Do Backbone High-Speed Rails Widen the North-South Gap in China?

AbstractThe economic disparity between southern and northern China has widened in the past decade. This article explores the roles of the north-south stretched backbone high-speed rails (HSRs) in the widened north-south economic gap in China. By constructing panel data of 283 cities between 2005 and 2016 and estimating the difference in GDP and per-capita GDP of northern and southern cities before and after the first north-south stretched HSR, we show that the north-south economic gap widened by about 8% as the opening of the Beijing-Shanghai HSR, the first north-south stretched HSR. Further channel analysis reveals that the north-south gaps in population, fixed asset investment, public expenditure, and the relative size of secondary industry to tertiary industry also widened. These results suggest that fast transportation improvement caused by long-distance backbone HSRs can contribute to accelerating the large-scale regional disparity.Keywords: High-speed railnorth-south economic gapChina AcknowledgmentsThe authors thank Miss Lin Zhang at the School of Economic and Management in Southeast University for her excellent research assistance and the funding support from the China National Social Science Foundations (Grant no. 22&ZD066 [Yanyan Gao] and 22BJY036 [Jun Sun]) and the Social Science Foundation of Jiangsu Province (Grant no. 22EYB016 [Jun Sun]).Disclosure statementThe authors declare no conflict of interest.Data availability statementThe data and STATA code replicating tables and figures in this article are available from the corresponding author upon request.Notes1 The southern and northern regions of mainland China are mainly divided by Qinling Mountain and the Huai River (see Figure 1).2 See https://baijiahao.baidu.com/s?id=1753244091798247208&wfr=spider&for=pc.3 See https://www.163.com/dy/article/GDP7CIOE0519DFFO.html.4 For example, the Jing-Hu HSR connected areas with a total population accounting for about 27% of national population, with 11 cities having a population over one million, with a total of 568 trains in service each day. This HSR line realized a net profit of 6.58 billion RMB in 2015, which increased to 9.5 billion in 2019. It has become the most profitable HSR line in the world. See https://en.wikipedia.org/wiki/Beijing%E2%80%93Shanghai_high-speed_railway.5 Panel B of Figure 2 is not contradictory to the result in Table B2 (Online Appendix), which shows that northern per-capita GDP is lower than the south. There are two differences. First, Panel B graphs the trends in the mean of logged per-capita GDP, while in Table B2 (Online Appendix) the per-capita GDP is the sum of northern (southern) GDP divided by the sum of northern (southern) population. Second, the northern and southern cities are defined by their centroids rather than simply by provinces used in Table B2 (Online Appendix). However, by graphing the trends in the same way to Table B2 (Online Appendix), i.e., the sum of northern (southern) GDP divided by the sum of northern (southern) population, we find consistent result to Table B2 (Online Appendix), that the northern per-capita GDP is lower than the southern per-capita GDP.6 Since the Jing-Guang HSR was opened one-year later than Jing-Hu HSR, we mostly introduce our results by referring to as the opening of Jing-Hu HSR. Of course, the average gap estimated is in relation to both HSRs. In heterogeneity effects analysis, we will separate the joint effects of both HSRs.7 While not reported but available upon request, the estimates increase to about 0.13 if we confine the data within provinces connected by the Jing-Guang HSR, suggesting that this longer backbone HSR produces a greater north-south economic division effect.8 We also conducted placebo tests in two dimensions. First, we moved forward the timing of the opening of Jing-Hu HSR and estimated its interaction effects with South within the data in years before 2011, when there was no backbone HSR opened. This leads to results similar to those in Figure 3, insignificant estimates for GDP and negative estimates for per-capita GDP. Second, we falsified a random south variable and estimated its interaction effect with the After variable on both outcomes. Results confirmed that no significant estimates can be attained in terms of the fake South variable. To save space, we do not report these results which, however, are available upon request.9 Based on our data, we can calculate that before 2011 the average share of tertiary GDP in northern cities is 34.8%, 1.81 percentage points lower than southern cities. Meanwhile, the average share of secondary GDP in northern cities is 51.03%, 3.81 percent points higher than southern cities. But, since then, northern cities experienced a faster increase in service industries than southern cities, realizing an average share in GDP of 37.45%, almost the same to that in the south. In contrast to the increase in service industries, the average share of secondary industries in northern cities dropped slightly to 49.87% after 2010. This is also in contrast to the case in the south, where the secondary-industry share increased from 47.22% to 49.82% during the same period.