.cn/wjw/#/home (accessed on 11 November 2021).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

#### *Article* **Quantification of Resilience Considering Different Migration Biographies: A Case Study of Pune, India**

**Ann-Christine Link 1, Yuanzao Zhu <sup>2</sup> and Raphael Karutz 3,\***

**Abstract:** Urbanization proceeds globally and is often driven by migration. Simultaneously, cities face severe exposure to environmental hazards such as floods and heatwaves posing threats to millions of urban households. Consequently, fostering urban households' resilience is imperative, yet often impeded by the lack of its accurate assessment. We developed a structural equation model to quantify households' resilience, considering their assets, housing, and health properties. Based on a household survey (*n* = 1872), we calculate the resilience of households in Pune, India with and without migration biography and compare different sub-groups. We further analyze how households are exposed to and affected by floods and heatwaves. Our results show that not migration as such but the type of migration, particularly, the residence zone at the migration destination (formal urban or slum) and migration origin (urban or rural) provide insights into households' resilience and affectedness by extreme weather events. While on average, migrants in our study have higher resilience than non-migrants, the sub-group of rural migrants living in slums score significantly lower than the respective non-migrant cohort. Further characteristics of the migration biography such as migration distance, time since arrival at the destination, and the reasons for migration contribute to households' resilience. Consequently, the opposing generalized notions in literature of migrants either as the least resilient group or as high performers, need to be overcome as our study shows that within one city, migrants are found both at the top and the bottom of the resilience range. Thus, we recommend that policymakers include migrants' biographies when assessing their resilience and when designing resilience improvement interventions to help the least resilient migrant groups more effectively.

**Keywords:** livelihood resilience; migration; urbanization; extreme events; India

#### **1. Introduction**

The megatrend of urbanization has largely become a phenomenon of the Global South [1]. The growth of urban centers is not only governed by natural population growth but rural to urban and urban to urban migration (For our analysis, we adopt IOM's general definition of migrants: "A person who moves away from his or her place of usual residence, whether within a country or across an international border, temporarily or permanently and for a variety of reasons" [2]). India is a prominent example of the wide practices of migration and associated urbanization. It features the world's largest emigration and domestic migration population [3], favored by persisting vertical and spatial inequality [4]. Simultaneously, India is among the countries most prone to natural disasters, placing an additional burden on these already stressed urban infrastructures [5]. Although there is little consensus on how environmental factors impact economic and socio-political crises and drive migration, changes in variabilities and anomalies of rainfall and rapid-onset disasters have been shown in the past to significantly impact migration [6].

**Citation:** Link, A.-C.; Zhu, Y.; Karutz, R. Quantification of Resilience Considering Different Migration Biographies: A Case Study of Pune, India. *Land* **2021**, *10*, 1134. https:// doi.org/10.3390/land10111134

Academic Editors: Stephan Bartke and Sigrun Kabisch

Received: 30 September 2021 Accepted: 22 October 2021 Published: 25 October 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Ultimately, diverse and complex systems of push-pull factors, including those associated with urbanization processes, are influencing domestic migration throughout India [7].

After decades of problematizing and attempting to reduce domestic migration [8], a more recent understanding of (domestic) mobility has been established where it is not seen as an outcome of failing governmental or developmental strategies but as a normal aspect of life [9]. Hence, migration can be conceptualized as a livelihood, adaptation, and resilience strategy to diverse external and internal stressors (political, social, economic, and environmental) to improve the situation of migrants and their families [10].

To ensure sustainable economic, social, and demographic growth of fast-growing cities in the face of stressors, households need to be resilient. By actively building the resilience of urban households, the negative impacts of extreme weather events can be alleviated. Effective strategies to increase resilience, however, require a detailed understanding of the status quo. As a highly context-specific concept, resilience needs to be well-conceptualized and, in a second step, quantified.

Resilience is a complex and contested concept with no universally agreed-upon definition [11]. First appearing in the ecological discourse, resilience was referred to as the magnitude of the disturbance that can be absorbed by a system before it redefines its structure [12]. Many other disciplines took up and expanded the concept since then. Particularly in the last decade, calls for a more integrated understanding of resilience have gained prominence, in particular from socio-political and economic perspectives [13–16].

For our analysis, we build on the concept of livelihood resilience, defined as the capacity of individuals to maintain and enhance their livelihood opportunities and wellbeing despite stressors [17]. Livelihood resilience is strongly linked to social resilience concepts, typically viewed as the "ability of human communities to withstand external shocks to their social infrastructures such as environmental variability or social, economic, and political upheaval." [18]. Livelihoods can be described as systems that comprise all capabilities, assets (material and social), and activities needed for living-making [19]. Consequently, livelihood resilience does not only describe the resources that people own but also the strategies that they develop to adapt, making livelihood resilience a dynamic approach to resilience.

People are viewed as the central actors in resilience, adaptation practice, and policy since it is the individuals themselves who are making a living, trying to meet their economic and social needs while coping and responding to diverse uncertainties and opportunities [20]. Furthermore, livelihood resilience acknowledges the importance of considering scale and uncertainty since livelihoods are actor-, place-, and context-specific [21]. Speranza et al. [17] developed an analytical framework where livelihood resilience is determined by an actor's buffer capacity, ability to self-organize, and capacity to learn, serving as the conceptual basis for our resilience model (RM).

The quantification of resilience is challenging due to the concept's nature since resilience is considered a latent variable that cannot be directly observed [16]. Most approaches to resilience quantification and modeling follow the understanding that households' or communities' resilience is shaped by socio-economic and organizational factors, which enable them to respond and adapt to stressors [11].

The Food and Agriculture Organization of the United Nations (FAO) pioneered the quantification of resilience, focusing on food shocks [22]. They conceptualize households' options for living making as defined by so-called pillars: access to social safety nets (food assistance, social security), basic services (water, healthcare, electricity), and assets (land, livestock, house). A household's resilience is described as the status and supply of all pillars over time and is linked to the pillar's stability [23]. Since 2008, the FAO developed a statistical model known as the resilience index and measurement analysis (RIMA) [24]. Based on factor analyses (FA) and structural equation models (SEM), RIMA produces a resilience capacity index (RCI) and a resilience structure matrix (RSM). The RCI identifies households most at risk and specifies the areas of resilience weaknesses whereas the RSM serves as a descriptive tool to explain how each resilience pillar relates to the resilience

capacity and how each observed variable correlates to its pillar [25,26]. RIMA has since evolved with RIMA-I building on mixed-effects models [27] and RIMA-II on multiple cause multiple indicator models (MIMIC) allowing for resilience predictions over time and the inclusion of indirect resilience such as speed of recovery or loss extent [28]. The different RIMA generations have been applied widely in various developing countries to either quantify current or predict future household resilience status to food shocks [27,29,30].

All RIMA approaches focus on resilience to food shocks, primarily targeting households in rural settings, and are hence not directly applicable to the purposes of this study regarding quantifying resilience in an urban setting. Even though the body of knowledge on urban resilience has been expanding rapidly [31], the majority of studies focus on urban areas in countries of the Global North and assess resilience at an aggregated (e.g., municipal), rather than household-scale [32]. Moreover, many urban RMs and indices require detailed time-series of socio-economic and biophysical data, which is often lacking in the Global South, especially at the household scale [33]. Examples of urban quantification indices include the socio-ecological index for flood events [34], the climate disaster resilience index [35], the urban resilience index [36], and the integrated resilience index [37].

To our knowledge, studies that quantify the resilience of migrant groups compared to non-migrant households from an urban destination perspective are lacking [38]. Previous studies set in an urban-migration context in India focus predominantly on the resilience to environmental stressors such as mountain hazards [39], climate change [40–43], and the combination of specific extreme events such as droughts, floods, heatwaves, and storm surges [44,45]. The methodological approaches of these studies range from primarily mixed-method designs based on interviews and household surveys [40,43,44] to literature reviews [39–42,44,46]. Rarely modelling approaches are pursued [41].

To that end, we address the aforementioned knowledge gaps by quantifying and comparing urban resilience of non-migrants and migrants at one of the main Indian migration destinations [47], namely the western-Indian city Pune while accounting for migration biographies. So far migration and resilience scholars have broadly highlighted that the resilience of migrants is related to the "circumstances of the journey" and "the reception in the receiving community" [48]. In this study, we investigate more deeply how migration biographies impact the resilience of migrants. We consider the following four specific migration characteristics to implement migrants' diverse socio-economic and cultural backgrounds as well as trajectories: time since arrival in Pune, reasons for migration, migration distance, and environmental exposure (flood, heat). The analyses are structured along six sub-groups based on current residence zone (formal urban, slum) and migration origin (non-migrants, urban origin, rural origin). This analysis is critical to unravelling the opposing generalized notions of migrants defined as possessing either extremely low [49] or extremely high resilience at the migration destination [50], and improve the targeting and effectiveness of resilience interventions.

#### **2. Materials and Methods**

#### *2.1. Study Site*

Pune Metropolitan Region (in the following: Pune) comprises Pune Municipal Corporation (PMC) and Pimpri Chinchwad Municipal Corporation (PCMC), as well as peri-urban areas. Pune has the eighth-largest metropolitan economy and the sixth-largest per capita income in India [51]. Known as the "new industrial and educational hub" with burgeoning manufacturing and IT industry as well as many renowned higher education facilities [52], the city has witnessed substantial population growth. Since the last decade, 50% of the population growth has been due to migration [53]. Pune district ranks second in Maharashtra's migration destinations with most of the arrivals coming from within Maharashtra (>80%) and many (>40%), even, from within the same district (Figure 1).

Located in the east of India and close to the Arabian Sea it is specifically prone to environmental stressors such as monsoonal floods and extreme heat [54,55]. Pune's households experienced three large flood events in the last three consecutive years (2018, 2019, and 2020), increasing in size and damage, involving dozens of casualties [56–58]. In these last three years, several heat warnings had to be issued during the summer months of April and May [59]. In 2019, Pune recorded 42.9 ◦C which is the highest temperature in 36 years [60].

**Figure 1.** Total number of Pune District migrants per state of origin in 64th NSSO round 2008 (*n* = 1438) [61].

#### *2.2. Data*

To build a reliable resilience model (RM), large and detailed data sets of relevant socio-economic and demographic variables are required. Therefore, our RM is based on a general sample of Pune's households (*n* = 1872), aiming at representativeness by randomly selecting households within pre-defined spatial and socio-economic clusters in central and peripheral Pune. The household survey (HHS) includes information on water and energy use, source and expenditure, housing conditions, income and food expenditure, and migration. The households were interviewed in the year 2020 based on an interview template (cf. SM data collection). About 30% of the households (*n* = 553) have a migration background. If available, migration information was gathered for the responder and partner. Response variables in the HHS are categorical, nominal, ordinal, interval, or text.

#### *2.3. Methods*

In this study, an RM based on socio-economic factors and the livelihood resilience concept was developed. It serves the purpose of quantifying and comparing the resilience of Pune migrants and non-migrants. Since resilience is a latent variable that cannot directly be observed, our RM is based on so-called pillars which are influenced by many other observable variables which contribute proportionally to their pillar. This structure makes our RM a hierarchical model (cf. SM, Figure S5). Due to the multidimensionality of resilience, this RM is specifically designed for the study region Pune to properly account for all relevant contexts.

The spreadsheet program Microsoft Excel 2019 was used to pre-process the HHS data. This step involved excluding all variables with more than 30% missing data. Variables directly linked to the household size such as income, space, or rooms were turned into per capita variables. Due to the lack of predictor variables in the HHS, RIMA based on a structural equation model (SEM) was pursued. However, contrary to Alinovi et al. [24], an iterative-inductive approach of determining principal components was chosen to avoid subjectivity and arbitrariness.

The selection of pillars and variables as well as the model building was implemented with the statistical program RStudio (cf. SM, R code). The variable and pillar selection was built on insights of the RIMA models [22,24,25,62], urban resilience models [34,36,37,63], statistical relationships (principal component analysis, PCA), and the criteria of only including variables with factor loading scores higher than 0.65 (cf. SM, model). A categorical PCA (PRINCALS) was applied not only to condense and scale the data because most of the HHS variables are nominal or ordinal while measured at different scales but also to produce weights for each variable, depending on the variation and covariation of the data matrix. Since PRINCALS was chosen to determine the principal components of the data set, continuous variables were categorized based on quartiles or quantiles where low values represent low performances and high variables represent high performances of the households.

This approach led to the determination of three foundation pillars: *assets, housing,* and *health*. The first pillar *assets* comprises of the variables; yearly income (per person, pp), years of education, and food–income ratio. The second pillar *housing* is composed of the variables; zone type, dwelling type, space (pp), and rooms (pp). The third pillar *health* encompasses the availability of flush toilets, electricity backup, number of cooling devices, and total water storage (Figure 2; cf. SI, Table S1).

**Figure 2.** Resilience model framework describing how resilience is calculated based on the three pillars: assets, housing, and health as well as their associated variables.

The weightings of each observable variable within the three pillars (RSM) are displayed using radar charts (cf. SM, Figure S7). The radar charts are valuable to understand the importance of each variable within a pillar. For the *assets* pillar, income has the highest influence, followed by the food–income ratio, and education. For the *housing* pillar, zone and dwelling contribute equally strongly, followed by space and rooms per capita. Then for the *health* pillar, flush toilets and electricity backup have the highest impact, followed by total cooling devices and total water storage (cf. SM, pillars).

In the next step, the object scores of the first component of each pillar were implemented into the SEM to explain the latent variable *resilience*. The path diagram of the SEM reveals that the three pillars, *assets* (0.78), *housing* (0.77), and *health* (0.69), contribute equally strongly to the latent variable resilience (cf. SM, Figure S6). All tests for the goodness of fit return satisfying results (CFI > 0.9, RMSEA < 0.08, and SMR < 0.08). The object scores of the SEM represent the resilience (RCI) of each household.

For more differentiated resilience comparisons, the results were organized using four migration characteristics: time since arrival, reasons for migration, migration distance, and environmental exposure (flood and heat exposure). For each migration characteristic, the results are presented in a matrix, employing six sub-groups based on the current residence zone (formal urban, slum) and migration origin (rural, urban; Figure 3). These are called in the following: non-migrant-urban, non-migrant-slum, urban-urban, urban-slum, ruralurban, and rural-slum.

**Figure 3.** Explanation of migration biography including four migration characteristics and six sub-groups from matrix.

In the following, the categorization and methodology of the migration characteristics are described in more depth. The *time since arrival* migration characteristic was organized into three classifications, namely ≤ 10, 10–20, and >20 years. The *reasons for migration* characteristic included in the HHS survey and this paper are work, living conditions, education, and family (cf. SM, Figure S1). The *migration origin* characteristic is structured using three classifications: same district (Pune), same state but other districts than Pune, and other states than Maharashtra. For the *flood exposure* characteristic, the sub-groups' resilience with and without flood experience based on the HHS was calculated. For the *temperature exposure* characteristic, we used moderate resolution imaging spectroradiometer (MODIS) satellite imagery on day surface temperature and normalized vegetation difference index (NDVI). The latter was included due to the moderating effects of vegetation on heat stress and other extreme events [64–66], expecting a positive correlation with households resilience. The remote sensing data was processed in the geoinformation software QGIS 3.10 and analyzed accordingly. The 90th percentile of day temperatures of each year was calculated. Then, the exceedance of this 90th percentile for each household was assessed based on its location and split into two categories, namely low (<7) and high heat exposure (≥7; cf. SI, migration characteristics). Additionally, the number of extreme heat exposures was interpolated to display spatial patterns in Pune (cf. SM, Figure S12). For the NDVI, an inclusion in the RM did not return any statistically significant results and was thus excluded (cf. SM, experiments with environmental data).

To test whether the sub-groups display significant resilience differences, suitable t-tests were applied (cf. S; data processing). The RCI results are displayed using kernel density estimations (kde) and a grid-cell map. In the kde, for comparison reasons, the average RCI of each sub-group is highlighted in form of a vertical line. In the grid-cell map (Figure 4), Pune's household resilience is displayed spatially using QGIS, applying a fishnet approach where the RCI values were averaged on 1000 m grids.

**Figure 4.** Resilience capacity index of Pune households using 1000 m grids. In the zoomed-in section (purple), slums are shown hatched in red. Dots correspond to individual households. For data security reasons, locations correspond to the next street crossing, not the exact building.

#### **3. Results**

The results of the RM are presented in increasing differentiation. We first illustrate the mean resilience score of the total sample spatially distributed across Pune. Second, we contrast the resilience of the six sub-groups with a specific focus on migrants and non-migrants. Third, we compare the resilience of the six sub-groups for each of the four migration characteristics to determine the role of migration biography on quantifying resilience.

#### *3.1. Overall Resilience Map*

The generated resilience map (Figure 4) shows that the north of Pune (Pimpri Chinchwad) and the west are associated with middle to high resilience, whereas the center, south, and east have mixed to low resilience. In general, resilience shows to be strongly associated with the residence zone since households living in formal urban areas tendentially have higher resilience than households living in slums. Additionally, small-scale spatial patterns become clear, with high-resilience areas often located next to low-resilience areas.

#### *3.2. Migrants vs. Non-Migrants*

The resilience of migrants and non-migrants is displayed using a kde (Figure 5). Comparing the resilience of migrants and non-migrants (Table 1), we see that on average, Pune's migrants are significantly more resilient than non-migrants (*p* < 0.05). The shape of the kde-curves is similar for both groups. However, the kde-curve of migrants is shifted to the right, indicating that they have fewer households with low resilience and more households with high resilience compared to non-migrants.

**Figure 5.** Kernel density estimation of RCI: migrants (urban, rural origin) and non-migrants.

**Table 1.** RCI and average object scores of 1st component of each RM pillar for migrants (rural, urban origin) and non-migrants.

When differentiating between migrants who moved to Pune from an urban origin and those that migrated from a rural origin, urban migrants are significantly more resilient (*p* < 0.001) than rural migrants. Moreover, rural migrants have an average RCI that is even lower than that of non-migrants (Figure 5, dotted lines).

The average object scores of the first component of each pillar, as well as the descriptive analysis of the observed variables underlying each pillar for migrants and non-migrants, support the results of our RM (Table 1).

Regarding the first pillar *assets*, fewer migrants are found in the lowest income category and more migrants are found in the highest income category. On average, migrants earn 39,000 Rs a year more than non-migrants, which represents a 19% higher income of migrants compared to non-migrants. The difference between urban migrants and non-migrants is even larger (115,000 Rs, 40% more). While the average educational attainment of the three focus groups is relatively similar (migrants: 12.40 years, urban migrants: 13.64 years, rural migrants: 11.54 years, and non-migrants: 12.53 years), urban migrants still have the highest educational attainment while rural migrants have the lowest. Rural migrants do not only have the highest share of illiterates but also the lowest share in the highest education category (cf. SM, Figure S8, Table S2).

For the second pillar *housing*, the proportion of migrants compared to non-migrants living in formal urban residence zones, associated with a middle to high standard of living, is greater (migrants: 84%, urban migrants: 92%, rural migrants: 77%, and non-migrants: 81%). Moreover, the numbers of rooms and space per capita are higher for migrants; however, again only due to the higher values of urban migrants (rooms: migrants (0.93), urban migrants (0.94), rural migrant (0.70), non-migrant (0.9); space: migrants (177 ft2), urban migrants (215 ft2), rural migrants (191 ft2), and non-migrants (163 ft2); cf. SM, Figure S9, Table S3).

The third pillar *health* provides a similar picture to the previous two pillars, except for the availability of electricity backup: It is the case again that migrants are scoring higher in all four categories *water storage* (migrants: 17,777 liters, urban migrants: 32,767 liters, rural migrants: 11,479 liters, and non-migrants: 11,580 liters), the number of total *cooling devices* (migrants: 1.3, urban migrants: 1.3, rural migrants: 1.4, and non-migrants: 1.2), percentage of households with *flush toilets* (migrants: 51%, urban migrants: 54%, rural migrants: 45%, and non-migrants: 39%), and percentage of households with *electricity backup* (migrants: 30%, urban migrants: 17%, rural migrants: 28%, and non-migrants: 24%). However, in the *electricity backup* category, the higher results of migrants are not due to the higher availability of electricity backups of urban migrants but rural migrants instead (cf. SM, Figure S10, Table S4).

When including the additional layer of complexity by considering residence zone and migration origin, significant differences between the six previously defined sub-groups become visible (Figure 6).

**Figure 6.** Kernel density estimation of RCI for the six analyzed sub-groups.

Slum residents have a significantly lower resilience compared to their formal urban counterparts (*p*-value < 0.001). Additionally, urban migrants have a higher resilience in both residence zones compared to rural migrants and non-migrants. Migrants are on average slightly more resilient than non-migrants in the formal urban category, which does not apply to the slum category where it is the other way around (Table 2).

#### *3.3. Intra-Migrant Comparions*

Literature and HHS data provide ample evidence for the high socio-economic diversity among different migrant groups within the city [67]. Therefore, in the next section, we contrast the resilience scores against four key characteristics of migration: (1) time since arrival, (2) reasons for migration, and (3) distance travelled from the migration origin. We further analyzed (4) how the exposure to extreme weather (flood, heat) at the migration destination correlates with the households' resilience. To allow for better comparison, we include non-migrants into the plots of the four migrant sub-groups.

#### 3.3.1. Time since Arrival

The resilience of migrants based on the following three categories is assessed: <10, 10–20, and >20 years since arrival in Pune. When classifying migrants based on their time since arrival in Pune, distinct differences between the sub-groups appear (Figure 7, Table 3). Compared to the non-migrant sub-groups, recently arrived migrants are in all cases more resilient, whereas those longest in Pune have lower resilience throughout the four sub-groups.

**Figure 7.** Resilience matrix displaying kernel density estimation of RCI of sub-groups based on the migration characteristic time since arrival.

**Table 3.** RCI of sub-groups based on the migration characteristic time since arrival (\* low sample size).

#### 3.3.2. Reasons for Migration

The following reasons for migration are considered in this analysis: work, living conditions, education, and family (Figure 8, Table 4). For the two sub-groups urbanurban and rural-urban, those who moved to Pune for education reasons have the highest resilience. In contrast, among both migrant groups living in slums (urban-slum and ruralslum), those who moved because of family reasons have the highest resilience. While most migrants moved to Pune for work reasons, their resilience tends to be one of the lowest, especially for migrants living in slums.

**Table 4.** RCI of sub-groups based on the migration characteristic reasons for migration (\* low sample size).

#### 3.3.3. Migration Distance

The resilience of migrants based on their migration distance is compared using the categories same district, same state but other districts, and other states (Figure 9, Table 5). For the urban-urban sub-group, migrants from other states have the highest resilience whereas rural-urban migrating from other states have the lowest resilience. For both subgroups of rural migrants, those who migrated shorter distances (same district, same state) have a higher resilience than migrants who migrated longer distances (other states).

**Figure 9.** Resilience matrix displaying kernel density estimation of RCI of sub-groups based on the migration characteristic migration distance.

**Table 5.** RCI sub-groups based on the migration characteristic migration distance (\* low sample size).

#### 3.3.4. Environmental Exposure: Flood and Heat

The resilience of migrants to environmental exposure is assessed based on (1) whether they experienced a flood event or not and (2) on their exposure to extreme heat events (high or low exposure) in the last three years in Pune.

Regarding floods, according to the HHS data (cf. SM, Figure S11), a higher share of migrants (5.6%) compared to non-migrants (2.1%) has been exposed to floods in the past. Again, it is especially the rural migrants who are prone to floods (rural migrants: 7.5%, urban migrants: 3.1%). Furthermore, the RM shows that flood exposure tends to reduce the resilience of migrants and non-migrants considerably, except for the urban-urban sub-group, where the opposite effect is found (Figure 10, Table 6).

**Figure 10.** Resilience matrix displaying kernel density estimation of RCI of sub-groups based on the migration characteristic environmental exposure, displaying flood exposure.

**Table 6.** RCI of sub-groups based on the migration characteristic environmental exposure, displaying flood exposure (\* low sample size).

Regarding heat stress, the interpolated extreme day surface temperature map of Pune shows distinct spatial and temporal differences (cf. SM, Figure S12) with heat islands as well as areas of low extreme temperature exposure. Overall, the west of Pune tends to experience more extreme day temperatures compared to the east.

A higher share of migrants (23%) compared to non-migrants (15%) experiences high heat exposure (cf. SM, Figure S13). Among migrants, it is rural migrants who are more prone to high heat exposure than urban migrants (rural migrants: 24%, urban migrants: 14%). In general, a higher heat exposure leads to a lower resilience except for the nonmigrant-urban sub-group where the opposite effect can be observed. Especially for ruralslum migrants, higher heat exposure is associated with a significantly lower resilience (*p* < 0.01) compared to low heat exposure (Figure 11, Table 7).

**Figure 11.** Resilience matrix displaying kernel density estimation of RCI of sub-groups based on the migration characteristic environmental exposure, displaying heat exposure.

**Table 7.** RCI of sub-groups based on the migration characteristic environmental exposure, displaying heat exposure (night; \* low sample size).

#### **4. Discussion**

The results of our RM show how the livelihood resilience of urban households is shaped by migration biographies. Several findings are of particular interest. *First*, resilience can vary greatly on small spatial scales, from neighborhood to neighborhood, which is linked with high differences in the availability of livelihood opportunities [68] and the nested, often informal built environment in cities such as Pune [69]. *Second*, comparing the total migration and non-migration cohorts, we noted a significantly higher resilience of those that have moved to Pune than those that were born in the city. On the one side, this finding is consistent with the comparisons of migrants and non-migrants by Revathy et al. [50] and Haan [70] but contrary to Foresight's [9] assessment of migrants to be a particularly vulnerable urban group. The prior found that migrants tend to perform well in urban labor markets mainly due to higher levels of education and advantageous age cohorts [71]. The latter argues that they often lack the human, social, or financial capital to protect themselves from the environmental risks in high-density settlements where they predominantly live. This apparent contradiction is reflected in our *third* main finding, showing that the high resilience of migrants is accounted for only by urban migrants while rural migrants have a slightly lower resilience than the non-migrant sub-group. Acknowledging this difference is critical, albeit often neglected in research and policy—as it points to the need to differentiate between migrant biographies.

The higher resilience of urban migrants compared to rural migrants is linked with higher levels in terms of income, education, residence zone, and housing quality which relates to research by Oberai [72] who presents evidence that the poor have a comparatively greater propensity to out-migrate from rural areas than the poor from urban areas. Additionally, according to Srivastava [73] nearly half of rural migrants are found at the bottom six consumption deciles and work predominantly as casual wage employed or as self-employed in the informal sector. As a result, they tend to have no civic identity and citizenship at the destination, limited access to housing and basic amenities, poor entitlements, deficient workplace conditions, and experience labor market discrimination [74]. Moreover, we found that rural migrants perform better compared to urban migrants in one of the input variables of the RM, namely in the availability of electricity backup. On the one side, this can be interpreted as a sign of increased resilience but also a sign for the higher necessity of having electricity backup since electricity is unreliable [75].

For the investigation of *the four migration characteristics*, we disaggregated the sample into six sub-groups displayed in the result matrices:

First, we found an inverse relationship between *time since arrival* at the destination and resilience for all migrant sub-groups. This contradicts the widespread assumption that migrants need time at their new migration destination to settle and establish themselves [76]. A possible explanation for this pattern may be past peaks of rural-urban migration to Pune (the last around the year 2000) due to severe droughts. These caused a major, unplanned migrant influx to Pune, especially to the city's slums (cf. SM, Figure S2). The HHS data suggests that more recently, the decision to leave the rural origin and move to the city may have been better planned and less displacement-driven (cf. SM, Figure S3). Nevertheless, Revi [77] highlights that climate change is expected to increase the occurrences and intensities of droughts in western India which may force more landless, small, and marginal farmers to migrate to the city's informal neighborhoods. These rural-urban migrants are specifically vulnerable as previously described since they tend to have low human and financial capital while predominantly living in slums which are associated with higher environmental risks.

Second, marked differences between the sub-groups are examined regarding the correlation between *reasons for migration* and resilience: formal urban migrants score highest when motivated by *education* and slum-based migrants when migrating due to *family* reasons. We see that the prior sub-group is dominated by young migrants with high financial capital that allows them to pursue further educational attainment (human capital). The importance of *family* for the latter sub-group's resilience supports the argument that networks through relationships of friendship and mutual origin are critical [78].

Third, comparing the effects of *migration distance* across the sub-groups, we find that while urban-urban migrants from other states feature the highest resilience, the exact opposite is the case for the rural-slum sub-group, where migrants from the same district have the highest resilience. The HHS (cf. SM, Figure S4) and evidence from literature suggest an inversely proportional relationship between migration flows and the distance between regions [79]. Possible earnings, distances, transportation costs, information on job opportunities, and psychic costs are weighed against each other. The higher financial and human capital of urban-urban migrants allows them to travel farther distances whereas rural migrants, especially those that live in the slums of Pune, have low financial and human capital and thus fewer means to travel long distances. This may serve as an explanation for these oppositional findings.

Fourth, considering Pune's increasing exposure to extreme weather events it is alarming to see how all migrant sub-groups have greater *environmental exposure* than the nonmigrant sub-groups, especially because exposure to floods or heat is generally correlated with lower resilience scores. It should be noted that the most resilient migrant group (urban-urban) is not significantly affected by extreme events. On the contrary, we see the paradoxical effect of higher resilience when exposed to floods. An explanation for this could be that they received compensation after the floods, irrespective of their losses, which may have been comparatively low. This, however, would need further investigation. Moreover, the rural-slum sub-group, on the other side, was severely affected both by floods and extreme heat, underlining its particularly high vulnerability. These results are supported by literature since rural-urban migrants tend to predominantly live-in slums which are linked with lower housing quality. Therefore, this sub-group's housing does not only provide less protection during extreme weather events such as strong winds or floods but is also destroyed more quickly and easily [49,80].

In summary, the subdivision of migrants in matrices by residence zone and migration origin yields two contrasting archetypical migrant groups in Pune: First, those that live in formal urban areas with urban migration origin, and second, those that live in slums with rural migration origins. On the one side, we find that in the first, most resilient group, the highest-scoring ones have moved recently to Pune and came from far (outside Maharashtra). This group was attracted by Pune's excellent education and the potentially good living conditions—the city has recently been ranked most livable in India by the Ministry of Housing and Urban Affairs [81]. On the other side, we find that those households have the lowest resilience who have moved from villages outside Maharashtra to Pune's slums long ago and came to find work in the city. Other, less clear-cut archetypes may be slum households that recently came from another city within Maharashtra for family or work reasons (relatively high resilience), or those settling in slum areas after leaving the village due to living conditions (relatively low resilience).

Furthermore, it needs to be highlighted that the livelihood resilience concept serves as an important input for the RM since it provides relevant insights into quantifying the resilience of different sub-groups with and without migration backgrounds. Its detailed conceptual and analytical framework makes it particularly suitable for translation into a quantitative model on urban migrants' resilience. Related concepts such as social resilience [18] or translocal social resilience [82], so far, either lack relevant aspects regarding the migration context or are too complex to be implemented in an RM [17].

Overall, the data shows how diverse and complex migration patterns and the associated household resilience are. While the clustering into stylized archetypes may help for a better understanding of qualitatively different groups, a categorization can never be absolute and should be treated with caution. Additional limitations of our RM include a limited sample size of the HHS, incomplete data on water and electricity access, quality, and quantity, as well as a lack of predictor variables. All the above resulted in both a limited number of pillars and prevented a quantification of indirect resilience. Moreover, two

specific methodological shortcomings of quantitative resilience approaches are recognized: first, a possible arbitrariness in the selection of input and predictor variables, and second, the challenges of accounting for the socio-cultural contexts in which resilience occurs. Furthermore, a statistical model can never capture the multi-facetted resilience complexity of urban households completely and we need to be cautious with causal inferences. Despite these limitations, we argue that our presented RM can provide valuable insights for policymakers to set and implement targeted resilience interventions.

#### **5. Conclusions**

This study shows that on average households with migration backgrounds are more resilient than households without. More importantly, we show that not migration, as such, but the migration biography matters. In particular, migrants' residence zone at the migration destination, migration origin, migration distance, time since arrival at the destination, and the reasons for migration strongly influence households' resilience and affectedness by extreme weather events. Only by including characteristics from the migration biographies, we detect the significant differences between rural and urban migrants and the impact of the residence zone, time since arrival or distance migrated on resilience. Our results thus paint a multi-faceted picture of Pune's migrant population which contradicts simplistic notions of migrants as socio-economic "high-performers" or "problem children."

The resilience of households in rapidly growing cities to environmental hazards is rising in importance: The exposed population is growing as well as the frequency and intensity of extreme weather events. Especially for emerging megacities across the world, efforts towards actively increasing urban household resilience are needed to allow for sustainable economic and demographic growth. While our results describe the situation in Pune, the approach can be applied to any other city facing large migration influx, urban informality, and natural hazards. This study provides valuable information for policymakers on the resilience structure of urban households with a specific focus on migrants, enhancing the targeting and effectiveness of resilience interventions. On the one side, our RM fosters the understanding of policymakers on the resilience of particular population groups and the reasons for it. On the other side, it can be used to estimate the resilience effects of specific proactive and reactive interventions.

Therefore, in the future, resilience research in Pune and other cities of the Global South should be continued using similar surveys to establish a time-series data set while addressing the previously established limitations. This can be achieved, for example, by increasing the sample size to decrease standard deviation and uncoverage bias. Additionally, expanding the comprehensives of the RM by including important insights from, for example, the social resilience and translocal social resilience concepts as well as environmental factors (vegetation) from remote sensing data could provide valuable insights into resilience dynamics. Moreover, future research should aim to implement a mixed-method design since the combination of quantitative and qualitative data allows for a more layered analysis at varying depths of meaning. The qualitative data could allow to test the accuracy of the RM results as well as reveal which additional parameters might have to be included in the RM.

Finally, we advocate that both policymakers and scientists account for migration biographies in assessments of resilience and adjacent concepts such as vulnerability and livability. The socio-economic and cultural properties are too diverse to justify treating migrants as one homogeneous group. Neglecting migration biographies can lead to the implementation of ineffective resilience interventions and an associated misallocation of scarce funds. Endeavors such as the RM presented in this work can lay the ground for sustaining the resilience of urban households in rapidly-growing cities despite the dual pressure of elevating urbanization and climate-change-induced hazards.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/land10111134/s1, Supplementary Material: 1. HHS, 1.1 Data Collection, 1.2 Reasons for Migration, 1.3 Descriptive Statistics, 2. Model, 2.1 Data Processing, 2.2 Structure, 2.3 Testing Resilience Interventions with Model, 2.4 Pillars, 2.4.1 Assets, 2.4.2 Housing, 2.4.3 Health, 3. Migration Characteristics, 3.1 Flood Exposure, 3.2 Heat Exposure, and 3.3 Experiments with environmental data. Figure S1: Reasons for migration categories used in HHS, Figure S2: (a) Yearly number of migrants in Pune and (b) yearly number of migrants in Pune's formal urban areas and slums (1924–2019) based on HHS, Figure S3: Share of Urban and Rural Migrants for years since arrival categories, Figure S4: Origin of migration of Pune migrants based on HHS, Figure S5: Path diagram of hierarchical Resilience Model based on assets (ass), housing (hos), health (hlt) where circles represent latent variables, rectangles observable variables, Figure S6: Path diagram of Structural Equation Model displaying how each pillar relates to resilience, Figure S7: Resilience Structure Matrix of three pillars assets, housing, and health, Figure S8: Figures of descriptive statistics of assets pillar (income, education, and food–income ratio) for migrant, urban migrant, rural migrant, and non-migrant, Figure S9: Figures of descriptive statistics of housing pillar (zone, dwelling, rooms, and space) for migrant, urban migrant, rural migrant, and non-migrant, Figure S10: Figures of descriptive statistics of *health* pillar (flush toilet, total water storage, total cooling devices, and electricity backup) for migrant, urban migrant, rural migrant, and non-migrant, Figure S11: Percentage of flood-affected households (migrant, urban migrant, rural migrant, and non-migrant), Figure S12: Interpolation of extreme temperature experiences per household (90th percentile), Figure S13: Heat exposure (low, middle, high) experienced by different focus groups (migrant, urban migrant, rural migrant, and non-migrant), Table S1: Categories and Indicators of the Resilience Model, Table S2: Tables of descriptive statistics of assets pillar (income, education, and food–income ratio) for migrant, urban migrant, rural migrant, and non-migrant (\* low sample size), Table S3: Tables of descriptive statistics of housing pillar (zone, dwelling, space, and rooms) for migrant, urban migrant, rural migrant, and non-migrant (\* low sample size), Table S4: Figures of descriptive statistics of health pillar (flush toilet, total water storage, total cooling devices, and electricity backup) for migrant, urban migrant, rural migrant, and non-migrant (\* low sample size).

**Author Contributions:** Conceptualization, A.-C.L.; Data curation, A.-C.L., Y.Z. and R.K.; Formal analysis, A.-C.L.; Investigation, A.-C.L.; Methodology, A.-C.L.; Software, A.-C.L.; Supervision, R.K.; Visualization, A.-C.L.; Writing—original draft, A.-C.L. and R.K.; Writing—review & editing, Y.Z. and R.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was conducted as part of the Belmont Forum Sustainable Urbanisation Global Initiative (SUGI)/Food-Water-Energy Nexus theme for which coordination was supported by the US National Science Foundation under grant ICER/EAR-1829999 to Stanford University. UFZ received funding from the Federal Ministry of Education and Research (BMBF) under grant 033WU002. Any opinions, findings, and conclusions, or recommendations expressed in this material do not necessarily reflect the views of the funding organizations.

**Institutional Review Board Statement:** Ethical review and approval were waived for this study. This study does not involve animals. The involvement of humans occurred during the survey data collection process. The data collection was conducted strictly in accordance with the European General Data Protection Regulation. Only non-sensitive personal data was collected and analyzed. All data has been treated confidentially.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study verbally. No written informed consent is available since the survey interviews were collected anonymously. All the interviews were conducted under the condition that verbal informed consent of the respondent was obtained.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy regulations with regards to the households survey results.

**Acknowledgments:** The authors are grateful to Christian Kuhlicke, Eberhard Rothfuß, and Sigrun Kabisch for their supervision and feedback. The authors would also like to extend their gratitude to Vishal Gaikwad and his team from the Gokhale Institute of Politics and Economics, Pune for data collection and cleaning, and two anonymous reviewers for helpful feedback.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**

#### *Article* **Spatial Diffusion of E-Commerce in China's Counties: Based on the Perspective of Regional Inequality**

**Fan Wang 1,2, Mingfeng Wang 1,2,\* and Shichen Yuan 1,2**

**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** In recent decades, China has been on a new journey toward a digital economy of which e-commerce accounts for a substantial proportion. Despite some controversy, the innovation diffusion hypothesis and efficiency hypothesis of online shopping have been tested in research on the urban– rural dual structure. However, research on the spatial diffusion model of online business is sparse. Based on the online business and online shopping index released by the Ali Research Institute, this article compares the spatial diffusion model of online shopping and online business in the core– periphery structure based on the inequality between the eastern and western regions of China. Our study suggests that online business trends are in line only with the innovation diffusion hypothesis, with marginal counties having lower levels of online business. Online shopping, on the other hand, is in line with the innovation diffusion hypothesis and partially with the efficiency hypothesis, with a higher index of online shopping in the core regions and some peripheral counties. The discrepancy in the spatial diffusion mode is due to the differences in aims and supporting elements between online business and online shopping. Apart from infrastructure, the diffusion of online business is largely constrained by the regional industrial base, while online shopping is influenced by income and savings levels, which is the main reason for the differences in the spatial diffusion of online business and online shopping. We argue that the diffusion of online business has not led to the ability to balance regional inequalities at the national scale, while online shopping has the potential to bridge core and peripheral disparities better than online businesses, not in terms of the ability to bridge economic disparities, but in terms of the potential to reduce spatial consumption inequalities and welfare gains.

**Keywords:** online business; online shopping; spatial diffusion model; innovation diffusion hypothesis; efficiency hypothesis; regional inequality

#### **1. Introduction**

Since the advent of information and communication technologies (ICTs), there have been ongoing debates about the spatio-temporal effects of the spatial expansion of the internet. The impact of the spread of the internet on the geographic location of economic activity has also been the subject of much scholarly attention [1,2]. In response to the technical characteristics of the internet, the theory of "time-space compression" was proposed [3]. Moreover, Cairncross [4] declared the "death of distance", and Friedman [5] stated that "the world is flat" in the internet era. It is indisputable that the application of the internet overcomes the friction caused by distance and reduces the communication, transportation and search costs for economic activities [6], and the development of networks creates decentralization in the core–periphery structure.

However, different views on the impact of the internet remain. In the urban–rural dichotomy, the central city is considered to be an important distribution area for internet technologies. Compared with urban areas, rural areas are obviously high-cost markets.

**Citation:** Wang, F.; Wang, M.; Yuan, S. Spatial Diffusion of E-Commerce in China's Counties: Based on the Perspective of Regional Inequality. *Land* **2021**, *10*, 1141. https://doi.org/ 10.3390/land10111141

Academic Editors: Stephan Bartke and Sigrun Kabisch

Received: 29 September 2021 Accepted: 22 October 2021 Published: 27 October 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Thus, investment in new technologies will continue to be made in urban regions, and this urban orientation has led to a gap in digital diffusion between urban and rural regions [7]. Furthermore, differences in social and economic characteristics, such as knowledge, skills, and income, have also led to social exclusion in the adoption of internet technologies [8]. Thus, the polarizing trend of the core area within the core–periphery structure of urban and rural regions has also been repeatedly emphasized in the adoption of the internet.

Research on the spatial expansion of the internet between urban and rural regions has focused on the commercial application of the internet—e-commerce—as a breakthrough. Anderson et al. [1] proposed two mutually targeted hypotheses regarding whether the adoption of online business might affect the spatial structure, namely, the innovation diffusion hypothesis, based on the innovation hypothesis, and the efficiency hypothesis, based on the demand hypothesis. Compared with residents in other regions, people living in urban regions tend to use e-commerce because of their higher education and internet skills [9]. However, the adoption of online shopping in remote areas and sparsely populated areas lacking stores is particularly valuable in overcoming spatial friction [2]. There has been an interesting debate as to whether e-commerce usage in rural areas will exceed that in urban areas [6].

Previous testing of the spatial diffusion hypothesis of e-commerce has focused on the municipal level. Two of Anderson's hypotheses on the spatial diffusion of online shopping are tested by comparing the differences in online shopping adoption between urban areas and suburban or rural regions. It is also common in research to equate e-commerce directly with online business or online shopping in order to study the differences between urban and rural areas, without clearly distinguishing between online business and online shopping. Our study contributes to the growing body of research on the diffusion of e-commerce by providing a different perspective for understanding the expansion of e-commerce at the national level. For example, we distinguish between online shopping and online business and extend the core–periphery structure from the urban scale to the regional scale.

This article takes mainland China as the research scale and counties, except for municipal districts, as the research unit. Compared with the city unit, the number of county units is greater, allowing the precise identification of more detailed features of diffusion. We adopt the core–periphery structure based on the regional inequality between the eastern and western regions of China. The eastern part of China is economically developed and technologically advanced, while the remote areas in the west have low population and economic density. There is a gap in technological adaptability between the western and eastern regions. Therefore, we adopt the core–periphery structure with the east as the core area and the west as the marginal area based on the socioeconomic gradients of different regions in China. Previous tests of the innovation diffusion hypothesis and efficiency hypothesis have mainly been based on the diffusion of online shopping, using regression methods to analyze the econometric correlation between online shopping and urban or rural areas as a way of testing the diffusion hypothesis. Starting from the two opposites of "retail" and "consumption" in e-commerce, this paper tests and compares the applicability of online retail and online shopping in the innovation diffusion hypothesis and efficiency hypothesis. In this process, we discard the regression method and return to the essence of diffusion by using the diffusion gradient layout characteristics to determine which hypothesis best explains the large-scale diffusion of e-commerce. The mechanisms underlying the differences in the spatial diffusion patterns between online business and online shopping are also analyzed.

The following section introduces the literature on the diffusion of the internet and the spatial diffusion of e-commerce. Our review focuses on the digital divide caused by internet diffusion, the mechanism of internet diffusion, and the spatial diffusion of e-commerce. Section 3 elaborates on the data sources. Section 4 compares the spatial diffusion model of online business and online shopping. Section 5 uses the ordinary least squares (OLS) method to analyze the diffusion mechanism of online shopping and online business in different samples. Conclusions are drawn and discussed in Section 6.

#### **2. Literature Review**

#### *2.1. Internet Diffusion and the Spatial Digital Divide*

The digital divide between regions plays a considerable role in the proliferation of ecommerce, and in particular, the layout of digital infrastructure can significantly influence the adopting of e-commerce in different regions. The term "digital divide" is used to describe the difference between the "haves" and "have-nots" of digital infrastructure, referring to the wide social and regional differences in people's capacity to access and adopt digital equipment and services as well as their capacity to access the internet in terms of physical connectivity and use of facilities [10,11]. OECD (2001) described the "digital divide" as the gap between groups of different socioeconomic levels in terms of access to ICTs and the use of the internet for various activities. The digital divide has also been described as disparities in internet access, usage, search strategies, social support, information evaluation, and user diversity [12].

To conceptualize the digital divide, the dichotomy of the digital divide between different scales was a valuable concept at the beginning of the development of the internet, and most of the policy discussions and research on telecommunications issues have focused on the issue of access to technical facilities [13–15]. However, as the digital infrastructure divide has narrowed, research in recent years has diversified this binary concept, and new intricacies have been recognized in internet-access disparities [16,17]. Access to ICT is not the same as adoption or, more importantly, effective use. As a result, statements such as redefining the digital divide and transcending access rights have emerged [18,19]. Numerous empirical studies have emphasized the importance of distinguishing between access and use, as having access to an internet infrastructure does not mean that people will use it [20]. The redefinition of the concept of the digital divide focuses on the inequality of internet use, which is referred to as the deepening digital divide [21,22]. Following discussions on adoption, the issue of digital usability has become the third level of the digital divide, where dissimilar skill levels lead to new disparities [23]. This third level of the digital divide is related to the knowledge gap, and therefore, a change in the concept of the digital divide is necessary. The digital divide is not only a technological issue, but also a social issue that reflects broader social, economic, cultural, and learning inequalities [24].

Apart from the conceptual issues of the digital divide, research on the inter-regional digital divide is more oriented toward spatial scale studies and different objects. The digital divide coexists in different dimensions and forms, including digital divisions at different spatial scales and with different socio-demographic characteristics, and they are also linked to the ownership of ICT. Unlike the divide that stems from technology, people in marginalized areas are also affected by their own social exclusion factors, such as geographic location and social characteristics. The existing social divide needs to be reproduced in studies of digital inequality, which is a more pressing issue than the digital infrastructure gap in marginalized areas.

Digital inequalities involving spatial scales, including between countries, between urban and rural areas, and within cities and villages, have been explored by scholars. Early studies of inequality on the internet dealt with the inequalities in connectivity between developed and developing countries, and how these differences might affect society [10]. Compared to individuals in suburban and urban areas, studies have found that rural residents are less likely to use internet technology for economic and other everyday activities, a relationship that is a product of the slow diffusion of advanced technology to rural areas [25]. ICT adopters can be categorized according to their stage of adoption, and there is evidence that marginal areas will continue to lag behind as technologies continue to iterate [26,27]. Not only do marginal communities have less broadband internet usage than metropolitan communities, but also the degree of broadband availability varies within marginal communities [28].

Factors contributing to differences in ICT systems, classified according to sociodemographic characteristics, are socio-economic status in relation to the state of the technological infrastructure, including age, income, highest level of education attained, ethnicity, etc. Looking back at rural issues a decade ago, low levels of education were a major barrier to ICT development in marginal areas [29]. Nowadays, education remains a huge factor in the internet use divide in marginalized communities [30]. For people living in marginal areas, rural communities may not be able to afford superfast broadband connections on their incomes, and even regular broadband can be no small challenge [31,32]. There is also a digital divide between the sexes, with the act of sharing online being very different in terms of gender, with men more likely to be involved [33,34]. Social factors affecting the digital divide also include age, with older people generally having lower access to ICT [35,36], and persistent gaps between different racial and ethnic groups [37].

#### *2.2. Mechanism of Internet Diffusion*

The diffusion of innovation is a long-term and continuous process, during which the share of companies adopting and applying new technologies grows [38]. Hägerstrand considers distance to be the main influencing factor for the diffusion of innovation, especially distance from innovation centers [39]. A number of empirical studies have proven that distance is negatively related to the degree of innovation diffusion [40,41]. Spatial proximity, cognitive proximity, social proximity, organizational proximity and institutional proximity have also received extensive attention from geographers in the study of innovation diffusion [42–44].

In terms of the diffusion mechanism, Hargittai summarized that the economic level, human capital, institutional environment and existing technology accumulation are all important elements in the diffusion of the internet in a country or city [45]. The spatial diffusion of the internet in the United States and the European Union is based on the theory of neoliberal economics [46]. Notably, the diffusion of internet access is a process driven by the market and profit, and cities always take the lead in internet diffusion [47]. The spatial diffusion of digital networks needs to create value for all major stakeholders. In particular, only when all participants can obtain appropriate value in the form of economy, efficiency, legitimacy, knowledge or social benefits can the network be successfully spread [48]. Guillen and Suarez conducted a survey using national-level data and demonstrated that internet adoption is a complex phenomenon and that the spatial diffusion of the internet is more influenced by per capita income, infrastructure, English language proficiency and entrepreneurial conditions than by the impact of public policy on telecommunications [49]. Additionally, Beilock and Dimitrova identified that per capita GDP is the most important factor affecting the rate of internet adoption [50]. According to the research of Lin et al., the proliferation of the internet is positively correlated with economic growth, with spillover effects being more pronounced in developed regions [51].

#### *2.3. Spatial Diffusion of E-Commerce*

From the perspective of "retail" and "consumption", the combination of the internet and commerce has resulted in two types of technology applications: business-oriented online business and consumer-oriented online shopping, which are collectively referred to as e-commerce [1]. Research on the spatial diffusion of e-commerce has focused on comparing the diffusion order and diffusion mode between core and marginal areas.

Most of the research on the spatial diffusion of online shopping has aimed to verify the two mutually targeted hypotheses proposed by Anderson. Farag et al., for example, indicated the spatial distribution of internet users and online buyers in the Netherlands from 1996 to 2001 and the impact of spatial variables on online shopping [2]. They found that the innovation diffusion hypothesis and the efficiency hypothesis coexist in the diffusion of e-commerce. On the one hand, residents living in highly urbanized regions are more likely to search or purchase online. On the other hand, residents living in less urbanized or non-urbanized areas with lower accessibility to stores can purchase products online more often. As De Blasio noted, the internet cannot reduce the role of distance, with urban consumers using the internet and e-commerce much more frequently than consumers in weakly urbanized areas [52]. According to the survey of Motte-Baumvol et al., the efficiency

hypothesis is partially validated by findings that suburban and urban households have very different patterns and that online purchases can remove spatial constraints on access to tangible goods [53]. Cao et al. examined the relationship between spatial attributes and online shopping and found supporting evidence for the innovation diffusion hypothesis, arguing that internet users living in cities or areas with high shopping convenience are inclined to shop online more frequently than those in other regions, as the former are more educated and use the internet more than the latter [54]. Beckers et al. highlighted the fact that there is no significant effect of the level of urbanization of a region on the probability of online shopping by analyzing data from a survey conducted by the Belgian Retail Federation with over 1500 respondents [55].

Research on the spatial diffusion hypothesis of e-commerce has long been centered on online shopping. However, does the spread of online business follow the innovation diffusion hypothesis and efficiency hypothesis? Do residents in marginal areas adopt the internet in order to overcome the distance barrier to sell their agricultural products? Previous studies of the spatial diffusion of online business included two main issues: the extent to which the diffusion of online business affects the number of visits to offline stores, and the comparison of the order of diffusion in urban and rural areas [6]. We focus primarily on the latter question. Retailers in urban areas are more likely to take on an online sales strategy, as urban areas typically have more advanced telecommunications infrastructures, stronger internet innovation capabilities and knowledge spillover, and more internet-related production services [56]. Clarke et al., for example, investigated a largescale commercial consumer survey in the U.K. to explore the expansion of e-commerce in British retail and to examine the spatial differences in e-commerce adoption [6]. According to their research, e-commerce initially spread from London and major cities but was not just an urban trend. Due to the improvement of broadband service quality, usage in rural areas is increasing.

#### *2.4. Digital Inequality and Spatial Diffusion of E-Commerce in China*

The distribution of internet users in China is characterized by significant spatial differences, with the eastern coastal regions accounting for more than 50% of the country's internet users. China is the world's largest ICT market in terms of the number of mobile devices used, the number of internet users and the number of broadband users, but there are significant numerical inequalities within and between provinces, municipalities and counties [57]. The growth of e-commerce in China exhibits national inequalities that are constrained by local economic, political and infrastructural conditions. The growth of e-commerce exhibits a spatial mix that relies on the structure of cyberspace and physical space [58]. As a rural e-commerce cluster with Chinese characteristics, there are many studies on the spatial distribution of Taobao villages that involve the spatial diffusion of e-commerce. Similar to the regional development of the ICT industry, Taobao Villages first emerged in more developed regions and then gradually spread to less developed regions, showing a tendency of agglomerative diffusion [59]. As Liu et al. noted, more than 90% of Taobao villages are located in East China, while Central China has the second highest number of Taobao villages and the least in the West [60]. The study by Geng et al. also shows that, overall, the concentration of e-commerce in the northern and western regions of China is decreasing, with a stable trend of geographical concentration of e-commerce activity in the eastern and southern regions [61]. The use of e-shopping technology is also very unevenly distributed across regions, with more developed cities in the east being higher than those in the less developed west, and cities at higher administrative levels being higher than those at lower administrative levels [62]. The level of online shopping in China is trending downwards from the eastern coastal areas to the western rural mountainous areas [57].

#### **3. Data and Method**

The data in this article are from the 2015 China County E-commerce Index, which is the online business index and online shopping index of all the counties in China published on the website of the Ali Research Institute. These indexes are based on the e-commerce data of Taobao.com (accessed on 15 September 2019), China's largest e-commerce website, and are very comprehensive. The Online Retail Index (OBI) and Online Shopping Index (OSI) are constructed by Ali Research to characterize the level of e-tailing and online shopping in each county. The formulae are as follows:

$$\text{OBI} = 0.6 \times \infty + 0.4 \times \beta \tag{1}$$

$$\text{OSI} = 0.6 \times \text{y} + 0.4 \times \text{p} \tag{2}$$

where OBI stands for Online Sales Index and OSI represents Online Shopping Index; α stands for e-tailing density, that is, the ratio of the number of e-retailers to the population; β indicates e-tailing trade level, which is the ratio of the number of e-retailers with an annual turnover of over RMB 240,000 to the total number of e-retailers; γ stands for online shopping density, that is, the ratio of the number of online consumers to the population; ρ means online shopping consumption level, which is the ratio of online shopping consumption to the d represents the level of online shopping consumption, that is, the ratio of online shopping consumption to the number of online shoppers. The values range from 0 to 100, with larger values indicating a higher level of online shopping development. The socioeconomic data used in the regression model are partly derived from the China County Statistical Yearbook and partly crawled from Tianyancha.com (accessed on 23 April 2020). The offline retailer, ICT service providers and logistics service provider data obtained from Tianyancha.com (accessed on 23 April 2020). The length of high-grade roads, per capita GDP, employment in service industry, industrial output value above designated size, number of manufacturing enterprises, population density, and per capita savings data come from the China County Statistical Yearbook. After sorting out the data, they are all unified to the county level for analysis of the mechanism.

This paper conducts an exploratory geographic data analysis based on ArcGIS software to explore the distribution patterns of spatial data and visualize the exponential grading of online business and online shopping. We calculate simultaneously the local autocorrelation index of online business and online shopping to obtain their cold and hot spots distribution and identify their local spatial autocorrelation characteristics. The average index per hundred kilometers is calculated to verify the spatial diffusion characteristics of e-commerce. Finally, a least squares regression model is used to explore the factors influencing the diffusion of online business and online shopping.

#### **4. Differences in Spatial Diffusion**

#### *4.1. Comparison of Spatial Diffusion Characteristics*

We use the core–periphery structure based on China's "east–west" structure. Due to the large number of county units, in verifying the spatial diffusion pattern of online business and online shopping, we can make judgements based on the transition characteristics from core to peripheral counties. If the spatial diffusion from east to west conforms to the pattern of gradient diffusion from core to edge, then it is in line with the innovation diffusion hypothesis at the county scale. Conversely, there is the efficiency hypothesis. Since the cross-sectional data of each year can be seen as the result of the previous diffusion, we use the 2015 online business and online shopping data as the diffusion results of the previous years to study the spatial diffusion of e-commerce.

The distribution of county-level online business in Figure 1 shows that the high value of the index is distributed in eastern coastal China, where high-value contiguous areas are formed. The central region, as a transitional region between the eastern and western regions, has a lower index than the eastern region. Online business in the western counties is dominated by a low-value distribution. The county-level online business index has obvious gradient distribution characteristics. High values are distributed in the eastern coastal areas from Bohai Bay to the Pearl River Delta. The mid-level is North China, Central China and South China, and the vast western area is the outermost level. The eastern region has a higher level of economic development and is also a center of technological innovation. The general forms of technology diffusion are hierarchical and contagious diffusion. The eastern region, being at the heart of online business technology innovation, is the first to be exposed to online business technology and therefore, has an advantage in online business adoption.

**Figure 1.** County-level online business and online shopping index in 2015.

The state of online shopping in counties shows that high-value areas are more widely distributed than are online businesses. High values are distributed not only in the eastern coastal areas, but also in the North China Plain and northern regions. Low values are located in the western region and are distributed over a smaller area. Comparing the spatial characteristics of online business and online shopping in counties, it can be seen that online shopping in general conforms to the same gradient distribution characteristics. However, there are many high values in the remote areas of Northeast and Northwest China, as well as on the North China Plain. The presence of these areas seems to diverge from the gradient distribution characteristics of the core–periphery structure from east to west. Compared to the eastern region, the peripheral counties have larger administrative districts but are more sparsely populated. Retail services are mainly located in administrative premises and most residents have difficulty accessing these services. The widespread distribution of internet infrastructure and high penetration of mobile internet in China, as well as the low technical threshold for online shopping, has led residents to switch from offline consumption to online shopping, with higher indices in some marginal areas.

#### *4.2. Spatial Agglomeration Characteristics*

This analysis of the distribution of spatial diffusion provides a preliminary perception of the diffusion characteristics of online business and online shopping. In the core and peripheral areas, we find some high values that presumably reflect agglomeration, but whether these areas are truly high-value clusters still needs to be explored using more scientific spatial analysis. In Figure 2, hot-spot analysis is used to explore the high-value agglomeration of online business and online shopping. The analysis of the online business index shows that the hot spots are concentrated in the eastern part from Bohai Bay to southwestern Fujian, with a significance level of more than 90%. Most of China's top 100 counties in terms of GDP are located in the eastern region, which have a good manufacturing base, a complete retail supply chain and abundant human capital. As a result, the high value areas for online business are clustered in the eastern regions. Because of the mixed distribution of high and low values in North China, Central China, and South China, the analysis results

are not significant. Most of the areas in the west are cold-spot clusters with a significance level of more than 90%, supplemented by the presence of some small patches of mixed high and low values in the west, which show non-significant results in the analysis of cold and hot spots. The hot-spot analysis shows the characteristics of the gradient distribution of the online business index more clearly, but there is a non-significant area in the northwest region, and further demonstration is needed.

**Figure 2.** County-level hot spots of online business and online shopping indexes in 2015.

The online shopping index has formed a very large hot spot in the eastern region and its hinterland, North China. We observe only that there are high values in North China in the previous analysis, but we do not know whether this area represents a "marginal reversal". The analysis here suggests that the high-value area in North China is not a "marginal reversal" but is part of the greater whole of the eastern core area. However, this result also needs further verification. Additionally, there are six small hot spots in the northern region. Whether there is a "revolution" in the peripheral regions also needs further investigation. The northern fringe regions are mostly mountainous, grassland or desert terrain, with poor accessibility to retail services and a strong incentive for residents to shop online. Moreover, the industries in the northern regions are mostly resource extraction and animal husbandry, where the inhabitants have a high per capita income and a strong spending power, thus creating high-value agglomerations.

#### *4.3. Differences in the Same County*

Having analyzed the spatial diffusion patterns of online shopping and online business indexes in different counties, we will explore which pattern is more dominant within the same county. Especially in remote regions, if one of these indexes has a greater advantage and is widely distributed, this can indicate the existence of marginal reversal. Therefore, the difference between the online business and online shopping indexes is used to represent the divide in Figure 3. Online shopping has an absolute advantage in the northern region, including the fringe areas from the northeast to the northwest, which form an agglomeration. There are few regions in eastern China, where online shopping has an absolute advantage except for the region on the west coast of the Taiwan Straits. This finding shows that peripheral areas have a demand for technology but are better adapted to online shopping and less well adapted to online business. Combined with the previous analysis, it can be seen that the eastern coastal region takes into account the application of business-oriented and consumption-oriented technology, which is a relatively comprehensive technical adaptation. Marginal areas are subject to regional endowments, industrial foundations and knowledge thresholds, resulting in poorer application of business-oriented technology, while the application of consumption-oriented technology has an advantage.

**Figure 3.** County-level differences in online business and online shopping indexes in 2015.

#### *4.4. Diffusion Features in the Core-Periphery Structure*