How does economic policy uncertainty affect corporate Innovation?–Evidence from China listed companies

Journal Pre-proof How does Economic Policy Uncertainty Affect Corporate Innovation?–Evidence from China listed companies Feng He, Yaming Ma, Xiaojie Zhang PII:

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https://doi.org/10.1016/j.iref.2020.01.006

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International Review of Economics and Finance

Received Date: 30 September 2019 Revised Date:

16 January 2020

Accepted Date: 16 January 2020

Please cite this article as: He F., Ma Y. & Zhang X., How does Economic Policy Uncertainty Affect Corporate Innovation?–Evidence from China listed companies International Review of Economics and Finance, https://doi.org/10.1016/j.iref.2020.01.006. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc.

Author statement Feng He: Conceptualization, Methodology, Formal analysis, Resources, Writing - Review & Editing, Supervision, Funding acquisition Yaming Ma: Conceptualization, Validation, Writing - Review & Editing, Project administration Xiaojie Zhang: Investigation, Software, Formal analysis, Data Curation, Writing - Original Draft, Visualization The authors have no conflicts of interests regarding this research.

How does Economic Policy Uncertainty Affect Corporate Innovation? –Evidence from China listed companies1 Feng He School of Finance, Tianjin University of Finance and Economics Email: [email protected] Yaming Ma School of Finance, Tianjin University of Finance and Economics Email: [email protected] Xiaojie Zhang School of Finance, Tianjin University of Finance and Economics Email: [email protected] Abstract: This paper examines the effects of economic policy uncertainty (EPU) on corporate innovation in China from 2000 to 2017. The monthly EPU index for China, developed by Huang et al. (2019), is applied as the measurement of EPU. We find that EPU is positively correlated with corporate innovation in general. We further conclude that in the low EPU period before 2008, EPU induced more innovation activity, but it decreased corporate innovation in the higher EPU period after 2008. Moreover, EPU has a stronger positive effect on state-owned enterprises, lower cash flow companies and companies with fewer financial constraints. This shows that EPU affects corporate innovation mainly through cash holdings and revenue growth rate. Finally, we use different corporate innovation indicators, the US EPU index and a different estimation method for endogeneity and robustness checks. Our results shed light on the relationship between economic policy environment and corporate innovation in China as an emerging economy.

Keywords: Economic policy uncertainty; innovation; cash holdings; revenue growth rate JEL: F4, G1

1

All authors are equally contributed to this research. This work is supported by National Natural Science Foundation of China(NSFC) project(71701106, 71703111); NSFC-ESRC project(71661137001) *Corresponding authors at Tianjin University of Finance and Economics: Yaming Ma([email protected]); Feng He([email protected]); Xiaojie Zhang([email protected])

1

How does Economic Policy Uncertainty Affect Corporate Innovation?

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-Evidence from China listed companies

3 4 5 6 7 8 9 10 11 12 13 14 15 16

Abstract: This paper examines the effects of economic policy uncertainty (EPU) on corporate innovation in China from 2000 to 2017. The monthly EPU index for China, developed by Huang et al. (2019), is applied as the measurement of EPU. We find that EPU is positively correlated with corporate innovation in general. We further conclude that in the low EPU period before 2008, EPU induced more innovation activity, but it decreased corporate innovation in the higher EPU period after 2008. Moreover, EPU has a stronger positive effect on state-owned enterprises, lower cash flow companies and companies with fewer financial constraints. This shows that EPU affects corporate innovation mainly through cash holdings and revenue growth rate. Finally, we use different corporate innovation indicators, the US EPU index and a different estimation method for endogeneity and robustness checks. Our results shed light on the relationship between economic policy environment and corporate innovation in China as an emerging economy.

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Keywords: Economic policy uncertainty; innovation; cash holdings; revenue growth rate

18 19

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1. Introduction

2

Economic policy uncertainty (EPU) is the risk of economic policy change that cannot be

3

accurately predicted by market participants and that leads to economic fluctuations and changes in

4

the macroeconomic environment (Gulen and Ion, 2016).

5

As policy affects the economic environment in which firms operate, the overall economic

6

policy-related risk affects corporate behaviour either directly or indirectly. We aim to provide

7

empirical evidence of a link between macroeconomic policy condition changes and corporate

8

innovation activity and to study micro-corporate behaviour under different market regimes.

9

Gulen et al. (2016) find that EPU refers to the difficulty economic participants face in

10

predicting changes to current economic policy, and it often leads to economic fluctuations and

11

complications in the economic environment. After the outbreak of the financial crisis in 2008, the

12

global economy plunged into a downturn. To avoid falling into economic difficulties, governments

13

have taken measures to strengthen their intervention in their economies and financial markets.

14

However, frequent government intervention has also increased EPU, leading to macroeconomic

15

fluctuations. Therefore, since the economic crisis, EPU has attracted the attention of scholars who

16

have sought to determine its impact on economic development. An important aspect of

17

international competition is the competition of technologies, which are created through innovation

18

and entrepreneurship. In 2018, China’s R&D expenditure accounted for 2.18% of its GDP, its

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number of patent applications and authorisations for inventions ranked first in the world and its

20

national comprehensive innovation capacity ranked seventeenth in the world. However, China is

21

still the largest developing country in the world. Compared with developed countries such as the

22

United States, China’s capability of independent innovation is not strong, there is a shortage of

1

core technologies and downward pressure on the domestic economy is increasing. At the same

2

time, the external environment has undergone profound changes, with the uncertainties of

3

instability obviously increasing due to trade conflict with the US. At present, China’s economy is

4

in a stage of transformation. Innovation is an important engine for future economic growth. It

5

needs to deepen innovation and enhance entrepreneurship creation, encouraging more innovation

6

from entrepreneurship. Thus, understanding how EPU affects corporate innovation is crucial for

7

academic research and policy makers.

8

In recent years, finding ways to improve the innovation ability of enterprises and promote

9

economic growth has been a hot topic. Scholars study the factors affecting innovation from

10

various perspectives, including both external and internal factors. Zhang and Guan (2018) find that

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direct government subsidies are beneficial to corporate innovation in the short term but hinder

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long-term innovation performance. Su et al. (2018) and Tsai et al. (2019) show that political

13

connections have positive effects on corporate innovation. Yet others believe that political

14

connections can inhibit enterprise innovation (Luo and Ma, 2013; Yuan and Hou, 2015). Wu (2011)

15

shows that the relationship between political connections and enterprise innovation is an inverted

16

‘U’. Fassio et al. (2019) find that immigrants with higher education have a positive impact on

17

innovation, but the impact varies across industries. In addition to the above external factors

18

affecting enterprise innovation, scholars have studied the impact of internal factors on enterprise

19

innovation. Silva and Carriera (2012) and Gorodnichenko and Schnitzer (2013) find that financing

20

constraints can significantly inhibit enterprise innovation. Almeida et al. (2004) discovers that

21

cash holdings and profitability are positively correlated with innovation. Wang et al. (2019) find

22

that executive team cognitive conflict has a positive impact on exploratory innovation, but

1

emotional conflict has a negative impact. In addition, some scholars find that technology directors

2

(Li et al., 2019) and foreign management experience (Yuan and Wen, 2018) have positive

3

relationships with enterprise innovation. Chi et al. (2019) finds that mutual fund ownership

4

significantly increases corporate innovation, but grey institutional ownership (such as insurance

5

companies and pension funds) and qualified foreign institutional investors (QFII) have little or no

6

significant impact on innovation. Chang et al. (2019) show that credit default swaps on corporate

7

debt have a positive impact on technological innovation output. Chen et al. (2018) find that

8

exports have a positive impact on enterprise innovation.

9

This paper contributes to the literature in three ways. First, based on the conflicting findings

10

of positive (Gu et al., 2018) and negative impacts (Wang et al., 2017) of EPU on corporate

11

innovation, we discover that EPU is in general positively correlated with corporate innovation,

12

supporting the conclusions of Gu et al. (2018). However, the impact of EPU on innovation varies

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among market regimes. In the low EPU period before 2008, EPU positively affected corporate

14

innovation; it switched to negatively affecting corporate innovation after 2008, when EPU became

15

high. Thus, the conflicting results of the previous research may be due to sample selection bias.

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Thus, we can conclude that the true effect of EPU on corporate innovation is contingent on the

17

level of EPU.

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Second, we use the Huang et al. (2019) EPU index as a proxy for EPU. This index is more

19

reliable for EPU in China than the index constructed by Baker et al. (2016). Huang et al. (2019)

20

follow Baker’s calculation method but use different newspaper sources. The index compiled by

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Baker et al. (2016) uses the Hong Kong’s South China Morning Post as its news retrieval platform,

22

but the new index uses 114 mainland newspapers for news retrieval, which better reflect the

1

uncertainty of China’s economic policy. Compared with Gu et al. (2018), we find that Baker et al.

2

(2016) EPU underestimate EPU’s impact.

3

Third, this paper studies the transmission mechanism between EPU and enterprise innovation,

4

which is not abundant in the literature. Firm characteristics are considered to explain the selective

5

effect and affecting channels. Corporate cash holdings, ownership control type, financial

6

constraints, earnings management and idiosyncratic volatility are considered when testing the

7

effect of EPU on corporate innovation.

8

The rest of this paper is organised as follows. We review the relevant literature and propose

9

our hypotheses in Section 2. The data description and research designs are given in Section 3.

10

Section 4 shows the empirical results, and we make some further analyses and robustness checks

11

in Section 5. Finally, we draw conclusions in Section 6.

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2. Related literature and hypotheses

13

Many studies published in the literature argue that EPU has adverse effects on the

14

macro-economy. These adverse effects are not only reflected in the fact that a rise in EPU will

15

restrain a country’s export growth (Grier and Smallwood, 2007) but also in the fact that frequent

16

EPU fluctuations adversely affect the stock market. Ferguson and Lam (2016) find that an increase

17

in EPU has a negative impact on stock prices, and the volatility of stock returns increases with an

18

increase in EPU . Arouri (2016) and Chiang (2019) find that an increase in policy uncertainty

19

significantly reduces stock returns. Yang and Jiang (2016) and Christou et al. (2017) find a

20

significant negative correlation between EPU and stock market returns. In addition, an increase in

21

EPU will lead to unnecessary fluctuations in house prices. Huang et al. (2018) show that when

22

economic policy is stable, the housing market is prosperous, and there is a positive relationship

1

between housing price changes and EPU. Housing market risk increases when economic policy is

2

unstable, and changes in policy uncertainty increase the risk premium of the housing market. At

3

the same time, some scholars find that EPU has an impact on the economic behaviour of

4

microenterprises. These studies find that EPU has a significant inhibitory effect on enterprise

5

investment (Julio and Yook, 2012; Wang et al., 2014; Dibiasi et al., 2018; Drobetz et al., 2018; Liu

6

and Zhang, 2019) and that there is a significant positive correlation between EPU and a company’s

7

cash holdings (Xu et al., 2016; Demir and Ersan, 2017; Cheng et al., 2018; Phan et al., 2019).

8

Meanwhile, Bonaime et al. (2018) find that EPU has a negative impact on M&A (merger and

9

acquisition) activities. Yung and Root (2019) show that firms increase (decrease) earnings

10

management when policy uncertainty is high (low). In addition, we find that earnings management

11

due to policy uncertainty damages enterprise value.

12

Although domestic and foreign scholars have carried out abundant studies on the impact of

13

EPU on macroeconomic development and microenterprise behaviour, there are few studies on the

14

impact of EPU on enterprise innovation. Marcus (1981) proposes that it is difficult to know

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whether policy uncertainty is merely a rationalisation of non-innovation or whether there is a

16

causal relationship between policy uncertainty and technological change because there is no

17

established criterion for judging industry performance. Recent studies on the relationship between

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EPU and enterprise innovation have two opposing views.

19

One view is that EPU is an unavoidable systemic risk. When EPU rises, banks and investors

20

will demand more compensation to bear risk. Therefore, EPU increases the cost of external

21

financing, and the financing difficulties of enterprises increase, reducing motivation for enterprise

22

innovation. Moreover, when EPU is high, it is difficult for management to predict the outcome of

1

innovation activities or future development trends, and they are more likely to postpone

2

innovation investment to wait for more information. Therefore, the option value of waiting to

3

invest in innovation will gradually increase and the cost of innovation will increase, leading to a

4

reduction in innovation activities. As a result, EPU may inhibit enterprise innovation. Goel and

5

Ram (2001) use annual data from nine OECD countries covering the 1981-1992 period and find

6

that uncertainty had a serious negative impact on R&D investment. Kang et al. (2014) find that

7

EPU, which interacts with uncertainty at the enterprise level, inhibits corporate investment

8

decisions. Using data from listed companies in China, Wang et al. (2017) find both policy and

9

market uncertainty have negative impacts on corporate R&D investment. Bhattacharya et al. (2017)

10

find that EPU caused by official changes has an inhibitory effect on corporate innovation.

11

However, it cannot be ignored that in recent years Chinese enterprises have been facing a high

12

level of EPU. Although the increase in EPU has restrained investment in material capital,

13

enterprises may transfer part of their investment to innovation. Bloom (2007) shows that although

14

EPU has negative impacts on investment, employment and productivity, its impact on enterprise

15

R&D may be different from its impacts on other economic activities due to differences in

16

adjustment cost characteristics.

17

The other view is that innovation activities are irreversible, which increases the value of

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waiting. However, from the perspective of corporate competition, when a company chooses to

19

wait, it may lose the opportunity to seize the market, and the resulting losses are much greater than

20

the cost of innovation and the value of waiting. According to game theory, the waiting value of a

21

company’s innovation activities will be affected by its competitors’ behaviour. If competitors

22

innovate in the waiting period, the value of waiting will be reduced or zero. Therefore, the

1

enhancement of EPU will not reduce the innovation level of enterprises. Thus, our first hypothesis

2

is proposed.

3

H1: There is a positive impact of economic policy uncertainty on enterprise innovation.

4

At present, the development of China’s financial market is still imperfect. First of all, the

5

supervision system of China's financial market is not perfect. Secondly, the legal system

6

construction of China's financial market is not in place. In addition, China's financial financing

7

channels are too single, the main channel of financial financing is through bank loans. As direct

8

financing and bond issuance have strict restrictions and access procedures, government-oriented

9

indirect financing has been the main financing system in China for a long time. Bank credit is the

10

main form of indirect financing, but its availability varies greatly among companies with different

11

attributes. State-owned enterprises are better protected by the government, and they enjoy implicit

12

or explicit loan guarantees, enabling them to borrow at preferential rates (Dewenter and Malatesta,

13

2001). At the same time, compared with non-state-owned enterprises, state-owned enterprises

14

have advantages in scale, supervision and other aspects, and thus they face less risk. He and

15

Ma(2019) showed that Stated-Owned enterprises even have different market reaction to analyst

16

recommendations.

17

more likely to obtain financial support than non-state-owned enterprises, so the financing

18

constraints faced by state-owned enterprises in innovation investment are usually smaller than

19

those faced by non-state-owned enterprises. In addition, compared with non-state-owned

20

enterprises, state-owned enterprises have greater advantages in information acquisition. Therefore,

21

changes in EPU may have different impacts on innovation for state-owned enterprises and

22

non-state-owned enterprises. Therefore, our second and third hypotheses are as follows.

Therefore, when enterprises invest in innovation, state-owned enterprises are

1 2 3 4

H2: The effect of economic policy uncertainty on enterprise innovation is stronger in state-owned enterprises. H3: Economic policy uncertainty promotes enterprise innovation more strongly in enterprises with less cash flow.

5

In addition to their individual attributes, the economic environment faced by enterprises also

6

leads to their heterogeneity. The financial constraints of enterprises can reflect the difficulty of

7

financing in the external economic environment. The capital that enterprises can freely control is

8

largely affected by financial constraints, and corporate innovation activities require continuous

9

investment of large amounts of capital, they are more susceptible to financial constraints (Hall et

10

al., 2015). When enterprises face relatively high financial constraints, they have financing

11

difficulties, thus production and operations are restrained. At such times, enterprises may forgo the

12

best operational decision because they cannot get enough financial support. Therefore, financial

13

constraints have a certain inhibitory effect on enterprise innovation (Denis and Sibilkov, 2007;

14

Silva and Carriera, 2012). EPU affects both the capital and credit markets, and higher EPU

15

increases external financing costs. In contrast, firms with few financial constraints can obtain more

16

disposable funds and thus make investment decisions more freely. Therefore, our forth hypothesis

17

is as follows.

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H4: The positive impact of economic policy uncertainty on enterprise innovation is stronger

19

in enterprises with lower financial constraints.

20

3. Research design

21

3.1. Sample selection and data sources

22

Data of A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2000

1

to 2017 are used as the research sample in this paper. The relevant financial data and basic

2

company information are from the CSMAR, RESSET and Wind databases. The patent application

3

data come from the CSMAR database, and the EPU index is derived from the EPU index website

4

(https://economicpolicyuncertaintyinchina.weebly.com/). The original data are processed as

5

follows: (1) listed companies in the financial and insurance industries are eliminated; (2) ST and

6

*ST listed companies are excluded; (3) listed companies with missing financial data are excluded;

7

(4) listed companies established in the year of observation are excluded; (5) the continuous

8

variables are winsorized at 1%.

9

3.2. Selection of variables

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3.2.1. Dependent variable

11

Compared with patent applications, patent authorisations have more human factors and a

12

certain lag period, whereas patent applications are more contemporaneous with a company’s

13

decision-making. Therefore, we use the number of patent applications to measure enterprise

14

innovation.

15

Patents are divided into invention patents, utility model patents and appearance design

16

patents. Invention patents are the most original, more representative of the three types of patents

17

and closely related to enterprise innovation. Therefore, in line with Yuan and Wen (2018), Phan

18

(2019) and Tsai et al. (2019), we use the number of invention patent applications in the enterprise

19

plus one and then takes the natural logarithm to measure enterprise innovation.

20

3.2.2. Independent variable

21

The China EPU index compiled by Huang et al. (2019) is used as the explanatory variable

22

(EPU). The China EPU monthly index starts in January 2000. Using Wisers Information Portal,

1

Huang et al. (2019) search for relevant keywords in ten newspapers: Beijing Youth Daily,

2

Guangzhou Daily, Jiefang Daily, People’s Daily Overseas Edition, Shanghai Morning Post,

3

Southern Metropolis Daily, The Beijing News, Today Evening Post, Wen Hui Daily and

4

Yangcheng Evening News. For each newspaper, they search for articles that contain at least one

5

keyword in each of three categories: (1) economics, (2) uncertainty and (3) policy. They scale the

6

number of articles in each month by the number of articles that meet criteria (1) for the same

7

month. The series is then standardised to have a standard deviation of unity during the period from

8

January 2000 to December 2011. They compute the simple average of the monthly series across

9

ten newspapers. Last, the index is normalised to have an average value of 100 in the study period.

10

As our research object is annual data, we use the arithmetic average of the monthly EPU index as

11

an annual variable, and we take the annual data obtained as a natural logarithm.

12

3.2.3. Characteristic variables

13

Our characteristic variables are as follows. (1) Nature of ownership (Soe): According to the

14

nature of the ultimate controlling shareholder, listed companies can be divided between

15

state-owned and non-state-owned enterprises. When listed companies are state-owned enterprises,

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Soe = 1; when listed companies are non-state-owned enterprises, Soe = 0. (2) Cashflow: The cash

17

flow of an enterprise is represented by the ratio of net cash flow from operating and investment

18

activities to total assets. (3) Financial constraints (SA): According to the SA index proposed by

19

Hadlock et al. (2010), SA = -0.737*Size + 0.043*Size2 - 0.040*Age.

20

3.2.4. Mediation variables

21

The mediation variables are as follows. (1) Cash holdings (Cash): The ratio of corporate cash

22

and cash equivalents to total assets and is used to indicate the cash holdings of the enterprise. (2)

1

Growth: (current operating income - last operating income)/last operating income.

2

3.2.5. Control variables

3

Referring to the related studies of Yuan and Wen (2018), Chang et al.(2019), Chi et al.(2019),

4

Li et al.(2019) and Liu and Zhang (2019), our main control variables are age (Age), size (Size),

5

leverage ratio (Lev), return on assets (ROA), Tobin’s Q (TQ), book-to-market ratio (MB) and

6

tangible asset ratio (Tangibility). In addition, the dummy variables year and industry are used in

7

the regression analysis. Definitions of the main variables can be found in Table 1, but they are not

8

discussed in detail.

9

10



3.3. Model construction

11

To test the impact of EPU on enterprise innovation, combined with the index selection and

12

logical hypotheses above, we take the natural logarithm after adding 1 to the number of invention

13

patents as the explanatory variable (Patent1). Because the Patent1 is censored greater than 0,

14

traditional OLS estimation will produce bias. Therefore, we use a panel Tobit model and set the

15

left truncation point to zero.

16

17

18 19

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To verify Hypothesis 1, we use the following basic regression model:

Patent1i ,t = α 0 + α1 EPU i ,t −1 + α 2 ln age + α 3 Sizei ,t −1 + α 4 Levi ,t −1 + α 5 ROAi ,t −1 + α 6TQi ,t −1 + α 7 MBi ,t −1 + α 8Tangibilityi ,t −1 + year + industry + ε i ,t

(1)

To verify Hypotheses 2 to 4, we use the following regression model based on the basic regression model:

Patent1i ,t = α 0 + α1 EPU i ,t −1 + α 2 X i ,t −1 * EPU i ,t −1 + α 3 ln age + α 4 Sizei ,t −1 + α 5 Levi ,t −1 + α 6 ROAi ,t −1 + α 7TQi ,t −1 + α 8 MBi ,t −1 + α 9Tangibilityi ,t −1 + year + industry + ε i ,t

（2）

1

All of the independent and control variables (except for enterprise age and the virtual

2

variables at the industry level and year level) are 1 lag to the independent variable. X represents

3

the characteristic variable, i represents the individual, t represents the year, α represents the

4

regression coefficient of each variable, ɛ is the random disturbance term, year represents the year

5

dummy variable, and industry represents the industry dummy variable.

6

4. Empirical analysis

7

4.1. Descriptive statistics

8

Table 2 reports the descriptive statistics of the main variables. It shows that the minimum

9

value of Patent 1 is 0 and the maximum value is 5.762, which indicated that companies differ

10

greatly in innovation. The mean value is 1.309 and the standard deviation is 1.476. The minimum

11

and maximum values of EPU are 3.963 and 5.110, respectively, with an average value of 4.784

12

and a standard deviation of 0.306. This shows that EPU fluctuates greatly during the observation

13

period. In Table 2, we find that there is sufficient variation in the main variables during the sample

14

period, so this study has research significance and feasibility.

15

16



4.2. Basic regression

17

In Table 3, the regression results of single variables are shown in models (1) to (3), in which

18

the regression coefficients of EPU are positive and significant at the 1% level. This basic

19

regression shows that EPU has a significant role in promoting enterprise innovation. After

20

controlling for other factors and fixed effects in (4) to (6), the regression coefficient of EPU is still

21

significantly positive at the 1% confidence level, which is consistent with Atanassov et al. (2016).

1

Model (4) in Table 3 shows the effect of EPU on invention patent applications. The regression

2

coefficient is 2.603 and is significant at the 1% level. Model (5) shows the impact of EPU on the

3

number of utility model patent applications of listed companies. The coefficient of EPU is still

4

significantly positive at 1%. The results of Model (6) show that EPU is specific to the appearance

5

design of listed companies. The impact of EPU on invention patent applications is significantly

6

greater than that on utility design patent applications. This shows that using the number of

7

invention patent applications as a proxy for innovation is reasonable.

8

In addition, the signs of the control variables are consistent with those of previous research.

9

The coefficient of enterprise age is significantly negative at the 1% level, which is consistent with

10

the results of Zhang and Guan (2018) and Fassioa et al. (2019). The older the enterprise is, the

11

weaker is its degree of innovation. This shows that young enterprises are eager to innovate. The

12

coefficient of enterprise scale is significantly positive at the 1% level, which is consistent with the

13

results of Yuan and Wen (2018) and Su et al. (2018). It shows that the larger the enterprise scale,

14

the higher the innovation level. Larger companies tend to have stronger risk resistance and more

15

capital than smaller ones. The innovation performance of listed companies with stronger solvency

16

and profitability is greater than that of other listed companies, but we do not find that the tangible

17

asset ratio and Tobin’s Q value have a significant impact on enterprise innovation. The coefficient

18

and significance of the control variables related to enterprise operating conditions show that the

19

degree of innovation is greater for listed companies with stronger solvency and profitability and

20

that both types of company are more innovative. The sign and significance of the parameter in test

21

result is consistent with Hall et al. (2015), Li et al. (2019) and other research results, and the

22

book-to-market ratio has a significant negative correlation with enterprise innovation, which is

1

consistent with the results of Tsai et al. (2019).

2

Overall, the regression results in Table 3 show that after controlling for other factors, the

3

innovation of listed companies will increase when EPU increases. The regression results support

4

our hypothesis that EPU promotes enterprise innovation, and Hypothesis 1 is verified.

5

6



4.3. Ownership control effect

7

To test the relationship between EPU and enterprise innovation, we introduce the interaction

8

term of enterprise ownership type with EPU. The regression results for Model (1) are reported in

9

Table 4. We find that the regression coefficient of EPU is positive and significant at 1%, which is

10

consistent with the results of basic regression; the regression coefficient of the cross-term of Soe

11

and EPU is significantly positive at the 1% level, which indicates that when EPU increases, both

12

state-owned enterprises and non-state-owned enterprises promote their innovation activity.

13

However, the increase of invention patent applications by state-owned enterprises is greater; that is,

14

the impact of EPU on enterprise innovation is more significant for state-owned enterprises.

15

Compared with non-state-owned enterprises, state-owned enterprises have easier access to loans

16

(Dewenter and Malatesta, 2001) to support long-term and risky innovation investment, so

17

state-owned enterprises are better positioned to innovate than non-state-owned enterprises. This

18

empirical result supports Hypothesis 2.

19

4.4. Cash flow effect

20

To test the impact of cash flow on the relationship between EPU and enterprise innovation,

21

we introduce the interaction term of Cash flow and EPU. The regression results of Model (2) are

1

reported in Table 4. The coefficient of EPU is significantly positive at the 1% level, which is

2

consistent with the previous results. The coefficient of the interaction term is -0.00066 and is

3

significant at the 1% level, indicating that a higher cash flow ratio lowers the stimulating effect of

4

EPU on enterprise innovation. The positive impact of EPU on enterprise innovation is stronger in

5

firms with a lower cash flow ratio because the higher an enterprise’s cash flow ratio, the more net

6

cash flow its business and investment activities generate. Net cash flow generated by investment

7

activities mostly means that its investment cash inflow is larger than its investment cash outflow

8

or that the enterprise has been forced to sell fixed assets or long-term investments, etc. This

9

reflects poor operating conditions for enterprises, and as a result, the enterprise may focus more on

10

current operations rather than on innovation. This result supports our Hypothesis 3.

11

4.5. Financial constraints effect

12

Based on prior research, we use SA to measure the financial constraints of enterprises. The

13

larger the SA value, the fewer are the financial constraints the enterprise faces. To investigate the

14

impact of financial constraints on the relationship between EPU and enterprise innovation, we

15

introduce the interaction term of SA and EPU (Model 3 in Table 4). The coefficients of EPU are

16

significantly positive at the 1% level, and the interaction term is 0.073 and statistically significant.

17

This shows that the positive impact of EPU on enterprise innovation is greater in enterprises with

18

easier access to financing. Denis and Sibilkov (2007) point out that the positive impact of EPU on

19

enterprise innovation is stronger when enterprises are confronted with uncertainties in economic

20

policies; however, their operational cash flow and profit will often be reduced. At the same time,

21

macroeconomic fluctuations are often accompanied by financial constraints, which are detrimental

22

to the R&D investment of enterprises and thus reduce their innovation performance. It is

1

consistent with previous research that financial constraints have an inhibitory effect on enterprise

2

innovation (Silva and Carriera, 2012; Guariglia and Liu, 2014). When EPU rises, enterprises with

3

few financial constraints can more easily obtain loans to support innovation, so enterprise

4

innovation increases, which supports Hypothesis 4.

5



6

5. Further discussion

7

5.1. Mediation effect

8

To analyse the impact of EPU on enterprise innovation, we use the mediation effect test

9

(Baron and Kenny, 1986) and Zmediation test (Iacobucci, 2012) to determine whether the mediation

10

effect is significant. We choose cash holdings and revenue growth rate as intermediary variables

11

and consider the impact of EPU on enterprise innovation through direct and intermediary effects.

12

We study the direct effect of EPU (independent variable) on enterprise innovation (dependent

13

variable) and whether EPU (independent variable) will produce an intermediary effect through

14

cash holdings or revenue growth rate (intermediary variables) and measure the intermediary effect.

15

16

The model setting is as follows, in which Mediator is our mediating variable.

Patent1 = α 0 + α1 EPU + α 2 ln age + α 3 Size + α 4 Lev + α 5 ROA + α 6TQ + α 7 MB + α 8Tangibility + ε 1

17

18

(3)

Mediator = β 0 + β1 EPU + β 2 ln age + β 3 Size + β 4 Lev + β 5 ROA + β 6TQ + β 7 MB + β 8Tangibility + ε 2

19

20

(4)

Patent1 = γ 0 + γ 1 EPU + γ 2 Mediator + γ 3 ln age + γ 4 Size + γ 5 Lev + γ 6 ROA + γ 7TQ + γ 8 MB + λ9Tangibility + ε 3

1

(5)

2

Table 5 shows the test results for the mediation effect of cash holdings. In Table 5, Model (1),

3

the coefficient of EPU is significantly positive at the 1% level, and the symbols of the other

4

control variables are the same as in the basic regression. This is consistent with the previous

5

findings; that is, EPU has a significant positive impact on enterprise innovation. The coefficient of

6

EPU is estimated to be 1.205%, which indicates that for every 1% increase in EPU, invention

7

patent applications increase by 1.205%. In Model (2), the regression coefficient of EPU is 21.711,

8

indicating that EPU has a significant positive impact on cash holdings of enterprises (Cheng et al.,

9

2018; Phan et al., 2019). In Model (3), the regression coefficient of the mediating variable Cash is

10

positive and significant at the 1% level. The regression coefficient of the independent variable

11

EPU is 1.157, which is still significant at 1%. This shows that there exists only a partial mediation

12

effect; that is, EPU affects enterprise innovation partly through corporate cash holdings. Cash

13

holdings are an important factor and play a significant role in promoting enterprise innovation.

14

When EPU rises, the risk faced by enterprises increases, and their investment will decrease. To

15

cope with the risk brought by uncertainty, enterprises increase their cash holdings, and abundant

16

cash can support enterprise innovation, so enterprise innovation increases.

17



18

Table 6 shows the test results for the intermediary effect of revenue growth rate. In Table 6,

19

Model (1), the coefficient of EPU is significantly positive at the 1% level; that is, EPU has a

20

significantly positive impact on enterprise innovation. In Model (2), the regression coefficient of

21

EPU is negative and significantly less than 0 at the 1% level. This shows that EPU has a

1

significantly negative impact on revenue growth rate. Higher EPU will inhibit the development

2

and revenue growth rate of enterprises. In Model (3), the regression coefficient of the mediating

3

variable is negative and significant at the 1% level; however, EPU is positive and still significant

4

at the 1% level. This indicates that EPU’s impact on enterprise innovation is partly achieved

5

through the revenue growth rate.

6



7

In addition, we further use Zmediation statistics to judge whether the mediation effect is

8

significant. Based on the t-values in Tables 5 and 6, the Zmediation values are calculated. From the

9

results in Table 7, we can see that the Zmediation statistics of the mediation variable enterprise cash

10

holdings is 2.899, and the Zmediation statistics of revenue growth rate is 3.826, both of which are

11

significant at the 1% level. This shows that there is a significant mediation effect between EPU

12

and enterprise innovation through enterprise cash holdings and revenue growth rate. By comparing

13

the significance of the regression coefficients in Tables 5 and 6, we can see that the coefficients of

14

EPU are significant, which shows that there is only a partial mediation effect.

15

16

17



5.2. Different regimes of EPU level



18

We can conclude that EPU has a positive impact on enterprise innovation, but will the

19

impact vary with the level of EPU? Figure 1 shows the levels of EPU in China from 2000 to 2017.

1

We observe that there are great differences in EPU before and after 2008. It was lower before 2008

2

and increased significantly after 2008. For this reason, we divide the sample into two parts and

3

conduct regressions with each sub-sample. The regression results are shown in Table 8.

4

Model (1) in Table 8 shows the regression results with the full sample as above, in which

5

EPU has a significant positive effect on enterprise innovation in the 2000-2017 period. Model (2)

6

shows that the coefficient of EPU is 4.415 in the 2000-2007 period, and it is significant at the 1%

7

level. However, in the second sub-sample period, from 2008 to 2017, the coefficient of EPU is

8

-8.790, indicating that EPU has a significant negative impact on enterprise innovation. These

9

results show that when EPU is low, it plays a significant role in promoting enterprise innovation,

10

but when EPU is high, it has a significant inhibitory effect on enterprise innovation.

11

5.3. Industry effect

12

Prior studies typically use a full sample and do not elaborate by industry. The impact of EPU

13

on innovation may vary by industry. It is necessary to explore whether the impact of EPU on

14

innovation in manufacturing and non-manufacturing enterprises is different. According to the

15

2012 industry classification standard of China Securities Regulatory Commission, we divide all

16

enterprises into manufacturing and non-manufacturing industries, and repeat the regressions to

17

check the impact of EPU on enterprise innovation in different industries. The regression results are

18

shown in Table 9.

19



20

Model (1) in Table 9 shows the impact of EPU on innovation for manufacturing enterprises.

21

The coefficient of EPU is 2.681 and significant at 1%. This shows that there is a significant

1

positive impact of EPU on innovation in manufacturing enterprises. The coefficient of EPU for

2

non-manufacturing enterprises is 2.415, indicating the same effect as for manufacturing

3

enterprises. However, we find that EPU has a greater impact on the innovation of manufacturing

4

enterprises. Manufacturing is the pillar industry of the Chinese economy and the main battlefield

5

of scientific and technological innovation, and it accounts for more than 50% of listed companies.

6

Compared with non-manufacturing industries, manufacturing has more intensive R&D activities, a

7

greater demand for innovation and is more sensitive to the policy environment. The R&D

8

achievements of non-manufacturing industries are relatively small and less sensitive to economic

9

policy changes. At the same time, from the results of models (2), (3), (5) and (6), we can see that

10

low EPU has a significant positive impact on enterprise innovation in both manufacturing and

11

non-manufacturing industries, and high EPU has a significant negative impact, which is consistent

12

with the previous results.

13

14



5.4. Ownership control effect

15

To study whether there are differences in the impact of EPU on innovation between different

16

shareholder control types, we classify enterprises into state-owned and non-state-owned and run

17

regressions with the sub-samples.

18

Models (1) and (4) in Table 10 show the impact of EPU on innovation for state-owned and

19

non-state-owned enterprises. The coefficients of EPU are 2.910 and 2.136, respectively, and

20

significant at the 1% level. It shows that EPU has a significant positive impact on both types of

21

enterprise. However, from the regression results, we find that EPU has a greater impact on

1

innovation for state-owned enterprises. In addition, from models (2), (3), (5) and (6), we find that

2

EPU’s impact on corporate innovation is consistent with the previous findings in both groups.

3

4



5.5. Idiosyncratic risk effect

5

Next, we divide idiosyncratic volatility into high and low groups to test the impact of EPU on

6

enterprise innovation at different levels of idiosyncratic volatility. The regression results are

7

shown in Table 11. Models (1) and (4) in Table 11 show that the impact of EPU on enterprise

8

innovation does not differ greatly between volatility groups. However, in a high EPU period, the

9

negative impact on the low idiosyncratic volatility group is much stronger.

10

11 12



5.6. Earnings management effect Further, we divide the sample into groups according to earnings management level and

13

examine the impact of EPU on enterprise innovation. The results are shown in Table 12. Models (1)

14

and (4) show that the impact of EPU on enterprise innovation does not differ greatly between

15

enterprises with high and low levels of earnings management. In addition, when EPU is low, EPU

16

has a significant positive impact on enterprise innovation, but when EPU is high, it has a negative

17

impact. In a high EPU period, the high earnings management group’s innovation activity is more

18

negatively affected by EPU.

19



1

5.7. Robustness check

2

To further verify the reliability of our results, we use the total patent applications of listed

3

companies and affiliated joint venture companies from 2000 to 2017 to take logarithm as the

4

explanatory variable (Patent) to measure enterprise innovation. The results are reported in Table

5

13, Model (1). The results show that the coefficient of EPU is 2.353, which is consistent with the

6

previous results. This proves the reliability of the patent application data.

7

As the EPU index is calculated with different newspaper sources, we further compare our

8

results using an alternative EPU index (Baker et al., 2016; Davis et al., 2019). The results shown

9

in Table 13, models (2) and (3), suggest that our conclusions are robust to EPU index calculation.

10

However, as Baker’s index mainly uses newspapers in Hong Kong, it underestimates the true

11

effect of EPU on corporate innovation.

12

Although there is almost no reverse causality between EPU and enterprise innovation, to

13

ensure the robustness of the test results we use the US Economic Policy Uncertainty Index

14

(USEPU) as an instrumental variable to perform a 2SLS regression. This instrumental variable is

15

chosen because China’s EPU is influenced by US EPU, which satisfies the relevant conditions of

16

an instrumental variable. In addition, US EPU does not directly affect the innovation of Chinese

17

enterprises, so it also satisfies the exogenous conditions of an instrumental variable. The results

18

are shown in Table 13, Model (4). Among them, the coefficient of EPU is positive and significant

19

at the 1% level, which indicates that there is a significant positive correlation between EPU and

20

enterprise innovation.

21

We mainly use the Tobit model to test the relationship between EPU and enterprise

22

innovation, as innovation number is centred above 0. To ensure the robustness of the empirical

1

method, we use a fixed effects model to retest. The results, given in Table 13, Model (3), show that

2

the relationship between EPU and enterprise innovation is still significantly positive at the 1%

3

level, and the symbols and significance of the main control variables have not changed greatly,

4

which indicates that the results are robust with the estimation method.

5

6



6. Conclusions

7

Using the data of A-share listed companies in China from 2000 to 2017 and the EPU index

8

compiled by Huang et al. (2019), this paper studies the impact of EPU on enterprise innovation.

9

The results show the following.

10

First, EPU has a significant positive impact on enterprise innovation, which shows that when

11

EPU increases, enterprise innovation increases. Second, the impact of EPU on enterprise

12

innovation is different between state-owned and non-state-owned enterprises, enterprises with

13

different cash flow ratios and among enterprises with different degrees of financial constraints.

14

EPU’s effect is stronger for state-owned enterprises, lower cash flow ratio enterprises and

15

enterprises with fewer financing constraints. Moreover, there is a significant mediation effect

16

between EPU and enterprise innovation. EPU affects enterprise innovation partly through cash

17

holdings and revenue growth rate. In addition, we find that when EPU is low, it has a significant

18

positive impact on enterprise innovation; when EPU is high, it significantly inhibits enterprise

19

innovation. Finally, we find that the impact of EPU on enterprise innovation varies by enterprise

20

characteristics, such as industry type, ownership control type, earnings management level and

21

idiosyncratic volatility.

1

The rise of low-level EPU has a significant role in promoting enterprise innovation, and when

2

EPU is high, it reduces enterprise innovation. Therefore, we should comprehensively and

3

objectively assess the impact of EPU on enterprise innovation, so that when formulating

4

macroeconomic policies it can fully consider the timeliness and possible impact of economic

5

policies to keep EPU at a reasonable level and strive to stabilise the market and enterprises.

6 7

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1

2 3

Table 1: Variable definitions Variable

Definition

Patent1

ln(number of invention patent applications + 1)

Patent2

ln(quantity of patent applications for utility models + 1)

Patent3

ln(number of patent applications for design + 1)

EPU

Average logarithm of annual EPU is obtained from the EPU index of Huang et al.(2019)

Soe

When listed companies are state-owned enterprises, Soe = 1; when listed companies are non-state-owned enterprises, Soe = 0.

Cashflow

(net operating cash flow + net investment cash flow)/total assets at the end of the period

SA

-0.737*Size + 0.043*Size2 - 0.040*Age

Cash

Cash and cash equivalents/total assets

Growth

(current operating income - last operating income)/last operating income

Age

ln(enterprise age)

Size

ln(total assets)

Lev

Total liabilities / total assets

ROA

Net profit/total assets

TQ

(equity market value + net debt market value)/total assets at the end of the period

MB

Market value / shareholder equity

Tangibility

Tangible assets / total assets

1

2 3

Table 2: Descriptive Statistics Variable name

Sample size

Mean

Standard D

Minimum

Maximum

Patent1

26418

1.309

1.476

0

5.762

Patent2

26418

1.321

1.556

0

5.805

Patent3

26418

0.525

1.037

0

4.605

EPU

26418

4.784

0.306

3.963

5.110

Soe

26418

0.527

0.499

0

1

Cashflow

26418

-1.727

10.075

-31.497

26.553

Cash

26418

12.231

13.025

0

60.117

Growth

26418

0.221

0.526

-0.602

3.646

SA

26418

3.929

1.458

1.120

8.948

lnage

26418

2.501

0.510

0

3.912

Size

26418

21.832

1.270

19.262

25.768

Lev

26418

0 .462

0.208

0 .0552

0.997

ROA

26418

0.0349

0.0579

-0.227

0.192

TQ

26418

2.060

1.817

0.112

10.241

MB

26418

0.910

0.829

0.0231

4.612

Tangibility

26418

0.940

0.0764

0.566

1

1 2 3 4 5

Table 3: Benchmark regression Model (1)-(3) showed single variable regression of lagged EPU on innovation, while we controlled for other factors, year effect and industry effect in Model (4)-(6). ***,** and * respectively indicate the statistical significance level of 1%, 5% and 10%, and the value in brackets is t value. dependent variable

Patent1

Patent2

Patent3

Patent1

Patent2

Patent3

independent variable

（1）

（2）

（3）

（4）

（5）

（6）

L.EPU

3.315***

3.519***

1.493***

2.603***

2.424***

0.612***

(64.06)

(59.30)

(19.47)

(25.91)

(23.71)

(3.94)

-0.209***

-0.221***

0.0912

(-6.52)

(-6.41)

(1.61)

0.816***

0.742***

0.719***

(57.20)

( 48.59)

(27.80)

-0.111

0.215

-0.928***

(-0.66)

(1.18)

(-3.06)

0.00930

-0.0254***

0.00896

(1.05)

(-2.65)

(0.57)

-0.271***

-0.117

-0.285**

(-3.65)

(-1.46)

(-2.13)

2.835***

3.129***

4.024***

(11.56)

(11.69)

(9.02)

-0.189***

-0.0938***

-0.0635

(-8.77)

(-4.08)

(-1.59)

lnage L.Size L.Tangibility L.TQ L.Lev L.ROA L.MB Constant

-15.19***

-16.36***

-8.756***

-30.01***

-28.63***

-19.89***

(-60.49)

(-56.73)

(-23.40)

(-51.05)

(-46.48)

(-21.23)

Year effects

NO

NO

NO

YES

YES

YES

Industry effects

NO

NO

NO

YES

YES

YES

Observations

26418

26418

26418

26418

26418

26418

2

0.0547

0.0479

0.0081

0.2117

0.2278

0.1249

Prob >chi2

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

Pseudo R 6 7

1 2 3 4 5 6

Table 4: Selective effects of economic policy uncertainty Model (1)-(3) showed regression of lagged EPU and its interaction terms on innovation. The interaction terms with EPU are corporate ownership control type, cash flow ratio and financial constrain, respectively. We all controlled for other factors, year effect and industry effect in Model. ***,** and * respectively indicate the statistical significance level of 1%, 5% and 10%, and the value in brackets is t value. dependent variable Model

Patent1 （1）

Patent1 （2）

Patent1 （3）

independent variable

X=Soe

X=Cashflow

X=Sa

L.EPU

2.644***

2.594***

2.327***

(26.08)

(25.82)

(19.91)

0.0166***

-0.000660***

0.0730***

(3.02)

(-2.74)

(4.50)

-0.222***

-0.197***

-0.0377

(-6.86)

(-6.07)

(-0.76)

0.809***

0.815***

0.397***

(55.92)

(57.13)

(4.23)

-0.162

-0.0721

-0.103

(-0.96)

(-0.43)

(-0.61)

0.00986

0.00975

0.00285

(1.12)

(1.10)

(0.32)

-0.284***

-0.263***

-0.282***

(-3.82)

(-3.54)

(-3.81)

2.896***

2.930***

2.899***

(11.77)

(11.82)

(11.84)

-0.191***

-0.189***

-0.206***

(-8.85)

(-8.76)

(-9.41)

-30.01***

-30.04***

-21.34***

(-51.06)

(-51.09)

(-10.62)

Year effects

YES

YES

YES

Industry effects

YES

YES

YES

Observations

L.(X*EPU) lnage L.Size L.Tangibility L.TQ L.Lev L.ROA L.MB Constant

26418

26418

26418

2

0.2118

0.2118

0.2119

Prob >chi2

0.0000

0.0000

0.0000

Pseudo R 7 8

1 2 3 4

Table 5: Mediating effect of cash holdings Model (1)-(2) show the regression results of EPU on Patent and Cash holding respectively, Model (3) shows the effect of EPU and cash holding on Patent. ***,** and * respectively indicate the statistical significance level of 1%, 5% and 10%, and the value in brackets is t value. dependent variable Model

Patent1 （1）

Cash （2）

Patent1 （3）

EPU

1.205*** (39.19)

21.711*** (88.91)

-0.0850*** (-4.71) 0.521*** (62.78) -0.298*** (-2.82) 0.0425*** (7.65) -0.532*** (-11.11) -0.175 (-1.12) -0.244*** (-17.92) -14.96*** (-66.13) 26418 0.2575

-0.700*** (-4.89) -0.134** (-2.03) 18.386*** (21.93) 0.998*** (22.63) -14.181*** (-37.28) 20.549*** (16.48) -0.302*** (-2.79) -100.20*** (-55.80) 26418 0.3993

1.157*** (32.99) 0.00225*** (2.90) -0.0834*** (-4.62) 0.522*** (62.82) -0.339*** (-3.18) 0.0403*** (7.18) -0.500*** (-10.18) -0.221 (-1.40) -0.243*** (-17.87) -14.73*** (-61.62) 26418 0.2577

Cash lnage Size Tangibility TQ Lev ROA MB Constant Observation Adj R2 5 6

1 2 3 4

Table 6: Mediating effect of revenue growth rate Model (1)-(2) show the regression results of EPU on Patent and revenue growth rate respectively, Model (3) shows the effect of EPU and revenue growth rate on Patent. ***,** and * respectively indicate the statistical significance level of 1%, 5% and 10%, and the value in brackets is t value. dependent variable Model

Patent1 （1）

Growth （2）

Patent1 （3）

EPU

1.205*** (39.19)

-0.0788*** (-6.43)

-0.0850*** (-4.71) 0.521*** (62.78) -0.298*** (-2.82) 0.0425*** (7.65) -0.532*** (-11.11) -0.175 (-1.12) -0.244*** (-17.92) -14.96*** (-66.13) 26418 0.2575

-0.0248*** (-3.45) 0.00253 (0.76) -0.618*** (-14.67) 0.00818*** (3.69) 0.472*** (24.72) 2.310*** (36.88) -0.0255*** (-4.71) 0.89*** (9.91) 26418 0.0694

1.200*** (38.99) -0.0734*** (-4.76) -0.0868*** (-4.81) 0.521*** (62.83) -0.343*** (-3.24) 0.0431*** (7.76) -0.498*** (-10.27) -0.00563 (-0.03) -0.246*** (-18.06) -14.89*** (-65.75) 26418 0.2581

Growth lnage Size Tangibility TQ Lev ROA MB Constant Observation Adj R2 5 6

1

2 3

Table 7: Zmediation statistics for mediation effect test Model

Zmediation

Table 5 Model (1), Model (2), Model (3) Table 6 Model (1), Model (2), Model (3)

2.899 3.826

1 2 3 4 5 6

Table 8: Intensity of economic policy uncertainty and enterprise innovation Model (1)-(3) showed regression of lagged EPU on innovation, while we controlled for other factors, year effect and industry effect. Model (1) is the full sample regression, while Model (2) and (3) are results of subsample in low EPU period from 2000-2008 and high EPU period from 2008-2017 respectively. ***,** and * respectively indicate the statistical significance level of 1%, 5% and 10%, and the value in brackets is t value. year dependent variable Model

2000-2017 Patent1 （1）

2000-2007 Patent1 （2）

2008-2017 Patent1 （3）

L.EPU

2.603*** (25.91) -0.209*** (-6.52) 0.816*** (57.20) -0.111 (-0.66) 0.00930 (1.05) -0.271*** (-3.65) 2.835*** (11.56) -0.189*** (-8.77) -30.01*** (-51.05) YES YES 26418 0.2117 0.0000

4.415*** (12.33) 0.00801 (0.10) 0.878*** (21.70) -0.956 (-1.37) 0.0269 (0.77) -0.662*** (-3.58) 3.137*** (5.45) -0.190*** (-3.10) -37.92*** (-19.47) YES YES 26418 0.1675 0.0000

-8.790*** (-7.36) -0.253*** (-7.04) 0.823*** (52.06) 0.0102 (0.06) 0.00991 (1.08) -0.131 (-1.59) 2.983*** (10.48) -0.224*** (-9.62) 26.96*** (4.37) YES YES 26418 0.1743 0.0000

lnage L.Size L.Tangibility L.TQ L.Lev L.ROA L.MB Constant Year effects Industry effects Observations Pseudo R2 Prob >chi2 7 8

1 2 3 4 5 6 7 8

Table 9: Economic policy uncertainty and enterprise innovation in different industries Model (1)-(3) show regression of lagged EPU on innovation of manufacturing industry companies, while Model (4)-(6) show regression of lagged EPU on innovation of non-manufacturing industry companies. We all control for other factors, year effect and industry effect. Model (1) and (4) are the full sample regression, while Model (2) and (4) are results of subsample in low EPU period from 2000-2008 and Model (3) and (6) are high EPU period from 2008-2017. ***,** and * respectively indicate the statistical significance level of 1%, 5% and 10%, and the value in brackets is t value. industry year dependent variable Model L.EPU lnage L.Size L.Tangibility L.TQ L.Lev L.ROA L.MB Constant Year effects Industry Observations Pseudo R2 Prob >chi2

9 10

manufacturing industry 2000-2017 2000-2007 2008-2017 Patent1 Patent1 Patent1 （1） （2） （3）

Non-manufacturing industry 2000-2017 2000-2007 2008-2017 Patent1 Patent1 Patent1 （4） （5） （6）

2.681*** (21.97) -0.280*** (-7.19) 0.784*** (42.99) -1.017*** (-3.99) -0.0132 (-1.16) -0.0579 (-0.63) 3.080*** (10.19) -0.308*** (-9.95) -28.33*** (-35.18) YES YES 14272 0.1787 0.0000

2.415*** (13.89) -0.0757 (-1.33) 0.858*** (36.24) 0.551** (2.38) 0.0271* (1.92) -0.550*** (-4.38) 2.094*** (5.00) -0.111*** (-3.51) -30.94*** (-31.48) YES YES 12146 0.2360 0.0000

4.819*** (11.15) -0.0933 (-1.00) 0.790*** (15.43) -0.931 (-1.00) -0.0655 (-1.43) -0.413* (-1.70) 3.828*** (5.05) -0.353*** (-4.39) -36.60*** (-15.15 ) YES YES 3938 0.1385 0.0000

-5.834*** (-3.98) -0.290*** (-6.65) 0.802*** ( 39.73) -0.995*** (-3.84) -0.00402 (-0.33) 0.0680 (0.67) 3.243*** (9.44) -0.351*** (-10.18) 13.05* (1.71) YES YES 10334 0.1283 0.0000

3.225*** (5.30) 0.351** (2.48) 0.902*** (13.10) -0.652 (-0.61) 0.0853 (1.56) -1.123*** (-3.76) 1.644* (1.87) -0.00559 (-0.06) -34.77*** (-10.62) YES YES 3758 0.2099 0.0000

-11.21*** (-5.43) -0.150** (-2.38) 0.864*** (33.09) 0.642*** (2.74) 0.0232 (1.62) -0.304** (-2.11) 2.194*** (4.32) -0.159*** (-4.69) 37.45*** (3.51) YES YES 8388 0.2047 0.0000

1 2 3 4 5 6 7

Table 10: EPU and enterprise innovation of enterprises of different ownership control type Model (1)-(3) show regression of lagged EPU on innovation of State-owned enterprise, while Model (4)-(6) show regression of lagged EPU on innovation of non- State-owned enterprise. We all control for other factors, year effect and industry effect. Model (1) and (4) are the full sample regression, while Model (2) and (4) are results of subsample in low EPU period from 2000-2008 and Model (3) and (6) are high EPU period from 2008-2017. ***,** and * respectively indicate the statistical significance level of 1%, 5% and 10%, and the value in brackets is t value. Enterprise nature year dependent variable Model L.EPU lnage L.Size L.Tangibility L.TQ L.Lev L.ROA L.MB Constant Year effects Industry Observations Pseudo R2 Prob >chi2

8 9

State-owned enterprise 2000-2017 2000-2007 2008-2017 Patent1 Patent1 Patent1 （1） （2） （3）

Non-state-owned Enterprises 2000-2017 2000-2007 2008-2017 Patent1 Patent1 Patent1 （4） （5） （6）

2.910*** (21.14) -0.153*** (-2.84) 0.811*** (41.51) -0.565* (-1.87) 0.0182 (1.06) -0.561*** (-4.85) 2.476*** (6.76) -0.115*** (-4.31) -31.28*** (-39.52) YES YES 13928 0.2355 0.0000

2.136*** (12.32) -0.280*** (-6.98) 0.818*** (36.45) 0.0183 (0.09) -0.00222 (-0.21) -0.146 (-1.50) 2.991*** (8.91) -0.428*** (-9.92) -26.98*** (-27.39) YES YES 12490 0.1896 0.0000

4.490*** (10.61) 0.107 (1.16) 0.777*** (16.26) -0.209 (-0.24) 0.00948 (0.22) -0.711*** (-3.02) 3.633*** (4.89) -0.0950 (-1.33) -36.94*** (-15.99) YES YES 5580 0.1740 0.0000

-14.74*** (-8.27) -0.321*** (-4.59) 0.817*** (37.09) -0.691** (-2.23) 0.0226 (1.15) -0.407*** (-3.01) 2.364*** (5.35) -0.143*** (-5.07) 57.58*** (6.22) YES YES 8348 0.2118 0.0000

4.511*** (6.62) -0.508*** (-3.37) 1.106*** (12.92) 1.085 (0.82) 0.0414 (0.65) -0.689** (-2.08) 2.489** (2.42) -0.454*** (-3.53) -43.75*** (-11.46) YES YES 2116 0.1997 0.0000

-4.127** (-2.35) -0.222*** (-5.30) 0.786*** (32.48) 0.00488 (0.02) -0.00861 (-0.81) 0.0258 (0.24) 3.248*** (8.57) -0.438*** (-9.23) 4.986 (0.55) YES YES 10374 0.1552 0.0000

1 2 3 4 5 6 7 8

Table 11: EPU and enterprise innovation under different idiosyncratic risk Model (1)-(3) show regression of lagged EPU on innovation of high idiosyncratic risk companies, while Model (4)-(6) show regression of lagged EPU on innovation of low idiosyncratic risk companies. We all control for other factors, year effect and industry effect. Model (1) and (4) are the full sample regression, while Model (2) and (4) are results of subsample in low EPU period from 2000-2008 and Model (3) and (6) are high EPU period from 2008-2017. ***,** and * respectively indicate the statistical significance level of 1%, 5% and 10%, and the value in brackets is t value. Idiosyncratic risk year dependent variable Model L.EPU lnage L.Size L.Tangibility L.TQ L.Lev L.ROA L.MB Constant Year effects Industry Observations Pseudo R2 Prob >chi2

9 10

High volatility 2000-2017 2000-2007 2008-2017 Patent1 Patent1 Patent1 （1） （2） （3）

Low volatility 2000-2017 2000-2007 2008-2017 Patent1 Patent1 Patent1 （4） （5） （6）

2.324*** (5.55) -0.333*** (-5.83) 0.755*** (29.11) 0.0209 (0.08) -0.00748 (-0.61) -0.551*** (-4.63) 2.227*** (6.26) -0.261*** (-5.84) -27.13*** (-12.52) YES YES 13209 0.1768 0.0000

2.395*** (16.67) -0.114** (-2.06) 0.863*** (34.07) -0.319 (-0.91) -0.00358 (-0.14) 0.0714 (0.48) 3.761*** (6.69) -0.127*** (-3.56) -30.30*** (-32.45) YES YES 13209 0.2592 0.0000

4.185*** (3.18) -0.252 (-1.37) 0.818*** (11.04) -0.365 (-0.27) -0.0148 (-0.21) -0.913*** (-3.06) 1.622* (1.79) -0.340*** (-3.12) -34.65*** (-5.73) YES YES 3111 0.1820 0.0000

-5.302** (-2.49) -0.354*** (-5.69) 0.765*** (24.62) 0.220 (0.78) -0.000100 (-0.01) -0.384*** (-2.82) 2.382*** (5.72) -0.279*** (-5.27) 10.49 (0.95) YES YES 10098 0.1549 0.0000

3.864** (2.43) 0.0458 (0.46) 0.932*** (14.68) -1.556 (-1.50) 0.0667 (1.18) -0.0878 (-0.31) 4.977*** (4.77) -0.0535 (-0.48) -36.70*** (-5.50) YES YES 4585 0.1729 0.0000

-9.580** (-2.04) -0.164** (-2.45) 0.871*** (31.74) -0.116 (-0.34) -0.000991 (-0.03) 0.148 (0.87) 3.504*** (5.16) -0.208*** (-5.74) 29.43 (1.24) YES YES 8624 0.2154 0.0000

1 2 3 4 5 6 7 8

Table 12: EPU and enterprise innovation with different earnings management levels Model (1)-(3) show regression of lagged EPU on innovation of high earnings management levels companies, while Model (4)-(6) show regression of lagged EPU on innovation of low earnings management levels companies. We all control for other factors, year effect and industry effect. Model (1) and (4) are the full sample regression, while Model (2) and (4) are results of subsample in low EPU period from 2000-2008 and Model (3) and (6) are high EPU period from 2008-2017. ***,** and * respectively indicate the statistical significance level of 1%, 5% and 10%, and the value in brackets is t value. Earnings management year dependent variable Model L.EPU lnage L.Size L.Tangibility L.TQ L.Lev L.ROA L.MB Constant Year effects Industry Observations Pseudo R2 Prob >chi2

9 10 11

High earnings management 2000-2017 2000-2007 2008-2017 Patent1 Patent1 Patent1 （1） （2） （3）

Low earnings management 2000-2017 2000-2007 2008-2017 Patent1 Patent1 Patent1 （4） （5） （6）

2.601*** (14.40) -0.115** (-2.21) 0.899*** (43.79) -0.376 (-1.28) -0.0104 (-0.51) -0.129 (-0.99) 2.435*** (5.74) -0.197*** (-7.41) -32.22*** (-31.61) YES YES 13624 0.2206 0.0000

2.429*** (16.52) -0.237*** (-4.72) 0.883*** (27.34) 0.521** (2.02) -0.00166 (-0.14) -0.479*** (-4.51) 2.803*** (7.54) -0.361*** (-6.13) -30.51*** (-30.67) YES YES 12794 0.2159 0.0000

3.241*** (5.23) 0.146 (1.02) 1.064*** (15.58) -1.394 (-0.91) -0.0425 (-0.51) -0.130 (-0.35) 2.884*** (2.59) -0.170* (-1.90) -37.09*** (-10.95) YES YES 3123 0.2194 0.0000

-10.98*** (-6.22) -0.167*** (-2.93) 0.886*** (39.90) -0.251 (-0.85) -0.00151 (-0.07) -0.108 (-0.76) 2.567*** (5.36) -0.209*** (-7.39) 36.19*** (3.99) YES YES 10501 0.1945 0.0000

5.147*** (9.78) -0.130 (-1.20) 0.876*** (11.65) -0.104 (-0.12) 0.0203 (0.45) -0.687*** (-2.72) 3.759*** (4.69) -0.346*** (-3.02) -41.13*** (-13.27) YES YES 4573 0.1629 0.0000

-2.642 (-1.18) -0.228*** (-3.94) 0.921*** (24.38) 0.585** (2.22) 0.00833 (0.66) -0.354*** (-2.89) 2.640*** (5.85) -0.332*** (-4.40) -5.929 (-0.51) YES YES 8221 0.1725 0.0000

1 2 3 4 5 6 7

Table 13: Robustness test Model (1) shows our basic regression as in before, and we replace the EPU index to Baker’s EPU(Baker et al., 2016) and Davis’s EPU(Davis et al., 2019) in Model (2) and (3). Model (4) shows the 2SLS result of using US EPU as instrument variables. Model (5) shows the result of replacing Tobit regression method to Fixed-effect estimation. Note: ***,** and * respectively indicate the statistical significance level of 1%, 5% and 10%, and the value in brackets is t value. All models include all control variables and dummy variables at the industry level and year level. Tobit model dependent

Patent

Patent1

Patent1

Patent1

Patent1

Model

（1）

（2）

（3）

（4）

（5）

independent

EPU

BEPU

DEPU

USEPU

EPU

L.Y

2.353***

1.472***

2.143***

6.788***

0.483

(26.42)

(25.91)

(25.91)

(25.91)

(3.29)

All Controls

YES

YES

YES

YES

YES

Observations

26418

26418

26418

26418

26418

0.2011

0.2117

0.2117

0.2117

0.3501

R 8 9

Fixed-effect

2

1

2 3

Fig. 1 Trend of China EPU index