Abstract
Purpose
The spatiotemporal compression effect of China–Europe Railway Express (CR-Express) can reduce the flow costs of resources between China’s node cities. Additionally, it can break through the limitations of low-added-value marine products, significantly impacting the logistics industry efficiency. However, there are few literature verifying and analyzing its heterogeneity. This study explores the impact of CR-Express on the efficiency of logistics industry in node cities and analyzes the heterogeneity.
Design/methodology/approach
First, this study uses panel data to measure the efficiency of node city logistics industry. Secondly, this study discusses the impact of the opening of CR-Express on the efficiency of logistics industry in node cities based on the multi-period differential model. Finally, according to the node city difference, the sample city experimental group is grouped for heterogeneity analysis.
Findings
The results show that CR-Express can promote the urban logistics industry efficiency, with an average effect of 4.55%. According to the urban characteristics classification, the heterogeneity analysis shows that the efficiency improvement effect of logistics industry in inland cities is more obvious. The improvement effect of node cities and central cities in central and western China is stronger, especially in the sample of megacities and type I big cities. Compared with non-value chain industrial products, the CR-Express has significant promotion effects on the logistics efficiency of the cities where main goods are value chain products.
Originality/value
Under the background of double cycle development, this paper can provide a scientific basis for the investment benefit evaluation of CR-Express construction and the follow-up route planning.
Keywords
Citation
Niu, Y., Liu, J., Yang, X. and Wang, C. (2024), "The heterogeneous impact of China–Europe railway express on the efficiency of logistics industry in node cities", Railway Sciences, Vol. 3 No. 3, pp. 279-294. https://doi.org/10.1108/RS-03-2024-0005
Publisher
:Emerald Publishing Limited
Copyright © 2024, Yanliang Niu, Jin Liu, Xining Yang and Chuan Wang
License
Published in Railway Sciences. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
The development of the urban logistics industry plays a vital role in facilitating “dual circulation”. The opening of China–Europe Railway Express (CR Express), an important channel for cross-border trade transport, will accelerate the flow of factors between cities and the optimization of resource allocation and drive inland areas to expedite their opening-up efforts and foster international economic and trade cooperation. CR Express brings about a time-space compression effect reducing the logistics costs for node cities engaged in foreign trade. Additionally, the cross-border land transport offered by CR Express improves the current situation characterized by lengthy sea transport, limited involvement from inland areas and low added value. Furthermore, it significantly amplifies the logistics output of node cities by promoting economic and trade cooperation along the CR Express routes (Fang, Lu, & Wei, 2020; Zhou & Zhang, 2021; Li, Min, & Wang, 2021). As of early 2022, 91 cities across China have launched CR Express services. However, several challenges persist, such as disorganized and unplanned operations, competition among node cities for freight sources, as well as high investment, long project cycles and difficulties in measuring the investment benefits of CR Express construction, which arise due to insufficient consideration given to the development of the logistics industry in node cities. Therefore, it is crucial to scientifically select node cities, enhance investment efficiency and effectively promote “dual circulation” by explicitly identifying the impact of CR Express on the efficiency of the logistics industry in node cities.
Currently, most research conducted on the logistics industry in CR Express node cities focuses on assessing and analyzing the current situation. On one hand, some scholars have observed a rapid improvement in the efficiency of the logistics industry in node cities along the “Belt and Road” by calculating its efficiency (Jiang, 2020). On the other hand, research has also demonstrated that improving logistics efficiency can have a substantial impact on promoting bilateral trade flows between regions or countries (Liu & Yin, 2017). However, these findings alone may not sufficiently reflect the differences before and after the opening of CR Express. There is a small amount of literature that specifically focuses on the development of the logistics industry in node cities after the opening of CR Express. Some studies have indicated that the opening of CR Express has significantly facilitated transport convenience for trade between Chongqing and Europe and resulted in a positive impact on the city’s development as an international logistics center (Yang, Sun, & Lee, 2020). While there are existing studies on the role of CR Express in improving transport accessibility and promoting domestic and foreign trade circulation (Zhou & Zhang, 2021; Yang et al., 2020), there is a scarcity of literature specifically discussing the impact of CR Express on logistics industry efficiency. By analyzing the logistics transport networks of current node cities, it has been observed that the density of logistics networks in these cities is relatively low. The logistics connections of other node cities mainly rely on intermediary hubs such as Suzhou, Chongqing, Beijing, Nanjing and Wuhan (Wang, Dong, Chen, & Sun, 2018). This finding indirectly suggests that there are shortcomings in the current route planning, including weak connections between node cities, low utilization rates of certain routes and inadequate fulfillment of freight transport demands in certain regions. As a result, it is necessary to further investigation into the subsequent route planning and investment focus for CR Express, providing study opportunities for this paper. Additionally, existing literature demonstrates that the promoting impact of CR Express on the economies of node cities varies depending on their unique characteristics. From a geographical perspective, scholars concur that the opening of CR Express has a more substantial promoting impact on western China (Zhou & Zhang, 2021; Wei & Gu, 2021a). From the standpoint of city type, the economic promotion impact resulting from the opening of CR Express is stronger for larger cities as well as cities with a lower level of economic development (Fang et al., 2020; Zhou & Zhang, 2021; Wei & Gu, 2021b).
From the perspective of the heterogeneity of node cities, this paper, based on panel data collected from 154 prefecture-level cities during the period of 2008 to 2021, adopts a time-varying difference-in-differences (DID) model to investigate the impact of CR Express on the efficiency of the urban logistics industry. Furthermore, the study also delves into the heterogeneity of node cities. The heterogeneity analysis in this study primarily relies on city-level data. The node cities are categorized based on the main types of goods, differentiating them according to the value level of products. Geographical location is also considered, as it can reveal the unique characteristics resulting from a city’s position within the railway network. Additionally, city size is taken into account as a reflection of the economic levels of different cities. Through this categorization, the study aims to analyze the varied effects of CR Express’s opening on the efficiency of the logistics industry in different types of cities. An accurate assessment of the impact of CR Express’s opening on the efficiency of the urban logistics industry is crucial. This analysis will not only broaden the research on the economic effects of CR Express’s opening but also enable the evaluation of the benefits derived from project construction investments, providing a valuable reference for future investment focus and route planning concerning CR Express.
2. Study design
2.1 Assumptions of the basic model
A literature review indicates that changes in the city’s internal and external environments (Liu & Yang, 2019; Liu, Li, & Li, 2022; He, Wang, & Xu, 2023) as well as the macroeconomic situation (Wang, Guan, & Dong, 2018) also lead to a higher efficiency of the urban logistics industry. Therefore, this study adopts the DID method to address potential endogenous problems (Thorsten, Ross, & Alexey, 2010). The traditional DID model requires the same time of policy implementation. However, since the opening time of CR Express varies across cities, it does not fit the traditional model. Thus, this study adopts a quasi-natural experiment of CR Express’s opening and utilizes a time-varying DID model with fixed time and individual to explore the impact of CR Express’s opening on the efficiency of the urban logistics industry. The specific model is set as follows:
2.2 Measurement of data and variables
This study utilizes a sample of 154 prefecture-level cities, which includes Beijing and municipalities directly under the central government, spanning the period of 2008–2021. The relevant data such as inputs, outputs and control variables of sample cities are obtained from the China City Statistical Yearbook, as well as local statistical yearbooks and annual reports. The actual opening time of CR Express for the sample cities (i.e. the policy and time variables) is based on publicly available information. This information is sourced from the Belt and Road Portal (www.yidaiyilu.gov.cn), the 2016–2020 Development Plan of CHINA RAILWAY Express, development reports of CHINA RAILWAY Express over the years and announcements on government websites. The opening time of CR Express for the node cities in this study refers to the first opening time of CR Express in these specific cities.
2.2.1 Explained variable: efficiency of the urban logistics industry
Data envelopment analysis (DEA) is primarily used in measuring the efficiency of the urban logistics industry. Its application in the field of logistics has been proven to be feasible (Markovits-Somogyi & Bokor, 2014). Indeed, there has beenmany researches on the application of DEA for measuring the efficiency of the urban logistics industry. Scholars in the field have developed a comprehensive system of models and indicators that are specifically designed to assess the efficiency of the urban logistics industry using DEA (Wang, Dong et al., 2018; Wang and Tan, 2013). Since DEA does not require making any hypothesis about weights, it can avoid measurement errors caused by subjective factors. However, the traditional DEA model can only proportionally reduce in the radial direction and adjust the input and output, failing to measure all slack variables. Consequently, it yields inflated efficiency values and situations where multiple assessment indicators result in a DEA efficiency value of 1 often occur. To address this, this study adopts the super-slacks-based measure-data envelopment analysis (SBM-DEA) model based on relevant literature to measure the efficiency of the urban logistics industry (He et al., 2023; Tone, 2001). Through literature screening, an assessment indicator system for logistics industry efficiency is established. The input indicators include labor input (Jiang, 2020; Liu & Yang, 2019), capital input (Liu, Li, & Li, 2022; Yu & Qian, 2018) and route input (Wang & Tan, 2013; Zhang & Wang, 2018). Specifically, the number of employees in transport, warehousing and postal industries, the fixed asset investment in these industries and the highway mileage are chosen as input indicators. Because the logistics industry is a typical production-oriented service sector, the freight volume is selected to represent its physical output, while the gross product of transport, warehousing and postal industries is chosen to represent its value output.
2.2.2 Explanatory variable: opening of CR express
To capture the direct impact of CR Express on the efficiency of the urban logistics industry, based on the 2016–2020 Development Plan of CHINA RAILWAY Express, this study defines a set of 38 cities with CR Express operations and direct access to Europe as “node cities”, including Chongqing, Zhengzhou, Chengdu, Yiwu (Jinhua), Xi’an and Wuhan, among others. These node cities are considered the treatment group in the analysis, with
2.2.3 Control variable
Based on previous literature (Yu & Qian, 2018; Zhang & Wang, 2018), this study adopts the following control variables: government support environment (Gc), i.e. the proportion of fixed asset investment in the logistics industry compared to the total social fixed asset investment; scientific and technological level (Net), i.e. the proportion of fixed asset investment in the information transmission, software and information technology service industry compared to the total social fixed asset investment; regional industrial structure (In), i.e. the ratio of the tertiary industry to the secondary industry in a city; economic development level (Eco), which is measured by the regional gross domestic product (GDP) per capita; extent of opening-up (Open), which is measured by the proportion of the total import and export volume of a city in its GDP; regional R&D level (Rd), which is measured by the number of granted patents in a city; foreign direct investment (Fdi), i.e. the actual amount of foreign direct investment in a city; urbanization rate (Urb), i.e. the proportion of the urban population in a city. Control variables are all subject to logarithmic value processing. The characteristics of the main variables are shown in Table 1.
3. Analysis of empirical results
3.1 Baseline regression results and analysis
The baseline estimation results are given in Table 2. Interaction terms and control variables are introduced in sequence. Both the first and second columns control the fixed effects of interaction terms to ensure that individual and time effects of sample cities will not affect the results. The results show the coefficients of
From Table 2’s second column that controls the two fixed effects, it is evident that among the control variables, the coefficient of the economic development level (Eco) is positively significant, indicating that cities with higher economic development levels exhibit higher efficiency in their logistics industry. This is because economically developed cities typically possess stronger support for the development of logistics infrastructure. Additionally, they provide logistics supplies that are aligned with the economic and social demands, thus facilitating improvement in urban logistics industry efficiency (Wang & Li, 2019). The coefficient of the regional R&D level (Rd) is positively significant, indicating that cities with higher R&D levels exhibit higher efficiency in their logistics industry. This can be attributed to the fact that cities with higher regional R&D levels tend to have advanced logistics technology and equipment. This, in turn, facilitates the adoption and utilization of advanced logistics technology, driving improvement in the efficiency of the urban logistics industry (Zhou, Chen, & Dan, 2023).
3.2 Robustness tests
3.2.1 Parallel trend test
The DID model relies on a basic hypothesis that, if the policy is not implemented, the efficiency levels of the logistics industry in two groups of sample cities would exhibit a parallel trend. In this study, the test is performed by the event study method (Jacobson & Sullivan, 1993). The specific model is as follows.
The initial year is defined as the current year when CR Express opens and 5 years before and after it is also considered. If
3.2.2 Placebo test
Another basic hypothesis of the DID model is that it is affected by unobservable factors. To exclude the impact of other unobservable missed variables, a placebo test is conducted following the empirical practice of previous studies (Li, Lu, & Wang, 2016). The placebo test involves randomly generating node cities. To be more specific, 1000 and 2000 trials are conducted on the efficiency of the urban logistics industry respectively according to the second column of Table 1. The regression coefficient
It can be seen from Figure 2 that most of the estimated coefficients concentrate around zero and their P-values are greater than 0.1 (not significant at the level of 10%). This demonstrates that the baseline regression results of this study are not accidental, so the probability of the study results being affected by other policies or random factors is extremely low. It proves that the impact of CR Express’s opening on the efficiency of the urban logistics industry is not affected by other factors. Namely, the impact of CR Express’s opening is robust in improving the efficiency of the urban logistics industry.
3.2.3 PSM-DID test
This study re-examines the impact of CR Express’s opening on the efficiency of the logistics industry based on the propensity score matching-differences-in-differences (PSM-DID) analysis. The purpose is to alleviate the endogenous and selective errors in the study and reduce the difference between the treatment group and the control group before the opening of CR Express (Heyman, Sjoholm, & Tingvall, 2007). This study adopts the 1:4 nearest neighbor matching with replacement, radius matching and kernel matching. According to the list of node cities obtained after matching, the DID model is used again. The test results in Table 3 show that the coefficient
3.2.4 Other robustness tests
In addition to the robustness tests described above, other robustness tests are also adopted. (1) Node cities of CR Express are selected from areas along the Belt and Road. This is an endogenous trouble. According to the practice of previous studies (Li et al., 2021; Wei & Gu, 2021b), the two-stage regression method is used to test the cities along the ancient “Silk Road” (Pcity) as an instrumental variable. The test results are given in the first and second columns of Table 4. The conclusions of this study are robust; (2) To account for the potential “pseudo regression” between the efficiency of the urban logistics industry and the opening of CR Express, the tests are conducted by shifting CR Express’s opening year
4. Heterogeneity analysis
To explore the heterogeneity in the impact on improving logistics industry efficiency related to the characteristics of node cities, these cities are grouped based on the type of goods, geographical location and city size. The categorization of the sample cities is presented in Schedule 1 in the annex.
4.1 Heterogeneity in improving the efficiency of the logistics industry in cities with different types of goods
The “point-to-point” through mode of CR Express is characterized by its timeliness, safety and regional accessibility. This mode of transport significantly reduces transport costs, which is particularly beneficial for value chain industries with high demands for efficient distribution (Liu, Li, & Li, 2022). According to the 2019 report “Globalization in transition: The future of trade and value chains” by McKinsey Global Institute, six industries are identified as value chain industries. These industries include chemical engineering, automotive, computers and electronics, machinery and equipment, electrical machinery and transport equipment. In this study, tests are conducted on the variable of products of value chain industries. If a city’s main export products come from these specific industries, the corresponding value for this variable is set to 1. Otherwise, the variable value is set to 0. The results are given in Table 5. The interaction term coefficients of products of value chain industries and core explanatory variables are found to be significantly positive. On the other hand, the coefficient of products of non-value chain industries is not significant. This suggests that CR Express has a significant impact on improving the efficiency of the logistics industry in cities where the main goods belong to value chain industries. This may be attributed to the fact that most of the goods transported by CR Express trains are chemical products and mechanical equipment and China and European countries cooperate more closely in the value chain of these industries (Liu, Li, & Li, 2022). This finding also proves that the opening of CR Express provides an important opportunity for China’s urban logistics industry to participate in the division of labor in the global value chain, which is conducive to promoting China’s position in the global value chain.
4.2 Heterogeneity in improving efficiency of the logistics industry in cities at different geographical locations
In this study, sample cities in the treatment group are divided into eastern, central, western and northeastern sub-samples as well as coastal and inland sub-samples based on their locations and coastal status. The division of these cities is determined by the locations of their provinces/municipalities. The comprehensive calculation results are given in Table 6. The opening of CR Express has a positive effect on improving the efficiency of the logistics industry in eastern, central, western, northeastern and inland regions while the impact on coastal cities is not significant. Moreover, the interaction term coefficients of sub-samples in central, western and inland regions are greater, proving that the opening of CR Express has a stronger effect on improving the efficiency of the logistics industry in node cities in central, western and inland regions. This also indicates that the opening of CR Express has brought new opportunities for the logistics industry development in these regions. It is conducive to minimizing the imbalance in the logistics industry development between various regions and narrowing the development gap between the central and western regions and the eastern coastal region of China, thus promoting the common improvement of the logistics industry development in cities at different locations. The impact of CR Express on improving the efficiency of the logistics industry in northeastern China is relatively weak. This can be attributed to the relatively weak high-tech industrial foundation of cities in the northeastern region (Wei & Gu, 2021b). During the study years, the main export products of these cities are low value-added goods. Additionally, the operations of CR Express in this region are insufficient to meet the demand for goods transport (Liu, Li, & Li, 2022). Similarly, the opening of CR Express does not yield a significant improvement in the efficiency of the logistics industry in coastal cities. This is because the logistics industry in coastal cities predominantly relies on water transport. As a result, the impact of CR Express on the logistics industry in these cities is insufficient.
Additionally, according to the “center-periphery” theory (Prebisch, 1962), it is easier for regional central cities to reduce factor transport costs and strengthen the trend of factor resource inflow (Li et al., 2021). Therefore, they certainly have more advantages than peripheral cities. Many central cities are also node cities and their industrial concentration effect and scale economy effect are stronger than peripheral cities. Therefore, the impact of CR Express’s opening on their logistics industry efficiency should be different. According to the construction plan issued by the National Development and Reform Commission, 8 CR Express hubs are classified as central cities, including Urumqi, Chengdu, Zhengzhou, Chongqing, Shenyang, Xi’an, Jinan and Hefei. Other node cities are classified as peripheral cities. Empirical analysis is conducted on the two city groups and the results are shown in Table 6. The interaction term coefficients of central cities and peripheral cities are all positively significant. However, the interaction term coefficients of central cities are greater, demonstrating that CR Express’s opening has a greater impact on the efficiency of the logistics industry in central cities compared to peripheral cities.
4.3 Heterogeneity in improving efficiency of the logistics industry in cities of different sizes
Considering that the different economic development levels of different cities due to city size have a great impact on the promotion role of CR Express, this study analyzes the heterogeneity from the aspect of city size. According to the latest classification criteria specified in the Tabulation on the 2020 China Population Census by County, empirical study is conducted on node city groups, i.e. super-large cities, megacities, Type-I large cities, Type-II large cities and small and medium-sized cities. As shown in Table 7, the opening of CR Express improves the efficiency of the logistics industry in cities of different sizes. The coefficients of interaction term
5. Conclusions and implications
Through the quasi-natural experiment of CR Express’s opening, this study establishes a time-varying DID model based on panel data from 154 prefecture-level cities in 2008–2021 to explore the effect of CR Express’s opening on the efficiency of the urban logistics industry. Furthermore, the parallel trend test, placebo test and PSM-DID regression are performed to test the robustness of this model. The empirical results indicate: (1) The opening of CR Express leads to a 4.55% improvement in the efficiency of the urban logistics industry on average; (2) The impact of CR Express on logistics industry efficiency is significantly greater in cities where the main goods are products of value chain industries when compared to cities where the main goods are products of non-value chain industries; (3) The improvement effect on logistics industry efficiency is more pronounced in central, western and inland regions, as well as in central cities, particularly megacities and Type-I large cities. Based on these conclusions, the following recommendations are proposed.
Actively expand the types and channels of goods for CR Express in node cities to enable products of value chain industries to dominate the goods transported. CR Express trains can be utilized for the cross-border transport of products of value chain industries to be exported near node cities. In contrast, products of non-value chain industries in node cities can be transported by other means or concentrated at nearby CR Express hubs for transport.
The operations of CR Express are primarily influenced by factors such as freight demand and government support. Moreover, cities in central and western China offer substantial subsidies to attract freight sources. As a result, CR Express operations can be increased in the plans for central cities in central and western China, especially megacities and I-type large cities. Meticulous investigations should be conducted for future construction of CR Express in the eastern and coastal regions, thus avoiding resource waste. For small and medium-sized cities with a significant demand for export freight, such as Yiwu, it is advisable to further increase CR Express operations. Additionally, comprehensive considerations should be given to enhancing the utilization rate of CR Express in coastal cities, leveraging its potential to catalyze the development of the logistics industry in these cities. Furthermore, northeastern China should accelerate its industrial upgrading process and focus on developing high-tech industries to increase the added value of products.
This study validates the role of CR Express in improving the efficiency of the logistics industry in node cities and performs heterogeneity analysis. However, due to the difficulty in obtaining railway freight data classified by the type of goods in node cities, it was not possible to conduct further examinations regarding the impacts of factors such as railway freight volume, types of goods, location characteristics and planning of railway logistics bases. We will incorporate such impacts in future studies to further consolidate the understanding of the role of CR Express in improving the efficiency of the logistics industry in node cities.
Figures
Main variable characteristics
| Variable | Symbol | Observed value | Mean value | Standard deviation | Minimum value | Maximum value | |
|---|---|---|---|---|---|---|---|
| Explained variable | Efficiency of the logistics industry | θ | 2156 | 0.011 | 0.106 | 0.00000157 | 1.865 |
| Environment variable | Government support environment | Gc | 2156 | 0.087 | 0.055 | 0.00000533 | 0.560 |
| Scientific and technological level | Net | 2156 | 0.012 | 0.026 | 0.00000003 | 0.468 | |
| Foreign direct investment | Fdi | 2156 | 146350.916 | 424857.722 | 3.00000000 | 13211603.214 | |
| Economic development level | Eco | 2156 | 55033.316 | 36014.498 | 99.00000000 | 467749.000 | |
| Regional industrial structure | In | 2156 | 0.998 | 0.546 | 0.13869299 | 5.304 | |
| Extent of opening-up | Open | 2156 | 7.340 | 328.286 | 0.00064436 | 15247.030 | |
| Regional R&D level | Rd | 2156 | 9191.397 | 20304.633 | 14.00000000 | 279509.000 | |
Source(s): Authors' own work
Baseline regression result
| Variable | Interaction item introduced | Control variable introduced |
|---|---|---|
| Treat×Post | 0.042* (0.023) | 0.045524** (0.022111) |
| Gc | – | 0.001340 (0.001055) |
| Net | – | 0.015278 (0.020176) |
| Fdi | – | −0.058276 (0.042854) |
| Eco | – | 0.259537* (0.1567230) |
| In | – | −0.254980 (0.170765) |
| Open | – | −0.025189 (0.057531) |
| Rd | – | 0.000009* (0.000005) |
| Urb | – | 0.000618 (0.002990) |
| Cons | −7.671*** (0.098) | −9.80571*** (0.596976) |
| City fixed effect | Y | Y |
| Year fixed effect | Y | Y |
| N | 2156 | 2156 |
| R2 | 0.401 | 0.659 |
Note(s): The number in the parentheses indicates the standard error (clustered to the city level); N represents the number of data entries; R2 represents the goodness of fit; ***, ** and * represent significance at levels of 1, 5 and 10% respectively
Source(s): Authors' own work
Estimated result of PSM-DID
| Matching method | Nearest neighbor matching | Radius matching | Kernel matching |
|---|---|---|---|
| Treat×Post | 0.058** (0.028) | 0.162** (0.069) | 0.047** (0.017) |
| Control variable | Y | Y | Y |
| City fixed effect | Y | Y | Y |
| Year fixed effect | Y | Y | Y |
| N | 667 | 2078 | 1549 |
| R2 | 0.824 | 0.634 | 0.554 |
Source(s): Authors' own work
Other robustness tests
| Test method | Instrumental variable method | Shifting the opening year forward | Conversion measurement method | Excluding outliers | |||
|---|---|---|---|---|---|---|---|
| Breakdown | Stage 1 | Stage 2 | One year forward | Two years forward | Gross product of the logistics industry | Winsorized at 1% | Winsorized at 5% |
| Treat×Post | – | 0.136* | – | – | 974089.500*** | 0.009** (0.004) | 0.053* (0.032) |
| Pcity×Post | 0.207*** | – | – | – | – | – | – |
| Treat×Post01 | – | – | 0.114 (0.061) | – | – | – | – |
| Treat×Post02 | – | – | – | 0.113 (0.061) | – | – | – |
| Control variable | Y | Y | Y | Y | Y | Y | Y |
| City fixed effect | Y | Y | Y | Y | Y | Y | Y |
| Year fixed effect | Y | Y | Y | Y | Y | Y | Y |
| N | 2156 | 2156 | 2156 | 2156 | 2156 | 2156 | 2156 |
| R2 | 0.228 | 0.477 | 0.585 | 0.145 | 0.576 | 0.559 | 0.585 |
| F–stat | 990.350 | – | – | – | – | – | – |
Source(s): Authors' own work
Heterogeneity of goods type
| Type of goods | Products of value chain industries | Products of non-value chain industries |
|---|---|---|
| Treat×Post | 0.164** (0.067) | 0.057 (0.040) |
| Control variable | Y | Y |
| City fixed effect | Y | Y |
| Year fixed effect | Y | Y |
| Number of treatment group cities | 29 | 9 |
| N | 2030 | 1750 |
| R2 | 0.685 | 0.707 |
Source(s): Authors' own work
Geographical location heterogeneity
| Classification criteria | City location | Coastal/Inland | Central/Peripheral | |||||
|---|---|---|---|---|---|---|---|---|
| Sub-sample | Eastern China | Central China | Western China | Northeastern China | Coastal area | Inland | Central city | Peripheral city |
| Treat×Post | 0.051* (0.039) | 0.347* (0.223) | 0.116* (0.074) | 0.005* (0.094) | 0.021 (0.020) | 0.159** (0.077) | 0.107* (0.058) | 0.030* (0.017) |
| Control variable | Y | Y | Y | Y | Y | Y | Y | Y |
| City fixed effect | Y | Y | Y | Y | Y | Y | Y | Y |
| Year fixed effect | Y | Y | Y | Y | Y | Y | Y | Y |
| N | 1890 | 1722 | 1750 | 1666 | 1791 | 1988 | 1736 | 2044 |
| R2 | 0.527 | 0.854 | 0.844 | 0.845 | 0.835 | 0.898 | 0.838 | 0.629 |
Source(s): Authors' own work
Urban size heterogeneity
| City size | Super-large cities | Megacities | Type I large cities | Type II large cities | Small and medium-sized cities |
|---|---|---|---|---|---|
| Treat×Post | 0.0706739* (0.14436) | 0.1010606** (0.05212) | 0.2454778*** (0.06750) | 0.014689* (0.00559) | 0.0907041* (0.05863) |
| Control variable | Y | Y | Y | Y | Y |
| City fixed effect | Y | Y | Y | Y | Y |
| Year fixed effect | Y | Y | Y | Y | Y |
| Number of treatment group cities | 5 | 11 | 6 | 10 | 6 |
| N | 1694 | 1778 | 1708 | 1764 | 1708 |
| R2 | 0.739 | 0.755 | 0.628 | 0.655 | 0.556 |
Source(s): Authors' own work
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Acknowledgements
This study was funded by the National Natural Science Foundation of China (No. 72071133) and the Hebei Provincial Department of Education Humanities and Social Science Research Major Projects (No. ZD202309).