Convergence clustering in the Chinese provinces: New evidence from several macroeconomic indicators

Abstract

In this paper, the convergence clustering in 31 Chinese provinces regarding several important economic indicators over the period 1952 to 2016 was empirically investigated.Several provincial clusters were identified in the per capita (real) gross domestic product (GDP), consumption-income ratio, retail price, and consumer price inflation rates, using a club convergence and clustering procedure. The empirical findings are as follows. First, it was found that all series of the original data contain a significant nonlinear component.Second, it was observed that there are five significant clusters for the per capita income in China. Third, it was found that there are four significant clusters for the consumptionincome ratio. Fourth, it was observed that there are four significant clusters for the retail inflation rates and two significant clusters for the consumer inflation rates in China.These results will enable local and central planners to implement economic growth, savings and price adjustment policies for different groups of provinces.

A review of Phillips‐Sul approach‐based club convergence tests

“…In this case, the authors controlled for the global financial crisis effect, which confirms the conclusions drawn by Antonakakis et al ( 2017) on the robustness of the standard PS approach to structural breaks. Gozgor et al (2019) also proposed the modification of the log t regression to take non-linearities into account. To this end, they added the square of logt to Equation (5) as follows:…”

“…Finally, it should be emphasised that, apart from the aforementioned work by Kerner and Wendler (2022), no other studies have used the approaches outlined by Arestis et al (2017), Gozgor et al (2019), Johnson (2020) and Kwak (2022) to identify convergence clubs.…”

“…Table 6 proposes a development path for the PS approach in the future by juxtaposing all developed clustering algorithms and log t regressions. In particular, it would be extremely interesting to investigate how the modifications to the standard log t regression introduced by Arestis et al (2017), Gozgor et al (2019) and Kwak (2022) perform with the clustering algorithms outlined by von Lyncker and Thoennessen (2017) and Haupt et al (2018). Ultimately, it would also be fruitful to investigate the usefulness of the modification outlined by Johnson (2020) for identifying convergence clubs.…”

“…Gozgor et al. (2019) also proposed the modification of the log t regression to take non‐linearities into account. To this end, they added the square of log t to Equation () as follows: $$$$\begin{equation} \log \left(\frac{{H}_{1}}{{H}_{t}}\right)-2\, \log L\left(t\right)=\widehat{a}+\widehat{b}\log t+\widehat{c}(\log t)^{2}+{\widehat{u}}_{t}.$$…”

Spatial Development Prospects for Geostrategic Territories of the Russian Federation

“…Исходя из зависимости процесса конвергенции от специфики набора экономических специализаций (Brenner & Kauermann, 2016;Chapman & Meliciani, 2017), будущие межрегиональные различия можно оценить по сочетанию перспективных экономических специализаций регионов России, приведенных в приложении 1 к Стратегии. Методика выявления клубов конвергенции обычно опирается на иерархический кластерный анализ (Barrios et al, 2019;Gozgor et al, 2019).…”