Centring or suburbanization? Changing locations of producer services in Shanghai

Abstract

Producer services are a critical indicator of global cities in advanced economies, whose spatial-temporal dynamics reflect the trajectory of urban transformation. However, the growth of producer services in China cannot be fully explained by current theories (e.g. neoclassical, institutional, global city and human capital theories), especially regarding their development process and geographical contexts. This study developed a context-sensitive analytical framework to comprehensively examine the location of producer services in Shanghai, a global city. We used rigorous geospatial analytical methods and considered sectoral differences and local contexts, especially institutional factors and urban spatial structure. We found that producer services in Shanghai were still concentrated in the city centre, but a dispersion trend could be detected, and subcentres were emerging in suburban areas. Further analysis highlighted producer service firms’ significant sector differences and various underlying spatiotemporal locational determinants. We identified positive effects of agglomeration on the emergence of centres for IT and research services. However, the concentration of financial and real estate services needs diversity, and agglomeration had a negative impact on them. Also, access to public transit promoted the development of IT and research services. Our study suggests that none of the existing theories alone can explain the location of producer servicer firms in Shanghai, and that sectoral heterogeneity and spatiality of producer services should be seriously considered in policy development and future studies.

Keywords

Global city producer service agglomeration human capital China

Introduction

Developed economies are service-oriented, and producer services are the most dynamic service sector. The term ‘producer services’ refers to services provided by firms mainly to other firms as intermediate inputs. Advanced producer services (APS), including accountancy, advertising, banking and law, form the backbone of global cities (Sassen, 2001) and global city networks (Taylor et al., 2014). Core cities in developing countries are also undergoing a rapid transformation towards service-oriented economies. Aspiring global cities, in particular, see APS as the key engine of economic growth and global city status (Timberlake et al., 2014).

The location of producer services is more dynamic than what grand theories describe. Producer services have become more spatially heterogenous over time and are experiencing suburbanization, especially since COVID-19 (Li and Wei, 2023; Xiao et al., 2024). The location of producer services is also highly sensitive to local contexts, such as spatial structure and local policies (Wu et al., 2022). Their location dynamics are critical to suburbanization patterns, polycentric development and more generally, sustainable development (Coffey, 2000; Yeh et al., 2017). Understanding the location dynamics of producer services not only enriches theories on industrial location and urban dynamics, but also has important implications for future development and urban governance (Taylor et al., 2014).

With the rise of production costs and global competition, China is ‘deindustrializing’ and sees the service economy, especially producer services, as a new engine of sustained growth. China’s leading cities are positioning themselves as leaders in the world system of cities (Timberlake et al., 2014) and rising rapidly in that hierarchy; Shanghai and Beijing have joined the list of top-tier global cities (Derudder et al., 2018). In China’s global cities, traditional industries have largely been suburbanized or relocated to peripheral cities to make space for the development of service industries (Wong et al., 2020; Wu et al., 2022; Zhang et al., 2018). Consequently, analysing the spatiotemporal dynamics of producer services in Shanghai is critical to a better understanding of urban transformation in global cities in emerging economies.

There are many types of producer services, and significant heterogeneity exists among them. So far, few studies have tried to compare different producer services in urban China, and theories on the development and location of producer services are inconsistent (Wu et al., 2022). While global city theory highlights the concentration of APS in city centres, other theories recognize diseconomies of agglomeration, such as higher cost on offices and increased competition (Mueller and Jungwirth, 2022; Zhang et al., 2019). These diseconomies may push businesses to relocate outside central business districts (CBDs) to reduce costs and improve efficiency, which global city theory may not fully capture. Thus, the location of producer service firms, particularly in developing countries, is not fully explained by current theories.

This study aimed to advance knowledge regarding the location of producer services by analysing the development and spatial transformation of producer services in Shanghai, an emerging global city in transitional China. The number of producer service firms in Shanghai has increased rapidly since 2000, and recently, over 50% of job opportunities have been in producer services (Li et al., 2019a). In particular, APS has become an important industry in Shanghai, making it the financial centre of China and a primary node in the global city network (Cheng and LeGates, 2018). We built an analytical framework integrating multiple theories with local contexts and sectoral differentials to better understand the locations of producer services in Shanghai. We analysed longitudinal data on producer service firms in Shanghai from 1990 to 2017, using spatial-temporal analysis tools, to answer the following questions: (a) Are producer services still highly concentrated in the city centre, or are they being decentralized? (b) What determines producer services’ location in the Chinese context? (c) How do location determinants differ across different producer service sectors? We highlighted the unique characteristics of global city formation and the significance of state institutions and local contexts, including urban space, in the development and location shifts of producer services in urban China.

Literature review

Producer services include various office-based industries generally specified at three levels (Coffey, 2000): (a) business services, (b) finance, insurance and real estate (FIRE) services and (c) any other service consumed by firms and organizations, such as transportation, storage and communications services. Producer services are critical to urbanization, economic growth and sustainable development and have drawn substantial attention in the literature and public policy. This section will review the merits and limitations of existing theories in explaining the location of producer services and discuss the role of urban space.

Spatial distribution of producer services: Theories

Various theories have been applied to understand the spatial distribution of producer services, including neoclassical location, institutional economic geography, global city and human capital theories, with different applicability and limitations. Neoclassical location theory provides a traditional explanation of the location of producer services, emphasizing supply-side and cost reduction (McCann and Sheppard, 2003). It suggests that producer service firms prefer to locate themselves in places that reduce costs, usually characterized as those with good accessibility to transportation hubs and business centres (Shearmur and Doloreux, 2008). It also recognizes some disadvantages associated with overconcentration, including higher land and labour costs and increased congestion.

Alternatively, institutional economic geography theory highlights the role of institutions in industrial locations, largely based on ‘thick’ descriptions. It pays particular attention to the role of agglomeration and networks in location decisions and views CBDs and industrial districts as places where such institutions flourish. Also recognized in research on Asia is the role of the state as a formal institution. The Asian development state strongly influences industrial location and urban development (Chang, 2006). CBDs, where government offices and high-quality public services are often located, attract producer services. However, the state also creates technology parks as subcentres, significantly contributing to the dispersion of producer services (Zhou, 1998). In addition, informal institutions are integral to producer services, providing the social foundations that shape how services are located and delivered.

With the rise of globalization, global city theory dominated the urban studies and geography literature from the mid-1990s to the mid-2000s. It emphasizes producer services, viewing APS as a growth engine and transformer of urban economies through which global cities exert their command-and-control functions (Sassen, 2001; Taylor, 2011). Emerging global cities in developing countries have pursued APS to capitalize on globalization and move up in global urban hierarchy (Timberlake et al., 2014). APS firms are highly concentrated in the global cities because these cities are ‘strategic places’ in the global city network, and APS plays a strategic role (Taylor et al., 2014). The development of APS globalizes cities (Parnreiter, 2018). APS firms are also highly centralized in the CBDs of global cities, clustering to benefit from globalization and agglomeration economies (Nelson, 2006; Smętkowski et al., 2021). Agglomeration is a cost-saving strategy when the concentration of production at a given location is more efficient than a single firm because of sharing, matching and learning mechanisms (Parr, 2002). However, competition and rising costs could reduce the efficiency of agglomeration and lead to diseconomies (Cook et al., 2007), forcing the dispersion of producer services (Halbert, 2004).

Also relevant is human capital theory, which emphasizes the role of people, such as entrepreneurs, talents, creative classes, etc., in determining industrial locations and urban development. Producer services firms are increasingly seeking places with desirable amenities, knowledge production capabilities and a tolerant atmosphere. Urban centres provide amenities that are attractive to talents, ranging from top-notch educational institutions to vibrant cultural hubs. Creative classes can also be found in locations outside city centres, such as old industrial districts (Markusen, 2006), gay communities (Florida, 2002), college towns (McGranahan and Wojan, 2007) and technology parks (Wilson and Spoehr, 2010), which offer opportunities for learning and collaboration. Thus, human capital theory suggests that producer services do not necessarily cluster in city centres but locate themselves based on the nature of places (Hermelin, 2007).

To summarize the limitations of these theories, neoclassical theory is often criticized for being supply-sided and weak in explaining the distribution of the creative class/industry (Storper and Scott, 2009). Institutional economic geography theory has difficulty quantifying institutional factors and measuring their effects. Human capital theory assumes that ‘jobs follow people’, which has sparked a debate on whether ‘jobs follow people’ or ‘people follow jobs’ (Scott, 2010). Global city theory overconcentrates on select cities in developed countries and deemphasizes the process of global city formation and the uniqueness of developing countries (Amin and Graham, 1997; Wei and Leung, 2005). These theories also provide inconsistent perspectives on the location of producer services, generally arguing for the agglomeration of producer services in CBDs, and do not adequately explain location dynamics.

Thus, each individual theory has limited power to explain the location of producer services in Shanghai. A comprehensive analytical framework is needed to integrate the theories while considering sector heterogeneity and the contexts of Shanghai. Such a framework will help to explore the power and limitations of existing theories and may better explain what is really happening in Shanghai.

Heterogenous producer services: Sectoral differences, colocation and urban space

The theories mentioned above provide general explanations for the development and location of producer services. However, producer services vary in their needs for proximity to clients and have high internal heterogeneity. Different subsectors have different location principles depending on their internal and external relations. Front-office subsectors such as FIRE and legal services tend to be more location-dependent and cluster with their customers. They need more face-to-face interaction and agglomeration mechanisms in general, and are more centralized and clustered (Zhou, 1998). APS firms are typically concentrated in city centres, especially in top-tier global cities. Back-office subsectors include knowledge-intensive services such as software and IT; they are more ‘footloose’ in location choice and less dependent on the CBD location. Thus, they are often located in suburban areas with lower costs and more natural amenities, which may be closer to their clients (e.g. IT manufacturers; Wernerheim and Sharpe, 2003) and tend to be decentralized (Wei et al., 2006, 2016). Consulting and engineering services are located close to talents and clients, such as universities and research parks (Edgington, 2008). Therefore, firm and sector characteristics influence the location of producer services.

A significant sectoral difference in location choice is the colocation between producer services and other industries. The colocation relationship between manufacturing and producer services is an expression of agglomeration economies and impacts urban development (Yuan et al., 2017). Furthermore, agglomeration can help create a more dynamic local economy by encouraging knowledge-sharing and collaboration. IT services may be located closer to their advanced manufacturing clients, largely located in development zones in suburban areas. Colocation is also promoted as a development policy for industrial parks, reflecting institutional power to shape urban development and space (Cheng et al., 2014).

The exact location determinants for producer services also depend on the local contexts in which the services are being provided. Spatial-temporal heterogeneity complicates producer services’ location choices. For example, Shanghai and Beijing exhibit a ring structure of urban space, and their suburban areas are connected through subway systems (Li et al., 2019a). This urban structure tends to make the spatial distribution of producer services more concentrated in the central urban areas (Han and Qin, 2009). With urbanization and suburbanization in recent years, producer services have begun to develop in suburban areas, making cities more polycentric. However, we still know little about the extent of suburbanization, sectoral differences and underlying mechanisms affecting producer service location.

An analytical framework for location of producer services in Shanghai

Based on the above literature review and research context, we developed a framework to analyse the location determinants of producer services in Shanghai (Figure 1). Our analysis indicated that none of the aforementioned theories alone could explain the reality in Shanghai. By integrating different perspectives in the same analytical framework, we could more rigorously test which theories best explained the location of producer services in Shanghai. Our analytical framework also considered local contexts in Shanghai, especially institutional factors and urban spatial structure, to better explain the spatial distribution of producer services. Local contexts were also embedded in the variable selection based on dominant theories.

Neoclassical location theory emphasizes cost reduction and profit maximization in location decisions, especially reduction of access/transportation costs and labour costs. The development of the transportation system in Shanghai has provided more possible locations for producer service firms, and a high population growth rate provides the labour force to support them. Our analysis also considered housing costs, which typically increase the cost of production. However, neoclassical location theory alone is insufficient to explain the location of producer services in Shanghai.

Institutions, including the Chinese state, play an important role in industrial location and urban transformation. The Chinese government established numerous development zones to make China the global manufacturing centre. With rising production costs, the Chinese state started to promote tertiarization (Yeh et al., 2015) and favoured producer services as a new engine of economic growth. Furthermore, the Chinese state dominates transportation planning, investing heavily in metro stations. Chinese metropolitan areas have experienced rapid urban expansion, and public transportation connects urban places, contributing to suburbanization and the development of producer services. There has been a significant increase in producer services around metro stations (Smętkowski et al., 2021; Wu et al., 2022; Xiao et al., 2022). Chinese state institutions, therefore, have significantly influenced the spatial distribution of producer services in Shanghai.

The global city literature is particularly important for interpreting the development of producer services and also partially reflects the status of Shanghai in China. Shanghai was a top treaty port in Asia under colonialism, called the Paris of the East. However, the establishment of socialism isolated Shanghai from the outside world, making it a socialist productive city. Economic reforms, globalization and institutional change have provided new opportunities for Shanghai to capitalize. As a centrally administered municipality, Shanghai enjoys preferential policies for economic development, including national development zones, and its Pudong district is a focus of China’s open-door policy. The city has also advanced from a manufacturing-based economic structure to a service-oriented one, and has been gradually transformed into a top-tier global city (Timberlake et al., 2014; Wei and Leung, 2005). The growth of producer services has been key to economic development and global city formation in Shanghai. However, as a global city in the emerging economy of China, Shanghai retains a large manufacturing sector and is largely a recipient of foreign direct investment, with limited global command and control functions. These characteristics may limit the power of global city theory to explain the location of producer services in Shanghai.

Also important to our research is human capital theory. Shanghai is a leading centre for the creative classes in China, attracting top national talent (Rao and Dai, 2017). The city’s universities and high-density, diverse urban amenities are the main reasons for the increase in high-quality human capital; universities improve knowledge production and provide well-educated young workers for producer service firms. Therefore, good access to universities and amenities that attract a well-educated population would benefit the growth of producer services.

Geographers have argued for the importance of local context in economic development, including urban spatial structure, associated with the suburbanization of firms and population (Horner, 2004). We use the term ‘urban space’ for simplification. There are many ways to capture urban space, such as spatial organization and population distribution. Chinese cities are generally more compact than US cities, and CBDs remain dominant in economic activities and housing markets. Also, China has the household registration (Hukou) system, which identifies each person as a resident of a particular place, dividing each city into a registered population of locals and a floating population of non-locals. The floating population in Shanghai is disadvantaged because they have limited access to public welfare in the city (Wu et al., 2022; Xiao et al., 2023). The effects of urban population, housing markets and spatial structure on producer services will also be explored in this study.

Our analytical framework attempted to integrate varied perspectives on producer services and consider the contexts of China and Shanghai. Our purpose was to provide a context-sensitive conceptual framework to guide our study of the location of producer services in Shanghai. It also helped us to identify potentially important variables influencing the spatial-temporal pattern of producer services. However, the mechanisms underlying the location of producer services are highly complex, and we do not expect our framework to fully explain the complex patterns. We do hope this conceptualization enriches the literature and provides important implications for analysing the development of producer services in other developing countries undergoing rapid urbanization and globalization.

Research setting and methodology

Study area and data sources

This study explored the spatial-temporal dynamics and underlying mechanisms of the location of producer services in Shanghai, the largest economic centre of China. After the reform, Shanghai became the centre of China’s open-door policy to capitalize on globalization, and it has quickly become a top-tier global city. The study area covered the 16 districts of Shanghai, including the main region of Shanghai and the Chongming district. Shanghai’s main region is classified into four areas, each consisting of several districts. These four areas are presented in Figure 2 and include the traditional city proper area (Huangpu and Jing’an), expanded central city area (Xuhui, Changning, Putuo, Hongkou and Yangpu), inner-suburban area (Pudong, Minhang and Boshan) and outer-suburban area (Fengxian, Jiading, Jinshan, Qingpu and Songjiang; Wei et al., 2016).

We used the National Enterprise Credit Information Publicity System online data set, collected in January 2019. As the data for 2018 were not complete, our analysis stopped at the end of 2017. This data set was cross-jointed with other open data platforms, such as tianyancha.com and qcc.com, which are highly reliable (Wu et al., 2022). Information we used from this data set included company addresses, types of companies and the years the companies were established. This helped overcome barriers to data availability, since the most recent industrial census data accessible to the public are from 2013. All together, we obtained data on 125,124 producer service firms, which would be difficult to analyse with conventional aspatial methods. Details about the types of producer services and the number of cases are shown in Table 1.

Table 1.

Type of producer services and number of cases.

Producer service type	Description	Number of cases
IT	Information transfer, computer service and software industry	9807
Finance	Insurance, monetary service and capital service	2892
Realty	Realty service	7318
Business	Leasehold and business service industry	72,004
Research	Scientific research, technical service and geologic examination industry	33,103

The general spatial pattern of producer service locations is presented in Figure 2 using a kernel density method. We selected five types of producer service firms dominant in Shanghai, following Coffey (2000): business, financial, research, real estate and information technology (Wu et al., 2022). Data to support further analysis, such as urban amenities and the locations of development zones, were obtained from map.baidu.com. The sociodemographic status of each district was derived from 2010 population census data, which was the most recent publicly available.

Spatial temporal dynamics: Space-time cube

Producer services in Shanghai showed a significant annual revenue increase of 13.2% from 2011 to 2016. Concurrently, the city experienced a dynamic transformation in its urban spatial structure, characterized by a blend of industrial suburbanization and centralization. This evolving urban spatial structure likely impacted the development patterns of producer services, leading to variations in their growth and distribution over time. Thus, to analyse the spatiotemporal patterns and identify the changing trends, we geocoded and analysed spatial-temporal data using the space-time cube, a powerful visualization tool for space-time data exploration (Nakaya and Yano, 2010). The space-time cube can help aggregate data using a predefined two-dimensional space and one vertical dimension of time; the attributions of each cube are statistical values such as mean and variance. This method is powerful to explore the changing trend between two-time points in a certain region (Hamed, 2009).

In this case, we constructed space-time cubes using a 1 km distance interval and a 1-year time interval. We used ArcGIS Pro to identify the space-time clusters based on the constructed space-time cubes. The Getis-Ord Gi* for each cube was calculated, and we used the Mann-Kendall trend analysis to evaluate the spatial-temporal patterns. Based on the changing trend, spatial location and time point, a space-time data mining algorithm was employed that helped detect three kinds of space-time clusters, shown in Table 2.

Table 2.

Three types of hot spots.

Type	Description
Emerging hot spots	The job centre that is a statistically significant hot spot recently, but has never been a statistically significant hot spot before.
Consecutive hot spots	A job centre with a single uninterrupted run of statistically significant hot spot bins in the final time-step intervals. The job centre has never been a statistically significant hot spot prior to the final hot spot run and less than 90% of all bins are statistically significant hot spots
Sporadic hot spots	A job centre that is an on-again then off-again hot spot. Less than 90% of the time-step intervals have been statistically significant hot spots and none of the time-step intervals have been statistically significant cold spots.

Multilevel and heterogenous determinants: Multilevel regression and regression tree models

Next, we employed multilevel regression and regression tree models, mainly focusing on the main region of Shanghai because the Chongming district is an isolated island. We used the multilevel model to explore the underlying mechanisms affecting the location of Shanghai’s producer service firms and to explore the role of urban space, which is administratively hierarchical and therefore multilevel, an essential characteristic of Chinese cities. Thus, a multilevel regression model could more effectively reveal the fixed effects of the city districts in Shanghai. This approach aligned with the complex, layered nature of urban areas and provided a more comprehensive understanding of spatial dynamics under the multi-level governance system. The minimum analytical unit was a subdistrict, and each subdistrict was nested with the district, revealing the impact of Shanghai’s administrative system. Regional location was also considered as a factor, reflected by the subregions, which were classified based on the distance to an urban centre and the historical environment in Shanghai (Table 3). The dependent variable was the percentage of coverage of emerging hot spots for producer services. The explanatory variables were selected based on our analytical framework in Figure 1. The method used to estimate the variables and their descriptions are provided in Table 3.

Table 3.

The independent variables and description.

Variable	Description	Type
Neoclassic location theory (Neo)
Metro	Density of metro stations	Continuous
Bus	Density of bus stations	Continuous
Population growth	Population growth rate from 2000 to 2010	Continuous
Housing	Average value of housing price in 2017	Continuous
Rent	Average value of rent in 2017	Continuous
Urban structure (US)
Region	1. Central urban area 2. Expanded central urban area 3. Inner suburban area 4. Outer suburban area	Category
Floating population	Percentage of floating population	Continuous
Human capital (HC)
University	Density of university in 2017	Continuous
Life service	Density of life service (Clinics, restaurants, etc.) in 2017	Continuous
Shopping centre	Density of shopping centre in 2017	Continuous
Institution (In)
Development zones	The percentage of the area located in the development zones	Continuous
Public culture	Density of public culture (Museum, etc.) in 2017	Continuous
Culture art	Density of culture art (Performing art groups, etc.) in 2017	Continuous
District	District and municipal governments. Expected to be solved using multi-level model	Category
Global city (GC)
Function	$Σ_{i = 1}^{n} - 1 * (P S_{i}) * \ln (P S_{i})$ /ln(n) $P S_{i}$ refers to the proportion of producer service type I, n is the number of producer service types	Continuous
Agglomeration	Density of all the producer service jobs in 2017	Continuous

We employed several theories to explain the spatial-temporal dynamics of producer service firms and the multi-level regression analysis could not determine the importance of variables for interpretation. Therefore, we employed a regression tree model to identify the importance of the variables in the location decisions of producer service firms (Guo et al., 2020; Xiao and Wei, 2023). A regression tree model is a machine learning based tool that can reveal intricate patterns and dependencies in data; it was particularly useful for examining how various factors collectively influenced the spatial distribution of producer services. By integrating this model, we aimed to gain deeper insights into the locational decisions of producer service firms.

Spatial heterogeneity of mechanisms: Geographically weighted regression

Shanghai has a large urban area and notably uneven urban development, which might contribute to the spatial heterogeneity of the locating mechanism of producer service firms. The significant values of global Moran’s I for producer services also indicated that the spatial effects were significant and should be addressed. Traditional global regression treating Shanghai as a point ignores the large spatial heterogeneity within the city. We therefore employed geographically weighted regression (GWR) to identify the spatial heterogeneity of mechanisms and reveal different effects of variables across space. By applying GWR, we could better understand how the influences of various factors on the location choices of producer service firms varied across the city. This approach was particularly significant for a metropolis like Shanghai, where local contexts and development trajectories differ widely, influencing the distribution and characteristics of producer services. GWR was not just an addition to our analytical toolkit but a necessary method to capture the complex dynamics of Shanghai’s urban space. It took the equation

Y_{i} = C_{i} + \sum_{k} β_{k i} F {(N e o, U S, H C, I n, G C)}_{k i} + ε

(1)

where $Y_{i}$ was the dependent variable to be regressed, percentage of neoclassical coverage of emerging hot spots. $C_{i}$ was constant, $β_{ki}$ was the parameter for individual explanatory variables F(Neo, US, HC, In, GC)_ki (k = 1, 2, 3. . . n) and $ε_{i}$ was the error term. The adaptive kernel was employed as the kernel function. Neo, US, HC, In and GC separately referred to neoclassical location theory, urban space, human capital, institution and global city. The specifications of the explanatory variables were the same as in Table 3. The five producer service industries were added to the model separately.

Spatial-temporal dynamics of producer services in Shanghai

Development of producer services and space-time cube analysis

Producer services in China emerged in the 1990s as the Chinese government recognized their importance, which was also explicitly stated in the Eighth Five-Year Plan (1991–1995). Nonetheless, labour-intensive manufacturing still dominated the economy, making China a global production centre. Producer services grew more rapidly after the global financial crisis when China promoted domestic consumption. Shanghai led China in developing producer services and remaking itself as a global city. Additionally, national-level development zones with generous preferential policies served as engines for the growth of the producer service industry in Shanghai, especially the Lujiazui Finance & Trade Zone (Cojanu and Pîslaru, 2011; Tian et al., 2020).

Spatially, the built-up area of Shanghai has expanded rapidly in the past 20 years, and the producer service industry keeps growing and suburbanizing (Figures 3 and 4). The overall spatial-temporal dynamics of producer service firms in Shanghai from 1990 to 2016 are reflected by Moran’s I in Figure 4. The results suggested a tendency to cluster around 1992, when the central government of China tried to accelerate economic reforms and the development of producer services. After 1992, more development zones were established in the suburban region, providing space for the dispersion of producer service firms in Shanghai.

The global financial crisis impacted the Chinese economy, although not as significantly as Western economies. Producer service firms in the central city tended to be more globalized and therefore were hit harder, resulting in a drop in spatial clustering. After a short recovery, the transformation of agricultural land to commercial land provided space for the emergence of commercial centres in suburban areas (Han and Qin, 2009; Wójcik, 2013), which were suitable locations for the development of producer service firms. After a great increase in producer service firms in Shanghai in 2010, these firms continued to disperse (Figure 4).

Our constructed space-time cube helped identify the spatial-temporal pattern of producer service firms in Shanghai (Figure 5). The results showed the locations of consecutive hot spots, with several new hot spots for producer service firms. Most of the new hot spots were found in the Songjiang district, a subcentre and a new college town, with the rapid development of producer services. Others were located in the outer urban area, where a large number of development zones were established that attracted many producer service firms. Highly concentrated consecutive hot spots were found in the central urban area of Shanghai, where most of the service industry was located.

Geographical differentials by sector

Spatially, producer services in Shanghai were still concentrated in the area within the inner ring road, the main CBD of Shanghai by 2017, despite suburban development (Figure 2). However, some subcentres attracted producer service firms, indicating a trend of dispersion of producer services in Shanghai. Figure 5 presents a more nuanced analysis, including the spatial-temporal patterns of five types of producer service firms: business, financial, information technology, real estate and research. The hot spot analysis suggests that the growth of producer services was not limited to the CBD, and could be detected in the centres of suburban districts. These figures show different spatial-temporal patterns for the five types, revealing the heterogeneity among producer services.

Business services were the major type of emerging producer services in Shanghai, and their consecutive hot spots were primarily in the subcentres. The clustering of business services in the Pudong and Minghang districts was close to the traditional CBD. Consecutive hot spots for research service firms manifested a dispersing spatial pattern, except in the southern Pudong district or the old Nanhui district, which was less developed. In 2009, this administrative division was changed by consolidating Pudong and Nanhui districts to stimulate the economic growth of Nanhui, which still lags behind in producer services. The consolidation did not contribute much to the growth of Nanhui, and the north-south division is evident in the spatial distribution of producer services. Jingan and Zhabei districts were combined in 2009, but no significant change can be identified in the 2017 patterns.

IT service firms have grown extensively in Shanghai, and were mainly found in the central urban area and northern part of the city. Compared with business and research services, the growing pattern of IT service firms was more concentrated in the main centre and the northern area where research universities clustered, rather than in the subcentres. The growth of real estate service firms was highly unstable, and there were many sporadic hot spots across districts. The consecutive hot spots for real estate service firms were mainly in suburban regions where more land was available for development.

Financial services were the main type of producer services in Shanghai and have been critical to the city’s economic development and globalization. Although financial service jobs were concentrated in the central urban regions, financial services did not keep growing in the city centre, with only some sporadic hot spots. The emergence of financial services was mainly found in the periphery of the central urban region and the northern part of the Pudong district, especially the designated Lujiazui financial district of Shanghai. Thus, the growth of financial services in Shanghai exhibited a divergence trend, which might be because of the development zones in suburban regions and the urban centre’s high housing and land costs.

Location determinants: Results of spatial analysis and modelling

Spatial clustering: Multilevel modelling of location determinants

We first applied a set of OLS regression models based on spatial clustering results using all the producer service firms, and the variables were added based on the theories we reviewed (Table 4). Model 1 only included the variables of neoclassical location theory and accessibility to bus stations showed high significance. However, this variable became insignificant after more variables were added to the models. Models 2, 3 and 4 suggested that high rent price was a barrier to the clustering of producer services and life services would contribute to the concentration of producer services. Also, the R² of these models suggested that global city theory had strong explanatory power because the R² improved after global city function and agglomeration were added to the model.

Table 4.

The result of OLS regression model.

	Model 1	Model 2	Model 3	Model 4	Model 5
Intercept	0.0619	0.188	0.127	0.128	−0.178
Neoclassic location theory
Metro	0.176*	0.1661*	0.0529	0.0497	0.122
Bus	7.52e⁻³***	6.16e⁻³***	1.44e⁻³	1.31e⁻³	−3.39e⁻³
Population growth	−0.0203	−0.0106	−4.99e⁻³	−5.29e⁻³	−5.38e⁻³
Housing	2.048e⁻⁶	2.284e⁻⁶	3.15e⁻⁶	3.21e⁻⁶	3.39e⁻⁶
Rent	−1.315e⁻⁶*	−1.042e⁻⁶*	−1.44e⁻⁶*	−1.56e⁻⁶*	−5.93e⁻⁷
Urban space
Floating population		−0.278	−0.234	−0.262	−0.239
Human capital
University			0.485	0.496	0.568
Life service			0.691e⁻⁴*	6.19e⁻⁴*	3.40e⁻⁴
Shopping centre			2.45e⁻³	−2.59e⁻³	−9.75e⁻³
Institution
Development zones				0.084	−0.075
Public culture				6.85e⁻³	0.017
Culture art				0.017	0.043
Global city
Function					0.354**
Agglomeration					0.0365***
R ²	0.279	0.291	0.327	0.330	0.497

***

Significant at 0.01 level. **Significant at 0.05 level. *Significant at 0.1 level.

The results of the multilevel model are presented in Table 5, which includes the estimate of the coefficient for independent variables and the variance explained by district and subdistrict levels. The high R² values suggested that our analytical framework was powerful in explaining the spatial-temporal pattern of producer service firms. The explanatory powers of districts and regions for business services, financial services and research services were similar, about 20%. The clustering of IT service firms manifested a significant core-periphery heterogeneity, while the mechanism of real estate firms’ location was sensitive to the district.

Table 5.

The result of multilevel models.

	Business	Finance	IT	Research	Real estate
Intercept	−0.097	−0.22	−0.23**	−0.088	−0.076
Neoclassic location theory
Metro	0.075	0.063	0.13**	0.060	0.019
Bus	0.0076***	0.0014	0.0072***	0.0093***	−0.0043*
Population growth	0.022	5.81e⁻⁴	0.0089	0.016	−0.011
Housing	4.39e⁻⁶*	1.12e⁻⁶	1.22e⁻⁶	2.84e⁻⁶*	−5.18e⁻⁷
Rent	1.51e⁻⁷	4.94e⁻⁷	4.17e⁻⁸	−1.03e⁻⁷	3.5e⁻⁷
Urban space
Region (%)	19.9	23.0	41	20.9	0.7
Floating population	−0.38***	−0.014	−0.13	−0.17*	0.024
Human capital
University	0.47	−0.030	0.74*	0.34	0.0022
Life service	4.59e⁻⁶	8.09e⁻⁴***	9.7e⁻⁵	−4.03e⁻⁵	0.00016
Shopping centre	−0.018*	−0.039***	−0.019*	−0.020*	0.021*
Institution
Development zones	0.12*	−0.12*	0.194***	0.18**	−0.020
Public culture	−0.017	−0.035**	−0.026*	−0.021*	−0.013
Culture art	−0.12*	0.015	−0.093*	−0.11**	0.015
District (%)	25.2	20.3	3	24.1	38.5
Global city
Function	0.49***	0.244*	0.39***	0.40***	0.25**
Agglomeration	0.042***	0.024***	0.054***	0.045***	0.036***
R ²	0.725	0.503	0.828	0.768	0.508

***

Significant at 0.01 level. **Significant at 0.05 level. *Significant at 0.1 level.

We found that public transportation was important to improve the accessibility of producer service firms in Shanghai. Our results indicated that bus stops contributed to the growth of most producer service clusters, while the metro system was significant to the concentration of IT service firms. The growth of e-commerce has made IT services more labour-intensive, and these white-collar workers rely more on public transportation for commuting, as housing prices are extremely high in central Shanghai; this is similar to the heavy reliance on the metro system of IT workers in Beijing (Lin et al., 2015). Therefore, public transportation is critical to the emergence of producer services, particularly IT services, whose employees rely on the metro system for commuting. On the other hand, the growth of real estate and financial services firms did not follow the development of public transportation, because they are more advanced and less labour-intensive compared to other producer service firms.

Urban space indicators, including housing and population, affected the growth of producer service firms as well. The urban space factors were mainly related to business and research service firms concentrating in regions with rising housing prices and declining floating populations, with medium significance (Table 5). These regions are usually highly gentrified, attracting many well-educated and high-skill workers, and thus preferred by producer service firms. Geographically, the producer service firms had a better awareness of the geospatial effects caused by the administrative system; districts and regions explained more than 40% variance for all the models. Region could explain 41% variance for the growing IT service firms, but less than 1% variance for the real estate service firms. The location of real estate service firms showed spatial heterogeneity at the district level, as development activities took place across districts, especially suburban development.

Urban amenities can reflect urban development and the environment, and access to urban amenities contributes to the agglomeration of human capital (Li et al., 2019a). High-quality human capital is critical for the growth of producer services (McGranahan and Wojan, 2007). However, the results of this study suggested that the density of urban amenities was not a good indicator of the growth of producer services. In particular, the density of shopping centres significantly increases both the flow of shoppers and land prices, which would make these areas less attractive to producer service firms. Another important reason is planned segregation between commercial districts and residential areas. Thus, shopping centres are an urban amenity with a negative effect on producer service firms’ agglomeration. Moreover, public culture and art facilities are heavily concentrated in the city centre, and usually have privilege in land use planning, which would institutionally limit land supply in the surrounding area. As a result, these urban amenities make the regions less attractive to the producer service industry.

Development zones create space to attract various kinds of firms. Shanghai’s economic development strategy has promoted producer service firms, which obtain numerous benefits when they are located in development zones (Huang et al., 2016; Liu et al., 2023; Wei and Leung, 2005). Also, development zones provide opportunities to maximize agglomeration effects. Thus, regarding the colocation relationship and the growth of the producer service industry, both density and diversity should be recognized.

Primary and spatial determinants: Regression tree and GWR models

Although the multilevel model revealed the determinants of the producer service firms’ spatial-temporal pattern, it was still unclear which variables were the primary ones and how their effects varied over space. We employed a regression tree model to compare the relative importance of the explanatory variables (Table 6). Agglomeration or multifunctional effects were important influencing factors. Specifically, the multifunctional effect was critical to business and financial services because they usually serve different kinds of firms. IT and research services are more independent and tend to have fewer contacts with other services. Although agglomeration is highlighted in the research on producer services, our results suggested that agglomeration primarily influenced IT and research services. Other types like business and financial services needed a diversity of producer services for more potential cooperation.

Table 6.

The relative important urban amenities of producer service based on regression tree models (top five factors).

Business	Finance	IT	Research	Real estate
Function (100%)	Function (100%)	Agglomeration (100%)	Agglomeration (100%)	Function (100%)
Floating (97%)	Floating (61%)	Bus (54%)	Bus (61%)	University (36%)
Culture public (93%)	Agglomeration (60%)	Life service (48%)	Life service (58%)	Travel (35%)
Shopping (83%)	Life service (58%)	Culture public (40%)	Culture public (49%)	Life service (34%)
Metro (71%)	Culture public (54%)	Travel (38%)	Travel (48%)	Agglomeration (23%)

The numbers in the brackets are the relative importance of each amenity. The most important one is 100%.

Other factors, such as floating population and accessibility of urban amenities, also played critical roles in shaping the spatial-temporal pattern of producer service firms. The results suggested that business service firms were equally influenced by diverse factors, as most determinants had similar relative importance. The negative effects of a floating population were highlighted in the regression tree model, suggesting that business and financial service firms did not favour regions with a high percentage of floating population, typically in less developed areas. Instead, accessibility of urban amenities showed high relative importance next to the agglomeration and multifunctional perspectives. Density of urban amenities, such as life services, contributed to the emergence of most producer service firms. Thus, the human capital attracted by access to urban amenities is a critical concern for the development of the producer service industry in Shanghai.

The regression model also revealed the relative importance of the variables. The results suggested that agglomeration and multifunctional effects were the dominating determinants. However, Shanghai covers a large urban area, and urban and economic development are highly uneven, contributing to significant spatial heterogeneity. Thus, the power of the determinants to explain the growth of producer service firms would be sensitive to the local context. A GWR model helped us identify how the impacts of the determinants varied across space, paying particular attention to agglomeration and multifunctional effects, and highlighting Shanghai’s role as a global city in developing the producer service industry.

The GWR model detected a core-periphery pattern regarding spatial heterogeneity (Figure 6). The core-periphery pattern followed the general urban structure of Shanghai, and accessibility of urban centres influenced the development of producer services. The core area of Shanghai is where public and private resources concentrate, especially administration and public services. Many semi-state functions, also agglomerate there and provide substantial attractiveness, such as hospitals and cultural facilities. Suburban areas tend to have poorer public services and fewer urban amenities, which are less attractive to human capital and producer services. Thus, we found that multifunctional effects benefited the growth of producer service firms in the central urban area and, to a lesser extent, suburban subcentres. There were some exceptions, such as financial service firms, which can provide services to almost all kinds of industries. Also, real estate service firms did not show significant spatial heterogeneity compared to other producer service firms.

Conclusion

Producer services are critical to urban development and global city formation, and their location has important implications for the development, transformation, vitality and dynamism of cities. The locations of producer services must be carefully considered in urban planning and policy-making. However, spatial-temporal changes in producer services and mechanisms for their location in rapidly developing cities remain less investigated. This study analysed the spatial-temporal pattern and location mechanisms of producer services in Shanghai, a top-tier global city, and their spatial clustering, sectoral differences and spatial heterogeneity.

Shanghai has experienced tremendous growth in producer services, including in its suburban areas. Our analysis suggests that Shanghai’s CBD remains the dominant centre for producer services, especially advanced business services, following the neoclassical location and global city theories. However, producer services are decentralizing in Shanghai, and a dispersing pattern was found for other producer service types, such as financial and real estate services. IT and research services have grown rapidly in outer urban regions, further indicating the dispersion trend. Producer service firms have been forming job centres in the outer urban area, and there are significant sectoral differences. Such outcomes suggest that decentralization does take place in global cities (Halbert, 2004), and IT and research services even experience more rapid suburbanization (Aranya, 2008).

Regarding the determinants of the location of producer service clusters, we found that the global city function was the most important dimension of urban development and transformation in Shanghai. On the one hand, despite recent literature highlighting human capital and accessibility to urban amenities (Li et al., 2019a; Yeh et al., 2017), the agglomerating and multifunctional natures of job centres are still dominating factors in producer service companies’ location. Agglomeration has been highlighted as a key factor explaining the location of producer services, and this study also found it as the dominating location determinant for IT and research services, while financial, business and real estate services were more sensitive to diversity in location. Therefore, the agglomeration mechanism is important in general to the formation of producer service clusters, but sectors like financial and real estate services tend to be more sensitive to local contexts, such as client location and available land. Research and IT services are usually knowledge-based and provide services for a broader range of customers, and their concentration could maximize agglomeration effects (Wu et al., 2022).

Furthermore, the core-periphery pattern could explain a large part of the variance in the agglomeration of IT services, while the agglomeration of real estate services was sensitive to districts. Also, accessibility of public transportation became a more important variable than accessibility of CBDs, represented by housing/rent price and population growth in this paper. Therefore, neoclassical location theory should pay more attention to the accessibility of public transportation rather than urban cores, as public transportation has significantly reshaped urban structure (Chang, 2006; Shearmur and Doloreux, 2008). Development zones in Shanghai have made similar impacts on urban structure by creating industrial sub-centres (Zhou, 1998). Among these factors, accessibility to buses was still the most important one in Shanghai, although the metro system and development zones had notable effects.

Another notable finding was that urban cultural amenities did not contribute to attracting producer service firms as much as expected. Human capital theory usually highlights the importance of arts and culture in attracting the creative class (Florida, 2002; Markusen, 2006), which can also contribute to the agglomeration of producer service firms. However, urban cultural amenities did not contribute to the concentration of producer service at the micro level in Shanghai, since such facilities usually cover a large area and are protected by policies that limit development surrounding them. Producer service firms require supporting urban amenities such as public transportation, living services and high-rise buildings, which might not be located close to cultural sites. Although cities with more cultural amenities would be more attractive to the creative class and producer service firms, producer service firms may avoid colocation with cultural sites at the intra-urban level. Positive amenities we found for producer services included universities and transportation stations, which are also critical triggers of urban development and vitality.

Our study also has important implications for the development of Shanghai and globalizing cities in developing countries. Although suburbanization is a global trend and polycentric urban development in Shanghai has gained currency, the overall pattern of findings suggested that producer services were still highly concentrated in the central urban region. Consequently, housing prices in Shanghai’s central city area are among the most expensive in the world (Li et al., 2019b), forcing young professionals to reside near metro stations for convenient commuting. Further research is needed to understand the effects of COVID-19 and the drop in urban population and expatriates on producer services. More efforts are also needed to promote polycentric development and provide public services in suburban areas, and the transportation network needs to be further developed to strengthen connections between districts, not just to the CBD.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We would like to acknowledge the funding of National Natural Science Foundation of China, Grant No. 42201231; China Postdoctoral Science Foundation, Grant No. 2022M713234; Jiangsu Funding Program for Excellent Postdoctoral Talent, 2022; International Postdoctoral Exchange Fellowship Program, Grant No. YJ20220202.

Author biography

Yehua Dennis Wei is a Professor in the Department of Geography at the University of Utah. His research focuses on urban/economic geography, spatial inequality, and sustainable development. Weiye Xiao is an assistant professor in Nanjing Institute of Geography and Limnology. His research interests include urban inequality, urbanization, transport geography and resilience.

Yangyi wu is an associated professor in department of urban design, Wuhan University. His research interests include urban/economic geography, sustainable development and injustice.

References

Amin

Graham

(1997) The ordinary city. Transactions of the Institute of British Geographers 22(4): 411–429.

Aranya

(2008) Location theory in reverse? Location for global production in the IT industry of Bangalore. Environment and Planning A: Economy and Space 40: 446–463.

Chang

(2006) The East Asian Development Experience. London: Zed Books.

Cheng

van Oort

Geertman

, et al. (2014) Science parks and the co-location of high-tech small-and medium-sized firms in China’s Shenzhen. Urban Studies 51(5): 1073–1089.

Cheng

LeGates

(2018) China’s hybrid global city region pathway: Evidence from the Yangtze River Delta. Cities 77: 81–91.

Coffey

(2000) The geographies of producer services. Urban Geography 21: 170–183.

Cojanu

Pîslaru

(2011) How important are agglomeration economies: A case study of Romanian industrial clusters. Review of Economic and Business Studies 4(1): 35–58.

Cook

Pandit

Beaverstock

, et al. (2007) The role of location in knowledge creation and diffusion. Environment and Planning A: Economy and Space 39(6): 1325–1345.

Derudder

Cao

Liu

, et al. (2018) Changing connectivities of Chinese cities in the world city network, 2010–2016. Chinese Geographical Science 28: 183–201.

10.

Edgington

(2008) The Kyoto research park and innovation in Japanese cities. Urban Geography 29(5): 411–450.

11.

Florida

(2002) The Rise of the Creative Class, vol. 9. New York, NY: Basic Books.

12.

Guo

Chen

(2020) Evaluation of spatially heterogeneous driving forces of the urban heat environment based on a regression tree model. Sustainable Cities and Society 54: 101960.

13.

Halbert

(2004) The decentralization of intrametropolitan business services in the Paris region: Patterns, interpretation, consequences. Economic Geography 80(4): 381–404.

14.

Hamed

(2009) Exact distribution of the Mann-Kendall trend test statistic for persistent data. Journal of Hydrology 365(1–2): 86–94.

15.

Han

Qin

(2009) The spatial distribution of producer services in Shanghai. Urban Studies 46(4): 877–896.

16.

Hermelin

(2007) The urbanization and suburbanization of the service economy: Producer services and specialization in Stockholm. Geografiska Annaler: Series B, Human Geography 89(sup1): 59–74.

17.

Horner

(2004) Exploring metropolitan accessibility and urban structure. Urban Geography 25(3): 264–284.

18.

Huang

Wei

(2016) A comparative study of land efficiency of electronics firms located within and outside development zones in Shanghai. Habitat International 56: 63–73.

19.

Wei

(2023) COVID-19, cities and inequality. Applied Geography 160: 103059.

20.

Wei

(2019a) Urban amenity, human capital and employment distribution in Shanghai. Habitat International 91: 102025.

21.

Wei

, et al. (2019b) Analyzing housing prices in Shanghai with open data: Amenity, accessibility and urban structure. Cities 91: 165–179.

22.

Lin

Allan

Cui

(2015) The impact of polycentric urban development on commuting behaviour in urban China: Evidence from four sub-centres of Beijing. Habitat International 50: 195–205.

23.

Liu

Zhu

(2023) Development zones and firms’ performance: The impact of development zones on firms’ performance for a Chinese industrial cluster. Regional Studies 57(5): 868–879.

24.

Markusen

(2006) Urban development and the politics of a creative class: evidence from a study of artists. Environment and planning A 38(10): 1921–1940.

25.

McCann

Sheppard

(2003) The rise, fall and rise again of industrial location theory. Regional Studies 37(6–7): 649–663.

26.

McGranahan

Wojan

(2007) Recasting the creative class to examine growth processes in rural and urban counties. Regional Studies 41(2): 197–216.

27.

Mueller

Jungwirth

(2022) Are cooperative firms more agile? A contingency perspective on small and medium-sized enterprises in agglomerations and peripheral areas. Small Business Economics 58(1): 281–302.

28.

Nakaya

Yano

(2010) Visualising crime clusters in a space-time cube. Transactions in GIS 14(3): 223–239.

29.

Nelson

(2006) Interpreting producer service suburbanization: The public accounting industry in Chicago and Minneapolis-St. Paul. Urban Geography 27(1): 45–71.

30.

Parnreiter

(2018) Producer service firms as global city makers: The cases of Mexico City and Hamburg. In: Hoyler

Parnreiter

Watson

(eds) Global City Makers: Economic Actors and Practices in the World City Network. Cheltenham and Northampton, MA: Edward Elgar Publishing, pp.23–40.

31.

Parr

(2002) Agglomeration economies: Ambiguities and confusions. Environment and Planning A: Economy and Space 34(4): 717–731.

32.

Rao

Dai

(2017) Creative class concentrations in Shanghai, China. Social Indicators Research 132(3): 1237–1246.

33.

Sassen

(2001) The Global City, Tokyo. Princeton, NJ: Princeton University Press.

34.

Scott

(2010) Jobs or amenities? Destination choices of migrant engineers in the USA. Papers in Regional Science 89(1): 43–63.

35.

Shearmur

Doloreux

(2008) Urban hierarchy or local buzz? High-order producer service and (or) knowledge-intensive business service location in Canada, 1991–2001. The Professional Geographer 60(3): 333–355.

36.

Smętkowski

Celińska-Janowicz

Wojnar

(2021) Location patterns of advanced producer service firms in Warsaw. Cities 108: 102937.

37.

Storper

Scott

(2009) Rethinking human capital, creativity and urban growth. Journal of Economic Geography 9(2): 147–167.

38.

Taylor

(2011) Advanced producer service centres in the world economy. In: Taylor

Derudder

, et al (eds) Global Urban Analysis. London: Earthscan, pp.22–39.

39.

Taylor

Derudder

Faulconbridge

, et al. (2014) Advanced producer service firms as strategic networks, global cities as strategic places. Economic Geography 90(3): 267–291.

40.

Tian

(2020) On spatial spillover and industrial agglomeration of financial crises to real economy. Journal of Mathematical Finance 10(3): 464–482.

41.

Timberlake

Wei

, et al. (2014) Global cities with Chinese characteristics. Cities 41: 162–170.

42.

Wei

Ning

(2016) Globalization, economic restructuring and locational trajectories of software firms in Shanghai. Professional Geographer 68(2): 211–226.

43.

Wei

Leung

(2005) Development zones, foreign investment, and global city formation in Shanghai. Growth and Change 36(1): 16–40.

44.

Wei

Leung

Luo

(2006) Globalizing Shanghai: Foreign investment and urban restructuring. Habitat International 30(2): 231–244.

45.

Wernerheim

Sharpe

(2003) “High order” producer services in metropolitan Canada: How footloose are they? Regional Studies 37(5): 469–490.

46.

Wilson

LOU

Spoehr

(2010) Labour relations and the transfer of knowledge in industrial clusters. Geographical Research 48(1): 42–51.

47.

Wójcik

(2013) The dark side of NY–LON: Financial centres and the global financial crisis. Urban Studies 50(13): 2736–2752.

48.

Wong

Lee

Zhao

, et al. (2020) Region-specific determinants of the foreign direct investment in China. Geographical Research 58(2): 126–140.

49.

Wei

, et al. (2022) Amenity, firm agglomeration, and local creativity of producer services in Shanghai. Cities 120: 103421.

50.

Xiao

Wei

(2022) Neighborhood, built environment and resilience in transportation during the COVID-19 pandemic. Transportation Research Part D: Transport and Environment 110:103428.

51.

Xiao

Wei

(2023) Assess the non-linear relationship between built environment and active travel around light-rail transit stations. Applied Geography 151: 102862.

52.

Xiao

Wei

Chen

(2023) Skills mismatch, jobs-housing relationship and urban commuting. Travel Behaviour and Society 33: 100610.

53.

Xiao

Yuan

, et al. (2024) The 2-month lockdown and commuting behavior transformation in Shanghai: A phone signal data-based analysis. Transport Policy 150: 149–161.

54.

Yeh

Yang

Wang

(2015) Economic transition and urban transformation of China: The interplay of the state and the market. Urban Studies 52(15): 2822–2848.

55.

Yeh

Yang

(2017) Will rural urbanization produce a new producer service space in China? Habitat International 67: 105–117.

56.

Yuan

Gao

Wang

, et al. (2017) Co-location of manufacturing and producer services in Nanjing, China. Cities 63: 81–91.

57.

Zhang

Yue

Liu

, et al. (2018) Suburban industrial land development in transitional China: Spatial restructuring and determinants. Cities 78: 96–107.

58.

Zhang

Sun

, et al. (2019) Polycentricity or dispersal? The spatial transformation of metropolitan Shanghai. Cities 95: 102352.

59.

Zhou

(1998) Beyond ethnic enclaves: Location strategies of Chinese producer service firms in Los Angeles. Economic Geography 74(3): 228–251.