The purpose of this paper is to investigate the impact of political instability at the local level on foreign firms in China. Building on the literature on political embeddedness and business power, the authors propose a theoretical framework to explain how political turnover can affect foreign firms’ performance and how they respond to such challenges by leveraging their power bases.
To test the hypotheses, the authors apply fixed effects regression to an unbalanced panel data set comprising 13,360 foreign firms from 1998 to 2013 and the political replacement that involved changes in provincial governors.
The findings confirm that political turnover incidents have a negative impact on the performance of foreign firms in China. However, the authors also found that this negative relationship is weaker for firms that can choose various types of power sources. Specifically, the study reveals that foreign firms with large firm size, government ownership and a strong foreign direct investment community are better qualified to mitigate the negative effects of political instability.
This study contributes to the literature by developing the understanding of how political uncertainties and risks affect the performance of foreign firms in China and the importance of firms’ power in counterbalancing these effects. The research provides valuable insights into how multinational corporations can exploit their power to manage the effects of local political turnover, which has practical implications for the strategy and management of foreign firms operating in China.
Introduction
The global political climate has been increasingly characterized by political uncertainty, shifting power dynamics and the growing influence of governments in regulating markets and industries (e.g. Ghauri et al., 2021; Temouri et al., 2023). These trends significantly affect international business, as companies must deal with changing regulations, trade and investment policies and government transitions in the countries where they operate. Frequent government transitions or political turnovers can significantly alter business environments. Although a growing number of studies in both economics and political science have examined the impact of political turnover on economic activities (e.g. Earle and Gehlbach, 2015; Li and Zhou, 2005; Zhong et al., 2019), the extent to which and the conditions under which subnational political turnover influences the performance of foreign firms remain under-researched. This study aims to answer the questions from a political embeddedness perspective and business power perspective, using the case of China.
Foreign firms face a variety of economic, political and institutional pressures in host countries, which they must navigate to thrive. One of such challenges is the frequent political turnover, which introduces uncertainty and policy inconsistency (e.g. Darendeli and Hill, 2016). As a business environment, China poses these types of challenges for foreign firms. Even being a unitary state with a central government and uniform laws, the provincial governments play an important role in implementing national policies within their jurisdictions. Furthermore, each province has its own people’s congress, which is responsible for enacting local laws and policies (Cho, 2002; Du et al., 2008). In such conditions, a local political official can unexpectedly be replaced by another official appointed by the central government.
Political turnover refers to a change in a ruling leader or leaders in a government (Horowitz et al., 2009). It amounts to an important institutional change that occurs in both national governments as well as in subnational governments. This phenomenon has been under the attention of international business research for some time under the umbrella of political embeddedness (Haveman et al., 2017; Wang et al., 2023). Political embeddedness “is defined broadly to include formal and informal, individual and organizational ties to the state” (Okhmatovskiy, 2010, p. 1023). It is often seen as a portfolio of a firm’s individual and institutional connections to some groups of the government, such as managerial ties to political actors and organizational ties to political institutions (Sun et al., 2010; Wang et al., 2023). While prior studies have suggested that political connections are of fundamental importance to business’s survival and thriving (Marquis and Qian, 2014; Okhmatovskiy, 2010; Sun et al., 2010), other studies advance the view that changes in political leadership alter the interdependencies between institutional and organizational actors (Dieleman and Widjaja, 2019; Rodrigues and Child, 2008).
Although foreign firms may have exhibited some advantages in comparison with domestic ones in China, prior studies have documented that foreign firms experienced a relatively high degree of liability of foreignness in China during our investigating period (Curran and Ng, 2018; Xu et al., 2006). Research evidence suggests that foreign firms may be more vulnerable to political reshuffle than other firms considering that political changes increase the liabilities of foreignness faced by foreign firms, and in so doing affect their performance (Marquis and Qian, 2014; Zhong et al., 2019). Studies of foreign firms operating in China (Haveman et al., 2017; Zhong et al., 2019) suggest that political turnover tends to generate instability by disrupting already established relationships and contracts between firms and government officials. Zhong et al. (2019) argued that political turnover may affect connections between firms and government agents by creating policy inconsistency and information asymmetry with implications for their performance. They suggest that turnover leads to failure in keeping promises, and thus creates mistrust and new sources of risks for foreign firms. Nevertheless, the negative impact of political turnover on firm performance can vary across the firms, depending on both their market position and the social and institutional environment (e.g. Child et al., 2012; Rodrigues and Dieleman, 2018). From a mixed embeddedness perspective, businesses are influenced by a combination of factors, including the microlevel of firm resources and the socioeconomic and politico-institutional environment of the country where they operate, and interactions between different domains of embeddedness (Bagwell, 2018; Kloosterman et al., 1999; Kloosterman, 2010). However, while studies have extensively discussed the impact of the macro environment and the dominance of conformity responses by firms, there has been insufficient investigation into how firms proactively take actions to mitigate the constraints imposed by the macro environment in which they are embedded (Mihailova and Svystunova, 2025; Saka-Helmhout et al., 2016; Yamamura and Lassalle, 2024), particularly those related to political turnover.
We contribute to the discussion on political turnover by examining how indicators of foreign firms’ power sources can mitigate the negative effects of turnover on their performance in China. Studies demonstrate that, instead of passively enduring institutional changes, firms can leverage certain intrinsic characteristics to compensate for and lessen the impact of institutional instability (Doh et al., 2017; Khanna and Palepu, 2010). Applying this to our research context, we argue that foreign firms can effectively address these challenges by leveraging their power resources valued by the host government (Eden et al., 2005). These resources may include their large size, institutional connections with local authorities (government ownership) and their position within the foreign direct investment (FDI) community.
To address the above issues and answer our research questions, we build upon the existing literature on political embeddedness and business power basis to develop a theoretical framework that explains the impact of political turnover on foreign firm performance, along with the conditions that influence this impact. Drawing from a political embeddedness perspective, we propose that political turnover can create unexpected political instability, thereby negatively affecting foreign firm performance through political disconnection, policy discontinuity and information asymmetry. Moreover, our study advances this line of inquiry by examining how foreign firms’ power sources can mitigate the negative effects of turnover on their performance in China from a business power perspective. We particularly shed light on the ways in which foreign firms can leverage their power resources to offset the challenges posed by political turnover. By integrating these two perspectives, our research aims to provide a comprehensive understanding of the intricate relationship between political turnover and foreign firm performance in the context of China.
Our paper proceeds as follows. After the introduction, we describe the main characteristics of the Chinese subnational political context and how it can create uncertainties for foreign firms operating in China. We then discuss the theoretical foundations of the hypotheses formulated. This takes us to the descriptions of the methods used, empirical testing results and robustness tests. The final section consists of discussion and conclusions.
Subnational political turnover in China
The Chinese political system is composed of five hierarchical levels: the center, provinces, prefectures, counties and townships (Wang, 2017). In this study, we focus on the provincial level. The Central Committee of the Chinese Communist Party (CCP) ultimately controls the movement of provincial government leaders within the system. The most senior position in provincial government is appointed by the CCP, with no right of veto, and no public influence as it happens in many Western democratic countries. Political promotion mainly depends on economic achievement (Guo, 2009; Li and Zhou, 2005). Thus, local leaders are motivated to adopt policies and integrate resources to improve their region’s economic performance (Guo, 2009). The official tenure of local leaders is five years, but the central government often replaces these leaders before their full term is completed, which increases the turnover frequency. According to our calculation, the average tenure of a provincial governor is around three years, the shortest is just two months and the longest is 10 years (including two reappointments). Firms may be unable to anticipate turnover because the appointment system is not transparent.
We chose China as the research context for two reasons. First, as a transition and fast-growing economy, the political environment in China poses greater uncertainty for firms given the weakness of formal institutions, and the local bureaucrats’ power to create and incentivize policies leading to market growth (Haveman et al., 2017). For firms, one way to reduce uncertainty is by developing political relations with bureaucrats. In a relational-oriented society (Hwang, 1987), establishing political connections is strategic for firms intending to expand their strategic resource base (Luo and Zhao, 2013; Shen et al., 2020). Second, despite being a unitary country, with uniform laws and policies, there are profound regional differences within China in terms of economic and institutional development. Accordingly, the amount and type of power the local authorities can exercise in each jurisdiction also differ, mainly in the creation of rules that directly shape the local economic environment (An et al., 2016; Li and Zhou, 2005; Luo, 2007; Qian and Weingast, 1997; Xu, 2011). Subsidies and other incentives can be an important attraction to foreign investors to carry on investing in each region.
Foreign firms, on the other hand, exhibit varying capacities in navigating the uncertainties and risks stemming from political turnover. Their substantial presence and contributions to the local economy, including significant investments and the introduction of advanced technologies to the region, endow them with legitimacy and bargaining power (e.g. Li and Sun, 2017). These help to align different interests and circumvent difficulties caused by political changes. Consequently, they are better equipped to shape a favorable business environment.
However, as the literature on foreign investment in China suggests, foreign firms are not evenly distributed across Chinese regions (Cheung and Lin, 2004). These regional differences provide a natural experiment to test how and under which conditions political turnover influences foreign firms′ performance. In addition, the choice of a single host-country context helps to avoid issues of cross-country variance and to reveal the consequences of intra-country environmental variations. These conditions lead to the development of a conceptual model with the potential to be tested in other countries with substantial subnational heterogeneity, such as India, Brazil, Russia and Mexico.
Theory and hypotheses
In the Chinese political regime, both central and local authorities retain coercive power over property rights, contracts and competition, and thereby control issues that matter to a foreign firm’s performance, i.e. licenses, fees, fines and taxes (Haveman et al., 2017). Political embeddedness is a crucial factor in determining foreign firms’ survival and growth in China (Marquis and Qian, 2014; Okhmatovskiy, 2010; Sun et al., 2010). Specifically, establishing stable relations with government officials is therefore critical, given the uncertainties generated by the absence of regulations and the role state bureaucrats play in substituting for their absence (Marquis and Qian, 2014). Not only can the local bureaucrats provide information on the market opportunities available, but also on other forms of state support, such as bank loans and contracts.
Political turnovers are closely linked to firms’ political embeddedness, which can alter the basis upon which political and economic relations are sealed, for example, the exchanges between partners in the form of implicit contracts and mutual rewards. Scholars argue that the institutional, political and economic systems are mutually embedded (Luo et al., 2012; Puffer et al., 2010; Xu et al., 2016). These arrangements can be relatively stable for extended periods but can also be affected by rapid changes, sometimes significant, such as when political turnovers are taking place. That those political uncertainties can have consequences for the economy and firms is an issue that has been explored from various perspectives: organizational sociology (e.g. Haveman et al., 2017), institutional (Xu et al., 2016) and governance (Yu and Mai, 2020; Zingales, 2017) and international business (Zhong et al., 2019). While prior research has drawn attention to how the state bureaucracy sets limits on, and supports, foreign firms’ operations in the Chinese market (Davies, 2013; Pan and Chi, 1999), more recent studies (Haveman et al., 2017; Yu and Mai, 2020) have highlighted the relevance of considering a foreign firms′ power in the context of their relationships with state authorities.
Foreign firms are also important to local authorities because their success is associated with the region’s economic development (Luo, 2004; Luo and Zhao, 2013). They can bring important contributions to development if they utilize the local supply chain (Johns and Wellhausen, 2016) by offering employment and stimulating the local economy. Multinationals can be critical in the implementation of governments’ plans because of their power to use a range of resources – knowledge, capabilities (e.g. new technologies, a wide geographical network of suppliers and markets), symbolic resources (e.g. reputation and legitimacy) and political resources (access to governments and international institutions in other parts of the globe) - in the interest of local authorities (Buckley et al., 2006).
This study brings a new angle to the analysis of the effect of local political turnover on foreign firm performance by drawing insights from the literature on the political embeddedness (Haveman et al., 2017; Wang et al., 2023) and business power perspective (Busemeyer and Thelen, 2020; Child and Rodrigues, 2011; Rollings, 2021), while also incorporating the concept of mixed embeddedness (Bagwell, 2018; Kloosterman et al., 1999; Kloosterman, 2010). By focusing on those relationships between business and government (Bohle and Regan, 2021), this study helps to understand the conditions under which subnational political turnover influences the performance of foreign firms. In adopting the business power perspective, we argue that powerful foreign firms can proactively take actions to limit the constraints associated with political turnover, thereby mitigating the effect of political turnover on performance.
Figure 1 proposes that depending on the power of a firm, the negative effects of political turnover can be lessened.
Subnatioanl political turnover and performance
As mentioned earlier, political turnover can impact a firm’s political embeddedness, leading to the disruption of the firm’s ties with political actors and institutions (Dieleman and Widjaja, 2019; Rodrigues and Child, 2008). This disruption may have an adverse effect on a foreign firm’s performance. In China, although it is governed by a single party, political turnover remains significant due to the potential for shifts in policy priorities and regulatory enforcement, even within the Communist Party. Leadership transitions at various levels can lead to changes in economic policies, development strategies and business regulations that directly impact foreign firms. Provincial governments play a significant role in executing national policies within their respective jurisdictions. Each province operates with its own people’s congress, responsible for enacting local laws and policies(Cho, 2002; Du et al., 2008). We argue that provincial political turnover negatively influences foreign firm performance by intensifying three constraints that are related to political embeddedness: political disconnection, policy discontinuity and information asymmetry.
Given the Chinese relation-oriented culture and legacies from the previous planning system, political connections, as “non-market resources,” are strategic for firms in obtaining political support and gaining other competitive advantages (Boddewyn and Doh, 2011; Cui et al., 2018; Wang et al., 2021). Foreign firms are no exception (Luo, 2001; Ma et al., 2021). The literature on Chinese political connections suggests that these have a positive effect on firm performance (Ding et al., 2018; Haveman et al., 2017; Ismail et al., 2013; Li et al., 2008; Yu and Mai, 2020). Political changes at the provincial level disconnect the existing political relationships that foreign firms have established (Feiock and Jang, 2009). Political disconnection affects firms′ performance by creating more unpredictability and costs.
Considering that the central government appoints leaders of provincial governments (Li and Zhou, 2005), it is expected that these leaders have the ultimate authority over economic functions and policies (Qian and Weingast, 1997; Yang, 2006). Due to the coexistence of decentralized economic power and centralized political power (An et al., 2016; Xu, 2011), provincial-level political turnover tends to lead to policy discontinuity (Quinn and Woolley, 2001). A new political coalition with different preferences than its predecessor may cancel or alter commitments made by the former provincial leaders to foreign investors (Blake, 2013). In a similar vein, Zhou and Xu (2012) suggest that because Chinese governance is relational and based on social relations and shared norms, change in this relational framework can lead to significant breaches of social contracts made between the firms and government members. For those firms that enjoyed the benefit of government support, these discontinuities amount to losses with implications for performance (Rothaermel and Hill, 2005; Zhong et al., 2019).
Moreover, the appointment of provincial leaders is not transparent and tightly controlled by the central government. This makes it more difficult for firms to anticipate political turnover and, therefore, adjust their political strategy. For foreign firms, it is especially difficult to acquire political information. Although, according to the law, the term of office for subnational leaders is five years, in many cases, it can be shorter (Zhong et al., 2019). Unpredictability from political environments intensifies the information asymmetry facing foreign firms and increases the costs and risks of doing business in China and, hence, reduces their performance (Zhong et al., 2019).
Consequently, we expect provincial political turnover to negatively influence foreign firms’ performance. Accordingly, we propose the following hypothesis.
There is a negative relationship between provincial political turnover and foreign firm performance.
Firms’ power sources and performance in the context of political turnover
Child and Rodrigues (2011, p. 804) defined the use of power as “inducing people or organizations to do what they would otherwise have chosen not to do, or preventing them from doing what they would otherwise have chosen to do.” Power provides its holder with the ability to achieve a high level of influence or control over the behavior of others (Shervani et al., 2007) and to shape the evolution of a system to suit its purposes (Child and Rodrigues, 2011). The present study explores how a firm’s ownership of certain resources can decrease uncertainties derived from its political embeddedness (Kim et al., 2004; Pfeffer and Salancik, 1974).
Related to the political environment, scholars in social politics have defined business power as the capacity of business actors to influence political and policy decisions (Farnsworth and Holden, 2006; Mercille and O’Neill, 2024) by identifying several sources of business power, structural, instrumental and institutional sources (Busemeyer and Thelen, 2020; Collier et al., 2018; Mercille and O’Neill, 2024). Structural sources are connected to firms’ privileged position in the economy, in such a way that their investments affect growth, employment and technology development (Collier et al., 2018). Instrumental sources are related to direct forms of lobbying by business actors (Van Der Ven, 2018). Institutional sources arise when state actors delegate public functions to private business actors (Busemeyer and Thelen, 2020). These sources are pertinent to foreign firms operating in China, as they play a significant role in the Chinese economy (Berthélemy and Démurger, 2000) and they are often connected to the local and central governments (Luo, 2001; Marquis and Qian, 2014).
Power provides foreign firms with two important capabilities. One is the ability to influence the agenda of external policymakers so that it is favorable to the firm in achieving its objectives; the other is the ability to mobilize political support and gain legitimacy and resources (Child and Rodrigues, 2011). Based on the business power perspective, we argue that, in the context of political replacements, firms can mitigate the negative impact of the changes on performance by utilizing power sources and then influencing policies in a way that suits their interests. To better understand how this could happen, we explore the different power sources a firm might own, display and unlock them.
Inspired by Child and Rodrigues’ (2011) three modes of organizational engagement with the external environment and the three sources of power discussed above, we identify three categories of power sources for foreign firms in China: endowed power, borrowed power and mediator power. These correspond respectively to the possession of material advantage, normative approval and a perception that cooperation is mutually beneficial (Child et al., 2013, p. 217). Accordingly, the possession of these three types of resources (material, knowledge and relational) represents the latent power in that they could be unlocked, mobilized and actioned through exerting influence if necessary. This power sources approach can also be understood through the mixed embeddedness perspective. The mixed embeddedness of foreign firms refers to their simultaneous contextualization within the market, society and politics (Deng et al., 2024; Kloosterman et al., 1999), which interact with the power sources of these firms and thereby influence their performance. In the following section, we develop an argument for the moderating role of each category of power in the turnover-performance relationship. In line with our arguments above, we analyze the moderating effect of the three sources of power on the relationship between turnover and performance (Figure 1).
Endowed power.
Endowed power associated with firms’ characteristics is critical for signaling the possession of critical resources that are either attractive or dire to competitors and allies. The visibility and the prospect that the holder can use such resources may be sufficient to encourage, or deter, actions by significant others (Child and Rodrigues, 2011; Mayer, 2000). Although there are other characteristics that may be associated with endowed power (e.g. productivity, brand equity, reputation and product variety), we focus on firm size given past evidence that this shapes a firm’s position of dominance or power in the market (Barnett and Amburgey, 1990). This endowed power interacts with the market embeddedness influencing foreign firm performance under political turnover.
In more detail, large firms that have a dominant position in the market, contribute to local economic performance and therefore have considerable bargaining power with the local government (Li and Sun, 2017). For example, Foxconn, one of the largest employers worldwide, had 12 factories in nine Chinese cities in 2010. Local Chinese governments compete to get Foxconn to set up a new factory or keep established factories in their territories to boost GDP growth and local employment within their jurisdiction. Foxconn thus had strong bargaining power that they could use to gain political resources, such as the provision of extensive land, infrastructural support and a supply of labor (Ngai and Chan, 2012). Large firms can influence a public agency’s decision making or key political actors, lobby for favorable rules in shaping the market (Hillman et al., 2004; Lord, 2000; Yim et al., 2017) and obtain political resources that could not be obtained through market exchange. Large firms can also “endeavor to integrate other business or nonbusiness actors that could contribute, or potentially hinder, their management of the political environment” (Child and Rodrigues, 2011, p. 813). Thus, large firms may be able to impose their own rules on an industry or region (Lord, 2000). Given their high market power, those firms can mitigate the negative effects of the uncertainty associated with provincial political turnover. More precisely, they can quickly build connections with new leaders in the region and rebuild the conditions needed to influence policy decisions to reduce the potential deficiencies associated with political disconnection, policy discontinuity and limited information. In contrast, small firms have little market power and are therefore more likely to be constrained by the external environment than larger firms (Elbanna et al., 2020), and are more vulnerable to the constraints created by provincial political turnover. Therefore, we would expect the negative effect of political turnover on performance to be weaker for large firms than for small firms. Based on this argument, we propose the following:
The negative relationship between provincial political turnover and foreign firm performance is weaker for large foreign firms than for small foreign firms.
Borrowed power.
Borrowed power refers to power that draws upon external sources (Betzold, 2010). A firm can borrow power from external parties such as local agents and collaborators (Child and Rodrigues, 2011), or it can extend its power by using institutions in other countries (Doh et al., 2017). Borrowed power is bestowed on a firm by its associates. Within this scenario, a firm can see its power increase if it is formally associated with other power holders through government participation in ownership (Pinkham and Peng, 2017). In our study, we focus on borrowed power achieved through government shared ownership, whether it’s at the local or central level. This borrowed power interacts with the institutional setting, influencing foreign firm performance under political turnover.
Local government ownership enhances a foreign firm’s ability to mitigate the uncertainty and costs associated with changes in local political leadership. First, through government ownership, a firm establishes a natural connection with the government (Cuervo-Cazurra and Li, 2020; Lu and Yao, 2006; Wu, 2011). Although provincial political turnover may disrupt the political connections between a foreign firm and the local government leaders, the government-ownership guarantees a route to political connection. The firm can build a connection with the new leaders through its ownership linkage, which reduces the costs that arise from political disconnection. Second, a foreign firm with government ownership can more easily create new alternatives relevant to its business and directly negotiate them with local policymakers. This special tie enables the firm to influence the government’s policymaking (Okhmatovskiy, 2010), reducing the costs that arise from policy discontinuity. Third, government ownership provides a channel for the firm to overcome information asymmetry by gaining information on provincial political turnover and potential policy changes. Previous studies have suggested that firms with government ownership can be better informed about changing policy (Chen et al., 2014; Okhmatovskiy, 2010), which helps them anticipate the uncertainty caused by provincial political turnover and take action to reduce the costs related to limited information. Central government ownership can also mitigate the negative effect of local political turnover. In emerging markets, like China, the central government controls the most critical resources in the economy (Wang et al., 2012). Foreign firms frequently receive support from the central government, which facilitates direct access to core government policies and additional resources. Thus, they may be less dependent on and influenced by local governments and their policies, which offsets the negative impact of local political turnover on the firm performance.
Based on these arguments, we conclude that a foreign firm with significant government ownership is better able than firms with no government ownership to overcome the negative effects of uncertainties. Therefore, we expect government ownership to mitigate the negative effect of political turnover on performance. Accordingly, we propose the following:
The negative relationship between political turnover and foreign firm performance in China is weaker for foreign firms with government ownership than for those with no government ownership.
Mediator power.
Mediator power refers to power enhancements gained through informal or temporary collaborators and networks (Baylis et al., 2005; Child and Rodrigues, 2011). These involve power by association and the term “power with” as used by organizational scholars. An important source of this power is the collective power of foreign firms, which can be reflected by the foreign firm community. This mediator power interacts with this specific social embeddedness, influencing foreign firm performance under political turnover.
The FDI community comprises foreign firms within a region. A strong FDI community helps foreign firms protect their interests during political turnovers. Foreign firms may establish associations with other multinationals operating in a particular country or region and in that way create a pressure group that fights for shared interests (e.g. Bouquet and Birkinshaw, 2008; Vivoda, 2011). In the context of political turnover, the power gained from the pressure group helps a foreign firm overcome turnover deficiencies. We first look at the political connection. Since political promotion in China is mainly determined by economic achievement within one’s jurisdiction (Guo, 2009; Li and Zhou, 2005), local leaders pay particular attention to firms that make important contributions to the local economy. In a region with a large FDI community, foreign firms are important players in regional economic development. The local governments are dependent on the firms for tax income, employment and knowledge (Eden and Molot, 2002; Luo, 2001; Rodrigues and Dieleman, 2018). This dependency enables the foreign firms to build a good connection with the new leaders, reducing the costs that arise from political disconnection, and limited information. In terms of policy changes, if new leaders indicate the intention to change a policy to favor domestic firms at the expense of foreign firms, the FDI community can negotiate with the government with a collective voice, mitigating the negative effect of policy discontinuity. The stronger the FDI community, the greater the access to resources dominated by its members. To improve economic performance, the new government leaders need to include these foreign firms as a group with common interests and resources. By association the stronger the FDI community, the more “mediator” power the more the number of sources of power foreign firms can use to mitigate and control the negative effects of political turnover. Hence, we propose the following:
The negative relationship between political turnover and foreign firm performance in China is weaker in regions with a strong FDI community than in those with a weak FDI community.
Data, variables and estimation approach
Sample
The sample for this study was identified from the China Industrial Enterprises Database (1998–2013). In the database, all types of firms registered in China with annual sales of at least 5m RMB are included from 1998 to 2007, while firms with annual sales of at least 20m RMB are included from 2008 to 2013. This database is one of the most comprehensive sources of firm-level data in China (Liu and Lu, 2015) and has been used by many researchers (Chang et al., 2013; Chang and Xu, 2008; Park et al., 2006; Zhang et al., 2019). We selected the sample based on two main criteria. The first criterion is a foreign capital ratio of no less than 25%. According to the Sino-Foreign Joint Venture Enterprise Law, companies are recognized as foreign-invested firms if foreign investors contribute at least 25% of the registered capital. The second criterion is state ownership of less than 30%. This is because firms with state ownership exceeding 30% are classified as state-owned enterprises (SOEs) (Huang et al., 2017). In our study, we focus on foreign firms. After excluding those with missing data, an unbalanced panel data set comprising 13,360 foreign firms, with 603,076 firm-year observations, is used in the main estimation. Each of these foreign-invested firms is identified by its operational location (subsidiaries), not by the name of a parent company. This means that each firm is located in a single place and maintains its own separate financial data.
Dependent variable (foreign firm performance)
Our selected dependent variable, ROA, or return on assets, is measured as the ratio of after-tax profit to total assets. ROA is a commonly used measurement of performance (Chang et al., 2013; He et al., 2015) and is used in our main estimations as the dependent variable. We also used return on equity (ROE) as an alternative measure of performance (e.g. Li et al., 2018; Peng, 2004) in our robustness test.
Key independent variable (political turnover)
It denotes subnational political turnover. It is measured based on the change in the provincial governor (sheng zhang) in a specific region and year. If a change in a provincial governor takes place in the first (second) half of year t, it is taken as occurring in year t (year t + 1) in subsequent calculations (e.g. An et al., 2016).
Moderating variables (power sources)
Size refers to a firm’s size, which is measured by the logarithm of total output (Li et al., 2018).
Government ownership denotes government involvement in a foreign firm. It is measured by a dummy variable, with a value of 1 if a firm has equity owned by the Chinese government and 0 otherwise.
FDI community refers to the collective presence and activities of foreign firms in a specific region. It is measured by the ratio of total investment of FDI to provincial GDP in a given year (Jiang et al., 2011).
Further, to test H2- H4, three interaction terms were created: Turnover×Size, Turnover× Government ownership and Turnover×FDI community.
Control variables
Based on existing studies, we included various control variables that might be influential.
Firm age refers to the duration of a foreign firm’s establishment in China, measured as the natural logarithm of the difference between the investigating year and its entry year into the Chinese market (Liu et al., 2021).
Capital intensity refers to a foreign firm’s capital intensity, the logarithm of the ratio of total assets to total staff (Chang et al., 2013).
Leverage denotes the financial leverage of a foreign firm, which is measured as the ratio of total debt to total assets (Zhang et al., 2019).
Local market orientation represents the local market connections of a foreign firm. It is measured as the ratio of sales in the host country to the total sales of a foreign firm (He et al., 2015; Pan and Chi, 1999).
Nationality refers to the origin of a foreign firm. It is measured by a dummy variable with a value of 1 if a firm is from one of the three special regions: Hong Kong, Macao and Taiwan, and 0 otherwise (Chang et al., 2013).
Herfindahl–Hirschman Index (HHI) is an indication of competition within an industry where a foreign company operates. It is measured by the HHI of a specific industry and year (Liu and Qiu, 2016).
Market development is an indication of the market institutional development in a specific region and year, measured by the ratio of fixed assets of non-SOEs to the total fixed assets in the region (Fan et al., 2011).
Wage reflects labor costs in a region and industry concerned and is measured as the average wage in an industry and region (NBS, 2014).
Governor tenure refers to the length of time a provincial governor remains in office within a specific region, measured in years from the starting year to a specific year concerned.
Governor origin denotes the origin of the provincial governor. It is measured by a dummy variable with a value of 1 if the provincial governor came from central government agencies and 0 otherwise (e.g. Zhang and Gao, 2008).
In addition to these control variables, we also included time and firm fixed effects.
The details of the control variables, including their definitions, measurements and impacts, are provided in the Supplementary Information Table.
Data sources
Information on the Turnover, Governor tenure and Governor origin were collected from several relevant internet sources [1]. Data on firm-level variables such as ROA, Size, Government ownership, Firm Age, Capital intensity, Leverage, Local market orientation, Nationality and HHI were obtained from the Annual Industrial Survey Database. Data on regional-level variables, such as FDI community, Market development and Wage, were acquired from NBS.
A summary of the descriptive information on these variables is shown in Table 1. The correlations between pairs of these independent variables were all below the generally accepted maximum value of 0.7. Furthermore, all the VIF values were computed based on the estimation models used and they were well below the generally accepted threshold value of 5. As such, these statistics suggest that multicollinearity is not an issue in this study.
Summary information and correlation matrix
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean | 0.075 | 0.158 | 0.202 | 10.721 | 0.012 | 0.784 | 1.966 | 5.25 | 1.338 | 0.519 | 0.498 | 0.001 | 0.71 | 26.284 | 3.693 | 0.735 | 0.303 |
| SD | 0.145 | 0.32 | 0.402 | 1.34 | 0.109 | 0.461 | 0.652 | 1.16 | 2.237 | 0.452 | 0.5 | 0.005 | 0.101 | 11.972 | 2.296 | 0.442 | 0.46 |
| 1 ROA | 1 | ||||||||||||||||
| 2 ROE | 0.748*** | 1 | |||||||||||||||
| 3 Turnover | 0.001 | 0.003*** | 1 | ||||||||||||||
| 4 Size | 0.258*** | 0.23*** | 0.044*** | 1 | |||||||||||||
| 5 Government ownership | −0.017*** | −0.018*** | 0.002 | 0.024*** | 1 | ||||||||||||
| 6 FDI community | −0.066*** | −0.056*** | −0.103*** | −0.106*** | 0.014*** | 1 | |||||||||||
| 7 Firm age | −0.034*** | −0.025*** | 0.051*** | 0.15*** | 0.023*** | −0.031*** | 1 | ||||||||||
| 8 Capital intensity | −0.064*** | −0.056*** | 0.02*** | 0.274*** | 0.056*** | 0.031*** | 0.049*** | 1 | |||||||||
| 9 Leverage | −0.007*** | 0.05*** | −0.085*** | 0.138*** | −0.004*** | −0.049*** | −0.084*** | −0.076*** | 1 | ||||||||
| 10 Local market orientation | 0.088*** | 0.068*** | −0.01*** | 0.078*** | 0.011*** | −0.024*** | −0.064*** | 0.226*** | 0.191*** | 1 | |||||||
| 11 Nationality | −0.074*** | −0.055*** | −0.021*** | −0.096*** | −0.02*** | 0.016*** | 0.053*** | −0.119*** | −0.033*** | 0.047*** | 1 | ||||||
| 12 HHI | −0.013*** | −0.009*** | −0.005*** | −0.03*** | 0.022*** | 0.048*** | −0.012*** | 0.013*** | −0.029*** | 0.058*** | 0.003** | 1 | |||||
| 13 Market development | 0.12*** | 0.106*** | 0.044*** | 0.248*** | −0.082*** | −0.235*** | 0.118*** | 0.034*** | 0.072*** | −0.108*** | −0.066*** | −0.126*** | 1 | ||||
| 14 Wage | 0.075*** | 0.048*** | 0.02*** | 0.288*** | −0.058*** | 0.022*** | 0.243*** | 0.121*** | 0.157 | −0.17*** | −0.1*** | −0.072*** | 0.465*** | 1 | |||
| 15 Governor tenure | −0.006*** | −0.016*** | −0.584*** | 0.007*** | −0.014*** | 0.023*** | 0.04*** | −0.007*** | 0.135*** | 0.026*** | 0.06*** | −0.014*** | −0.018*** | 0.219*** | 1 | ||
| 16 Governor origin | 0.017*** | 0.011*** | −0.154*** | −0.038*** | −0.001*** | 0.137*** | −0.023*** | 0.022*** | 0.018*** | 0.006*** | −0.026*** | −0.012*** | 0.084*** | 0.055*** | 0.226*** | 1 | |
| 17 Age63 | −0.002*** | −0.008*** | −0.331*** | 0.004*** | −0.014*** | −0.087*** | 0.036*** | −0.013*** | 0.041*** | 0.009*** | 0.054*** | −0.006*** | 0.096*** | 0.054*** | 0.619*** | 0.138*** | 1 |
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean | 0.075 | 0.158 | 0.202 | 10.721 | 0.012 | 0.784 | 1.966 | 5.25 | 1.338 | 0.519 | 0.498 | 0.001 | 0.71 | 26.284 | 3.693 | 0.735 | 0.303 |
| SD | 0.145 | 0.32 | 0.402 | 1.34 | 0.109 | 0.461 | 0.652 | 1.16 | 2.237 | 0.452 | 0.5 | 0.005 | 0.101 | 11.972 | 2.296 | 0.442 | 0.46 |
| 1 ROA | 1 | ||||||||||||||||
| 2 ROE | 0.748 | 1 | |||||||||||||||
| 3 Turnover | 0.001 | 0.003 | 1 | ||||||||||||||
| 4 Size | 0.258 | 0.23 | 0.044 | 1 | |||||||||||||
| 5 Government ownership | −0.017 | −0.018 | 0.002 | 0.024 | 1 | ||||||||||||
| 6 FDI community | −0.066 | −0.056 | −0.103 | −0.106 | 0.014 | 1 | |||||||||||
| 7 Firm age | −0.034 | −0.025 | 0.051 | 0.15 | 0.023 | −0.031 | 1 | ||||||||||
| 8 Capital intensity | −0.064 | −0.056 | 0.02 | 0.274 | 0.056 | 0.031 | 0.049 | 1 | |||||||||
| 9 Leverage | −0.007 | 0.05 | −0.085 | 0.138 | −0.004 | −0.049 | −0.084 | −0.076 | 1 | ||||||||
| 10 Local market orientation | 0.088 | 0.068 | −0.01 | 0.078 | 0.011 | −0.024 | −0.064 | 0.226 | 0.191 | 1 | |||||||
| 11 Nationality | −0.074 | −0.055 | −0.021 | −0.096 | −0.02 | 0.016 | 0.053 | −0.119 | −0.033 | 0.047 | 1 | ||||||
| 12 HHI | −0.013 | −0.009 | −0.005 | −0.03 | 0.022 | 0.048 | −0.012 | 0.013 | −0.029 | 0.058 | 0.003 | 1 | |||||
| 13 Market development | 0.12 | 0.106 | 0.044 | 0.248 | −0.082 | −0.235 | 0.118 | 0.034 | 0.072 | −0.108 | −0.066 | −0.126 | 1 | ||||
| 14 Wage | 0.075 | 0.048 | 0.02 | 0.288 | −0.058 | 0.022 | 0.243 | 0.121 | 0.157 | −0.17 | −0.1 | −0.072 | 0.465 | 1 | |||
| 15 Governor tenure | −0.006 | −0.016 | −0.584 | 0.007 | −0.014 | 0.023 | 0.04 | −0.007 | 0.135 | 0.026 | 0.06 | −0.014 | −0.018 | 0.219 | 1 | ||
| 16 Governor origin | 0.017 | 0.011 | −0.154 | −0.038 | −0.001 | 0.137 | −0.023 | 0.022 | 0.018 | 0.006 | −0.026 | −0.012 | 0.084 | 0.055 | 0.226 | 1 | |
| 17 Age63 | −0.002 | −0.008 | −0.331 | 0.004 | −0.014 | −0.087 | 0.036 | −0.013 | 0.041 | 0.009 | 0.054 | −0.006 | 0.096 | 0.054 | 0.619 | 0.138 | 1 |
Notes:
**p < 0.05; ***p < 0.01; n = 603,076
Estimation approach
We employ linear regression with the reghdfe estimator (Correia, 2016) to estimate the parameters, which allows us to control for firm (individual) fixed effects across multiple levels. We then used the method suggested by Brambor et al. (2006), and Kingsley et al., to test for a moderating effect. Using this method, we were able to evaluate the moderation effects by considering not only the significance and sign of the interaction term but also the marginal effect of political turnover conditioned by the moderators, which can eliminate the possibility of overstating or understating a moderating effect (Kingsley et al., 2017).
Endogeneity has been identified as an important issue in organizations and management research (Bascle, 2008; Hamilton and Nickerson, 2003). A foreign firm’s decision-making is nonrandom, which can be dependent partially on other organizational, industrial and national attributes that are difficult to measure and that cannot be included in the model. Therefore, the endogeneity problem may be present in the study, ignoring endogeneity can lead to biased parameter estimations (He et al., 2015). The often-discussed sources of endogeneity are errors in variables, omitted variables and simultaneous causality (Bascle, 2008; Hamilton and Nickerson, 2003). The reghdfe model captures the firm fixed effect, which can avoid the potential endogeneity problem resulting from omitted variables problems. To further check for potential endogeneity bias, we conducted a series of robustness tests, including alternative measurement for the dependent variable, and instrumental variables (IVs) estimation.
Results
Table 2 presents the results of the reghdfe regression analysis. Model 1 only contains the control variables. Model 2 includes the key variable. Model 3 adds the four moderating variables. Models 4–6 include an interaction term: in turn, Turnover×Size, Turnover×Government ownership and Turnover×FDI community. Model 7 contains all the interaction terms. Model 1 is used to evaluate the effects of the control variables. Models 2 to 3 are used to investigate the direct effect of the key variable, Turnover. Models 4–6 are used to evaluate the moderating effects. In Model 2 to 3, the coefficient for Turnover is significantly negative at the 0.01 level, providing support for H1. Specifically, the coefficient for Turnover is −0.0043, indicating that subnational political turnover is associated with a decrease in ROA (%) by 0.43%. Models 4–6 are used to estimate the moderating effect of three moderators. To mitigate the potential overestimation or underestimation that the traditional method may lead to, we follow the approach recommended by Kingsley et al. (2017) to investigate the association between a moderator and the marginal effect of Turnover on ROA. We calculate the marginal effects and standard errors using the variance and covariance estimated from Models 4–6. The results are shown in Figures 2–4.
The main estimation results
| Variables | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 |
|---|---|---|---|---|---|---|---|
| Turnover | −0.0077*** (0.0011) | −0.0043*** (0.0010) | −0.0123*** (0.0036) | −0.0043*** (0.0010) | −0.0057*** (0.0013) | −0.0146*** (0.0037) | |
| Power sources: Size | 0.0580*** | 0.0578*** | 0.0580*** | 0.0580*** | 0.0578*** | ||
| Government ownership | (0.0009) –0.0017 | (0.0009) –0.0017 | (0.0009) –0.0020 | (0.0009) –0.0017 | (0.0009) –0.0019 | ||
| FDI community | (0.0018) –0.0060*** | (0.0018) –0.0061*** | (0.0019) –0.0060*** | (0.0018) –0.0061*** | (0.0019) –0.0061*** | ||
| Interactions terms: | (0.0016) | (0.0016) | (0.0016) | (0.0016) | (0.0016) | ||
| Turnover_Size | 0.0007** (0.0003) | 0.0008** (0.0003) | |||||
| Turnover_government ownership | 0.0014 (0.0023) | 0.0011 (0.0023) | |||||
| Turnover_FDI community | 0.0020** (0.0010) | 0.0023** (0.0010) | |||||
| Control variables: | |||||||
| Firm age | 0.0136*** (0.0014) | 0.0136*** (0.0014) | −0.0079*** (0.0010) | −0.0079*** (0.0010) | −0.0079*** (0.0010) | −0.0079*** (0.0010) | −0.0079*** (0.0010) |
| Capital intensity | −0.0157*** (0.0006) | −0.0156*** (0.0006) | −0.0097*** (0.0007) | −0.0097*** (0.0007) | −0.0097*** (0.0007) | −0.0097*** (0.0007) | −0.0097*** (0.0007) |
| Leverage | −0.0056*** (0.0002) | −0.0057*** (0.0002) | −0.0110*** (0.0002) | −0.0110*** (0.0002) | −0.0110*** (0.0002) | −0.0110*** (0.0002) | −0.0110*** (0.0002) |
| Local market orientation | 0.0041*** (0.0009) | 0.0039*** (0.0009) | 0.0013 (0.0008) | 0.0013 (0.0008) | 0.0013 (0.0008) | 0.0013 (0.0008) | 0.0013 (0.0008) |
| Nationality | −0.0013 (0.0011) | −0.0013 (0.0011) | −0.0006 (0.0010) | −0.0006 (0.0010) | −0.0006 (0.0010) | −0.0006 (0.0010) | −0.0006 (0.0010) |
| HHI | −0.0037 (0.0586) | −0.0048 (0.0588) | 0.0684 (0.0471) | 0.0686 (0.0471) | 0.0685 (0.0471) | 0.0679 (0.0471) | 0.0681 (0.0471) |
| Market development | 0.0693*** (0.0074) | 0.0666*** (0.0073) | 0.0333*** (0.0068) | 0.0335*** (0.0067) | 0.0333*** (0.0068) | 0.0312*** (0.0066) | 0.0313*** (0.0066) |
| Wage | −0.0021*** (0.0002) | −0.0021*** (0.0002) | −0.0017*** (0.0001) | −0.0017*** (0.0001) | −0.0017*** (0.0001) | −0.0017*** (0.0001) | −0.0017*** (0.0001) |
| Governor tenure | −0.0000 (0.0001) | −0.0010*** (0.0002) | −0.0003 (0.0002) | −0.0003 (0.0002) | −0.0003 (0.0002) | −0.0003 (0.0002) | −0.0003 (0.0002) |
| Governor origin | 0.0034*** | 0.0031*** | 0.0023** | 0.0023*** | 0.0023** | 0.0023*** | 0.0024*** |
| (0.0010) | (0.0010) | (0.0009) | (0.0009) | (0.0009) | (0.0009) | (0.0009) | |
| Year fixed effect | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Firm fixed effect | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Constant | 0.1391*** (0.0082) | 0.1459*** (0.0086) | −0.4387*** (0.0103) | −0.4373*** (0.0103) | −0.4387*** (0.0103) | −0.4372*** (0.0102) | −0.4356*** (0.0102) |
| R2 | 0.5572 | 0.5574 | 0.6025 | 0.6025 | 0.6025 | 0.6025 | 0.6025 |
| F | 199.6638 | 183.7226 | 474.5444 | 442.9972 | 448.7210 | 442.6393 | 395.8724 |
| n | 604,957 | 604,957 | 603,076 | 603,076 | 603,076 | 603,076 | 603,076 |
| Variables | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 |
|---|---|---|---|---|---|---|---|
| Turnover | −0.0077 | −0.0043 | −0.0123 | −0.0043 | −0.0057 | −0.0146 | |
| 0.0580 | 0.0578 | 0.0580 | 0.0580 | 0.0578 | |||
| Government ownership | (0.0009) | (0.0009) | (0.0009) | (0.0009) | (0.0009) | ||
| FDI community | (0.0018) | (0.0018) | (0.0019) | (0.0018) | (0.0019) | ||
| (0.0016) | (0.0016) | (0.0016) | (0.0016) | (0.0016) | |||
| Turnover_Size | 0.0007 | 0.0008 | |||||
| Turnover_government ownership | 0.0014 | 0.0011 | |||||
| Turnover_FDI community | 0.0020 | 0.0023 | |||||
| Firm age | 0.0136 | 0.0136 | −0.0079 | −0.0079 | −0.0079 | −0.0079 | −0.0079 |
| Capital intensity | −0.0157 | −0.0156 | −0.0097 | −0.0097 | −0.0097 | −0.0097 | −0.0097 |
| Leverage | −0.0056 | −0.0057 | −0.0110 | −0.0110 | −0.0110 | −0.0110 | −0.0110 |
| Local market orientation | 0.0041 | 0.0039 | 0.0013 | 0.0013 | 0.0013 | 0.0013 | 0.0013 |
| Nationality | −0.0013 | −0.0013 | −0.0006 | −0.0006 | −0.0006 | −0.0006 | −0.0006 |
| HHI | −0.0037 | −0.0048 | 0.0684 | 0.0686 | 0.0685 | 0.0679 | 0.0681 |
| Market development | 0.0693 | 0.0666 | 0.0333 | 0.0335 | 0.0333 | 0.0312 | 0.0313 |
| Wage | −0.0021 | −0.0021 | −0.0017 | −0.0017 | −0.0017 | −0.0017 | −0.0017 |
| Governor tenure | −0.0000 | −0.0010 | −0.0003 | −0.0003 | −0.0003 | −0.0003 | −0.0003 |
| Governor origin | 0.0034 | 0.0031 | 0.0023 | 0.0023 | 0.0023 | 0.0023 | 0.0024 |
| (0.0010) | (0.0010) | (0.0009) | (0.0009) | (0.0009) | (0.0009) | (0.0009) | |
| Year fixed effect | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Firm fixed effect | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Constant | 0.1391 | 0.1459 | −0.4387 | −0.4373 | −0.4387 | −0.4372 | −0.4356 |
| R2 | 0.5572 | 0.5574 | 0.6025 | 0.6025 | 0.6025 | 0.6025 | 0.6025 |
| F | 199.6638 | 183.7226 | 474.5444 | 442.9972 | 448.7210 | 442.6393 | 395.8724 |
| n | 604,957 | 604,957 | 603,076 | 603,076 | 603,076 | 603,076 | 603,076 |
Notes:
**p < 0.05; ***p < 0.01; Standard errors clustered at industry in parentheses in model
Moderating effect of government ownership on Turnover-ROA relationship
Figure 2 presents the association between Size and the marginal effect of Turnover on ROA. The positive slope of the solid line indicates a positive moderating effect. When Size is less than 12.959, the negative marginal effect of Turnover is statistically significant at the 0.05 level, with 93.74% of the sample falling in this range. With larger firm sizes, the negative effect of Turnover on ROA is no longer significant. This statistical result indicates that the turnover–performance relationship is influenced by Size, but that, above a certain size, changes in the negative relationship become statistically nonsignificant. Therefore, H2 is supported.
Figure 3 illustrates the association between Government ownership and the marginal effect of Turnover on ROA. The positive slope of the line indicates that government ownership has a positive moderating effect on the turnover-performance relationship. When Government ownership is 1, the marginal effect of Turnover is −0.029 (−0.0043 + 0.0014) and statistically insignificant at the 0.05 level. However, when Government ownership is 0, the marginal effect of Turnover is −0.0035 and statistically significant at the level of 0.05. These statistics show that the turnover–performance relationship is dependent on Government ownership. The negative relationship is statistically significant only in cases where there is no government ownership involved. This result supports H3.
Figure 4 presents the marginal effect of Turnover on ROA, conditional on the scale of the FDI community. The slope of the solid line suggests that FDI community has a positive moderating effect on the turnover–performance relationship. When the FDI community is lower than 1.601, 92.68% of the sample are in communities below this point, the negative marginal effect of Turnover is significant at the 0.05 level. Where the FDI community is above the point, the marginal effect is insignificant. This result shows that the turnover–performance relationship is conditional on the size of the FDI community, the negative relationship becomes insignificant when the size of the FDI community exceeds a certain level. This result confirms H4.
Robustness tests
Considering the potential endogeneity problems as we discussed earlier, we conducted two supplementary analyses to check the robustness of our findings. First, we used an alternative measurement (ROE) of the dependent variable (performance) in the models with reghdfe estimator. The findings listed in Table 3 are also in line with the main estimation in Table 2, which provides additional evidence supporting H1-4.
Robustness estimation results with ROE
| Variables | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
|---|---|---|---|---|---|
| Turnover | −0.0034* (0.0019) | −0.0337*** (0.0089) | −0.0035* (0.0019) | −0.0049* (0.0029) | −0.0368*** (0.0090) |
| Power sources: Size | 0.1042*** | 0.1037*** | 0.1042*** | 0.1043*** | 0.1037*** |
| Government ownership | (0.0015) –0.0057 | (0.0016) –0.0057 | (0.0015) –0.0071 | (0.0015) –0.0058 | (0.0016) –0.0069 |
| FDI community | (0.0045) –0.0105*** | (0.0045) –0.0108*** | (0.0047) –0.0105*** | (0.0045) –0.0106*** | (0.0047) –0.0109*** |
| Interactions terms: | (0.0032) | (0.0032) | (0.0032) | (0.0032) | (0.0032) |
| Turnover_Size | 0.0028*** (0.0009) | 0.0029*** (0.0008) | |||
| Turnover_government ownership | 0.0065 (0.0075) | 0.0057 (0.0075) | |||
| Turnover_FDI community | 0.0022 (0.0024) | 0.0031 (0.0024) | |||
| Control variables: | |||||
| Firm age | −0.0046** (0.0019) | −0.0046** (0.0019) | −0.0046** (0.0019) | −0.0046** (0.0019) | −0.0046** (0.0019) |
| Capital intensity | −0.0006 (0.0021) | −0.0007 (0.0021) | −0.0006 (0.0021) | −0.0006 (0.0021) | −0.0007 (0.0021) |
| Leverage | −0.0049*** (0.0004) | −0.0048*** (0.0004) | −0.0049*** (0.0004) | −0.0049*** (0.0004) | −0.0048*** (0.0004) |
| Local market orientation | −0.0014 (0.0019) | −0.0013 (0.0019) | −0.0014 (0.0019) | −0.0014 (0.0019) | −0.0013 (0.0019) |
| Nationality | −0.0036 (0.0023) | −0.0036 (0.0023) | −0.0036 (0.0023) | −0.0036 (0.0023) | −0.0036 (0.0023) |
| HHI | 0.2119* (0.1136) | 0.2126* (0.1137) | 0.2120* (0.1136) | 0.2113* (0.1136) | 0.2119* (0.1137) |
| Market development | 0.0614*** (0.0146) | 0.0624*** (0.0145) | 0.0614*** (0.0146) | 0.0592*** (0.0141) | 0.0594*** (0.0141) |
| Wage | −0.0027*** (0.0002) | −0.0027*** (0.0002) | −0.0027*** (0.0002) | −0.0027*** (0.0002) | −0.0027*** (0.0002) |
| Governor tenure | 0.0007* (0.0004) | 0.0007* (0.0004) | 0.0007* (0.0004) | 0.0007* (0.0004) | 0.0007* (0.0004) |
| Governor origin | 0.0035** (0.0017) | 0.0036** (0.0016) | 0.0035** (0.0017) | 0.0036** (0.0017) | 0.0036** (0.0017) |
| Year fixed effect | Yes | Yes | Yes | Yes | Yes |
| Firm fixed effect | Yes | Yes | Yes | Yes | Yes |
| Constant | −0.9040*** (0.0200) | −0.8988*** (0.0200) | −0.9040*** (0.0200) | −0.9024*** (0.0201) | −0.8965*** (0.0201) |
| R2 | 0.4939 | 0.4940 | 0.4939 | 0.4939 | 0.4940 |
| F | 494.3198 | 463.4541 | 461.5192 | 464.5279 | 412.3915 |
| n | 59,1492 | 591,492 | 591,492 | 591,492 | 591,492 |
| Variables | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
|---|---|---|---|---|---|
| Turnover | −0.0034 | −0.0337 | −0.0035 | −0.0049 | −0.0368 |
| Power sources: Size | 0.1042 | 0.1037 | 0.1042 | 0.1043 | 0.1037 |
| Government ownership | (0.0015) –0.0057 | (0.0016) –0.0057 | (0.0015) –0.0071 | (0.0015) –0.0058 | (0.0016) –0.0069 |
| FDI community | (0.0045) –0.0105 | (0.0045) –0.0108 | (0.0047) –0.0105 | (0.0045) –0.0106 | (0.0047) –0.0109 |
| Interactions terms: | (0.0032) | (0.0032) | (0.0032) | (0.0032) | (0.0032) |
| Turnover_Size | 0.0028 | 0.0029 | |||
| Turnover_government ownership | 0.0065 (0.0075) | 0.0057 (0.0075) | |||
| Turnover_FDI community | 0.0022 (0.0024) | 0.0031 (0.0024) | |||
| Control variables: | |||||
| Firm age | −0.0046 | −0.0046 | −0.0046 | −0.0046 | −0.0046 |
| Capital intensity | −0.0006 (0.0021) | −0.0007 (0.0021) | −0.0006 (0.0021) | −0.0006 (0.0021) | −0.0007 (0.0021) |
| Leverage | −0.0049 | −0.0048 | −0.0049 | −0.0049 | −0.0048 |
| Local market orientation | −0.0014 (0.0019) | −0.0013 (0.0019) | −0.0014 (0.0019) | −0.0014 (0.0019) | −0.0013 (0.0019) |
| Nationality | −0.0036 (0.0023) | −0.0036 (0.0023) | −0.0036 (0.0023) | −0.0036 (0.0023) | −0.0036 (0.0023) |
| HHI | 0.2119 | 0.2126 | 0.2120 | 0.2113 | 0.2119 |
| Market development | 0.0614 | 0.0624 | 0.0614 | 0.0592 | 0.0594 |
| Wage | −0.0027 | −0.0027 | −0.0027 | −0.0027 | −0.0027 |
| Governor tenure | 0.0007 | 0.0007 | 0.0007 | 0.0007 | 0.0007 |
| Governor origin | 0.0035 | 0.0036 | 0.0035 | 0.0036 | 0.0036 |
| Year fixed effect | Yes | Yes | Yes | Yes | Yes |
| Firm fixed effect | Yes | Yes | Yes | Yes | Yes |
| Constant | −0.9040 | −0.8988 | −0.9040 | −0.9024 | −0.8965 |
| R2 | 0.4939 | 0.4940 | 0.4939 | 0.4939 | 0.4940 |
| F | 494.3198 | 463.4541 | 461.5192 | 464.5279 | 412.3915 |
| n | 59,1492 | 591,492 | 591,492 | 591,492 | 591,492 |
Notes:
*p < 0.10; **p < 0.05; ***p < 0.01; standard errors clustered at industry in parentheses
Second, to check for potential endogeneity bias caused by simultaneous causality, we conducted an instrumental estimation, a two-stage least squares estimation (2SLS). While we argue that political turnover impacts foreign firm performance, it is likely that foreign firm performance also influences political turnover. Since foreign firms have played an important role in Chinese regional economic development (Liu et al., 2014; Yao and Wei, 2007), the performance of foreign firms in a province can influence the economic achievement of the province. Regional economic achievement is an important criterion for political evaluation in China (Li and Zhou, 2005; Lü and Landry, 2014). Therefore, with poor regional economic performance, the provincial leaders are likely to be replaced, which implies a simultaneous causality. To address this endogeneity issue, we apply an instrumental approach. We use the age of provincial governors as the IV. The reason to choose this IV is as follows. On the one hand, the age of a leader may decide if he/she will be replaced (turnover). According to the promotion policy, provincial leaders are required to retire at the age of 65 if they are not promoted to higher positions in the central government. When the age of current leaders is approaching the retiring age, they are very likely to step down and be moved to a less important position. This means that age influences the endogenous variable (turnover). On the other hand, age is exogenous to the firm performance, meaning the age does not directly influence the dependent variable. By examining the turnover information, we found that the age of 63 is a critical value. The turnover takes place increasingly when a leader reaches this age. We therefore created a dummy variable, Age63, with a value of 1 if the age is 63 and above and 0 otherwise, as IV. The results of 2SLS analysis are presented in Table 4. The first-stage model indicates that the instrument (Age63) is positively and significantly (p < 0.01) related to Turnover, satisfying the first requirement for being a valid instrument. To further test the validity of the IV, the following two statistics were computed. Kleibergen-Paap rk LM statistic is 72.187 (p = 0.000), which confirms that the IV is not under-identified. Cragg-Donald Wald F statistic is 6,780.285, higher than the threshold value of 10, which confirms that the IV is not weakly identified. All these statistics support the validity of the IV. The endogeneity test statistic is 3.039 (p < 0.1), which supports the endogenous issue. The second stage model shows that the coefficient of the instrumented Turnover is negatively (b = −0.0173) and significantly (p < 0.05) related to foreign firm performance (ROA), confirming the findings in Table 2. The results demonstrate that when considering endogenous issues, subnational political turnover leads to a decrease of 1.73% in ROA (%).
2SLS endogenous estimation results
| Variables | Model 1 | Model 2 |
|---|---|---|
| First-stage Turnover | Second-stage ROA | |
| Age63 | 0.1013*** (0.0061) | |
| Turnover-instrumented | −0.0173** (0.0079) | |
| Size | −0.0117*** (0.0010) | 0.0578*** (0.0009) |
| Government ownership | 0.0034 (0.0062) | −0.0017 (0.0018) |
| FDI community | −0.0934*** (0.0048) | −0.0071*** (0.0016) |
| Age | 0.0087*** (0.0022) | −0.0077*** (0.0010) |
| Capital intensity | 0.0050*** (0.0011) | −0.0097*** (0.0007) |
| Leverage | −0.0087*** (0.0006) | −0.0111*** (0.0002) |
| Local market orientation | −0.0293*** (0.0037) | 0.0010 (0.0009) |
| Nationality | 0.0070*** (0.0019) | −0.0005 (0.0010) |
| HHI | −0.1576 (0.1437) | 0.0664 (0.0474) |
| Market development | −0.2828*** (0.0417) | 0.0304*** (0.0067) |
| Wage | 0.0068*** (0.0005) | −0.0016*** (0.0001) |
| Governor tenure | −0.1485*** (0.0012) | −0.0020** (0.0010) |
| Governor orientation | −0.0446*** (0.0044) | 0.0017 (0.0011) |
| Year fixed effect | Yes | Yes |
| Firm fixed effect | Yes | Yes |
| Kleibergen-Paap rk LM statistic | 72.187 | |
| Kleibergen-Paap rk LM statistic (P-value) | 0.0000 | |
| Cragg-Donald wald F statistic | 6,780.285 | |
| Kleibergen-Paap rk wald F statistic | 280.554 | |
| Endogeneity test | 3.039 | |
| Endogeneity test (P-value) | 0.0813 | |
| lefted R2 | 0.1152 | |
| F | 465.68 | |
| n | 603,076 |
| Variables | Model 1 | Model 2 |
|---|---|---|
| First-stage Turnover | Second-stage ROA | |
| Age63 | 0.1013 | |
| Turnover-instrumented | −0.0173 | |
| Size | −0.0117 | 0.0578 |
| Government ownership | 0.0034 (0.0062) | −0.0017 (0.0018) |
| FDI community | −0.0934 | −0.0071 |
| Age | 0.0087 | −0.0077 |
| Capital intensity | 0.0050 | −0.0097 |
| Leverage | −0.0087 | −0.0111 |
| Local market orientation | −0.0293 | 0.0010 (0.0009) |
| Nationality | 0.0070 | −0.0005 (0.0010) |
| HHI | −0.1576 (0.1437) | 0.0664 (0.0474) |
| Market development | −0.2828 | 0.0304 |
| Wage | 0.0068 | −0.0016 |
| Governor tenure | −0.1485 | −0.0020 |
| Governor orientation | −0.0446 | 0.0017 (0.0011) |
| Year fixed effect | Yes | Yes |
| Firm fixed effect | Yes | Yes |
| Kleibergen-Paap rk LM statistic | 72.187 | |
| Kleibergen-Paap rk LM statistic (P-value) | 0.0000 | |
| Cragg-Donald wald F statistic | 6,780.285 | |
| Kleibergen-Paap rk wald F statistic | 280.554 | |
| Endogeneity test | 3.039 | |
| Endogeneity test (P-value) | 0.0813 | |
| lefted R2 | 0.1152 | |
| F | 465.68 | |
| n | 603,076 |
Notes:
**p < 0.05; ***p < 0.01; standard errors clustered at industry in parentheses
Discussion and conclusions
The study investigates an important but under-researched institutional factor, political turnover, and how it affects foreign business. As we have argued throughout this paper, the discretion of the central government in replacing local officials gives rise to uncertainties and poses significant risks to foreign investors. On the other hand, the power of foreign firms enables them to take action to mitigate the negative effects of political changes.
The paper operationally identifies three sources of power: endowed power, borrowed power and mediator power and investigates their moderating roles in the relationship between political turnover and performance. Our framework assumes that political turnover influences foreign firm performance by creating institutional instabilities – political disconnection, policy discontinuity and limited information – leading to relational vulnerabilities. We investigated how foreign firms can take action to reduce these uncertainties and associated costs caused by political turnover. The results suggest that foreign firms′ sources of power play an important role in counterbalancing the negative effect of political turnover. We argue that the negative effect of political turnover can be weakened by the resources foreign firms are able to deploy. The empirical results of this study support the theoretical propositions, showing that the negative effect of turnover on foreign firm performance is softened under certain conditions: when the firm is large, has government ownership and is part of a strong FDI community. These conditions reflect the power sources of these foreign firms. The findings contribute to the literature by providing nuanced insights into the relationship between political changes and business performance, while also offering practical implications for practitioners consistent with the use of power sources.
Contributions
Our study offers an important contribution to the current debate in international business on the political embeddedness of foreign firms and how the replacement of the local coalition is likely to disturb relationships between foreign firms and authorities. Primarily, we complement the work of Zhong et al. (2019) by further investigating the impact of political turnover on multinationals and, more importantly, by exploring their capacity to handle such challenges by leveraging power resources. Specifically, we contribute to literature in three ways.
First, this study contributes to political embeddedness research by investigating the consequences of political turnover, providing new insights into how foreign firms use their power to contain and neutralize the negative effects of political changes on their performance. A fundamental tenet in political embeddedness research posits that there is a positive relationship between political embeddedness and organizational performance (Haveman et al., 2017; Luo et al., 2012; Sun et al., 2010). Our findings do not challenge these core premises, by also indicating that political turnover can introduce constraints related to political embeddedness, thereby decreasing foreign firms’ performance. Chiefly, our findings add a more nuanced understanding of how firms can use their power to mitigate the negative effects of changes in political embeddedness. By integrating the political embeddedness perspective with the business power perspective and the mixed embeddedness approach, this study highlights not only the significance of political connections but also generates important insights into the role of business power in shaping business actions and performance. As a result, this research offers a more nuanced understanding of the interaction between political institutions and business power sources, extending the political embeddedness perspective in particular and institutional theory in general. In practice, it draws attention to how firms can gain political power by making visible their market relevance. As Zingales (2017, p. 120) comments “money is used to gain political power and political power is then used to make more money.”
Second, this research enriches the studies on mechanisms that explain how political events influence businesses and extends the line of research on business power. Our analysis of the literature, suggests how political changes create information asymmetry (Zhong et al., 2019) and new transaction costs (e.g. Clinger et al., 2008) and. Although transaction costs and information asymmetry can explain how political events affect business organizations’ performance, they do not cover all mechanisms that explain their impact. This study complements previous research on the political embeddedness perspective by indicating how political turnover creates and exacerbates three constraints to foreign firms that affect the relationship between political turnover and performance. Our empirical study found that power-related factors, which alleviate these constraints, mitigate the negative effects of political turnover on performance. Consistently, the findings of this paper show that three operationalized sources of business power – firm size, state ownership and the FDI community – help resist the negative effects of political uncertainty on firm performance. This extends the understanding of business power from the perspectives of social politics and political economy to international business.
Third, this study extends our knowledge of the effect of business-state connection. In the literature, government ownership has often been considered a firm-level political connection that importantly influences firms’ performance (Achsanta et al., 2022; Marquis and Qian, 2014; Okhmatovskiy, 2010; Sun et al., 2010). Studies also found that government ownership moderates the effect of external environments on firms’ performance (Wu and Chen, 2014), thus highlighting the moderating role of government ownership in the relationship between political turnover and performance. Equally important, is the focus on government ownership as a source of power, as an issue that interferes with the relationship between political turnover and performance, a finding that extends our understanding of the influence exerted by government ownership in international business.
Additionally, this study developed a theoretical framework analyzing the mutual interdependence between the political environment and business actors in shaping economic performance within the context of political turnover in China. This framework provides a basis for future research to extend its scope to encompass a broader range of political issues that contribute to political uncertainty, such as electoral instability, geopolitical conflicts and social movements. By doing so, researchers can gain deeper insights into how varying political contexts interact with business actors in influencing economic outcomes.
Limitations and suggestions for future studies
The study understandably has some limitations, some of which can be tackled by future research. First, although this study has provided an analytical framework to understand the impact of political events on foreign firms, the framework is only developed and tested in the context of China. This research has laid down some of the contextual boundary conditions insufficiently explored. For example, our argument posits that political turnover often introduces instability for foreign firms by disrupting established relationships and contracts with government officials. This political disconnection can adversely affect firms’ performance. However, this political connection may be more salient in large countries with strong governments than in other countries. This presents an intriguing boundary condition that could be better examined through a broader research scope. Therefore, future research could replicate this study in other countries with different political contexts to shed light on the robustness of the framework and/or include more countries in the same framework to further investigate the boundary conditions of the relationship between political turnover and performance.
Second, while this study explored how power sources can mitigate the negative effect of political turnover, it mainly focused on firms’ potential power, leaving other possible influential factors untouched. Future research could pay special attention to these factors. They could usefully explore the moderating effect of foreign firms’ external and internal environments. For example, the external environment, such as formal and informal institutions and industry features (e.g. essentialness, potential and resilience of industries), could be investigated. In addition, the characteristics of the old and new leaders, such as factional ties (Wong and Zeng, 2019), could be factors that influence the effects of political turnover. Furthermore, future work could deepen the analysis of power by including other categories. For example, two other categories that we did not consider in our analysis of firms’ responses are market power and political power (Zingales, 2017). Future studies could extend the present research by operationalizing and empirically identifying institutional constraints.
The third limitation in our study relates to the measures of power adopted that primarily reflect a firm’s potential to act (Berger, 2005), or mobilization capacity, rather than actual influence (Child et al., 2012). Future studies could explore firms’ abilities to transform sources of power into genuine influence, for example by exploring and identifying different forms of mobilization such as forming coalitions with other parties and lobbying. From a methodological viewpoint, future investigations of political turnover could cover even more extensive periods of analysis to incorporate more variations in the context of political instabilities and transitions. For example, firm size, as a measurement of borrowed power may have different effects. When the political environment is favorable for foreign firms, a large size could render a firm less vulnerable to political turnover. However, in less favorable political circumstances, prominent foreign firms could also become targets for aspiring politicians aiming to demonstrate their influence, hence, large size could make a firm more vulnerable to political turnover.
In addition, our investigation is limited to the period ending in 2013 due to data availability constraints, consistent with the approach taken in recent studies (Liu and Kang, 2023; Zuo et al., 2023). Future studies can extend the analysis to include more recent data, thus facilitating a comprehensive examination of trends and developments beyond this timeframe by incorporating recent political changes. Moreover, firm performance can be influenced by a wide range of factors, including macro-environmental conditions, industry characteristics, managerial decisions and strategic orientations. While our study primarily focuses on the first two categories, future research should consider including additional variables that reflect managerial practices and strategic orientations in their models to reduce the risk of omitted variable bias. Alternatively, employing qualitative studies could provide valuable insights and complement quantitative findings, offering a more holistic understanding of the relationship between political turnover and foreign firm performance.
Finally, this study focuses on the immediate responses of foreign firms to political turnover. Future research could also investigate the long-term effects of political turnover on foreign investment strategies. This could include examining how political changes influence foreign firms’ market entry decisions, investment levels and strategic adjustments over time. Additionally, exploring the impact on supply chain stability, and overall business sustainability would provide a more comprehensive understanding of the enduring consequences of political turnover.
Practical implications
The findings of this study have useful and practical implications for policymakers and foreign investors. Regarding policymakers in China, they should be aware that political turnover can lower foreign firms’ performance and take necessary actions to mitigate the potential negative impact on the economy and businesses. Consistent with the above analysis, we suggest that local governments can take proactive actions to lessen the constraints that foreign firms encounter during political turnover. For example, fostering stable political environments, ensuring policy continuity, reducing government intervention, and collaboration combating corruption, and improving government transparency and efficiency. Additionally, they can prioritize open communication with foreign firms, seeking their input and addressing their concerns during times of political change. Furthermore, governments should recognize the considerable influence of business power, particularly from large foreign firms with significant market presence and regions with strong FDI concentrations. By establishing regular dialogue with major business leaders from foreign firms in their region, local governments can better align economic policies with national development goals. This proactive engagement will help anticipate business responses to political shifts, enabling policymakers to craft policies that mitigate market disruptions. By implementing these measures, local authorities can create a more conducive environment for foreign businesses to thrive, thereby boosting economic growth and attracting more foreign investments to contribute to long-term regional development.
Regarding managers of foreign companies, it is important to be aware of political turnover, its negative effects and ways to mitigate them. First, they could mitigate the negative impact of political turnover by choosing good locations and leveraging their organizational power. They could seek locations where the frequency of political turnover is relatively low, and the market system is relatively well developed.
Second, they could employ pertinent strategies by leveraging their power. Firms with considerable endowed power could use this source of power to actively protect their interests and mitigate the negative effects resulting from political turnover. For example, large firms can employ effective political strategies (Oliver and Holzinger, 2008) to gain support from new political leaders. These strategies might include demonstrating their commitment to adhering to local regulations and policies, and aligning their operations with government priorities. They can also proactively engage with policymakers by organizing regular consultations, offering expert advice on industry-specific challenges and collaborating on initiatives that align with national or regional goals. Furthermore, firms can highlight their contributions to the local economy – such as job creation, infrastructure development or social responsibility initiatives – through public reports or partnerships with community organizations. By doing so, they can establish a positive reputation, which may lead to favorable treatment and greater resilience during times of political transition.
Firms with limited endowed power could leverage external power sources, such as their FDI community. They can utilize the collective influence of their FDI community to address shared concerns and present unified positions to the government. This coordinated approach can carry more weight when influencing policy decisions, leading to a more favorable business environment for all members of the FDI community. Furthermore, they can set up institutional connections with local government, for example through government ownership participation or partnerships with SOEs. Establishing such connections can provide these firms with increased access to information, resources and support from the government, even during times of political turnover. In addition, they should avoid becoming overly dependent on personal connections with political actors and focus on building broader relationships with various stakeholders in the region. Relying solely on individual relationships may leave these firms vulnerable to sudden changes in political leadership or shifts in government priorities.
Jiangang Jiang thanks the financial support from the National Office for Philosophy and Social Sciences (grant number 20BJL051).
Suzana B. Rodrigues thanks CNPq – The Brazilian National Research Council – for supporting this research
Note
These include the State Council of the People’s Republic of China website, www.gov.cn; Who’s Who in the CCP database, http://xinhuanet.com/; and China institutions and leaders’ database, http://people.com.cn/
References
Supplementary material
The supplementary material for this article can be found online.




