Consumption volatility is a key source of economic growth volatility; thus, it is an important factor in designing macroeconomic policy. The purpose of this paper is to investigate the factors that determine household consumption volatility, using urban household survey (UHS) data over the period 2002–2009 in 18 provinces in China.
Both a traditional variance decomposition method and an advanced variance decomposition method are used.
The traditional variance decomposition method suggests that heterogeneity of consumption goods is the key to analyze consumption volatility in China. Consumption of transportation makes the highest aggregate contribution and per-unit volatility in consumption volatility, whereas consumption of food makes the second highest aggregate contribution and the lowest per-unit volatility. Further investigation with the advanced variance decomposition method, which allows the authors to capture intertemporal dynamics and cross-household differences simultaneously, finds that the main factor determining the consumption volatility in China is intertemporal dynamics, rather than cross-household differences.
Future research could fruitfully explore four issues. First, consumption upgrading has increased the volatility of China’s household consumption. How much will this affect economic growth in China under its “new normal” conditions, and how should the Chinese government respond? Second, differences between UHS data and aggregate data in the calculations of consumption risk sharing need to be investigated. Third, it is important to investigate the channels through which the Chinese government can enhance its ability to spread consumption risks and thus reduce consumer consumption volatility. Finally, further study could extend the current 18 provinces to a nation-wide sample and update the data beyond 2009 to estimate the impact of the global financial crisis.
The results suggest that when policy makers design macroeconomic policies to smooth consumption volatility, they should consider heterogeneity in household consumption goods, regional disparity and intertemporal dynamics simultaneously. Well-managed volatility of Chinese household consumption can contribute to a stable economic growth in China and the world.
Well-managed volatility of Chinese household consumption can contribute to a stable economic growth in China and the world.
This paper fills this gap by using China’s UHS data to assess consumption volatility from the perspectives of heterogeneity in household consumption goods, cross-household differences and intertemporal dynamics. We make three contributions to the literature. The first contribution of this paper consists of demonstrating the contributions of heterogeneity in household consumption goods to consumption volatility. The second contribution consists of using the advanced variance decomposition method proposed by Crucini and Telmer (2012). This decomposition methodology allows the authors to examine whether household consumption volatility is due to cross-household differences or intertemporal dynamics. The third contribution is that this paper takes Chinese residents’ consumption fluctuations as the starting point to analyze the impact of consumption fluctuations on the future trend of China’s economy.
The authors are grateful to Binkai Chen, Chuanchuan Zhang, Dong Cheng, Chris Bei Zhang, Wenping Zheng, Peng Yao for their valuable comments and feedback. The authors are also grateful to three anonymous referees and the editor for providing valuable comments that significantly improve our paper. Jian acknowledges financial support from the National Social Science Fund of China (No. 14ZDB120), the Program for Young Talents (QYP1907) at the Central University of Finance and Economics, and the Excellent Young Scientist Program of Beijing’s Higher Education Institutions. Xunpeng acknowledges financial support from the National Natural Science Foundation of China (71828401, 71873029).
Yu, J., Shi, X. and Laurenceson, J. (2020), "Will the Chinese economy be more volatile in the future? Insights from urban household survey data", International Journal of Emerging Markets, Vol. 15 No. 4, pp. 790-808. https://doi.org/10.1108/IJOEM-04-2019-0290
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