Economic policy uncertainty, the dynamic adjustment of enterprises’ capital structure and stabilizing leverage

WANG Chaoyang1 ZHANG Xuelan2 BAO Huina3

(1.National Academy of Economic Strategy, CASS)
(2.School of Finance, Zhongnan University of Economics and Law)
(3.China Nonferrous Metals Techno-Economic Research Institute)

【Abstract】The essence of stabilizing leverage is the dynamic adjustment process of capital structure. Starting from the financing demand and supply, which is critical to the capital structure dynamic adjustment, this paper establishes a logical framework to interpret the effect of economic policy uncertainty on the dynamic adjustment of enterprise capital structure through the uncertainty avoidance of both enterprises and financial intermediaries, and then empirically tested with the data of the manufacturing industry in the China Industrial Enterprise Database from1998 to 2013. Results show that economic policy uncertainty hinders the dynamic adjustment of capital structure through uncertainty avoidance; with the increase of economic policy uncertainty, in order to deal effectively with the decreasing income and increasing cost of capital structure adjustment, enterprises become more cautious in making investment decisions, and the financial intermediaries represented by banks would reduce the availability of financing by credit rationing, which ultimately leads to a slowdown in capital structure adjustment; moreover, the difference in sensitivity of policy changes in different industries does affect the capital structure dynamic adjustment of enterprises within the industry. This indicates that stable policy expectations are a prerequisite for stabilizing leverage. Therefore, under the new situation, in order to enterprisely promote the stabilizing leverage and reduce the friction of the dynamic adjustment of enterprise capital structure, we should not rely solely on monetary and financial policies, but should also focus on stabilizing market expectations in terms of the key points affecting enterprises’ confidence in long-term investment and financial intermediary financing, grasping the impact of macroeconomic policy adjustments and their overlap on enterprises of different industries to improve policy orientation, as well as strengthening the forward-looking, consistency and stability of policies.

【Keywords】 economic policy uncertainty; uncertainty avoidance; capital structure; adjustment speed;


【Funds】 The National Social Science Fund of China (15BJY161) The National Social Science Fund of China (14AJY026)

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    [1]. ① Take Tangshan iron and steel production restriction policy from July to August 2018 as an example. At the end of July, a document was issued demanding that the blast furnace production of the steel plant be restricted by 30% until the end of August. On August 10, a document was issued demanding that the production limit be increased from 30% to 50%. On August 17, it issued a document requesting that it should not be recovered at the end of August. In September, most of Tangshan’s steel mills would no longer be restricted and some would resume production. In just over a month, the policy of restricting production was adjusted four times. With each adjustment of the policy, the prices of steel futures and spot prices soared and plummeted. Enterprises were at a loss and did not dare to make long-term investments. See the survey on Tangshan iron and steel industry in Li, B. 经济能不能起来,就看能否破解这三大困局 ( [^Back]

    [2]. ① Tian and Lin (2016) used conditional Markov model to find that many peaks of the uncertainty sequence of economic policies correspond to the political and economic time points when a large number of economic policies are issued in China, proving that the index can capture the uncertainty of China’s economic policies to a large extent. [^Back]

    [3]. ② Since listed enterprises are all regulated equity financing, their capital structure cannot reflect the whole picture of domestic enterprises. The research on this subject based on this sample may have sample deviation. [^Back]

    [4]. ① For example, Ma (2010) pointed out that from the tax system reform in 1993 to 2010, China’s export tax rebate policy changed no less than dozens of times, resulting in enterprises investing in the production of certain products for export under the conditions of complete legality and compliance with the policy. However, due to the policy change, the products were listed as prohibited from export or restricted from export, leaving enterprises in a dilemma. According to the current market situation, the enterprise estimated and signed the export contract, which could have been profitable, but the change in the export tax rebate policy led to losses or made the contract impossible to perform. [^Back]

    [5]. ① The enterprise characteristic indicators represented by controli,there are panel data, and macroeconomic indicators are time series data. [^Back]

    [6]. ① The epu indicated by Ci,t here is time series data, and uoe and uof are panel data. [^Back]

    [7]. ① Some documents use two-step method to estimate the adjustment speed, which first calculate the enterprise’s optimal leverage ratio level, and then use the fitting value of the optimal leverage ratio level to estimate the adjustment speed of capital structure. This two-step method will not only cause large deviation of adjustment speed due to the low fitting degree of target capital structure, but also fail to consider the interaction between capital structure determining factors and adjustment factors (Huang, 2010), which may lead to variable error bias. [^Back]

    [8]. ② When Mi,t is enterprise uncertainty avoidance (uoe), the control variables are enterprise characteristic variables and the data form is panel data. When Mi,t is financial intermediary uncertainty avoidance, the control variables are macro variables and the data form is time series data. [^Back]

    [9]. ① For the regression results, please refer to the public annex of the website of China Industrial Economics (中国工业经济) ( [^Back]

    [10]. ② In order to avoid multicollinearity, the correlation coefficient and variance expansion factor of independent and control variables are calculated. The results show that the correlation coefficients of almost all variables are relatively small. Even if there are some large correlation coefficients, the variance expansion factor is far less than 10, which basically can eliminate the influence of multicollinearity. For the results of descriptive statistics, correlation coefficient and variance expansion factor, please refer to the public annex of the website of China Industrial Economics (中国工业经济) ( [^Back]

    [11]. ① For the test results and fitting results of this part, please refer to the public annex of the website of China Industrial Economics (中国工业经济) ( [^Back]

    [12]. ② At the same time, this paper also considers that the model needs to control the annual effect and the industry effect, but there will be serious multicollinearity after adding the annual and industry dummy variables, because the macroeconomic data money supply (lnM2) and entrepreneur confidence index (eei) in this paper have the same values in each year, and adding these two variables in the fitting is equivalent to controlling the annual effect at the same time. Similarly, the median industry leverage (medlev) has the same value for each industry every year. Adding this variable is equivalent to controlling the industry effect at the same time. Therefore, the annual effect and industry effect in the existing model in this paper are controlled. [^Back]

    [13]. ① Since the financial intermediary uncertainty avoidance index is based on macro level, macro variables are selected as the control variables here. [^Back]

    [14]. ① Results of robustness test are omitted. Please refer to the public annex of the website of China Industrial Economics (中国工业经济). [^Back]


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This Article


CN: 11-3536/F

Vol , No. 12, Pages 134-151

December 2018


Article Outline


  • 1 Introduction
  • 2 Logical mechanism and research hypotheses
  • 3 Research design
  • 4 Empirical process and result analysis
  • 5 Conclusion and enlightenment
  • Footnote