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The impact of cities’ transportation network connections on regional market integration: the case of China’s urban agglomerations

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Abstract

Despite growing scholarly attention on the role of urban networks for understanding regional dynamics, there has been limited research examining the impact of cities’ transportation network connections on regional market integration. This paper analyzes China’s four major urban agglomerations: the Yangtze River Delta, the Pearl River Delta, Beijing-Tianjin-Hebei, and Chengdu-Chongqing. Applying a spatial Durbin model to cross-sectional datasets for 2019, we provide insight into the role of cities’ transportation network connections in promoting regional market integration, considering both the potentially heterogeneous impact of network connections and the interplay between network and agglomeration externalities. Our results indicate that: (1) cities’ transportation network connections have an inverted ‘U’-shaped effect on regional market integration; (2) transportation network connections have spatial spillover effects; (3) the positive impact of transportation network connections on regional market integration becomes more pronounced as city size decreases; and (4) there are neither complementary nor substitution effects between network and agglomeration externalities. We reflect on the broader implications of our empirical findings for regional development strategies and discuss possible avenues for further research.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Notes

  1. Recent examples are the state-orchestrated ‘The Yangtze River Delta Urban Cluster Development Plan (2016)’ and ‘Construction of a Unified National Market (2022)’.

  2. China’s 13th Five-Year Plan (2016–2020), 14th Five-Year Plan (2021–2025), Opinions of the CPC Central Committee and the State Council on Accelerating the Construction of a Unified National Market (2022).

  3. The pursuit of national urban agglomerations has been a central aspect of China's ‘new urbanization’ policy, including initiatives aimed at coordinating the development of the Beijing-Tianjin-Hebei, integrating the Yangtze River Delta, promoting growth in the Pearl River Delta, and constructing the Chengdu-Chongqing dual-city economic zone.

  4. The directly-controlled municipalities refer to the unique administrative divisions in China. These municipalities, including Beijing, Shanghai, Tianjin, and Chongqing, are under the direct jurisdiction of the central government and are on par with provinces in terms of administrative powers.

  5. The timing of data collection is set to filter the impact of the seasonal influences on train frequencies, include the weekends, summer/winter vacation for students and national holidays.

  6. The retail price indices of 16 commodities include food, beverage and tobacco, clothing, textiles, household appliances, office articles, daily necessities, sports and recreation, transportation and communication, furniture, cosmetics, jewelry, medicine, newspaper and magazine, fuels and construction materials.

  7. The reason for calculating the first-order difference of price differentials here is twofold: 1) considering market fragmentation as a specific case of ‘ice-berg’ costs, the convergence of relative prices can be reflected by the first-order difference of relative prices. 2) Our original data consists of year-on-year retail price indices; the first differences can be used to construct relative price differentials that capture time-varying features.

  8. The price differences in commodities can be attributed to the heterogeneity of the products and market fragmentation. So, we need filter the unobservable effects that caused by the specific attributes of products.

  9. We have considered other control variables, such as openness (with FDI as indicator), infrastructure (with road area per capita as indicator) and industrial structure (with ratio of tertiary industry output/total industry output). However, the estimates of these variables are not significant. Hence, we have removed these variables from the model and manuscript.

  10. The model selection test contains two statistics: LM-Error and LM-Lag. If the LM-Error is significant, then it indicates SEM; if a significant LM-Lag statistic is observed, SAR is considered as an option. If both statistics are significant, we perform a Robust LM test to gain further insight.

  11. A valid instrumental variable should be correlated to the endogenous variables and not to the dependent variable.

  12. Under the rating system for tourist attractions in China, 5A (AAAAA) scenic spots represent the highest level of tourist scenic spots certificated by the National Tourism Administration of China.

  13. The “Labor Law” that took effect in China on January 1, 1995 established a minimum wage system and set corresponding wage standards, which have had a significant impact on the regional labor mobility. Thus, we select the average level of income in 1995.

  14. The UTEST gives an exact test of the presence of an inverted U-shaped relationship between a predictor and a response variable on a specific interval after the benchmark estimation model.

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Authors and Affiliations

  1. Department of Geography, Ghent University, Ghent, Belgium

    Xueqing Liu, Ben Derudder & Frank Witlox

  2. Public Governance Institute, KU Leuven, Leuven, Belgium

    Ben Derudder

  3. Department of Land Resource Management, Nanjing Agricultural University, Nanjing, China

    Xueqing Liu, Bo Yu & Qun Wu

  4. Department of Urban and Regional Development Studies, Nicolaus Copernicus University, Torun, Poland

    Ben Derudder

  5. Department of Geography, University of Tartu, Tartu, Estonia

    Frank Witlox

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  1. Xueqing Liu
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Appendices

Appendix 1: Descriptive statistics for variables

See Table 4.

Table 4 Summary statistics of variables
Full size table

Appendix 2: Results of UTEST estimation

We conduct a UTEST estimation based on the results of the benchmark OLS model. The results in Table 5 reveal that the extreme point calculated is 3.55, and the value range of logDC is [1.4563, 7.0403]. The value of the extreme point falls within the data value interval and can reject the null hypothesis at the 1% statistical level. The slope of the upper bound has a negative value in the interval, indicating that transportation network connections and RMI have an inverted U-shaped relationship.

Table 5 Results of UTEST estimation
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Appendix 3: Results of the regional heterogeneity

See Table 6.

Table 6 Estimations of regional heterogeneity: different quantiles of city sizes
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Appendix 4: Dealing with endogeneity

The tests of our IV regressions are reported in Table 7, alongside the standard tests for the variables' relevance and exogeneity. The weak identification test of the Cragg–Donald Wald F statistic is 13.44 for 5A scenic spots and 18.47 for historical regional income, which are greater than the critical values of Stock–Yogo at a 10% maximal IV size at a 5% confidence interval. The results suggest the IVs are effective. From the endogeneity test results, both statistics are not significant at a 10% confidence level, so it can be concluded that our key variable of interest—DC—can be treated as exogenous.

Table 7 Instrumental variable tests
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We reported the second-stage result of IV regression in Table 8 (see Model 1). Following Pan et al. (2020), we also apply the IVs in the SARAR model (see Model 2–4); our results provide consistent and compelling evidence for the main findings and address the spatial autocorrelation and endogeneity issues.

Table 8 Results of the SARAR-IV model
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Appendix 5: Robustness tests

We use four different methods to examine the robustness of our empirical results. First, as the random forest method outperforms the traditional multivariate linear regression in fitting and prediction (Cootes et al., 2012), we adopt the random forest method to randomly fit 100 rounds to generate a predicted dependent variable RMI and conduct the regression analysis. Second, as China’s high-speed rail enhances regional connectivity, we use different independent variables, i.e., the degree centrality of urban network constructed by high-speed rail frequencies to proxy transportation network connections (Huang et al., 2020). Third, we use the spatial estimation method of generalized spatial two-stage least squares, which makes the spatial inference more reliable (Kelejian & Prucha, 1998; Jin & Lee, 2013). And finally, we introduced the inverse distance matrix as an alternative. Detailed results are tabulated in Table 9. The changing directions of coefficients are consistent with our estimates, indicating that the main findings also remain robust with different estimations.

Table 9 Estimates of robustness tests
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Liu, X., Derudder, B., Yu, B. et al. The impact of cities’ transportation network connections on regional market integration: the case of China’s urban agglomerations. GeoJournal 88, 6539–6559 (2023). https://doi.org/10.1007/s10708-023-10984-6

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