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Economic policy uncertainty and corporate investment: evidence from China

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Abstract

We investigate the impact of economic policy uncertainty (EPU) on corporate investment. Employing the EPU index at the province level in China and firm level data from the Annual Census of Industrial Enterprises, our analysis reveals that the heightened EPU significantly reduces corporate investment. Specifically, empirical estimates show that 1% increase in the EPU index is associated with 0.051% decrease in corporate investment. Furthermore, the adverse repercussions of EPU on corporate investment are significantly more pronounced for the smaller, younger and less efficient firms as well as those with higher financial vulnerability and less credit loans.

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Notes

  1. Some other literature use election events (Julio and Yook 2012; Jens 2017) or government officials turnover (An et al. 2016) to mainly capture political uncertainty.

  2. The source newspapers are localized and the most authoritative in each province which enable them to well reflect local economic policy. Please refer to sYu et al. (2021) to find more details on the coverage of representative newspapers for each province.

  3. Table 17 in the appendix shows the correlation coefficient between the EPU index of various provinces. We find that the EPU index among the provinces are different and not highly correlated.

  4. As variance inflation factor (VIF) is a measure of the severity of complex (multiple) collinearity in a multiple linear regression model, we present Table 18 in the Appendix.

  5. We provide marginal plots for Table 2, 3 and 4 in the Appendix, and marginal plots for all regressions using interactions are available upon request.

  6. See Gu et al. (2021), Bai et al. (2021), Chen et al. (2022), etc.

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Author information

Authors and Affiliations

  1. Shandong University of Finance and Economics, Jinan, China

    Xiangyuan Meng

  2. Central University of Finance and Economics, Beijing, China

    Haoge Guo & Jie Li

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  1. Xiangyuan Meng
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  2. Haoge Guo
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  3. Jie Li
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Correspondence to Jie Li.

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The original version of this article was revised: the correct affiliation of first author was provided.

We thank the participants of the third China Development Conference, and the CUFE Workshop of Money and Finance for valuable comments. Financial support from China National Social Science Fund (Project #: 21AZD066) and the CUFE Key Research Project of “financial opening strategy and global economic governance” is gratefully acknowledged. Any remaining errors are the authors’ responsibility. The authors have no competing interests to declare that are relevant to the content of this article.

Appendix

Appendix

Table 16 reports the descriptive statistics by province for the annual EPU index including 31 provinces in China from 2000 to 2017. After sorting by the province level mean in descending order, we find that, on average, Shanghai has the highest level of EPU index, followed by Tianjin, Xizang again, and Jiangsu Province has the lowest average EPU index. Regarding the standard deviation of the EPU index, Xinjiang has the largest deviation, indicating that its economic policy uncertainty fluctuates greatly, while Jilin Province has the smallest deviation, indicating that its economic policy uncertainty is relatively stable.

Table 15 Descriptive statistics for firm-level variables
Full size table
Table 16 Descriptive statistics for province level EPU Index
Full size table

Table 17 shows the statistical results of the correlation coefficients of EPU index among various provinces. It shows that most of the correlation coefficients are not highly correlated. This is in line with our prior that the promotion tournament system in China leads to various local economic policy uncertainties. Therefore, using the EPU index at the province level captures such provincial variation.

Table 17 Correlation coefficient statistics for province level EPU Index
Full size table

As variance inflation factor (VIF)F is a measure of the severity of complex (multiple) collinearity in a multiple linear regression model, we calculate VIF for all independent variables and present the results in Table 18. The VIF for all variables are extensively less than 10, indicating no high correlation.

Table 18 Variance inflation factor (VIF) for all independent variables
Full size table

Figures 1 and 2 present marginal plots for Tables 2, 3 and 4. We provide three robustness regressions as additional support evidence.

First, one concern is that province-year variable may affect the EPU index and corporate investment simultaneously. Though we add the year fixed effect in all regressions, we do not control the variations among different provinces. Therefore, we add an additional control variable, the province-year level GDP growth rate, to control the province level macro-economy. Table 19 shows the estimation results. The coefficients on LagEPU are still significantly negative, consistent with the baseline results.

Table 19 Robustness regression—Additional control variable
Full size table

Second, we replace the EPU with the standardized EPU index to make the index comparable. The regression model is the same as specification (1). Table 20 provides the estimation results using the standardized EPU index. The coefficients on LagEPU_std are significantly negative, consistent with the baseline results.

Table 20 Robustness regression—Standardized EPU
Full size table

Third, in our main analysis, we use firm sample from ACIE. Most of the firms in ACIE are unlisted, restricting them from public equity finance, thus they rely more on credit loans to raise funds. For robustness, we use the sample of listed firms to rerun the baseline regressions. The data range of listed firms is from 2001 to 2017. The regression model is the same as specification (1). Table 21 reports the estimation results. The coefficients on LagEPU are significantly negative, in line with the baseline results.

Table 21 Robustness regression - Listed firms sample
Full size table
Fig. 1
figure 1

Sub-section 4.3: Marginal plot for Table 2. Notes: This figure is the marginal plot for Table 2. It shows that the key variable LagEPU has a significantly negative effect on corporate investment and the credit-loan variable mitigates the above negative effect with a positive marginal effect for \(LagEPU \times Crdgrowth\) and \(LagEPU \times Crddummy\) respectively

Full size image
Fig. 2
figure 2

Sub-section 4.3: Marginal plot for Tables 3 and 4. Notes: This figure is the marginal plot for Tables 3 and 4. It shows that the key variable LagEPU has a significantly negative effect on corporate investment and higher accessibility to capital mitigates the above negative effect with a positive marginal effect for \(LagEPU \times Crdproxy\) and \(LagEPU \times SOB\) respectively

Full size image

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Meng, X., Guo, H. & Li, J. Economic policy uncertainty and corporate investment: evidence from China. Econ Change Restruct 56, 4491–4529 (2023). https://doi.org/10.1007/s10644-023-09564-y

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