Elsevier

Transport Policy

Volume 112, October 2021, Pages 22-31
Transport Policy

High-speed rail and industrial movement: Evidence from China's Greater Bay Area

https://doi.org/10.1016/j.tranpol.2021.08.013Get rights and content

Highlights

  • HSR has causal impact on firm/industry relocation in China GBA.

  • Large-scale manufacturing firms exhibit a decentralization trend.

  • Service sector shows a clustering pattern in the GBA.

  • Industrial movement pattern differs between the GBA and other regions.

Abstract

In the context of location choice, firms face a trade-off between the increasing agglomeration benefits and the rising costs of doing business along the high-speed rail (HSR) corridors. This study examines the causal impact of the HSR extension on industrial movement patterns in China's Greater Bay Area (GBA). We analyze two firm-level datasets for the period 2007–2018 using the difference-in-differences method. We find that after the HSR extension, large-scale manufacturing firms exhibit a decentralization trend in the central GBA. We also find that the service sector shows a clustering pattern in the GBA. However, this pattern differs between the GBA and other regions, urban districts, and suburban counties, highlighting the redistribution effect of the HSR extension on industrial growth across regions. These findings have important implications for industrial policymaking, as they help decision-makers reflect on the potential industrial movement trend in relation to the impact of HSR expansion.

Introduction

The high-speed rail (HSR) is a modern rail transport system that provides high-speed, comfortable, and prompt rail transit service. China started to develop the HSR in the 2000s, and by 2019 35,000 kms were completed, accounting for over two-thirds of the global network of HSR. Scholars have paid significant attention to estimating the broader socioeconomic impact of the HSR on cities. Related topics include the effect of the HSR on economic growth (Zheng et al., 2019), accessibility gain (Levinson, 2012), land and housing markets (Chang and Diao, 2021, Diao et al., 2017), air-rail competition (Castillo-Manzano et al., 2015), and environmental issues (Chang et al., 2021, Chen et al., 2016). Regarding the role of HSR on economic growth, many studies show that HSR can promote economic growth for large cities, and that this growth is at the expense of the medium and small size cities nearby (Meng et al., 2018; Qin, 2017). The redistribution effects of the HSR on cities are quite common. Chang (2021) reviewed the empirical literature on the HSR's impact on economic growth in Japan, the EU, and China and concluded that while the HSR is beneficial for the economic growth of megacities, this gain is realized at the expense of the neighboring smaller cities.

As the growth of cities is largely determined by the performance of firms/industries, one perspective to explore the redistribution effect of the HSR on cities, is to understand its impact on firms’ location and industrial movement patterns. There are two major driving forces determining the location of firms and industries. One is agglomeration economies, referring to the benefits accruing from the spatial proximity of firms and people (Rosenthal and Strange, 2004). The literature has found that agglomeration can enhance productivity, facilitate innovation, accelerate interaction and learning, and increase wages (Arzaghi and Henderson, 2008; Combes et al., 2012; Glaeser and Mare, 2001). Agglomeration benefits suggest that a new firm is more likely to be located with an existing cluster of other firms. For example, Chang and Zheng (2020) showed that the elasticity of the number of new firms to an existing plant stock is around 0.52 in China. The other crucial factor influencing the location of firms is the transportation system. In the new economic geography framework, transportation cost is regarded as one of the key elements determining the location of industrial agglomeration (Krugman, 1991). A superior transportation network also strengthens agglomeration benefits, promotes market integration, accelerates interregional mobility and trade, and accelerates innovation (Ahlfeldt and Feddersen, 2018; Donaldson and Hornbeck, 2016; Duranton et al., 2014; Gibbons et al., 2019).

However, agglomeration benefits for firms are not free from costs. Many studies have shown that labor, housing and land costs will be high due to agglomeration economies (Combes et al., 2019; Glaeser and Mare, 2001; Partridgem et al., 2009; Wheaton and Lewis, 2002). As the transportation infrastructure strengthens urban agglomeration economics, it raises labor and land costs; thus, firms face a trade-off to adjust their location choices to rebalance agglomeration benefits and the associated costs. The classic monocentric city model (also called the AMM model, see Alonso, 1964; Mills, 1967; Muth, 1969) predicts that the service sector would cluster in the central business district (CBD) whereas manufacturing would move to the urban periphery where the cost of land is low. The model further suggests that the firms in the service sector value the agglomeration benefits more, whereas manufacturing firms are more cost sensitive. However, the theoretical prediction on firms’ responses to transportation improvement is not conclusive, as multiple equilibriums exist, depending on the parameter values employed (Fujita and Ogawa, 1982). Nevertheless, empirical studies have shown that highway construction can decentralize the service sector, and the railway can decentralize manufacturing to suburban districts (Baum-Snow, 2007; Baum-Snow et al., 2017).

As the HSR mainly transports people, it can stimulate non-tradable goods consumption and contribute to the development of the integrated market, which is attractive for service firms. Several studies have examined the impact of HSR on industrial agglomerations especially for the service sector (Dai et al., 2018; Dong, 2018; Lin, 2017; Shao et al., 2018; Tian et al., 2021). These studies employ official statistics, especially urban employment data to examine the impact of the HSR on China's urban specialization patterns, however, their conclusions are mutually inconsistent. For example, Lin (2017) examined the impact of the HSR on urban employment in 81 Chinese cities and found that employment growth in the service sector is about 4.5%, which is lower than that of the manufacturing and construction sectors. Dong (2018) found that the HSR significantly enhanced employment growth in the retail and hotel/catering sectors, whereas other sectors were not affected. Shao et al. (2018) studied the effect of HSR on the service sector for 25 cities in China's Yangtze River Delta region. They found that the HSR can strengthen service industry agglomeration for cities along the HSR corridor without weakening the agglomeration of medium and small cities. Further, they found that the effect is more robust for producer service sectors such as transportation, IT, and finance. The consumer service industry, retail, accommodation and catering, and real estate were not affected. These three studies apply the difference-in-differences (DID) method and cover similar study periods; however, as mentioned earlier, their findings are inconsistent with each other. One caveat is that local governments often change statistical methods to measure urban employment, which then raises questions on the accuracy of the studies that use those employment data.

Recent studies have leveraged firm data to examine the effect of the HSR on industry specialization patterns. For example, Chang and Zheng (2020) examined the impact of the HSR on the spatial pattern of establishment of new firms in China. They focused on the inter-regional pattern and found that the central region of China attracts firms from the service sectors due to HSR access, while other regions do not receive significant benefits. Chang (2021) examined the impact of the HSR on the socioeconomic development of China's Guangdong province. He finds evidence that the HSR can strengthen the agglomeration benefits for primate cities by attracting population, employment, and service-sector firms. However, this study is largely descriptive in nature and does not offer a comprehensive statistical examination. Moreover, there are two reasons that a city-level examination cannot contribute toward ascertaining the impact of the HSR on industrial agglomeration. First, the literature suggests that the agglomeration benefits are more relevant within 5 km of the agglomeration force for the manufacturing sector and several hundred meters for the service sector, such as advertising (Arzaghi and Arzaghi, 2008; Rosenthal and Strange, 2003). The spatial coverage of the agglomeration force is closer to the size of a county/district than that of megacities. Second, industrial movement patterns are also affected by industry policy, which are not considered in this study.

Given the limitations of existing studies, this study examines the causal effect of the HSR on industrial movement by analyzing firm-level data in districts/counties in the Guangdong province. Specifically, this study intends to answer the following questions: how firms respond to HSR-associated shocks; whether different firms respond differently to an HSR; how an HSR influences industrial relocation and agglomeration; and whether the industrial movement pattern favors megacities over small cities.

This study contributes to the literature on agglomeration economies, firm location, and the role of the HSR on market integration. First, existing studies on the impact of HSR on industrial agglomeration and urban specialization in China are inconclusive. This study leverages an innovative data source to provide new evidence on industry agglomeration in megacities, and contributes to agglomeration literature. Second, this study enriches the literature on the impact of transportation on a firm's location choice and market integration. Third, theories suggest that value-added firms should appreciate the rich information environment encountered in urban settings. Many large-scale manufacturing firms cannot afford high wages and land prices, and thus move to locations with low rentals. Our study provides empirical evidence on changing industrial patterns. Finally, our findings have important policy implications; like China, many countries are planning and developing the HSR, so these findings can prove useful to them.

The rest of the paper proceeds as follows. Section 2 provides the background on HSR development and industrial policy in the GBA. Section 3 introduces the empirical strategy and data sources. Section 4 presents and discusses the empirical results. Section 5 concludes the paper.

Section snippets

HSR expansion and industrial development in the Greater Bay Area

Guangdong is one of the most developed regions in South China. In 2018, the province had a population of 113.46 million and a GDP of RMB 9.73 billion (or USD 1.47 trillion). Both statistics placed Guangdong in the first position, among 31 provinces in mainland China. There are 21 prefecture-level municipalities in the province, and each municipality covers urban and rural areas. Except for Dongguan and Zhongshan, each municipality contains a few urban districts, county-level municipalities,

Empirical strategy

Existing studies regard the expansion of transportation infrastructure as a quasi-experimental shock, especially for intermediate cities. The idea is that the intercity transport network is built mainly for connecting large cities, but also passes through other areas between the terminal points; this affects economic outcomes of the route areas as a quasi-random shock. Redding and Turner (2015) named this an “inconsequential places approach”. HSR literature has adopted this approach to estimate

Stylized facts

We present several stylized facts on the relationship between the HSR network expansion and the evolution of industrial movement patterns. Fig. 2 demonstrates the trends for large-scale manufacturing after the HSR expansion. Panel 2-1 shows the employment distribution in 2009, one year before the launch of the HSR service. This shows that the manufacturing sector is heavily concentrated in the central GBA. Since the industrial survey data ended in 2013, Panel 2-2 shows the ratio of employment

Conclusion

The empirical results demonstrate the causal effect of the HSR service on the evolution of industrial clusters in the context of the GBA. The pattern depends on the industrial classification and the locations of the HSR. Owing to HSR access, the large-scale manufacturing sector shows a decentralization trend. Among eight major service sectors, we find that most show a concentration trend due to HSR, except for the real estate sector. It must be noted that the agglomeration of service sectors

Statement

This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. We have read and understood your journal's policies, and we believe that neither the manuscript nor the study violates any of these. There are no conflicts of interest to declare.

Acknowledgements

We thank Prof Roger Vickerman for constructive comments and support. The work described in this paper was substantially supported by a research grant under the 2020–2021 China Program International Fellowship program from the Lincoln Institute of Land Policy (C24R003-CZC030520).

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