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Impact of volatility in China’s economy on growth: the perspective of resource reallocation among firms

FANG Fuqian1 XING Wei1 WANG Kang2

(1.School of Economics, Renmin University of China)
(2.School of Statistics, Renmin University of China)

【Abstract】After the 2008 global financial crisis, China’s economy has witnessed sharp volatility and continuous drop in growth rate. Based on the creative destruction theory by Schumpeter, this paper analyzed the impact of China’s short-term economic volatility on growth by improving the AABM model and empirically tested some findings of the theoretic model with the provincial panel data of China in 1979–2012. It is found that volatility and growth of China’s economy had a negative correlation before 1978; these two had a positive correlation between 1978 and the round of economic downturn; they have become negatively correlated again since the Chinese economy entered a new normal. That is, in the economic history of China, the relation between economic volatility and growth shows the phased feature of being “negative—positive—negative” due to different reasons. The unleashing of benefits brought about by reforms is the primary contributor to the positive correlation after 1978 when economic growth was stimulated by eliminating low-efficiency firms and investment projects and optimizing the allocation of resources among firms through economic volatility. As low-efficiency firms and investment projects are reduced and China’s reform has entered a deep-water zone, the facilitating effect of economic volatility on economic growth decreases progressively. The fundamental solution to Chinese economic downturn is to optimize the resource reallocation among regions and industries through marketization promotion and financial development brought by the deepening structural reform.

【Keywords】 economic volatility; economic growth; heterogeneous firm; cleaning effect;

【DOI】

【Funds】 Major Program of National Social Science Foundation of China (15ZDB133)

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    Footnote

    [1]. ① The data in Figure 1 are the periodic terms of the mean value of the real GDP per capita growth rate and standard deviation. The calculation is as follows. The GDP per capita during 1978–2014 in China are adjusted by the GDP deflator into real values with 1978 as the base period, the real GDP per capita growth rate is obtained based on the real GDP per capita, the mean value and standard deviation of the real GDP per capita growth rate are calculated with five years as a calculation interval, and the periodic term of the mean value and standard deviation of the per capita real GDP growth rate is obtained with the HP filter method, with the trend variation factor λ = 100. [^Back]

    [2]. ② Aghion et al. (2010) constructed a corporate decision-making model incorporating short-term investment and long-term investment, in which each firm can survive three periods. In the first period, the firm determines the amount of funds for short-term investment and long-term investment, among which the former brings a return in the second period, while the latter brings a return in the third period. Though it may be subject to a liquidity shock, long-term investment is conductive to effective labor accumulation. [^Back]

    [3]. ③ Equations (1)–(9) and (12)–(15) in the theoretical model basically follow the construction method of the AABM model, but to facilitate the analysis, three survival periods are adjusted to two. Equations (10), (11) and (16) are simplified and expanded based on the basic model for better calibration and simulation to intuitively reflect the correlation between economic volatility and economic growth. Equations (17)–(20) are created by this paper, aiming to explain the mechanism for the formation of the correlation between economic volatility and economic growth. [^Back]

    [4]. ④ According to the experience in the existing literature and China’s five-year national economic plans, the interval of five years is adopted for empirical analysis. Besides, three years and eight years are also taken as the intervals for robustness test. [^Back]

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This Article

ISSN:1002-5502

CN: 11-1235/F

Vol , No. 01, Pages 30-50

January 2017

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Article Outline

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Abstract

  • 1 Introduction
  • 2 Theoretical model ③
  • 3 Empirical analysis
  • 4 Conclusion and policy suggestion
  • Footnote

    References