What 31 provinces reveal about growth in China

Summary

Focus

Chinese growth has been the main engine of the global economy for the past two decades. However, after the government announced in 2012 that it aimed to double real GDP by 2020, the country's aggregate growth statistics have been disappointingly flat and econometric analyses have been increasingly difficult to conduct. In this paper, instead of using aggregate growth statistics, we exploit a Chinese macroeconomic data set at the provincial level for 1999–2019 and take advantage of its larger statistical variation.

Contribution

We show that provincial data help to forecast aggregate economic activity in China and introduce a new alternative indicator for Chinese growth that will be updated and published quarterly. We also reveal that the growth determinants have changed substantially over time. The importance of government expenditure and credit is increasing, while that of investment and productivity in urban areas is falling. Lastly, we introduce an easy-to-update method for pinpointing changes in the underlying determinants of Chinese growth.

Findings

We find robust evidence that, when used, the richness of provincial data helps us understand and project Chinese aggregate growth. Our alternative growth indicator can reveal fluctuations not present in the official statistical series. Looking at the determinants of growth, we find that before 2010 Chinese growth was more dependent on productivity, urban employment and investment. After 2010, however, growth has been increasingly supported by government expenditure and credit. These new growth determinants now also apply more uniformly to a larger group of Chinese provinces.

Abstract

It is important to understand the growth process under way in China. However, analyses of Chinese growth became increasingly more difficult after the real GDP doubling target was announced in 2012 and the official real GDP statistics lost their fluctuations. With a dataset covering 31 Chinese provinces from two decades, we have substantially more variation to work with. We find robust evidence that the richness of the provincial data provides information relevant to understand and project Chinese aggregates. Using this provincial data, we build an alternative indicator for Chinese growth that is able to reveal fluctuations not present in the official statistical series. Additionally, we concentrate on the determinants of Chinese growth and show how the drivers have gone through a substantial change over time both across economic variables and provinces. We introduce a method to understand the changing nature of Chinese growth that can be updated regularly using principal components derived from the provincial data.

JEL Codes: C38, E01, E3, P2

Keywords: China, GDP, provincial data, business cycles, principal component