Impact of Economic Policy Uncertainty on Industrial Convergence: Evidence from China

Abstract

The uncertainties in current economic policies have increased, and Decisions about industrial convergence by businesses are impacted by uncertain macroeconomic policy. Using a fixed-effects model, the article selects annual data from A-share listed Chinese companies from 2010 to 2021 and empirically analyzes the relationship between economic policy uncertainty (EPU) and industrial convergence, as well as the mediating and moderating effects of diversification strategies and financial distress under this path of action. The findings are as follows: (1) EPU has a significant negative impact on industrial convergence, and this phenomenon is most obvious for non-state enterprises. (2) Enterprise diversification strategy mediate the relationship between industrial convergence and EPU (3) Corporate financial distress has a negative moderating effect on the relationship between EPU and industrial convergence. (4) The nature of firm property rights has a negative moderating effect on the relationship between EPU and industrial convergence. The aforementioned conclusions have implications for policy: the government should always ensure the long-term stability of economic policies and direct and encourage the development of cross-industry convergence of enterprises. Enterprises need to actively empower development with joint efforts to achieve win-win cooperation.

1. Introduction

As a new form of cross-industry competition and cooperation, industrial convergence breaks industry boundaries, accelerates the dissemination of information and knowledge, improves operational efficiency, and achieves transformation and upgrading through inter-industrial synergy [1]. In recent years, both traditional enterprises and new economic enterprises have ventured outward in their own industry fields, which has accelerated the pace of industrial convergence and seen a new round of convergence boom has opened. The present era is in an uncertain situation. EPU has become a normalized trend as a result of the regularity of major financial and political events such as the US financial crisis, the UK’s exit from the EU, and the US-China trade tension. Especially at higher levels of EPU, the unpredictability of the economy and policies may lead to ambiguity about what the company will do in the future, the intensity of execution, and the prospects for impact, ultimately forcing companies to change their decision-making behavior [2]. Enterprises are the most fundamental and crucial carriers of industrial convergence, and industrial convergence is a way of making business decisions; industrial convergence decisions of enterprises are bound to be influenced by this macro EPU.

As technology has advanced and the market environment has changed, industrial convergence has gradually become an important way for enterprises to transform and upgrade. Academic research on industrial convergence has also grown in depth, with an emphasis on industrial convergence models, mechanisms, and measurements, as well as the impact of industrial convergence on regional development. Choi [3] investigated the automotive industrial convergence using a perspective analysis of patent data, arguing that technological convergence leads to industrial convergence and examining the pattern of industrial convergence through the degree of convergence between the automotive industry and other industrial technology areas. Zhang [4] employed a Bayesian network causal inference model to explain the operational and formative mechanisms of the convergence of culture and tourism industries. Jung [5] and Kim [6] use M&A data as a representative form of “collaboration” for industry analysis from the perspective of corporate M&A to measure the level of industry convergence. Dong and Li (2022) find that industrial convergence is positively associated with regional high-quality development in China [7]. Further, Dong and Li (2022) used spatial econometric models to also find that industrial convergence is beneficial to improve the efficiency of regional green development [8]. In addition, Cao and Li (2020) found that industrial convergence has a significant contribution to the improvement of regional metabolic efficiency [9]. As can be seen, the existing literature has performed extensive research on industrial convergence and achieved some progress, providing a solid theoretical foundation for this paper’s investigation. However, very few researchers have studied the antecedent influences of industrial convergence, and even fewer studies have looked at how the macro environment affected industrial convergence. This paper analyzes industrial convergence from the perspective of micro-subject enterprises and studies the influence of external macro-environmental economic policy uncertainty on the industrial convergence of enterprises.

Industrial convergence is a development model in which companies use each other and work together between two or more industries, and its convergence process needs to be carried out in a precise and transparent environment. Nowadays, the world is in an eventful period, and the complex and turbulent macro environment, as well as the never-ending market turbulence events, can lead to a dramatic increase in economic policy uncertainty, which in turn causes ambiguity in the business environment and has an impact on the effect of industrial convergence. According to the theory of information asymmetry, there are differences in the degree of information mastery among market participants, especially in the uncertain economic policy environment. The fuzziness and complexity of the market will aggravate the information asymmetry among the participants, and industrial convergence, as an activity involving multiple participants, the existence of “incomplete information” will naturally affect industrial convergence decision-making. Simultaneously, according to the real option theory, firms will choose to minimize current expenditure in order to reduce the sunk cost caused by an uncertain environment. Because of the irreversibility of investment, the value of waiting for possibilities and selecting future investments grows. Industrial convergence is a special investment behavior of enterprises. In the uncertain environment of economic policy, enterprises will adjust their decision-making behavior of industrial convergence accordingly. In summary, the research in this paper aims to research the impact of EPU on industrial convergence and its mechanism of action from the perspective of the external macro environment. The paper also explores the impact of diversification strategy and financial distress on the relationship between EPU and industrial convergence from the viewpoint of company diversification and financial position, with the goal of providing a reference for companies making industrial convergence decisions in the face of uncertain economic policy environments.

With the frequent occurrence of global uncertainties, China has proposed a number of strategies for economic transformation and upgrading, regional economic synergistic development, etc., and its policy introductions and shifts have been characterized by accelerated frequency. While these policies have stimulated China’s economic development, they have also exposed Chinese firms to greater economic policy volatility and uncertainty, so this paper uses Chinese firms as the research object to better explain the impact of EPU on industrial convergence.

The paper’s primary contributions are as follows: First, It advances and widens the field of study on the causes of industrial convergence. While previous studies have mostly focused on industrial convergence models, mechanisms, and measurements, as well as the impact of industrial convergence on regional development, this one begins with the macro environment and comprehensively and methodically analyzes the impact of EPU on industrial convergence, offering new perspectives and ideas for a deeper comprehension of industrial convergence in the VUCA era. Second, To better comprehend the impact path of EPU on industrial convergence, we examine the relationship between EPU and industrial convergence from the perspectives of business diversity and financial status. Third, industrial convergence is a current trend, and the study has significant practical implications for attaining corporate sustainability.

2. Literature Review and Research Hypothesis

2.1. EPU and Industrial Convergence

Industrial convergence is a strategic decision made by an enterprise to integrate technology, information, products, and management between different industrial fields for collaborative innovation or transformation and upgrading in the face of market competition and resource competition, which necessitates the collaborative work of many subjects [10]. If enterprises wish to conduct seamless and effective industrial connectivity business activities, they must work in a transparent atmosphere as much as feasible. According to the theory of information asymmetry, due to the inconsistent information held by each trading party, the party lacking information may lead to a miscalculation in decision-making and loss of benefits because it has incomplete information. Frequent changes in economic policies increase information volatility and ambiguity in the business process, forcing enterprises to operate in a hazy and unpredictable business environment [11] and increasing the asymmetry of information exchange between enterprises. It can be seen that the high EPU increases the difficulty of supervision of enterprises in the decision-making process of industrial convergence, which breeds the possibility of counterparties misrepresenting or concealing the true operation of their own enterprises in the process of industrial convergence due to greed for personal gain, and raises the opportunity cost of industrial convergence for other participants, This makes it difficult for decision-makers to assess the value of industrial convergence under the EPU. Industrial convergence will only be chosen by rational business decision-makers if the synergistic benefits it produces outweigh the opportunity costs of convergence. Investment decisions made by businesses are unpredictable due to shifting economic policies, and the occurrence of industrial convergence may expose businesses to new market dynamics and competitive situations, heightening the uncertainty of their operations [12,13]. In order to avoid potential risks under economic policy uncertainty from affecting the smooth implementation of industrial convergence, companies are often reluctant to pursue industrial convergence in an environment of EPU. According to real options theory, companies take a cautious approach to their investment behavior in the face of an uncertain environment, frequently waiting for the best opportunity. Deferred investments by a company in an uncertain environment are equivalent to having an option. Since industrial convergence is a project with high transaction costs for companies, it is more likely that companies will postpone or scale back their decisions regarding industrial convergence [14,15].

Hypothesis 1 (H1):

EPU is negatively related to industrial convergence.

2.2. The Mediating Role of Diversification Strategy

The implementation of corporate diversification strategy is bound to bring about the expansion of business scope and increase in variability. The dynamic and complex environment of high economic policy makes it difficult for corporate management to make a rational assessment of strategic decision-making options [16], which in turn leads to a lack of scientific validity of the diversification strategy. In addition, management is more cautious when making investment decisions in order to avoid losing positions as a result of poor judgment in an uncertain environment [10] and is, therefore, more likely to abandon the diversification strategy. The implementation of a company’s diversification strategy necessitates significant capital expenditures and is extremely capital-dependent. According to the precautionary motive of cash management theory, firms maintain a certain amount of cash holdings ahead of time as a protective mechanism against potential future shocks, so they are as protected as possible from unexpected losses caused by significant changes in macroeconomic policies. As a result, firms will use their funds for unknown risk prevention in an environment of uncertain economic policy [17]. Because of this, businesses may spend capital to cover unknown risks in a climate of unpredictable economic policy, which prevents the implementation of their diversification initiatives. According to the principal-agent theory, Bergh stated that there was a diversity discount in the principal-agent problem in enterprises [18]. The uncertain economic policy environment will aggravate the principal-agent problem of enterprises [19], and managers may decide to diversify, which is detrimental to the company’s interests and can lead to a decline in the firm’s value and the diversification discount phenomena [20,21]. Managers will prioritize their own interests over those of shareholders, for example, by adopting self-serving tactics such as scale expansion, resulting in higher diversification costs than predicted benefits. Thus firms are often reluctant to implement diversification strategies in an environment of EPU.

Diversification is a technique used by businesses that operate in multiple product or service marketplaces at the same time. Its diversified business model can effectively diversify corporate risks and achieve resource cross-utilization [22]. Compared with the specialized strategies of enterprises, enterprises with diversified strategies have differentiating business concepts that make them excellent development platforms for cross-industry convergence and exchange of components, technologies, and markets. According to the Synergy Effects theory, enterprises implementing diversification strategies tend to be more willing to share resources and diffuse capabilities [23], which has an important effect on resource allocation and synergy among the industries they are involved in promotion, which makes the cross-industry convergence of enterprise technology, resources, and talents more rapid. Simultaneously, diversification is the business strategy adopted by firms, and its successful execution necessitates the synergy effect of many management elements [24]. As a new kind of cross-border cooperation among firms, industrial convergence adds value by leveraging the complementary characteristics of many industries. As a result, organizations with diversification strategies will be more active in industrial convergence since they require the synergy effects provided by industrial convergence. Based on signaling theory, since there are differences in the degree of knowledge of information among market transaction subjects, companies that actively implement diversification strategies can convey the message that they are willing to conduct business in multiple fields and express the signal that they want to cooperate, which can attract partners from other industries to cooperate and promote industrial convergence among companies. Additionally, the implementation of a diversification strategy can help enterprises improve their ability to cope with complex industrial environments, such as identifying resources for industrial convergence development more quickly and then flexibly integrating and applying the identified industrial resources to another industrial business so that enterprises can be free to advance or retreat in the process of industrial convergence.

Hypothesis 2 (H2):

Diversification strategy has a mediating effect in the relationship between EPU and industrial convergence. In other words, EPU further influences industrial convergence by affecting diversification strategy.

2.3. The Moderating Effect of Financial Distress

In a way, the financial situation of a company can have an impact on industrial convergence in an environment of economic policy uncertainty. From the management’s point of view, when companies are financially stable or on a positive trend, corporate managers prefer low-risk activities out of hedonistic attitudes; when companies become resistant to high-risk industrial convergence decisions, making the inhibiting effect of the already frequently changing economic policies on industrial convergence further aggravated. According to Kahneman D’s prospect theory [25], companies are more likely to awaken their own risk-taking spirit in the face of losses, demonstrating that companies in financial distress can reduce managerial inertia, and management is willing to take the risk to choose high-performance but risky industrial convergence decisions in the face of economic policy uncertainty. Based on risk diversification theory, financially distressed enterprises have a higher risk of bankruptcy. In order to avoid the increase of negative shocks such as bankruptcy risk under EPU, financially distressed enterprises will actively engage in industrial convergence to seek the synergy effect brought by it under EPU. Because industrial convergence can transfer risks by integrating enterprises in different industries and fields, expanding their own business fields and market shares, reducing the influence of a single business on enterprises, and resetting enterprise resource allocation, enterprises in financial distress are willing to engage in industrial convergence as an important way to reduce risks in an environment of economic policy uncertainty. Based on the hypothesis of “change in poverty”, companies in financial distress are more likely to adopt change activities and find growth opportunities through alternative methods.

External environmental variations are both hazards and opportunities. Industrial convergence can be a useful strategy for enhancing a firm’s solvency since it makes use of each other’s industrial resources in a cooperative partnership to create new growth chances for its own company [26]. This shows that financially distressed companies are more willing to seize opportunities in an EPU environment full of opportunities by means of industrial convergence.

Hypothesis 3 (H3):

Financial distress has a negative moderating effect on the relationship between EPU and industrial convergence. In other words, financial distress can inhibit the negative effect of EPU on industrial convergence.

The mechanism by which EPU affects industrial convergence is shown in Figure 1.

The theoretical model built using the theoretical presumptions is depicted in diagram form in Figure 1. In Figure 1, the independent variable is economic policy uncertainty, the dependent variable is industrial convergence, the mediating variable is diversification strategy, and the moderating variable is financial distress. According to the graph, economic policy uncertainty can directly affect industrial convergence, and economic policy uncertainty can also indirectly affect industrial convergence through diversification strategy, i.e., diversification strategy has a mediating role. The relationship between economic policy uncertainty and industrial convergence can be affected by financial distress; that is, financial distress plays a moderating role.

3. Study Design

3.1. Sample Selection and Data Sources

The annual data of listed Chinese A-share companies from 2010 to 2021 are used as the research sample in this paper, and the screening criteria are as follows: (1) exclude the companies in ST and PT status; (2) exclude the financial industries; (3) exclude the data samples with missing variable data. Finally, 10,648 research samples of listed companies were obtained. In this paper, we exclude the effect of extreme values by applying a variable tailing of 1% above and below to the continuous variables. The EPU data were obtained from the EPU index constructed by Baker, and the rest of the data were obtained from the CSMAR and WIND databases.

3.2. Variable Description

3.2.1. Economic Policy Uncertainty (EPU)

This paper adopts the Economic Policy Uncertainty Index (EPU) developed by Baker et al. [2]. Baker et al. used the respected English newspaper South China Morning Post as a search platform for news sources to create an index of Chinese economic policy uncertainty through text search. The index can accurately reflect the state of China’s economic policy uncertainty. The index, on the other hand, is released once per month, which has the advantages of continuity and temporal variation and may more accurately reflect the current state of China’s economic policy uncertainties. The index’s data comes from news media stories and event-based research techniques, which can improve the data’s scientific validity and reliability. Therefore, the Chinese EPU index measure developed by Baker [2] was chosen to convert the monthly data into an annual economic policy uncertainty index using the arithmetic average method and divided by 1000. The following equation was used.

$${\mathrm{EPU}}_b=\frac{1}{12}{\displaystyle \sum }_{a=1}^{12}{\mathrm{EPU}}_a$$

(1)

where b indicates the year, ranging from 2010 to 2021, and a indicates the month, taking values between 1 and 12.

3.2.2. Industry Convergence (IG_i)

Rhéaume and Bhabra [27] found that M&A can lead to value creation based on the convergence process. According to Christensen [28], M&As can be viewed as a key means of achieving convergence. Cross-industry mergers and acquisitions can reflect a shift from a technology-based and lead-user-driven specialized approach to convergence. This creates the necessity to analyze M&A records to understand convergence. Jung [5] and Kim [6] examined the phenomenon of industrial convergence via the lens of corporate M&A, using M&A data as a representative form to examine “cooperation” amongst industries. Enterprise mergers and acquisitions can be watched and compared in real-time, allowing for a more rapid understanding of cross-industry convergence trends, making assessment results more real-time and accurate. As a result, this paper refers to Jung [5] and Kim’s [6] approach and uses the data of cross-industry mergers and acquisitions is used in this research as a gauge of industrial convergence.

Specific Approach 1: extracting M&A information from the database and taking cross-industry M&A as the standard of industrial convergence, with cross-industry M&A recorded as 1 and 0 otherwise. Approach 2: Extract corporate M&A information from the database, aggregate cross-industry M&A events by year from the M&A information, and calculate the degree of industry convergence.

3.2.3. Diversification Strategy (DS)

With reference to Chen and Sun [29], the income entropy index is used to measure the diversification strategy, which is calculated as follows. The entropy method was chosen to measure the degree of industry diversification because of its consistency and objectivity. The approach is calculated using industry-standard codes, which allows for a more accurate assessment of the extent of diversification and is quantitatively feasible in terms of using the industry distribution of listed companies’ main business revenues for quantitative analysis. This metric is more accurate in its measurement results and also provides a larger amount of information.

$$DS=\sum _{i=1}^n{P}_i\ln \left(1/P_i\right)$$

(2)

where P_i is the proportion of revenue from industry i to main business revenue, and n is the number of industries in which the company operates using a three-digit industry code; the higher the degree of diversification, the higher the index value.

3.2.4. Financial Distress (Z_Score)

The Z_score created by Altman is utilized to quantify financial distress in this paper, referring to the practice of Shi [30] and Guan [31], and the specific formula is as follows. Altman’s Z_Score model takes into account several financial indicators of a company, including current ratio, profitability and debt ratio, which allows for a more comprehensive assessment of a company’s financial position. Simultaneously, Altman’s Z_Score methodology is also widely employed by businesses across numerous industries, demonstrating its viability and accuracy. The model is a standardized financial indication that is unaffected by elements such as company size, industry, or region.

$$\begin{array}{ll}Z\_\mathit{score}=1.2& \mathit{Working} \mathit{capital}/\mathit{total} \mathit{assets}+1.4 \mathit{Retained} \mathit{earnings}/\mathit{total} \mathit{assets}\\ & +3.3 \mathit{EBITDA}/\mathit{total} \mathit{assets}\\ & +0.6 \mathit{Total} \mathit{market} \mathit{value} \mathit{of} \mathit{stock}/\mathit{book} \mathit{value} \mathit{of} \mathit{liabilities}\\ & +0.999 \mathit{Sales} \mathit{revenue}/\mathit{total} \mathit{assets}\end{array}$$

(3)

where the higher the Z_score value, the better the financial position.

3.2.5. Control Variables

Based on the research related to industrial convergence, the control variables are selected as follows: total asset turnover (Ato), years of listing (Listage), nature of ownership (Soe), top 10 shareholders’ shareholding (Top10), number of directors (Board), growth rate of operating income (Growth), book-to-market ratio (Bm), dual job (Dual). This research also takes into account the industry to which all enterprises belong by using a dummy variable; according to the 2012 industry classification of the China Securities Regulatory Commission, the manufacturing industry takes two codes, and other industries use major categories, including mining, construction, and other 21 industries. Each variable is defined as shown in

Table 1. Variable descriptions.

Variables	Meaning	Metrics
EPU	Economic Policy Uncertainty	Arithmetic mean of data from January to December of each year/1000
IC1	Measure of Industry Convergence 1	Cross-industry M&A is recorded as 1, otherwise, it is 0
IC2	Measure of Industry Convergence 2	Total number of cross-industry M&A by firm-year
DS	Diversification Strategy	Entropy of enterprise’s main business income
Z_score	Financial distress	Multi-indicator measurement method, see Equation (3) above
Ato	Total assets turnover ratio	Operating income/average total assets
Listage	Listing year	Ln (current year − year of Listing + 1)
Soe	Nature of property rights	State-controlled enterprises take the value of 1; others are 0
Top10	Shareholding ratio of top ten shareholders	Number of shares held by top ten shareholders/total number of shares
Board	Number of Directors	The number of board members is taken as the natural logarithm
Growth	Operating income growth rate	Operating income for the year/Operating income for the previous year − 1
Bm	Book-to-market ratio	Book value/total market value
Dual	duality	The chairman and general manager are the same as 1, otherwise 0
Industry	Industry dummy variables	0–1 variable

.

3.3. Model Construction

Model (1) is constructed to test Hypothesis 1.

$$I{C}_{\mathit{it}}={\beta }_0+{\lambda }_1\times EP{U}_t+{\lambda }_2\sum {\mathit{Control}}_{\mathit{it}}+\sum \mathit{Industry}+{\epsilon }_{\mathit{it}}$$

(4)

Construct Models (2) and (3) to test Hypothesis 2.

Models (2):

$$D{S}_{\mathit{it}}={\beta }_0+{\lambda }_1\times EP{U}_t+{\lambda }_2\sum {\mathit{Control}}_{\mathit{it}}+\sum \mathit{Industry}+{\epsilon }_{\mathit{it}}$$

(5)

Models (3):

$$I{C}_{\mathit{it}}={\beta }_0+{\lambda }_1\times EP{U}_t+{\lambda }_2\times D{S}_{\mathit{it}}+{\lambda }_3\sum {\mathit{Control}}_{\mathit{it}}+\sum \mathit{Industry}+{\epsilon }_{\mathit{it}}$$

(6)

Model (4) was constructed to test Hypothesis 3.

$$I{C}_{\mathit{it}}={\beta }_0+{\lambda }_1\times EP{U}_t+{\lambda }_2\times \mathrm{Z}\_{\mathrm{score}}_{\mathit{it}}+{\lambda }_{3}EP{U}_t\times \mathrm{Z}\_{\mathrm{score}}_{\mathit{it}}+{\lambda }_4\sum {\mathit{Control}}_{\mathit{it}}+\sum \mathit{Industry}+{\epsilon }_{\mathit{it}}$$

(7)

where the subscripts i and t represent the company and the year, ${\beta }_0$ represents the intercept term; ${\lambda }_i$ means regression coefficients; Control is the control variable; Industry means industry fixed effect, and ${\epsilon }_{\mathit{it}}$ represents the error term; IC represents industrial convergence; EPU represents Economic Policy Uncertainty; DS represents Diversification Strategy; Z_score represents Financial Distress.

4. Analysis of Empirical Results

4.1. Descriptive Statistics and Correlation Analysis

Table 2. Descriptive statistics.

Variables	Obs	Mean	Std. Dev.	P5	P50	P95	Min	Max
IC1	23,668	0.338	0.473	0.000	0.000	1.000	0	1
IC2	23,668	0.515	0.965	0.000	0.000	2.000	0	17
EPU	23,668	0.407	0.234	0.114	0.365	0.792	0.0989	0.792
DS	23,668	0.379	0.438	0.000	0.194	1.254	0	1.606
Z_score	23,668	5.081	6.253	0.679	3.105	16.197	0	40.85
Ato	23,668	0.644	0.445	0.161	0.545	1.485	0.0761	2.693
Listage	23,668	2.119	0.828	0.693	2.197	3.178	0	3.332
Soe	23,668	0.323	0.468	0.000	0.000	1.000	0	1
Top10	23,668	0.58	0.15	0.321	0.588	0.812	0.233	0.901
Board	23,668	2.123	0.197	1.792	2.197	2.398	1.609	2.639
Growth	23,668	0.192	0.418	−0.281	0.125	0.840	−0.559	2.612
Bm	23,668	1.048	1.201	0.158	0.653	3.414	0.072	7.349
Dual	23,668	0.286	0.452	0.000	0.000	1.000	0	1

reports the results of descriptive statistics. The standard deviations of IC1 and IC2 are 0.473 and 0.965, respectively, and the mean values of IC1 and IC2 are 0.338 and 0.515, with the highest values of 1 and 17, indicating that the level of industrial convergence in China is not high, while the degree of industrial convergence among enterprises has obvious individual differences and large gaps. The standard deviation of EPU is 0.234, indicating that the EPU in China has fluctuated considerably in recent years. The mean value of diversification strategy (DS) is 0.379, the median is 0.194, and the maximum value is 1.606, indicating that the number of companies implementing diversification strategy in China is average; the standard deviation of financial distress (Z_score) is 6.253, implying that the situation of financial distress differs between companies and there are significant differences on an individual basis. The rest of the variables are within a reasonable range and will not be discussed further.

From the correlation coefficients among variables in

Table 3. Correlation analysis.

	IC1	IC2	EPU	DS	Z_Score	Ato	Listage	Soe	Top10	Board	Growth	Bm
IC1	1
IC2	0.746 ***	1
EPU	−0.010	−0.048 ***	1
DS	0.096 ***	0.110 ***	−0.007	1
Z_score	−0.049 ***	−0.041 ***	−0.030 ***	−0.129 ***	1
Ato	−0.026 ***	−0.023 ***	−0.048 ***	−0.067 ***	−0.035 ***	1
Listage	0.173 ***	0.140 ***	0.051 ***	0.226 ***	−0.136 ***	−0.016 **	1
Soe	0.002	−0.010	−0.106 ***	0.147 ***	−0.162 ***	0.034 ***	0.418 ***	1
Top10	−0.096 ***	−0.078 ***	0.012 *	−0.119 ***	−0.003	0.066 ***	−0.376 ***	−0.008	1
Board	−0.004	−0.006	−0.103 ***	0.049 ***	−0.119 ***	0.019 ***	0.134 ***	0.260 ***	0.019 ***	1
Growth	−0.007	0	−0.069 ***	0.002	0.007	0.132 ***	−0.070 ***	−0.060 ***	0.097 ***	−0.007	1
Bm	0.067 ***	0.060 ***	0.092 ***	0.118 ***	−0.354 ***	−0.015 **	0.339 ***	0.339 ***	0.024 ***	0.167 ***	−0.031 ***	1
Dual	−0.016 **	−0.006	0.068 ***	−0.047 ***	0.085 ***	−0.027 ***	−0.234 ***	−0.288 ***	0.027 ***	−0.173 ***	0.026 ***	−0.135 ***

*** p < 0.01, ** p < 0.05, * p < 0.1.

, it can be seen that the correlation coefficients among variables are less than 0.5, indicating that there is no serious multicollinearity problem in the empirical test of this paper, and the vif value is much less than 10 by the multicollinearity test, which means that the multicollinearity problem can be excluded.

4.2. Analysis of Empirical Results

In order to correctly identify the regression model in this paper, the F test and Hausman test were conducted for each model prior to testing each hypothesis. Models (1)–(4) should be chosen as fixed effects models for multiple regression analysis, according to the findings.

Table 4. Results of multiple regression analysis.

Variables	Model (1)		Model (2)	Model (3)		Model (4)
	IC1	IC2	DS	IC1	IC2	IC1	IC2
EPU	−0.0700 ***	−0.2950 ***	−0.0325 ***	−0.0679 ***	−0.2892 ***	−0.0703 ***	−0.2951 ***
	(−5.22)	(−10.74)	(−2.70)	(−5.07)	(−10.56)	(−5.24)	(−10.74)
DS				0.0649 ***	0.1800 ***
				(8.96)	(12.16)
Z_score						−0.0025 ***	−0.0042 ***
						(−4.69)	(−3.93)
EPU × Z_score						−0.0065 ***	−0.0139 ***
						(−3.14)	(−3.27)
Ato	−0.0259 ***	−0.0499 ***	−0.0666 ***	−0.0216 ***	−0.0379 **	−0.0276 ***	−0.0527 ***
	(−3.39)	(−3.18)	(−9.70)	(−2.82)	(−2.42)	(−3.60)	(−3.36)
Listage	0.1092 ***	0.1889 ***	0.0889 ***	0.1035 ***	0.1729 ***	0.1093 ***	0.1889 ***
	(22.91)	(19.36)	(20.79)	(21.54)	(17.62)	(22.92)	(19.36)
Soe	−0.0893 ***	−0.1966 ***	0.0368 ***	−0.0917 ***	−0.2033 ***	−0.0888 ***	−0.1952 ***
	(−11.31)	(−12.17)	(5.19)	(−11.63)	(−12.61)	(−11.23)	(−12.07)
Top10	−0.0728 ***	−0.1064 **	−0.1504 ***	−0.0630 ***	−0.0793 *	−0.0698 ***	−0.1009 **
	(−3.17)	(−2.27)	(−7.31)	(−2.75)	(−1.69)	(−3.04)	(−2.15)
Board	−0.0326 **	−0.0761 **	0.0055	−0.0330 **	−0.0770 **	−0.0351 **	−0.0796 **
	(−2.02)	(−2.30)	(0.38)	(−2.04)	(−2.33)	(−2.16)	(−2.40)
Growth	0.0051	0.0151	0.0354 ***	0.0028	0.0087	0.0061	0.0172
	(0.69)	(1.00)	(5.36)	(0.38)	(0.58)	(0.82)	(1.14)
Bm	0.0132 ***	0.0335 ***	0.0190 ***	0.0120 ***	0.0301 ***	0.0087 ***	0.0256 ***
	(4.34)	(5.39)	(6.96)	(3.94)	(4.85)	(2.76)	(3.96)
Dual	0.0077	0.0269 *	0.0197 ***	0.0064	0.0233	0.0085	0.0283 *
	(1.09)	(1.85)	(3.10)	(0.91)	(1.61)	(1.20)	(1.95)
Constant	0.2194 ***	0.3683 ***	0.3819 ***	0.1946 ***	0.2996 ***	0.2397 ***	0.4016 ***
	(4.68)	(3.84)	(9.10)	(4.16)	(3.13)	(5.09)	(4.17)
Industry	Yes	Yes	Yes	Yes	Yes	Yes	Yes
R2	0.0408	0.0356	0.0991	0.0441	0.0416	0.0420	0.0365
Observations	23,668	23,668	23,668	23,668	23,668	23,668	23,668

*** p < 0.01, ** p < 0.05, * p < 0.1.

Model (1) shows the baseline regression analysis of the explanatory variables EPU on the explanatory variables industrial convergence (IC1, IC2) after controlling for the control variables of ListAge, Dual, and industry, etc. Models (2) and (3) report the results of mediating effects of diversification strategies (DS). Model (4) tests the moderating utility of financial distress (Z_score) in EPU and industrial convergence (IC1, IC2). It is clear that the higher the uncertainty of economic policy, the more unfavorable it is for enterprises to make industrial convergence decisions.

The data from Model (1) show that the regression coefficients of EPU on industry convergence (IC1 and IC2) are −0.0700 and −0.2950, respectively, which are significant at the 1% level, indicating that EPU and industry convergence are negatively related and Hypothesis 1 holds.

In the regression of EPU on diversification strategy (DS) reported in Model (2), the regression coefficient of EPU is −0.0325, which is significant at the 1% level, indicating that EPU can inhibit firms’ diversification strategies. After Model (3) includes diversification strategy (DS) in the baseline regression model, the coefficient of EPU remains significantly negative at the 1% level, and the coefficients of diversification strategy (DS) are 0.0649 and 0.1800, respectively, which are significant at the 1% level, indicating that the EPU-diversification strategy-industrial convergence transmission channel is effective. And the robustness of the diversification strategy as a mediating effect was verified by the Sobel and Goodman methods. In the Sobel test, for IC1 and IC2, the Sobel Z values were −2.582 and −2.632, respectively; both p was 0.000, and the mediating effect accounted for 3.01% and 1.98%, and the Z_score for Goodman1 were −2.567 and −2.624, and Goodman2 with Z values of −2.597, −2.641, all results are significant, Hypothesis 2 is true. The diversification strategy plays an intermediary role in the relationship between EPU and industrial convergence.

Model (4) incorporates the financial distress (Z_score) and the interaction term between EPU and financial distress (EPU × Z_score), and it is found that the coefficients of the cross terms of EPU and financial distress are −0.0065 and −0.0139, respectively, which are both significantly positive at the 1% level, indicating that financial distress significantly moderates the relationship between EPU and industrial convergence, supporting Hypothesis 3.

4.3. Robustness Tests

To verify the reliability of the above findings, this paper uses substitution and instrumental variables to test the robustness.

4.3.1. Substitution Variable Method

In this paper, monthly data were transformed into annual data and divided by 1000 using the EPU index compiled by Baker et al. using the geometric mean method. Diversification strategy is measured by income Herfindahl Index replacement; the greater the index value, the lower the degree of diversification. Financial distress is tested for robustness using the dummy variable method, specifically by taking a value of 1 for Z_score when the firm’s Z_score index is lower than the industry average and 0 otherwise. Model (1) shows that EPU is significantly and negatively related to industrial convergence. Models (2) and (3) in

Table 5. Robustness test results of the substitution variable method.

Variables	Model (1)		Model (2)	Model (3)		Model (4)
	IC1	IC2	DS	IC1	IC2	IC1	IC2
EPU	−0.0719 ***	−0.2950 ***	0.0147 **	−0.0702 ***	−0.2904 ***	−0.0720 ***	−0.2951 ***
	(−5.39)	(−10.81)	(2.20)	(−5.27)	(−10.67)	(−5.40)	(−10.81)
DS				−0.1150 ***	−0.3133 ***
				(−8.85)	(−11.80)
Z_score						0.0413 ***	0.0742 ***
						(5.67)	(4.98)
EPU × Z_score						0.0757 ***	0.1521 ***
						(2.65)	(2.60)
Ato	−0.0260 ***	−0.0498 ***	0.0374 ***	−0.0217 ***	−0.0381 **	−0.0271 ***	−0.0518 ***
	(−3.39)	(−3.18)	(9.76)	(−2.83)	(−2.43)	(−3.54)	(−3.30)
Listage	0.1093 ***	0.1890 ***	−0.0517 ***	0.1034 ***	0.1728 ***	0.1089 ***	0.1882 ***
	(22.93)	(19.37)	(−21.69)	(21.51)	(17.58)	(22.86)	(19.30)
Soe	−0.0894 ***	−0.1962 ***	−0.0182 ***	−0.0915 ***	−0.2019 ***	−0.0889 ***	−0.1952 ***
	(−11.32)	(−12.15)	(−4.62)	(−11.60)	(−12.53)	(−11.27)	(−12.09)
Top10	−0.0729 ***	−0.1078 **	0.0865 ***	−0.0630 ***	−0.0807 *	−0.0694 ***	−0.1014 **
	(−3.18)	(−2.30)	(7.55)	(−2.75)	(−1.72)	(−3.03)	(−2.16)
Board	−0.0327 **	−0.0757 **	0.0026	−0.0324 **	−0.0749 **	−0.0354 **	−0.0802 **
	(−2.02)	(−2.29)	(0.32)	(−2.01)	(−2.27)	(−2.19)	(−2.42)
Growth	0.0050	0.0151	−0.0224 ***	0.0025	0.0080	0.0055	0.0161
	(0.68)	(1.00)	(−6.08)	(0.33)	(0.53)	(0.75)	(1.07)
Bm	0.0132 ***	0.0331 ***	−0.0075 ***	0.0123 ***	0.0308 ***	0.0078 **	0.0234 ***
	(4.34)	(5.33)	(−4.91)	(4.06)	(4.97)	(2.47)	(3.62)
Dual	0.0078	0.0269 *	−0.0102 ***	0.0066	0.0237	0.0087	0.0287 **
	(1.10)	(1.85)	(−2.89)	(0.93)	(1.63)	(1.23)	(1.97)
Constant	0.2190 ***	0.3630 ***	0.7679 ***	0.3072 ***	0.6036 ***	0.1981 ***	0.3248 ***
	(4.68)	(3.79)	(32.83)	(6.43)	(6.18)	(4.22)	(3.39)
Industry	Yes	Yes	Yes	Yes	Yes	Yes	Yes
R2	0.0409	0.0356	0.0940	0.0441	0.0413	0.0425	0.0369
Observations	23,668	23,668	23,668	23,668	23,668	23,668	23,668

*** p < 0.01, ** p < 0.05, * p < 0.1.

show that EPU can influence industrial convergence by influencing diversification strategies; diversification strategy plays an intermediary role. Model (4) reports the regression coefficients of EPU, Z_score, and EPU × Z_score, and the results show that financial distress has a negative regulatory effect, which further proves the robustness of the above findings.

4.3.2. Instrumental Variable Method

This paper uses the instrumental variables method to mitigate the endogeneity problem arising from omitted variables and further test the robustness of the results. The global EPU was re-tested as an exogenous instrumental variable using the 2SLS method, and the results of the study are shown in

Table 6. Robustness test results of the instrumental variable method.

Variables	Model (1)		Model (2)	Model (3)		Model (4)
	IC1	IC2	DS	IC1	IC2	IC1	IC2
EPU	−0.0789 ***	−0.3123 ***	−0.0438 ***	−0.0760 ***	−0.3044 ***	−0.0781 ***	−0.3105 ***
	(−5.61)	(−10.86)	(−3.48)	(−5.42)	(−10.62)	(−5.56)	(−10.80)
DS				0.0648 ***	0.1799 ***
				(8.96)	(12.16)
EPU × Z_score						−0.0053 **	−0.0111 **
						(−2.44)	(−2.51)
Z_score						−0.0024 ***	−0.0042 ***
						(−4.65)	(−3.88)
Ato	−0.0261 ***	−0.0503 ***	−0.0669 ***	−0.0218 ***	−0.0382 **	−0.0278 ***	−0.0530 ***
	(−3.41)	(−3.21)	(−9.74)	(−2.85)	(−2.44)	(−3.63)	(−3.38)
Listage	0.1096 ***	0.1897 ***	0.0894 ***	0.1038 ***	0.1736 ***	0.1096 ***	0.1896 ***
	(22.99)	(19.44)	(20.91)	(21.61)	(17.68)	(23.00)	(19.44)
SOE	−0.0900 ***	−0.1980 ***	0.0359 ***	−0.0924 ***	−0.2045 ***	−0.0896 ***	−0.1969 ***
	(−11.40)	(−12.25)	(5.06)	(−11.71)	(−12.68)	(−11.34)	(−12.17)
Top10	−0.0717 ***	−0.1043 **	−0.1490 ***	−0.0621 ***	−0.0775 *	−0.0690 ***	−0.0995 **
	(−3.13)	(−2.22)	(−7.25)	(−2.71)	(−1.65)	(−3.01)	(−2.12)
Board	−0.0336 **	−0.0780 **	0.0042	−0.0339 **	−0.0787 **	−0.0362 **	−0.0818 **
	(−2.08)	(−2.36)	(0.29)	(−2.10)	(−2.39)	(−2.23)	(−2.47)
Growth	0.0048	0.0145	0.0350 ***	0.0025	0.0082	0.0056	0.0162
	(0.65)	(0.96)	(5.30)	(0.34)	(0.54)	(0.76)	(1.07)
Bm	0.0135 ***	0.0341 ***	0.0194 ***	0.0122 ***	0.0306 ***	0.0091 ***	0.0265 ***
	(4.44)	(5.48)	(7.10)	(4.03)	(4.93)	(2.89)	(4.09)
Dual	0.0079	0.0273 *	0.0200 ***	0.0066	0.0237	0.0087	0.0286 **
	(1.12)	(1.88)	(3.14)	(0.94)	(1.63)	(1.22)	(1.97)
Constant	0.2234 ***	0.3761 ***	0.3870 ***	0.1983 ***	0.3065 ***	0.2436 ***	0.4094 ***
	(4.77)	(3.92)	(9.22)	(4.23)	(3.20)	(5.17)	(4.25)
Industry	Yes	Yes	Yes	Yes	Yes	Yes	Yes
R2	0.0408	0.0355	0.0991	0.0441	0.0415	0.0420	0.0365
Observations	23,668	23,668	23,668	23,668	23,668	23,668	23,668

*** p < 0.01, ** p < 0.05, * p < 0.1.

, where the findings remain unchanged, there is no endogeneity problem, and the results are reliable.

5. Further Analysis

Due to several institutional factors, state-owned and non-state-owned Chinese companies run very differently from one another. To test whether the effect of EPU on industrial convergence is related to the nature of property rights, this paper further tests the effect of the nature of property rights on it.

Table 7. Effect of the nature of property rights.

Variables	Model (1)
	IC1	IC2
EPU	−0.0699 ***	−0.2949 ***
	(−5.21)	(−10.74)
SOE	−0.0872 ***	−0.1924 ***
	(−11.00)	(−11.86)
EPU × SOE	0.0778 ***	0.1565 ***
	(2.80)	(2.76)
Ato	−0.0250 ***	−0.0479 ***
	(−3.25)	(−3.05)
Listage	0.1089 ***	0.1883 ***
	(22.84)	(19.29)
Top10	−0.0762 ***	−0.1132 **
	(−3.32)	(−2.41)
Board	−0.0322 **	−0.0753 **
	(−1.99)	(−2.27)
Growth	0.0046	0.0140
	(0.62)	(0.93)
Bm	0.0125 ***	0.0322 ***
	(4.11)	(5.16)
Dual	0.0083	0.0279 *
	(1.16)	(1.92)
Constant	0.2203 ***	0.3701 ***
	(4.70)	(3.86)
Industry	Yes	Yes
R2	0.0412	0.0359
Observations	23,668	23,668

*** p < 0.01, ** p < 0.05, * p < 0.1.

reports the results of the tests under different property rights properties. The results in Table 7 show that the regression coefficients of EPU × SOE on industrial convergence (IC1 and IC2) are 0.0778 and 0.1565, respectively, which are significantly positive at the 1% level. It is clear that the nature of property rights negatively moderates the relationship between EPU and industrial convergence. Compared to SOEs, non-SOEs are more reluctant to engage in industrial convergence under EPU.

The reason for this is that SOEs, with their close ties to the government, enjoy greater access to information in order to accurately capture policy orientations. They can use the knowledge already available to make future predictions, thus reducing the impact of future EPU on their businesses. At the same time, state-owned firms benefit from matching national support policies and preferential treatment, as well as better access to resources, which might help enterprises under EPU with industrial convergence. However, non-state-owned enterprises are more sensitive to the uncertainties brought about by frequent changes in economic policies, and industrial convergence decisions involve many subjects and a lot of money, so they dare not take the risk of industrial convergence easily. Therefore, compared with state-owned enterprises, non-state-owned enterprises will refuse to implement industrial convergence decisions under frequent changes in economic policies.

6. Discussion and Conclusions

Although industrial convergence is the driving force behind the transformation, modernization, and expansion of businesses, the unpredictability and instability of the macroeconomic policy environment restrict the industrial convergence behavior of enterprises. This paper investigates the annual data of A-share listed companies in China from 2010 to 2021 and discusses the influence of EPU on industrial convergence, the mechanism of financial distress and diversification strategy on the relationship between EPU and industrial convergence, and the differential influence of EPU on industrial convergence under different enterprise property rights.

According to the test results, firstly, the macro EPU has a significant inhibitory effect on the industrial convergence of micro-enterprises. The ambiguity of macroeconomic policies will intensify the phenomenon of information asymmetry, and industrial convergence involves the participation of multiple subjects, and the ambiguous market environment increases the possibility of other participating subjects concealing information, leading to poor corporate decisions, and enterprises are often reluctant to carry out industrial convergence in an environment of EPU.

Second, the diversification strategy plays a mediating role in the relationship between EPU and industrial convergence. Diversification strategy implementation involves a considerable amount of capital, and the EPU environment compels enterprises to store a specific amount of cash for emergencies, which plainly hampers diversification strategy implementation. Companies that implement diversification strategies are willing to share and cross-utilize resources, which facilitates industrial convergence. In conclusion, EPU can indirectly inhibit the development of industrial convergence of companies by influencing diversification strategies first.

Next, corporate financial distress has a negative moderating effect on the relationship between EPU and industrial convergence. Companies in financial distress will be eager to grow value through change. External EPU creates opportunities for business change, and companies will want to utilize each other’s industrial resources through industrial convergence in a partnership to provide new growth opportunities for their own companies in an uncertain environment.

Finally, the different property rights of enterprises will also have a differentiated impact on the relationship between EPU and industrial convergence. State-owned firms have natural advantages, such as national support policies, and will not be concerned about losses generated by miscalculation of industrial convergence decisions in an uncertain economic policy environment. However, non-state enterprises are more sensitive to the uncertainty factors brought by frequent changes in economic policies, and the decision of industrial convergence involves many subjects and a large amount of capital, so they are often reluctant to carry out industrial convergence under EPU in order to avoid major losses caused by wrong decisions.

Based on the above conclusions, this paper makes the following recommendations: (1) Because the current state of the economy is uncertain, policymakers should reduce the frequency of economic policy variations and always ensure long-term economic policy stability. Because of inconsistencies in economic policy, businesses may oscillate in their daily decision-making. As a result, when performing macroeconomic regulation obligations, the government must consider the potential effects of policy changes on business operations. (2) Enterprises should always improve their policy research and decision-making activities. They can endeavor to ensure the efficacy of their job by establishing a permanent policy research department, paying close attention to national economic policy trends, and examining economic policies rationally. (3) With the benefit of resources in various industries, industrial convergence is a new way for businesses to collaborate across borders in order to reduce costs and increase efficiency. Enterprises should foster the rapid expansion of businesses through industrial convergence and pay attention to the benefits of industrial convergence, particularly in the context of an unpredictable economic policy environment. (4) Recognize the importance of diversification strategies in fostering industrial convergence throughout the era of economic globalization. Diversification strategies are advantageous for businesses since they help transfer risks throughout the industrial convergence process and speed up resource exchange and information sharing. (5) In an uncertain economic policy environment, pay closer attention to businesses in financial distress. Such firms are desperate for poverty alleviation in order to change the status quo, and they will be willing to carry out industrial convergence in an uncertain environment in order to accomplish poverty alleviation through synergistic effects or even value creation for the enterprise.