Regional Clustering and Synchronization of Provincial Business Fluctuations in China

Abstract

In this article, we propose a novel, multilevel, dynamic factor model, to determine endogenously clustered regions for the investigation of regional clustering and synchronization of provincial business fluctuations in China. The parameter identification and model estimation was conducted using the Markov Chain Monte Carlo method. We then conducted an empirical study of the provincial business fluctuations in China (31 Chinese provinces are considered except Hong Kong, Macau, and Taiwan due to the data unavailability), which were sampled from January 2000 to December 2015. Our results indicated that these provinces could be clustered into four regions: leading, coincident, lagging, and overshooting. In comparison with traditional geographical divisions, this novel clustering into four regions enabled the regional business cycle synchronization to be more accurately captured. Within the four regional clusters it was possible to identify substantial heterogeneities among regional business cycle fluctuations, especially during the periods of the 2008 financial crisis and the 'four-trillion economic stimulus plan'.

Regional business cycles in emerging economies: a review of the literature

“…The determinants of synchronization are also estimate by using a gravity model (Huang et al. , 2015) and multilevel dynamic factor model (Song et al. , 2018).…”

“…Fang et al (2013), unlike previous studies, estimate the determinants of synchronization by OLS in a regression in which the dependent variable is the correlation between the cities' GDP growth rates. The determinants of synchronization are also estimate by using a gravity model (Huang et al, 2015) and multilevel dynamic factor model (Song et al, 2018). To cope with the effects of the idiosyncratic factors of regions, Liu et al (2020) use a hierarchical dynamic factors model to estimate the determinants of synchronization.…”

“…Narayan and Narayan (2008) provide a fifth methodology using the cointegration analysis to find the synchronization between regions. The sixth methodology is the Bai and Wang's (2015) dynamic multilevel factor model employed later by Song et al (2018) to determining synchronization. The seventh methodology is implemented by Tang (1998), Xu (2006), Lan and Sylwester (2011) and Huang et al (2015).…”

“…These divergent results on synchronization across Chinese regions are related to the fact that the regional division, which depends heavily upon geographical factors, cannot adequately describe its cyclical synchronization. Song et al (2018) propose to group the regions according to their similarities in terms of economic fluctuations. Accordingly, the authors suggest a classification grouping the 31 Chinese provinces into four regions, namely leading, coincident, lagging and overshooting.…”

“…Yet, there are other factors that explain the synchronization of regional cycles in China. These factors include fiscal divergence (Lan and Sylwester, 2010), the border-effect (Fang et al, 2013;Huang et al, 2015), shifts in the output structure (Gatfaoui and Girardin, 2015), the distance between provinces and economic size (Huang et al, 2015), geographic location (Song et al, 2018), the scarcity of resources, the growth of the financial system, the growth patterns of each region and the policies of market liberalization (Song et al, 2018;Liu et al, 2020).…”