Abstract
Purpose
The China–Europe Railway Express (CR Express) in Chongqing has operated regularly and undergone large-scale development. Its impact on Chongqing’s economic growth has become increasingly evident, necessitating further research in this field.
Design/methodology/approach
This study employs the opening of CR Express as a quasi-natural experiment, designating Chongqing, which inaugurated the CR Express in 2011, as the treatment group. 13 provinces and cities that had not yet opened the CR Express until 2017 were selected as the control group. Utilizing panel data from 14 provinces across China spanning from 2006 to 2017, the synthetic control method (SCM) is employed to synthetically construct Chongqing. To quantify the difference in economic development levels between Chongqing with the operation of the CR express and Chongqing without its operation. Key metrics such as gross domestic product (GDP), per capita GDP, total retail sales of consumer goods, import and export value and the proportions of the secondary and tertiary industries are employed to measure urban economic development capabilities. Chongqing is designated as the experimental group, and a double-difference model is constructed to regress the operation of the CR Express against economic development capabilities. Robustness tests are conducted to validate the analytical results.
Findings
The results indicate that, compared to provinces without the operation of the CR Express, the initiation of the CR Express in Chongqing significantly enhances the economic development level of the city. The opening of the CR Express exhibits a pronounced positive impact on Chongqing’s economic development, and these findings remain robust and effective even after parallel trend tests and placebo tests.
Originality/value
The study represents an expansion of the theoretical framework. In contrast to previous studies that relied on a single indicator such as GDP, this study selects six indicators from the dimensions of economy, trade and industry to measure regional economic development capabilities. Furthermore, employing the grey relational analysis method, the study screens these indicators, thereby providing a theoretical basis for the selection of indicators for measuring regional economic development capabilities.
Keywords
Citation
Zhang, R. and Li, Q. (2024), "A synthetic difference-in-differences model for the influence of CR Express on Chongqing’s economy", Railway Sciences, Vol. 3 No. 3, pp. 295-310. https://doi.org/10.1108/RS-03-2024-0008
Publisher
:Emerald Publishing Limited
Copyright © 2024, Rong Zhang and Qi Li
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 China–Europe Railway Express (CR Express) has revolutionized the international trade transport landscape traditionally dominated by sea and air transport. It advances the reform and development of global supply chains and plays a crucial role in facilitating import and export trade and international economic cooperation between cities where it operates and other countries worldwide. Government subsidies have reduced the freight costs of CR Express by 60%, providing them with a significant competitive advantage over maritime shipping in terms of cost. Specifically, this advantage is more pronounced in the category of information technology (IT) products within CR Express service (Jiang, Shen, Peng, & Yu, 2018). CR Express stimulates greater trade growth in the cities of origin and destination than in the cities along its route (Liu & Li, 2020). Since its opening, CR Express (Chongqing) has experienced substantial growth, with an increasing number and frequency of train trips. Achieving a two-way balance, the volume of loaded containers has consistently risen, and the quality of CR Express operations has improved continuously. Moreover, there has been a gradual diversification in the types of goods being transported. The CR Express (Chongqing) catalyzes the transformation and upgrading of Chongqing’s transport, foreign trade growth and industrial structure changes. It stands as an important initiative towards accelerating this city’s quest to become a pacesetter for opening-up in China’s inland areas.
Following CR Express’s opening, various studies have shed light on its impacts on import and export trade, production factors and industrial structure. Most studies typically analyze China’s trade relations and outward foreign direct investment with countries along the “Belt and Road” through the construction of classical gravity models or multiple difference models (Lu, Dong, & Ye, 2020; Julia, Mathilde, & Anthony, 2020). CR Express significantly promoted my country’s export trade to the regions along the “Belt and Road” through the two intermediary mechanisms of trade cost effect and policy subsidy effect (Yang & Li, 2023). Based on the difference-in-differences (DID) method, study reveals that CR Express operations have a significant impact on enhancing trade opening-up extent in the western regions and large cities of China. However, they did not yield a significant impact on the eastern and central regions and small and medium-sized cities (Fang, Lu, & Wei, 2020). The opening of CR Express has significantly enhanced the bilateral trade potential between countries along the routes and China (Huang, 2021) and has a particularly significant impact on industrial upgrading in the central and western regions of China. CR Express, with its reliance on central areas, transport corridors and inland free trade zones amplifies its effect on industrial upgrading (Li, Wen, & Wang, 2021). Through research on the total factor productivity of exporting enterprises, Fang and Zhao (2022), Xie and Fang (2022) and Xie (2021) concluded that the opening of the CR Express has increased the productivity of exporting enterprises and significantly enhanced the total factor productivity of the cities where they operate. The Belt and Road Initiative can drive the upgrading of the urban industrial structure by leveraging the technological promotion effect, industrial transfer effect and tertiary industry development effect (Fang & Zhao, 2021) and can promote enterprise upgrading through research and development innovation, and enterprise upgrading is also a micro-level manifestation of industrial structural upgrading. Therefore, this indirectly demonstrates that the Belt and Road Initiative can influence industrial structural upgrading (Wang & Lu, 2019).
Over the past decade, CR Express (Chongqing) has operated regularly and undergone large-scale development. Its impact on Chongqing’s economic growth has become increasingly evident, necessitating further research in this field. Considering the changes in Chongqing’s economic growth following the opening of the CR Express (Chongqing), both the synthetic control method (SCM) and DID method are used to quantitatively analyze the contribution of CR Express (Chongqing) to the city’s economic growth. The relationship between its opening and the city’s economic development is examined. Additionally, a placebo test is conducted to demonstrate the robustness of the study conclusions. This paper aims to verify the positive impact of CR Express (Chongqing) on the regional economic development of this city, specifically in the context of China–Europe connectivity under the Belt and Road Initiative. While previous studies have primarily focused on foreign trade, this study expands on the existing theories by broadening the scope of the study. In terms of study methods, this study employs a combination of the SCM and the DID method. In terms of the selection of explained variables, previous studies often relied on a single indicator, such as gross domestic product (GDP) or GDP per capita. In contrast, this study utilizes a set of six indicators concerning the economy, trade and industry to comprehensively measure the regional economic development capacity. Moreover, grey relation analysis is utilized to filter and select the indicators, providing a theoretical basis for choosing indicators that measure the index of regional economic development capacity.
2. Mechanism underlying the impacts of CR Express operations on regional economic development capacity
Following the operation of the first train in Chongqing in 2011, many cities embraced CR Express operations, resulting in a positive trend of growth. The number and density of trips have increased, while the quality of operations has steadily improved. Therefore, the impact of CR Express on the regional economy has gradually become evident. It serves as a new platform for the trade development of China, facilitating the opening-up of inland cities in China. It is closely intertwined with the economic progress of the cities.
The regular operations of CR Express have promoted the economic and trade development of cities where it operates. Moreover, CR Express has expanded China’s network of international trade transport services while effectively reducing transport costs for both inland and border ports. Compared to sea transport, CR Express offers notable advantages in terms of timeliness and service stability. It is particularly efficient in transporting time-sensitive goods with high added value. Furthermore, CR Express’s timeliness benefits enterprises by reducing inventory and minimizing tie-up capital. This, in turn, improves capital turnover. In addition, CR Express presents a cost advantage compared to air transport. The regular operations of CR Express can enhance logistics services in CR Express operating areas and facilitate regional business clusters, enabling enterprises involved to intensively participate in the global industrial chain. As a result, it accelerates the division of labor and cooperation among enterprises, reducing unnecessary links and costs in the industrial chain. This will lead to economies of scale, intensive utilization of resources and the production of more cost-effective products, thereby promoting a well-structured industrial development in cities where CR Express operates.
Figure 1 illustrates the theoretical framework of economic development in areas under the impact of CR Express.
This paper posits that the opening of CR Express can enhance regional economic development.
3. Measurement and analysis of index of regional economic development capacity
3.1 Indicator establishment
Regional economic development capacity serves as a comprehensive measure of regional economic development. Based on the principles of diversity, measurability and independence, 11 indicators are selected from three fields, i.e. economy, trade and industry, to establish the index of regional economic development capacity through weighted addition. See Table 1 for details.
3.2 Indicator screening and weight determination
Since CR officially launched the family brand “CHINA RAILWAY Express” on June 8, 2016, Chongqing, Chengdu, Xi’an, Zhengzhou and Urumqi have operated a large number of CR Express trains (See Figure 2 for details). During the 2016–2022 period, the total number of CR Express trips of these five cities averagely accounted for 81.4% of the annual total number (See Table 2 for details). In 2020, these representative cities were listed as CR Express hub demonstration cities. Therefore, the five cities mentioned above are used as the objects of this study.
After factors with impacts on regional economic development capacity are qualitatively analyzed, the impact extent and significance of each factor need to be quantitatively analyzed. The grey relation analysis is employed to assess the relation degree between the number of CR Express trips and each economic indicator to indicate the intensity of CR Express’s impacts on all aspects of the regional economy. The calculation steps are described below:
Step 1: Determine the reference sequence and comparison sequence. The number of CR Express trips is used as the reference sequence and economic indicators are used as the comparative sequence. The sequence
Step 2: Non-dimensionalize each indicator with the normalization method:
After the sequence dimension difference is processed, the value of each variable sequence is within [0, 1].
Step 3: Calculate the coefficient of grey relation between factors:
Step 4: Calculate the grey relation degree:
Use the MATLAB tool to perform relevant operations of grey relation analysis and work out the values of the relation degree between CR Express and economic indicators. The results are given in Table 3 below.
According to the results above, the indicators with an average relation degree of less than 0.6 are excluded. The SPSS software is used to check the multicollinearity of the remaining indicators, including the regional GDP, regional GDP per capita, total retail sales of consumer goods, total import and export value, share of the secondary industry, share of the tertiary industry and railway freight volume indicators. The number of CR Express trips of each province/municipality represents the regression variable. The variance inflation factors (VIFs) of the regional GDP, regional GDP per capita, share of the secondary industry and share of the tertiary industry are all greater than 10. This indicates that the multicollinearity of the model is serious, so the regional GDP per capita is excluded. As products from the manufacturing sector constitute the majority of the CR Express freight, the share of the tertiary industry is excluded. Then, the model is regressed again. The VIFs of all the variables are less than 10, indicating that the multicollinearity of the model is fully eliminated.
To enhance the robustness of assessment results, the entropy method is used to determine the weight of each indicator of regional economic development capacity. Results of the weights of regional economic development capacity indicators are given in Table 4.
3.3 Measurement model for index of regional economic development capability
A weighted regression model for the index of regional economic development capacity is established as follows:
4. Model settings and data description
4.1 Model settings
According to the natural experiment concept, to study the impact of CR Express on Chongqing’s economic development, it is necessary to control the impact of other factors. Based on the SCM proposed by Abadie and Gardeazabal (2003), the economic situation of Chongqing before the opening of CR Express is fitted through the weighted synthesis of 13 provinces without CR Express operations before 2017. The results are used for comparison. The model is as follows:
For the provinces with CR Express operations, the
The “Synthetic Chongqing” obtained by weighting the data of provinces without CR Express operations can be used to simulate how Chongqing’s economy would develop without CR Express (Chongqing) operations. The difference in regional economic development capacity between “Synthetic Chongqing” and Chongqing with CR Express (Chongqing) operations represents the impact of CR Express (Chongqing) operations on the regional economic development capacity of this city.
The “Synthetic Chongqing” obtained by SCM serves as the control group for further study by DID. The model is established as follows:
4.2 Data sources
The data of the model are sourced from the statistical yearbooks of the provinces and municipalities of the corresponding years and the website of the National Bureau of Statistics. Due to the significant rise in the number of provinces with CR Express operations after 2017, excluding these provinces from the analysis would make the sample data too little. This decrease in sample size may impact the robustness and validity of the experiment. Therefore, the data from the 12 years of 2006–2017 are used for the study.
CR Express’s opening years in various provinces and municipalities are listed in Table 5.
The provinces without CR Express operations before 2017 constitute the control group for Chongqing. However, certain provinces are excluded from the control group for two reasons: (1) These provinces had already opened CR Express operations before 2017; (2) Xizang, Hong Kong, Macao and Taiwan have limited or missing data.
4.3 Description of variables
- (1)
Explained variable
The explained variable refers to the indicator system established in Chapter 2, and the index of regional economic development capacity (
- (2)
Explanatory variables
- (3)
Control variables
With reference to the results of previous studies, the endogenous problems caused by missing variables of the model are alleviated as much as possible. Finally, the railway freight volume (
The data in this study undergo logarithmic value processing to establish the six indicators for the regional economic development capacity. In this way, the measurement problems caused by different dimensions of data are eliminated. Additionally, the data are weighted using the entropy value method based on predetermined weights. This process determines the indicator data for the regional economic development capacity of each province in each year. Furthermore, to eliminate potential heteroscedasticity, the explained variable and the control variable undergo logarithmic transformations in the empirical study. Refer to Table 6 for the descriptive statistical results.
5. Empirical study
5.1 Analysis of empirical results
- (1)
SCM
The opening of CR Express (Chongqing) is considered a natural experiment and 13 provinces of the control group are weighted to form “Synthetic Chongqing”. Fitting is performed to acquire the regional economic development capacity in the case of Chongqing without CR Express operations. The result serves as the control group for Chongqing to investigate the impact of CR Express operations (Chongqing-Xinjiang-Europe) on Chongqing’s regional economic development capacity. The weight components of “Synthetic Chongqing” are given in Table 7.
Table 8 gives the mean values of all relevant variables of “Synthetic Chongqing” and Chongqing before the opening of CR Express (Chongqing-Xinjiang-Europe) (referred to as “before intervention”). It can be seen that their deviations are very small (within 8%). Furthermore, the mean square prediction error (MSPE) before intervention is very small, only 0.111716. All these indicate a good fitting effect, so “Synthetic Chongqing” is a suitable control group for Chongqing.
Figure 3 illustrates the curve fitting of Chongqing and “Synthetic Chongqing”. It is evident that before 2011, namely, before the opening of CR Express (Chongqing), the solid line representing Chongqing and the dotted line representing “Synthetic Chongqing” exhibited a similar upward trend. This suggests that the “Synthetic Chongqing” successfully fitted with Chongqing before CR Express’s opening. So, “Synthetic Chongqing” is a suitable control group for Chongqing.
In early 2011 (the data in 2010 can be deemed as data of early 2011 due to their year-end aggregation), a gap started to emerge between the solid line and the dotted line. The solid line representing Chongqing rises at a much higher rate, and the gap between Chongqing and “Synthetic Chongqing” continues to widen. This shows that compared with “Synthetic Chongqing”, Chongqing’s regional economic development improved rapidly after the opening of CR Express (Chongqing), demonstrating that the opening can promote the regional economic development capacity of this city.
Figure 4 shows the impact of CR Express (Chongqing-Xinjiang-Europe) on Chongqing’s regional economic development capacity, i.e. the difference between the real export value and the synthetic export value. It can be seen that before 2011, the economic development gap between Chongqing and “Synthetic Chongqing” fluctuated around the zero axis within a range of plus or minus 0.5. However, after 2011, the impact broke through the zero axis and experienced a sharp rise. It reached a value of 3.5 at the end of the sample period.
Figures 3 and 4 reveal that there existed a gap between the actual value and the synthetic value of Chongqing’s regional development capacity before the opening of CR Express. However, this gap remained within a certain range. Following the official opening of CR Express (Chongqing), the gap widened noticeably. This suggests that the opening of CR Express has significantly enhanced the economic development capacity of the city and contributed to the economy.
- (2)
DID method – investigating the impact of CR Express (Chongqing) from the perspective of statistics
A DID model is created to assess the impact of CR Express on Chongqing’s economic development. “Synthetic Chongqing” serves as the control group and Chongqing as the experimental group for DID analysis. Table 9 shows the results based on empirical model (3).
According to the regression results, the SCM-DID policy treatment effect of CR Express (Chongqing) is 0.32298. It shows that compared with provinces and municipalities without CR Express operations, the index of regional economic development capacity of Chongqing has increased by 0.32298 after the opening of CR Express. The specific effect is shown in Figure 5. The core explanatory variable
5.2 Robustness tests
- (1)
Parallel trend test
A parallel trend test is conducted on the DID model for “Synthetic Chongqing” fitted by SCM. The results of the parallel trend test are shown in Figure 6. It can be seen that Chongqing and “Synthetic Chongqing” exhibited the same development trend before 2011, and the result passed the parallel trend test.
A hypothetical opening year is defined. This allows for the grouping of three years before the opening, the year of opening and three years after the opening as the policy implementation years for the treatment group. Seven indicator variables are then established: Before3, Before2, Before1, Current, After1, After2 and After3. A regression analysis is conducted using the DID model. This involves performing separate regressions each year. Figure 7 presents the coefficient regression results of the parallel trend test of the DID model. It can be seen that the regression coefficient of the first three years before the policy implementation is not significant, indicating that the result passes the placebo test. This demonstrates that the policy effect previously identified is reliable. However, the regression coefficient became significant immediately after 2011, indicating that the opening of CR Express has significantly promoted Chongqing’s economy.
- (2)
Placebo test
The placebo test primarily aims to examine whether changes in the impacted objects are only related to the policy implementation. Following the practices of Eliana, Alberto, and Suzanne (2012) and Li, Lu, and Jin (2016) a hypothetical treatment group is defined, an interaction term
Based on the experiment above, an assessment is conducted on whether Chongqing’s economic development is under the impacts other than the opening of CR Express. If the estimated coefficients of
6. Conclusion
In this study, the opening of CR Express (Chongqing) is deemed as an exogenous impact. The panel data in 2006–2017 of several provinces and Chongqing Municipality are utilized to establish a panel regression model employing the SCM and DID methods. Regression analysis is performed on the opening of CR Express (Chongqing) and Chongqing’s economic development capacity. The data of 14 provinces and municipalities are used for the study. The “whether CR Express opens” variable is treated as a dummy variable and deemed as the core explanatory variable. The regression results demonstrate that the opening of CR Express significantly promotes the regional economic development capacity. Then, a parallel trend test and a placebo test are conducted on the regression results, thereby validating the robustness of the study conclusions. Therefore, it is essential for Chongqing to continue vigorously implementing the Belt and Road Initiative in the following aspects:
Chongqing, being located in southwest China without convenient sea transport channels like coastal cities, can still leverage its geographical advantage in terms of connectivity between Asia and Europe. Inland cities are comparatively closer to new trade partners, presenting a significant opportunity. The following efforts should be undertaken: (1) Enhancing CR Express improvement and expanding the construction of supporting facilities for CR Express guided by the comprehensive top-level design and reasonable arrangement of the government; (2) Increasing the freight volume and the number of train trips of CR Express; (3) Continuously increasing the volume of import and export trade; (4) Attracting international direct investment; (5) Implementing a reasonable industrial distribution plan and promoting business clusters, thus enhancing the economic development capacity of Chongqing.
Chongqing should leverage the capital flow effect and regional innovation effect of CR Express to boost its industrial upgrading and enhance its comprehensive competitiveness. The opening of CR Express will stimulate enterprises to increase investment in scientific research and innovation. Additionally, the improved connectivity with the European market enables Chongqing to access high-end technology and management expertise at a reduced cost, thereby facilitating industrial upgrading and enhancing regional economic strength.
Chongqing should enhance the advantages of CR Express and develop it into a well-established international freight channel. CR Express services are more suited for time-sensitive products with high added value. Therefore, Chongqing should capitalize on the unique advantages of CR Express as a distinct third transportation option, different from sea and air transport. By consistently securing freight orders for such products and generating continuous value and profit, Chongqing should leverage CR Express to reap benefits for its development. Consequently, CR Express will evolve into an independent and mature international transport brand.
Figures
Figure 1
Mechanism underlying CR express’s impact on promoting regional economic development
Figure 2
Number and share of CR express trips of five hubs in 2016–2022
Figure 3
Curve fitting of Chongqing and “synthetic Chongqing”
Figure 4
Differences in regional economic development capacity between Chongqing and “synthetic Chongqing”
Figure 5
Treatment effect of SCM-DID model
Figure 6
Parallel trend test of SCM-DID model
Figure 7
Results of parallel trend test of SCM-DID model
Figure 8
Placebo test
Indicators of regional economic development capacity
| Type of variables | Field | Indicator | Unit |
|---|---|---|---|
| Regional economic development capacity | Economy | Regional GDP | RMB 100 million yuan |
| Regional GDP per capita | RMB yuan | ||
| Fixed asset investment | RMB 10,000 yuan | ||
| General public budget revenue | RMB 10,000 yuan | ||
| Trade | Total retail sales of consumer goods | RMB 100 million yuan | |
| Total import and export value | RMB 100 million yuan | ||
| Amount of foreign direct investment | USD 100 million | ||
| Industry | Share of the secondary industry | % | |
| Share of the tertiary industry | % | ||
| Railway freight volume | 10,000t | ||
| Operating length of railway | 10,000km |
Source(s): Authors’ own
Number of CR express trips of the five hubs in 2016–2022
| Year | Chongqing | Chengdu | Xi’an | Zhengzhou | Urumqi | Number of CR express trips | Share of CR express trips of five hubs (%) |
|---|---|---|---|---|---|---|---|
| 2016 | 313 | 460 | 151 | 251 | 223 | 1,702 | 82.14 |
| 2017 | 530 | 1,020 | 194 | 501 | 806 | 3,673 | 83.07 |
| 2018 | 1,002 | 1,591 | 1,235 | 752 | 1,002 | 6,363 | 87.73 |
| 2019 | 1,513 | 1,576 | 2,133 | 1,000 | 1,102 | 8,225 | 89.05 |
| 2020 | 2,604 | 2,177 | 3,720 | 1,106 | 1,068 | 12,406 | 86.05 |
| 2021 | 2,480 | 2,398 | 3,800 | 1,508 | 1,000 | 15,183 | 73.67 |
| 2022 | 3,101 | 2,297 | 4,639 | 1,167 | 65 | 1,6562 | 68.04 |
Source(s): https://www.crexpress.cn/#/home and statistical communique of Zhengzhou over these years
Relation degree between CR express and economic indicators
| Indicator\operating province/municipality | Hebei | Chongqing | Sichuan | Shaanxi | Xinjiang | Average relation degree |
|---|---|---|---|---|---|---|
| Regional GDP | 0.6612 | 0.7532 | 0.7122 | 0.8542 | 0.6496 | 0.7261 |
| Regional GDP per capita | 0.6772 | 0.6212 | 0.6857 | 0.7542 | 0.6496 | 0.6776 |
| Fixed asset investment | 0.5017 | 0.5844 | 0.5219 | 0.4511 | 0.5623 | 0.5243 |
| General public budget revenue | 0.4304 | 0.5331 | 0.5711 | 0.4256 | 0.5211 | 0.4963 |
| Total retail sales of consumer goods | 0.6987 | 0.6118 | 0.6624 | 0.6792 | 0.6747 | 0.6654 |
| Total import and export value | 0.7573 | 0.8852 | 0.8008 | 0.8144 | 0.8756 | 0.8267 |
| Amount of foreign direct investment | 0.5366 | 0.5867 | 0.5921 | 0.5304 | 0.4211 | 0.5334 |
| Share of the secondary industry | 0.8124 | 0.8641 | 0.8011 | 0.7895 | 0.6417 | 0.7818 |
| Share of the tertiary industry | 0.7211 | 0.6241 | 0.6351 | 0.7117 | 0.6842 | 0.6752 |
| Railway freight volume | 0.7851 | 0.8624 | 0.8103 | 0.7941 | 0.8213 | 0.8146 |
| Length of operating railways | 0.4622 | 0.7104 | 0.6512 | 0.4851 | 0.4311 | 0.5480 |
Source(s): Authors’ own
Measurement indicator system for regional economic development capacity
| Indicator | Weight |
|---|---|
| Regional GDP ( | 0.22 |
| Total retail sales of consumer goods ( | 0.18 |
| Total import and export value ( | 0.25 |
| Share of the secondary industry ( | 0.20 |
| Railway freight volume ( | 0.15 |
Source(s): Authors’ own
CR express’s opening years in provinces and municipalities
| Province/municipality | Opening year | Province/municipality | Opening year |
|---|---|---|---|
| Beijing | – | Henan | 2014 |
| Tianjin | 2017 | Hubei | 2015 |
| Hebei | 2016 | Hunan | 2013 |
| Shanxi | 2017 | Guangdong | 2017 |
| Inner Mongolia | 2017 | Guangxi | – |
| Liaoning | 2015 | Hainan | – |
| Jilin | 2016 | Chongqing | 2011 |
| Heilongjiang | 2016 | Sichuan | 2013 |
| Shanghai | – | Guizhou | – |
| Jiangsu | 2014 | Yunnan | 2016 |
| Zhejiang | 2015 | Shaanxi | 2015 |
| Anhui | 2015 | Gansu | 2016 |
| Fujian | 2016 | Qinghai | 2017 |
| Jiangxi | – | Ningxia | – |
| Shandong | 2017 | Xinjiang | 2016 |
| Xizang | – |
Source(s): https://www.imsilkroad.com/
Descriptive statistical results of variables
| Variable | Mean value | Standard deviation | Minimum value | Maximum value | |
|---|---|---|---|---|---|
| Explained variable | 6.01456 | 0.9780979 | 3.8444 | 7.9501 | |
| Explanatory variable | 0.5833333 | 0.4944805 | 0 | 1 | |
| Explanatory variable | 0.0714286 | 0.2583093 | 0 | 1 | |
| Core explanatory variables | 0.0416667 | 0.2004237 | 0 | 1 | |
| Control variable | 8.651606 | 1.351971 | 6.1334 | 11.2894 | |
| −1.812568 | 0.5485251 | −2.6443 | −0.1567 | ||
| 9.330802 | 0.5529073 | 8.0577 | 10.5914 | ||
| 9.685325 | 0.5640076 | 8.298 | 10.9851 |
Source(s): Authors’ own
Weight components of “synthetic Chongqing”
| Province/municipality | Jiangxi | Beijing | Ningxia | Qinghai |
|---|---|---|---|---|
| Weight | 0.563 | 0.223 | 0.136 | 0.078 |
Source(s): Authors’ own
Comparison of mean values of variables between Chongqing and “synthetic Chongqing”
| Variable | Chongqing | “Synthetic Chongqing” | Deviation (%) |
|---|---|---|---|
| 7.66 | 8.27 | 8.02 | |
| −1.43 | −1.49 | 3.86 | |
| 8.85 | 8.92 | 0.72 | |
| 9.58 | 9.21 | −3.84 | |
| 5.27 | 5.27 | −0.01 | |
| 5.57 | 5.57 | 0.04 | |
| 5.79 | 5.79 | −0.04 |
Source(s): Authors’ own
DID analysis results of “synthetic Chongqing” and Chongqing
| ATT | Std. Err | t | p>|t| | [95%Conf.Interval] | ||
|---|---|---|---|---|---|---|
| 0.32298 | 0.09721 | 3.32 | 0.001 | 0.13246 | 0.51350 | |
Source(s): Authors’ own
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