Index

• Introduction
• Literature Review
• Research Methodology
• Results and Discussion
• Discussion
• Conclusion
• References

Abstract

This research seeks to determine whether politically connected independent directors (PCIDs) have a substantial effect in lowering the cost of debt (CoD). Therefore, the research aims to explain the relationship between political ties and the cost of debt, politically connected independent directors and the cost of debt, and state ownership and the relationship of politically connected independent directors and the cost of debt. In addition, we analyze the influence of corporate ownership on the connection. To illustrate this, we empirically study panel data which was separated into two periods (2011–2012 and 2013–2014) in state-owned and non-state-owned firms (SOEs and non-SOEs). We discovered that PCIDs had a considerable and unfavorable effect on the CoD, particularly in non-SOEs. As a result of the shared resource knowledge of PCIDs in government procurement contracts, finance, and law, the CoD in non-SOEs is reduced. The findings of this research add to the existing literature by employing data from China and demonstrating the impact of PCIDs in decreasing the cost of debt, particularly for non-SOEs.

Keywords: Corporate ownership (SOEs & non-SOEs), Cost of debt, Politically connected independent directors, Regulatory change and China.

Received: 26 August 2022 / Revised: 8 November 2022 / Accepted: 23 January 2023 / Published: 2 February 2023

Contribution/ Originality

This study's objective was to build models based on data collected from listed Chinese companies to assess the practical relationship between politically connected independent directors and the cost of debt. This study enriches the body of knowledge in the context of China and developing countries by providing more information on the link between political ties and the cost of debt.

1. INTRODUCTION

In the last two decades, financial experts and researchers have focused on the independent director system. However, there is no agreement between researchers about the influence of independent directors on firm performance (Core, Holthausen, & Larcker, 1999; Hermalin & Weisbach, 1991; Nguyen & Nielsen, 2010; Rosenstein & Wyatt, 1990; Shi, Xu, & Zhang, 2018) . An independent director helps to mitigate agency problems and provide resources to firms (Fama & Jensen, 1983; Pfeffer & Salancik, 2003; Wang, 2015; Ye, 2014) . These studies were informed by the agency theory and resource dependence theory; this study is based on the resource dependence theory. All independent directors have different backgrounds, so they can provide different roles and resources.

Due to underdeveloped legal structures and a strong control holder (government) for resource distribution, it is difficult for businesses to obtain resources in emerging markets (Adhikari, Derashid, & Zhang, 2006; Faccio, 2006). Thus, businesses began using political ties to obtain a competitive edge (Allen, Qian, & Qian, 2005). Consequently, corporations appointed independent directors with political ties. A number of studies have concluded that politically linked enterprises face more risk than non-politically linked firms. There are a number of reasons for this, one being that companies with strong political ties became less efficient and were connected with more leverage (Bliss & Gul, 2012). This study investigates the influence of politically connected independent directors (PCIDs) on the cost of debt (CoD), particularly in China.

Several listed companies have hired independent directors with political connections; however, this has received criticism from business press and from the perspective of social fairness. First, business press describes PCIDs as “Rubber Stamp” because they lack financial expertise and experience. Second, from the perspective of social fairness, it could lead to unfair competition due to politically connected firms gaining an advantage in obtaining various resources. As a result, Document No. 18 was released by the Central Committee of the Communist Party of China on October 30, 2013, to prohibit politicians from holding positions in businesses (Harjan, Teng, Shah, & Mohammed, 2019; Shi et al., 2018).

In October 2013, exogenous regulatory change was issued under the title “Opinion Regarding Further Regulating Party and Government Officials' Part-Time (and Full-Time) Careers in Enterprises”. In contrast to previous literature, we investigate the value effect of PCIDs on the cost of debt by employing regulation change as an exogenous event that will be free from severe endogeneity bias. In the capital market, 10% of independent directors have a political connection; however, in the emerging market, the iFind database shows that approximately 45% of political figures were also independent directors before the regulatory change (Shi et al., 2018).

This study aims to determine whether PCIDs decrease or increase the cost of debt compared to non-PCIDs, and also if PCIDs have a different influence on the cost of debt in state-owned enterprises versus non-state-owned enterprises. To answer these questions, we investigate Chinese enterprises listed on the Shanghai and Shenzhen Stock Exchanges in relation to regulatory reform.

This article shows that lenders charge politically connected firms (PCFs) cheaper interest rates than non-PCFs and PCIDs, which increases debt costs. Creditors believe that PCIDs share their legal and financial experience and also serve on other boards. PCIDs help firms to obtain government contracts, equity finance, and bank loans. PCIDs also help organizations to benefit from advantageous government policies (Boubakri, Guedhami, Mishra, & Saffar, 2012; Claessens, Feijen, & Laeven, 2008; Faccio., Masulis, & McConnell, 2006; Shi et al., 2018) . These considerations provide PCFs with the opportunity to obtain interest rates that correspond to our expectation. Another finding is that firm ownership positively moderates the relationship between PCIDs and the CoD. Finally, the resignation of PCIDs positively impacts the CoD, supporting our main hypothesis that political connections play a significant role in reducing the cost of debt.

This work adds to the body of knowledge in three ways. First, this article offers more information on the relationship between political ties and the CoD in China and emerging markets. In the literature, there are contradictory conclusions regarding the influence of political linkages on global debt finance. Leuz and Oberholzer-Gee (2006) discovered that linked enterprises are able to obtain finance at reduced rates in Indonesia, which is corroborated by a number of studies conducted in countries such as the United States, Taiwan, Pakistan, and Brazil (Chen, Shen, & Lin, 2014; Claessens et al., 2008; Houston, Jiang, Lin, & Ma, 2014; Khwaja & Mian, 2005) . In Malaysia, however, Bliss and Gul (2012) discovered that politically linked enterprises must pay high borrowing rates. According to our understanding, there is minimal research that examines the link between China's political ties and the cost of debt (Harjan et al., 2019). This research focuses on the influence of conventional political ties, such as PCIDs, on the cost of debt. The findings show a significant and negative correlation between politically connected independent directors and debt cost.

Second, in general, the value of independent directors has been investigated by several previous studies, such as their effect on the performance and decision making of firms (Basuil & Datta, 2017; Core et al., 1999; Kim & Lim, 2010; Rosenstein & Wyatt, 1990) , while this study specifically investigates politically connected independent directors and if they can affect the decision making in Chinese firms. Few studies have investigated the impact of PCFs and debt financing decisions (Bliss & Gul, 2012; Pittman & Fortin, 2004). To our knowledge, there is minimal research in China examining the influence of conventional political connections on the CoD and PCIDs.

Third, the results are arguably free from endogeneity because it takes advantage of the regulatory change as an exogenous event and focuses on separate periods before and after. Board independence and the selection of independent directors are exogenous, so the result of the value of independent directors could be biased (Hermalin. & Weisbach, 1998; Shivdasani & Yermack, 1999; Wang, 2015) . Hence, this paper follows several studies in the literature as they use an exogenous event to avoid endogeneity problems (Dewally & Peck, 2010; Nguyen & Nielsen, 2010; Shi et al., 2018) .

Finally, we complement the literature by using the nature of firms’ ownership as a moderating variable on the causal association between PCIDs and CoD by investigating the distinctions between SOEs and non-SOEs.

2. LITERATURE REVIEW

2.1. Political Ties and Cost of Debt (CoD)

According to “the rent-seeking theory”, rent-seeking enables enterprises to gain rare resources, for example a reduced tax rate and government subsidies. In a phase of transition characterized by a shift in the economic structure, rent-seeking seems to generate significant gains (Chen, Li, Su, & Sun, 2011). As stated by Bertrand et al. (2006), corporations engage in rent-seeking when they have political ties.

There are several reasons behind following this theory. Firstly, the influence of political connections and CoD is reflected in the interest rate (Sapienza, 2004), the requirements of the loan (Khwaja & Mian, 2005), and the term of the loan (Fan, Rui, & Zhao, 2008). Secondly, according to Faccio and Parsley (2006), political connection is a direct and strong guarantee for lenders as it indicates that borrowing firms have strong solvency. The authors also documented that politically connected firms are given implicit guarantees by the government’s special preferential policies, which reduces the cost of debt.

In addition, Fisman (2001) contended that government-affiliated businesses may readily receive bank loans at reduced rates. In contrast, Johnson and Mitton (2003) demonstrated that political connections influence loan amounts. Therefore, political ties are advantageous in reducing the cost of financing for businesses. As PCIDs have a unique sort of political linkage, this research examines the relationship between PCIDs and CoD.

2.2. PCIDs and CoD

In accordance with “the board of resource dependency theory” (Pfeffer & Salancik, 2003), independent directors provide enterprises with the means to access crucial resources. Independent directors can decrease the CoD if they can provide access to resources. They can provide this access to critical resources through sharing their experience in certain areas, for instance, foreign markets, finance, industry, and law (Shi et al., 2018; Shivdasani & Yermack, 1999).

PCIDs are a unique form of independent director that enable businesses to access a certain type of valuable resource, namely a government relationship. For instance, Claessens et al. (2008) and Khwaja and Mian (2005) suggested that political ties offer businesses increased access to financial resources, for example bank loans. Boubakri et al. (2012) and Francis, Hasan, and Sun (2009) also argued that firms with political connections have easier access to equity finance. In addition to financial resources, government bailouts and government procurement contracts can be easily accessed by these firms (Faccio et al., 2006; Goldman, Rocholl, and So (2013); Johnson and Mitton (2003). Faccio (2010) concluded that enterprises with political ties benefit from favorable government policies, such as advantageous tax rates. Recruiting PCIDs is thus considered a value-adding technique. For instance, Goldman, Rocholl, and So (2009) discovered abnormally strong stock returns after the news of politically linked directors being nominated to a board. Moreover, the authors demonstrated that the market value of a company rises when its board of directors is affiliated with the winning presidential party. According to records compiled by Fisman (2001), in Indonesia, 16% of a company's worth may be traced to relationships with the previous president. Wang (2015) further implied that PCIDs might facilitate enterprises' access to financing and government subsidies in China, resulting in a decrease in the cost of debt due to politically linked independent directors. Therefore, we hypothesize that:  

H1: Ceteris paribus, the association between politically connected independent directors and the cost of debt is negative.

2.3. State Ownership and the Relationship between PCIDs and the CoD

There are several studies that have theoretically explained some mechanisms underlying the effect of political connections on debt financing (Bliss & Gul, 2012; Houston et al., 2014; Liedong & Rajwani, 2018; Yeh, Shu, & Chiu, 2013) . Yeh et al. (2013) argued that government ownership might mediate or moderate the influence of political connection on debt financing. Furthermore, Barth, Lin, Lin, and Song (2009) argued that weak institutions and the high level of corruption could influence lending decisions. Bliss and Gul (2012) and Fraser, Zhang, and Derashid (2006) highlighted that riskiness could be another mechanism that moderates the relationship. This leads to high interest rates because these kinds of firms have high leverage and a higher propensity to record negative equity.

In China, Wu, Wu, Zhou, and Wu (2012) found that politically connected firms have lower tax rates than firms with no political connection. Yang, Lu, and Luo (2014) found that politically connected non-SOEs received more long-term loans with longer maturities. Yeh et al. (2013) argued that government ownership might influence the effect of political connection on debt financing. As this paper focuses on PCIDs, it is necessary to investigate the impact of state ownership on the relationship between PCIDs and the cost of debt. So, we hypothesize that:

H2: Corporate ownership has a positive association with politically connected independent directors and the cost of debt.

From the standpoint of social justice, the relationship between political ties and corporate enterprises may result in unfair competition. This means that political connections give firms a competitive advantage in accessing sources. China has thus enacted new restrictions. After the release of Document No. 18 by the Central Committee of the Communist Party of China on October 30, 2013, PCIDs have resigned from PCFs, as this document prohibits politicians from holding roles in firms (Shi et al., 2018). SOEs are controlled by the government, which means that SOEs might not need political connections to reduce the cost of debt. However, in transitional economies, especially in China, the political connections are more helpful for non-SOEs compared with SOEs, as argued by Shi et al. (2018). So, that give us motivation to investigate the impact on two types of state ownership. We thus hypothesize that:
H3: Compared with state-owned enterprises, politically connected independent directors have a high but significantly negative impact on the cost of debt in non-state-owned enterprises.

3. RESEARCH METHODOLOGY

3.1. The Sample

All Chinese listed companies on both the Shanghai and Shenzhen Stock Exchanges at the time of the regulation change are included in this first sample of companies. The examination of regulatory change necessitates that we gather data from 2011–2016, which includes the periods before and after the change occurred. Researching listed companies was done through the China Stock Market and Accounting Research (CSMAR). As a first step, we tallied all 10,760 publicly traded companies registered on the Shanghai and Shenzhen Stock Exchanges between 2011 and 2016. From there, we omitted the financial services sector and companies without readily accessible data due to regulatory differences. As a result, 10,649 companies were left in the database. SOEs and non-SOEs were the two groups from which we drew data. There is a total of 8,541 observations for non-SOEs and 2,091 for SOEs.

3.2. Measurement of Variables

3.2.1. Dependent Variable

The measurements of CoD used in previous studies were followed (e.g., (Bliss & Gul, 2012; Liedong & Rajwani, 2018)) as they utilize interest expenses (IR) to determine the cost of debt by dividing a firm's annual interest expenditure by its average short- and long-term debt. The interest expenditures were gathered from the published income statements of Chinese enterprises in the CSMAR database, and the short and long periods of debt were obtained from the firms’ balance sheets.

3.3. Experimental Variables

We utilize CSMAR to acquire information on the qualities of independent directors. In this study, we use earlier research to determine if an independent director is politically linked (Shi et al., 2018; Wang, 2015). A politically independent director is defined as a current or former government or party official, a member of the National People's Congress (NPC) or a member of the Chinese People's Political Consultative Conference (CPPCC). We use a dummy variable for PCIDs with a value of one if at least one independent director in a company is politically linked, and zero otherwise. We also consider the PCID ratio (total PCIDs/total independent directors) and the PCID2 ratio (total PCIDs/board size).

We also utilize a dummy variable of time, applying the value of one for the time after the regulatory change, and otherwise zero. In addition, we use the years after the regulatory change to present the resignation of PCIDs.

Furthermore, to determine whether a firm is government-controlled or not, we use a dummy variable. When central government or provincial government has control of a firm, this is referred to as a state-owned enterprise (SOE), which takes a value of one, and firms not controlled by government are referred to as non-state owned enterprises (non-SOEs), which take a value of zero.

3.4. Control Variable

For the literature on the cost of debt, see (Bliss & Gul, 2012; Francis, Khurana, & Pereira, 2005; Harjan et al., 2019; Petersen & Rajan, 1994; Pittman & Fortin, 2004; Qiu & Yu, 2009; Shah, Xinping, Khan, & Harjan, 2018) . Our regression model considers a number of factors as control variables: leverage (LEV); total assets (SIZE); sales growth (SGRTH), cash flow (CFlow), current ratio (CRat) and executive directors’ duality (Duality).

Bliss and Gul (2012) emphasized that large businesses have more assets, resulting in a negative relationship between the size of a company (Size) and the CoD. According to Chaney, Faccio, and Parsley (2011), cash flow (CFlow) is one method by which lenders can assess a company's risk of loan repayment, resulting in a lower cost of debt. Chaney et al. (2011) provided evidence that greater leverage is associated with a higher CoD. Higher sales growth (SGRTH) facilitates loan repayment, allowing lenders to offer lower interest rates to enterprises. Furthermore, Pittman and Fortin (2004) claimed that the current ratio is another signal for lenders that businesses have a high level of current ratio that allows them to repay their loan, resulting in a lower cost of debt.

To prevent endogeneity problems and economic shocks, we also utilize a dummy variable for years, with a value of one for a particular year and zero for the others. We use six-year dummy variables for the time in question (2011–2016).

3.5. Regression Models

The regression models are listed below:

Equations 1, 2, 3 and 4 present the multiple regression models to test the relationship between the cost of debt and firms with PCIDs to test the first hypothesis. In addition, Equations 5, 6, 7 and 8 argue the interaction of corporate ownership and test our second hypothesis in this study.

All study variables are defined the Table 1.

Variable	Definition/Measurement
CoD	The ratio of a company's finance expenditure to its average short-term and long-term debt.
PCIDs	Dummy variable, 1 if the firm is politically connected, otherwise 0.
TPCIDs	Total number of independent directors who are politically connected.
RPCIDs1	Total PCIDs/total independent directors.
RPCIDs2	Total PCIDs/board size.
Resignation of PCIDs	We use 2014, 2015 and 2016 for the years after the regulation change (After the resignation of PCIDs), and 2011, 2012 and 2013 as the years before the regulatory change (Before the resignation).
Ownership	Dummy variable with a value of 1 if the government is the ultimate controlling shareholder and 0 otherwise.
SIZE	Natural logarithm of total assets.
LEV	Sum of total short-term and long-term debts divided by total assets.
CFlow	Cash flow from operations divided by total assets.
SGRTH	Sales revenue in year T minus sales revenue in year t-1 divided by sales revenue in year t-1.
CRat	Current assets divided by current liabilities.
Duality	This is given a value of 1 if the Chief Executive Officer (CEO) is also board chair and 0 otherwise.

Note:

CoD = Cost of debt; PCIDs = Politically connected independent directors; TPCIDs = Total politically connected independent directors; RPCIDs = Ratio of politically connected independent directors; LEV = Leverage; SIZE = Total assets; SGRTH = Sales growth; CFlow = Cash flow;  CRat = Current ratio; Duality = Executive directors’ duality.

4. RESULTS AND DISCUSSION

4.1. Descriptive Statistics

Table 2 provides a statistical overview of all research variables for Chinese listed enterprises. The mean values for PCIDs, TPCIDs, RPCIDs1 and RPCIDs2 are 0.152, 0.195, 0.05, and 0.023, respectively, indicating that PCIDs represent about 15.2% of all observations of Chinese listed enterprises. The data for non-SOEs and SOEs are shown in Tables 3 and 4, respectively. The average CoDs of our samples are 7.06 for all Chinese enterprises, 6.989 for non-SOEs, and 7.35 for SOEs. The average ownership for all listed companies is 19.7%. The average leverage for all listed companies is 21.76%, whereas it is 20% for non-SOEs and 23.1% for SOEs. The mean of company size is almost the same in Tables 2, 3, and 4. (22.242, 22.133 and 22.688, respectively). As seen in Tables 3 and 4, the cash flow of non-SOEs is smaller than that of SOEs. In addition, the median revenue growth for public companies is 10.196%, and the median Chief Executive Officer (CEO) duality rate is 1.784%. The average board size of SOEs and non-SOEs differ, with results of 9.149 and 8.131, respectively.

Variable	Obs.	Mean	Std. dev.	Min.	Max.
PCIDs	10632	0.152	0.359	0.000	1.000
TPCIDs	10632	0.195	0.510	0.000	4.000
RPCIDs1	10632	0.050	0.134	0.000	1.000
RPCIDs2	10632	0.023	0.064	0.000	1.000
Ownership	10632	0.197	0.397	0.000	1.000
CoD	10632	7.060	6.967	0.000	22.024
LEV	10632	0.206	0.153	0.000	3.064
SIZE	10632	22.242	1.318	16.52	28.036
CFlow	10632	0.033	0.121	-1.283	9.214
CRat	10632	1.595	1.347	0.000	40.845
SGRTH	10632	10.196	612.742	-25.369	59411.6
Duality	10632	1.784	0.412	1.000	2.000
Totalind	10632	3.629	1.738	0.000	12.000
Boardsize	10632	8.331	2.667	0.000	18.000

Note:

PCIDs = Politically connected independent directors; TPCIDs = Total politically connected independent directors; RPCIDs = Ratio of politically connected independent directors; RPCIDs1 = Total PCIDs/total independent directors; RPCIDs2 = Total PCIDs/board size; Ownership = Dummy variable with a value of 1 if the government is the ultimate controlling shareholder and 0 otherwise; CoD = Cost of debt; LEV = Leverage; SIZE = Total assets; CFlow = Cash flow; CRat = Current ratio; SGRTH = Sales growth; Duality = Executive directors’ duality; Totalind = Total number of independent directors; Boardsize = The number of board members.

Variable	Obs.	Mean	Std. dev.	Min.	Max.
PCIDs	8541	0.145	0.352	0.000	1.000
TPCIDs	8541	0.185	0.497	0.000	4.000
RPCIDs1	8541	0.049	0.134	0.000	1.000
RPCIDs2	8541	0.022	0.065	0.000	1.000
CoD	8541	6.989	6.939	0.000	22.024
LEV	8541	0.200	0.144	0.000	2.652
SIZE	8541	22.133	1.283	16.520	28.036
CFlow	8541	0.033	0.129	-1.283	9.214
CRat	8541	1.659	1.413	0.000	40.845
SGRTH	8541	5.031	230.087	-25.369	20370.60
Ownership	8541	0.000	0.000	0.000	0.000
Duality	8540	1.755	0.430	1.000	2.000
Boardsize	8541	8.131	2.728	0.000	18.000
Totalind	8541	3.494	1.737	0.000	12.000

Note:

PCIDs = Politically connected independent directors; TPCIDs = Total politically connected independent directors; RPCIDs = Ratio of politically connected independent directors; RPCIDs1 = Total PCIDs/total independent directors; RPCIDs2 = Total PCIDs/board size; Ownership = Dummy variable with a value of 1 if the government is the ultimate controlling shareholder and 0 otherwise;  CoD = Cost of debt; LEV = Leverage; SIZE = Total assets; CFlow = Cash flow; CRat = Current ratio; SGRTH = Sales growth; Duality = Executive directors’ duality; Boardsize = The number of board members; Totalind = Total number of independent directors.

Variable	Obs.	Mean	Std. dev.	Min.	Max.
PCIDs	2091	0.181	0.385	0.000	1.000
TPCIDs	2091	0.235	0.558	0.000	4.000
RPCIDs1	2091	0.056	0.134	0.000	1.000
RPCIDs2	2091	0.026	0.062	0.000	.500
CoD	2091	7.350	7.074	0.000	22.024
LEV	2091	0.231	0.182	0.000	3.064
SIZE	2091	22.688	1.363	18.291	27.349
CFlow	2091	0.035	0.076	-0.571	0.958
CRat	2091	1.337	0.994	0.003	13.288
SGRTH	2091	31.297	1301.116	-3.175	59411.6
Ownership	2091	1.000	0.000	1.000	1.000
Duality	2091	1.899	0.301	1.000	2.000
Boardsize	2091	9.149	2.219	0.000	18.000
Totalind	2091	4.183	1.626	0.000	10.000

Note:

PCIDs = Politically connected independent directors; TPCIDs = Total politically connected independent directors; RPCIDs = Ratio of politically connected independent directors; RPCIDs1 = Total PCIDs/total independent directors; RPCIDs2 = Total PCIDs/board size; Ownership = Dummy variable with a value of 1 if the government is the ultimate controlling shareholder and 0 otherwise;  CoD = Cost of debt; LEV = Leverage; SIZE = Total assets; CFlow = Cash flow; CRat = Current ratio; SGRTH = Sales growth; Duality = Executive directors’ duality; Boardsize = The number of board members; Totalind = Total number of independent directors.

4.2. Correlation

In general, the Pearson correlation coefficients between all variables in our whole sample are shown in Table 5. As expected, all four PCID measures (PCIDs, TPCIDs, RPCIDs1 and RPCIDs2) have coefficients that are positively and significantly correlated with each other. The CoD is significantly and negatively correlated with the four PCID measures, leverage, and ownership. It was also found that the CoD is negatively correlated with the current ratio and sales growth, while it is positively correlated with size and duality.

4.3. Regression Results

Table 6 reveals the multiple regression results of our four first models that measure the association between the cost of debt and politically connected independent directors through evidence from all listed Chinese firms. The table contains a column for each PCID measurement in this study, and the results show that there is a negative relationship between politically connected independent directors and the cost of debt. Models 1, 2, 3 and 4 report the multiple regression test results of the relationship between the cost of debt and firms with PCIDs to test the first hypothesis. Bliss and Gul (2012) provided evidence from Malaysian corporations regarding a positive association between the cost of debt and politically connected firms The coefficients of politically connected independent director measurements (PCIDs, TPCIDs, RPCIDs1 and RPCIDs2) are negative and highly significant (-1.963, t = -9.612, p < 0.01; -1.023, t = -7.263, p < 0.01; -4.872, t = -9.169, p < 0.01; -5.611, t = -5.093, p < 0.01, respectively), and thus support the first hypothesis. From a political connection perspective, the results are consistent with Houston et al. (2014), who stated that because of the reduced cost of financing that comes from government assurances, companies with ties to politics have a lower systemic risk.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)
(1) PCIDs	1.000
(2) TPCIDs	0.903***	1.000
(3) RPCIDs1	0.883***	0.941***	1.000
(4) RPCIDs2	0.838***	0.940***	0.907***	1.000
(5) Ownership	0.040***	0.039***	0.020**	0.021	1.000
(6) CoD	-0.058***	-0.035***	-0.060***	-0.022	0.021**	1.000
(7) LEV	0.069***	0.078***	0.068***	0.079	0.081***	0.037***	1.000
(8) SIZE	-0.003	0.027***	-0.002	0.016	0.167***	0.184	0.135***	1.000
(9) CFlow	-0.024**	-0.025**	-0.026***	-0.024	0.006	0.003	-0.037***	0.050***	1.000
(10) CRat	-0.016*	-0.027**	-0.009	-0.022	-0.095***	-0.114	-0.318***	-0.208***	-0.043***	1.000
(11) SGRTH	0.004	0.002	0.003	0.003	0.017**	-0.010	0.002	0.010	0.007	-0.006	1.000
(12) Duality	0.004	0.012	-0.003	0.004	0.139***	0.170	0.040***	0.163***	0.021**	-0.077***	0.000	1.000
(13) Tlind	0.157***	0.164***	0.057***	0.130	0.158***	0.073	0.070***	0.191***	-0.007	-0.093***	-0.003	0.076***	1.000
(14) Bsize	0.098***	0.074***	0.056***	-0.026***	0.152***	-0.129	0.010	0.098***	0.039***	-0.034***	-0.007	0.011	0.399***	1.000

Note:

t-statistics are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. PCIDs = Politically connected independent directors; TPCIDs = Total politically connected independent directors; RPCIDs = Ratio of politically connected independent directors; RPCIDs1 = Total PCIDs/total independent directors; RPCIDs2 = Total PCIDs/board size; Ownership = Dummy variable with a value of 1 if the government is the ultimate controlling shareholder and 0 otherwise;  CoD = Cost of debt; LEV = Leverage; SIZE = Total assets; CFlow = Cash flow; CRat = Current ratio; SGRTH = Sales growth; Duality = Executive directors’ duality; Tlind = Total number of independent directors; Bsize = The number of board members.

Variable	(1) CoD	(2) CoD	(3) CoD	(4) CoD
PCIDs	-1.963*** (-9.612)
TPCIDs		-1.023*** (-7.263)
RPCIDs1			-4.872*** (-9.169)
RPCIDs2				-5.611*** (-5.093)
LEV	-0.905** (-1.987)	-0.892* (-1.954)	-0.880* (-1.931)	-0.901** (-1.972)
SIZE	0.868*** (16.60)	0.868*** (16.56)	0.863*** (16.51)	0.854*** (16.29)
CFlow	-0.566 (-1.043)	-0.566 (-1.041)	-0.578 (-1.064)	-0.551 (-1.013)
CRat	-0.392*** (-7.552)	-0.395*** (-7.591)	-0.390*** (-7.498)	-0.395*** (-7.582)
SGRTH	-0.000122 (-1.151)	-0.000126 (-1.186)	-0.000124 (-1.163)	-0.000126 (-1.181)
Duality	2.268*** (14.08)	2.285*** (14.17)	2.265*** (14.06)	2.289*** (14.17)
Y1	-	1.145*** (4.780)	-	-
Y2	-0.130 (-0.546)	1.012*** (4.272)	-0.133 (-0.562)	-0.129 (-0.541)
Y3	-0.539** (-2.305)	0.597** (2.564)	-0.544** (-2.324)	-0.539** (-2.295)
Y4	-1.353*** (-5.615)	-	-1.239*** (-5.191)	-0.985*** (-4.130)
Y5	-1.612*** (-6.681)	-0.259 (-1.173)	-1.497*** (-6.266)	-1.242*** (-5.202)
Y6	-1.733*** (-7.200)	-0.379* (-1.730)	-1.617*** (-6.787)	-1.361*** (-5.715)
Constant	-14.20*** (-12.23)	-15.60*** (-13.21)	-14.22*** (-12.24)	-14.31*** (-12.28)
Observations	10,632	10,632	10,632	10,632
R-squared	0.071	0.067	0.070	0.065

Note:

t-statistics are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. PCIDs = Politically connected independent directors; TPCIDs = Total politically connected independent directors; RPCIDs = Ratio of politically connected independent directors; RPCIDs1 = Total PCIDs/total independent directors; RPCIDs2 = Total PCIDs/board size; LEV = Leverage; SIZE = Total assets; CFlow = Cash flow; CRat = Current ratio; SGRTH = Sales growth; Duality = Executive directors’ duality; Y = Is a dummy variable for years, with a value of 1 for a particular year and 0 for the others (Y1 to Y6 = year 1 to year 6).

To test our second hypothesis and the interaction of corporate ownership, we applied the last four regression models in this study. The results are shown in Table 7, and they show that corporate ownership has a significantly negative association with the cost of debt, its coefficients are -0.784, t = -4.266, p < 0.01; -0.798, t = -4.401, p < 0.01; -0.718, t = -3.978, p < 0.01; -0.714, t = -3.952, p < 0.01), while the interaction of corporate ownership on the relationship between PCIDs and the cost of debt is highly significantly positive. In Model 5, the coefficients of the interaction on the relationship between PCIDs and CoD are 1.260, t = 2.905, p < 0.01. In Models 6, 7 and 8, corporate ownership also has a significantly positive interaction in the relationship between total politically connected independent directors and the cost of debt.

Variable	(5) CoD	(6) CoD	(7) CoD	(8) CoD
PCIDs	-2.238*** (-9.862)
PCIDs*own	1.260*** (2.905)
TPCIDs		-1.246*** (-7.883)
TPCIDs*own		0.959*** (3.177)
RPCIDs1			-5.306*** (-9.108)
RPCIDs1*own			2.207* (1.806)
RPCIDs2				-6.445*** (-5.377)
RPCIDs2*own				4.527* (1.730)
Ownership	-0.784*** (-4.266)	-0.798*** (-4.401)	-0.718*** (-3.978)	-0.714*** (-3.952)
LEV	-0.890* (-1.953)	-0.864* (-1.894)	-0.843* (-1.850)	-0.861* (-1.883)
SIZE	0.891*** (16.91)	0.894*** (16.90)	0.889*** (16.87)	0.880*** (16.64)
CFlow	-0.577 (-1.065)	-0.579 (-1.067)	-0.581 (-1.072)	-0.557 (-1.024)
CRat	-0.400*** (-7.698)	-0.404*** (-7.751)	-0.398*** (-7.643)	-0.404*** (-7.741)
SGRTH	-0.000112 (-1.057)	-0.000117 (-1.095)	-0.000115 (-1.085)	-0.000118 (-1.105)
Duality	2.326*** (14.37)	2.347*** (14.48)	2.327*** (14.36)	2.353*** (14.49)
Y1	-	-	1.159*** (4.844)	-
Y2	-0.128 (-0.542)	1.030*** (4.351)	-0.134 (-0.564)	-0.129 (-0.543)
Y3	-0.542** (-2.317)	0.613*** (2.634)	-0.548** (-2.343)	-0.543** (-2.317)
Y4	-1.364*** (-5.667)	-	-1.252*** (-5.248)	-0.995*** (-4.173)
Y5	-1.634*** (-6.774)	-0.269 (-1.220)	-1.520*** (-6.361)	-1.262*** (-5.284)
Y6	-1.765*** (-7.334)	-0.400* (-1.824)	-1.649*** (-6.921)	-1.389*** (-5.834)
Constant	-14.66*** (-12.52)	-16.11*** (-13.54)	-14.76*** (-12.60)	-14.85*** (-12.64)
Observations	10,632	10,632	10,632	10,632
R-squared	0.072	0.069	0.071	0.066

Note:

t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. PCIDs = Politically connected independent directors; TPCIDs = Total politically connected independent directors; RPCIDs = Ratio of politically connected independent directors; RPCIDs1 = Total PCIDs/total independent directors; RPCIDs2 =Total PCIDs/board size; Ownership = Dummy variable with a value of 1 if the government is the ultimate controlling shareholder and 0 otherwise; LEV = Leverage; SIZE = Total assets; CFlow = Cash flow; CRat = Current ratio; SGRTH = Sales growth; Duality = Executive directors’ duality Y = A dummy variable for years, with a value of 1 for a particular year and 0 for the others (Y1 to Y6 = year 1 to year 6).

The comparison between the results of the impact of PCIDs on CoD in SOEs and non-SOEs is shown in Tables 8 and 9. In the first model in Table 8, the results report that the association between the PCIDs and the CoD for SOEs is negative with a low significance (-0.746, t = -1.657, p < 0.1), while the results for the non-SOEs in Table 9 report a highly significantly negative association (-2.287, t = -9.966, p < 0.01). On the other hand, the second model is related to total PCIDs and the CoD, and the result is not significant in SOEs but it is highly significant in non-SOEs, which is also the case in other models. Therefore, we can say that politically connected independent directors play an important role in reducing the cost of debt in non-SOEs compared with SOEs. The results thus support our third hypothesis and are consistent with Shi et al. (2018), who suggested that PCIDs are helpful for firms, especially non-SOEs. In addition, the control variables produced several different results. Leverage, cash flow, current ratio and sales growth are negatively associated with the cost of debt but are not stable in the level of significance, as shown in Tables 6, 7, 8 and 9. Size is positively associated with the cost of debt and is also unstable in the level of significance. However, duality is positively associated with the cost of debt and is highly significant in all model tests.

Variable	(1) CoD	(2) CoD	(3) CoD	(4) CoD
PCIDs	-0.746* (-1.657)
TPCIDs		-0.130 (-0.429)
RPCIDs1			-2.800** (-2.244)
RPCIDs2				-1.195 (-0.445)
LEV	-1.491* (-1.665)	-1.567* (-1.751)	-1.461 (-1.633)	-1.562* (-1.744)
SIZE	0.774*** (6.538)	0.760*** (6.406)	0.772*** (6.545)	0.759*** (6.413)
CFlow	-3.229 (-1.600)	-3.181 (-1.575)	-3.332* (-1.651)	-3.189 (-1.579)
CRat	-0.213 (-1.282)	-0.217 (-1.303)	-0.212 (-1.276)	-0.216 (-1.299)
SGRTH	-0.000130 (-1.114)	-0.000130 (-1.108)	-0.000130 (-1.113)	-0.000130 (-1.107)
Duality	2.627*** (5.189)	2.622*** (5.176)	2.627*** (5.191)	2.618*** (5.168)
Y1	1.357** (2.372)	1.136** (2.001)	1.403** (2.485)	1.134** (2.007)
Y2	1.389** (2.473)	1.178** (2.117)	1.426** (2.574)	1.177** (2.122)
Y3	1.102** (1.979)	0.892 (1.619)	1.132** (2.062)	0.892 (1.622)
Y4	0.505 (0.952)	0.502 (0.946)	0.503 (0.949)	0.502 (0.946)
Y5	0.205 (0.384)	0.203 (0.381)	0.205 (0.384)	0.203 (0.381)
Y6	-	-	-	-
Constant	-15.08*** (-5.166)	-14.72*** (-5.036)	-15.04*** (-5.170)	-14.69*** (-5.038)
Observations	2,091	2,091	2,091	2,091
R-squared	0.040	0.039	0.041	0.039

Note:

t-statistics are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. PCIDs = Politically connected independent directors; TPCIDs = Total politically connected independent directors; RPCIDs = Ratio of politically connected independent directors; RPCIDs1 = Total PCIDs/total independent directors; RPCIDs2 = Total PCIDs/board size; Ownership = Dummy variable with a value of 1 if the government is the ultimate controlling shareholder and 0 otherwise;  LEV = Leverage; SIZE = Total assets; CFlow = Cash flow; CRat = Current ratio; SGRTH = Sales growth; Duality = Executive directors’ duality; Y = A dummy variable for years, with a value of 1 for a particular year and 0 for the others (Y1 to Y6 = year 1 to year 6).

4.4. Robustness Results

There are two distinct periods of time in which independent directors who are politically linked are examined for their effect on the CoD. For instance, 2011–12 and 2013–2014 are the first and second intervals, respectively. Results from the PCIDs, TPCIDs, RPCIDs1 and RPCIDs2 metrics are included in these annual time periods. Table 10 shows that the PCIDs from all proxies has a negative and significant effect on the cost of debt during the first period for all Chinese listed firms. In addition, ownership has a significantly negative impact on the cost of debt in all time periods. For the interaction of ownership, all results show a positive impact on the relationship between PCIDs and the CoD, but the level of significance is unstable.

Variable	(1) CoD	(2) CoD	(3) CoD	(4) CoD
PCIDs	-2.287*** (-9.966)
TPCIDs		-1.284*** (-8.059)
RPCIDs1			-5.377*** (-9.162)
RPCIDs2				-6.611*** (-5.483)
LEV	-0.559 (-1.048)	-0.488 (-0.911)	-0.499 (-0.934)	-0.483 (-0.901)
SIZE	0.928*** (15.72)	0.935*** (15.79)	0.928*** (15.70)	0.919*** (15.50)
CFlow	-0.298 (-0.532)	-0.301 (-0.536)	-0.295 (-0.527)	-0.279 (-0.495)
CRat	-0.418*** (-7.652)	-0.421*** (-7.685)	-0.415*** (-7.583)	-0.421*** (-7.669)
SGRTH	0.000109 (0.348)	7.13e-05 (0.227)	8.93e-05 (0.285)	6.59e-05 (0.210)
Duality	2.266*** (13.30)	2.288*** (13.40)	2.268*** (13.30)	2.299*** (13.44)
Y1	0.179 (0.675)	0.181 (0.681)	0.184 (0.692)	0.183 (0.686)
Y2	-	-	-	-
Y3	-0.447* (-1.730)	-0.452* (-1.744)	-0.446* (-1.723)	-0.448* (-1.725)
Y4	-1.314*** (-4.963)	-1.111*** (-4.220)	-1.161*** (-4.424)	-0.906*** (-3.456)
Y5	-1.572*** (-5.953)	-1.370*** (-5.216)	-1.419*** (-5.422)	-1.163*** (-4.447)
Y6	-1.683*** (-6.405)	-1.479*** (-5.664)	-1.528*** (-5.873)	-1.270*** (-4.885)
Constant	-15.50*** (-11.82)	-15.91*** (-12.11)	-15.67*** (-11.94)	-15.79*** (-11.99)
Observations	8,540	8,540	8,540	8,540
R-squared	0.081	0.078	0.080	0.074

Note:

t-statistics are in parentheses. *** p < 0.01, * p < 0.1. PCIDs = Politically connected independent directors; TPCIDs = Total politically connected independent directors; RPCIDs = Ratio of politically connected independent directors; RPCIDs1 = Total PCIDs/total independent directors; RPCIDs2 = Total PCIDs/board size; Ownership = dummy variable with a value of 1 if the government is the ultimate controlling shareholder and 0 otherwise; LEV = Leverage; SIZE = Total assets; CFlow = Cash flow; CRat = Current ratio; SGRTH = Sales growth; Duality = Executive directors’ duality; Y = A dummy variable for years, with a value of 1 for a particular year and 0 for the others (Y1 to Y6 = year 1 to year 6).

Table 11 shows the results of the eight models for the second interval (2013–2014). The results report that PCIDs, with all its measurements, has a significantly negative association with the cost of debt, thus supporting our first hypothesis. Corporate ownership interacts positively in the association between PCIDs and the cost of debt; this interaction is highly significant in all models except model 6, whereas ownership is highly significant and negatively associated with the cost of debt. Therefore, this result gives us motivation to carry out another robustness test, dividing our sample into subsamples for both SOEs and non-SOEs, and the whole period into two intervals.

Table 12 illustrates the robustness test for the two periods for SOEs to investigate the association between PCIDs and the CoD. In the first interval, the results report that politically connected independent directors have a negative impact on the cost of debt, which is not significant in the third model. This means that politically connected independent directors do not have a significant impact on the reduction of the cost of debt in SOEs. In the second interval, the results show that politically connected independent directors have a negative but insignificant influence on the cost of debt.

Variable	(1) CoD	(2) CoD	(3) CoD	(4) CoD	(5) CoD	(6) CoD	(7) CoD	(8) CoD
PCIDs	-1.964*** (-7.449)				-2.289*** (-7.652)
PCIDs*own					1.513** (2.418)
TPCIDs		-1.031*** (-5.685)				-1.293*** (-6.227)
TPCIDs*own						1.123*** (2.686)
RPCIDs1			-4.796*** (-7.031)				-5.246*** (-6.881)
RPCIDs1*own							2.285 (1.355)
RPCIDs2				-5.429*** (-3.818)				-6.404*** (-4.076)
RPCIDs2*own								5.308 (1.465)
Ownership					-1.149*** (-3.078)	-1.162*** (-3.235)	-0.932*** (-2.615)	-0.963*** (-2.702)
LEV	-1.092 (-1.451)	-1.075 (-1.423)	-1.078 (-1.431)	-1.076 (-1.420)	-1.112 (-1.477)	-1.075 (-1.425)	-1.048 (-1.392)	-1.052 (-1.389)
SIZE	0.888*** (9.111)	0.888*** (9.040)	0.875*** (8.980)	0.854*** (8.698)	0.900*** (9.196)	0.904*** (9.169)	0.896*** (9.148)	0.874*** (8.862)
CFlow	-1.954 (-1.333)	-1.917 (-1.303)	-1.942 (-1.324)	-1.812 (-1.228)	-1.882 (-1.286)	-1.848 (-1.258)	-1.845 (-1.258)	-1.719 (-1.166)
CRat	-0.483*** (-5.332)	-0.489*** (-5.378)	-0.476*** (-5.251)	-0.488*** (-5.355)	-0.496*** (-5.475)	-0.504*** (-5.542)	-0.489*** (-5.391)	-0.503*** (-5.517)
SGRTH	0.000249 (0.741)	0.000220 (0.654)	0.000239 (0.711)	0.000215 (0.638)	0.000261 (0.779)	0.000230 (0.685)	0.000246 (0.731)	0.000222 (0.658)
Duality	2.663*** (8.359)	2.700*** (8.450)	2.661*** (8.345)	2.705*** (8.439)	2.705*** (8.466)	2.750*** (8.584)	2.715*** (8.487)	2.766*** (8.604)
Y1	0.121 (0.490)	0.125 (0.502)	0.124 (0.499)	0.119 (0.479)	0.119 (0.480)	0.120 (0.483)	0.125 (0.504)	0.120 (0.482)
o.Y2	-	-	-	-	-	-	-	-
Constant	-15.27*** (-7.055)	-15.53*** (-7.129)	-15.12*** (-6.981)	-14.97*** (-6.862)	-15.36*** (-7.059)	-15.71*** (-7.181)	-15.47*** (-7.108)	-15.31*** (-6.989)
Observations	3,221	3,221	3,221	3,221	3,221	3,221	3,221	3,221
R-squared	0.082	0.075	0.080	0.070	0.085	0.078	0.082	0.072
Adjusted R-squared	0.0794	0.0728	0.0777	0.0677	0.0817	0.0756	0.0791	0.0693

Note:

t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. PCIDs = Politically connected independent directors; PCIDs*own = The interaction of ownership; TPCIDs = Total politically connected independent directors; TPCIDs*own = The interaction of ownership; RPCIDs = Ratio of politically connected independent directors; RPCIDs*own = The interaction of ownership; RPCIDs1 = Total PCIDs/total independent directors; RPCIDs1*own = The interaction of ownership; RPCIDs2 = Total PCIDs/board size; RPCIDs2*own = The interaction of ownership; Ownership = Dummy variable with a value of 1 if the government is the ultimate controlling shareholder and 0 otherwise; LEV = Leverage; SIZE = Total assets; CFlow = Cash flow; CRat = current ratio; SGRTH = Sales growth; Duality = Executive directors’ duality; Y = A dummy variable for years, with a value of 1 for a particular year and 0 for the others (Y1 to Y6 = year 1 to year 6).

Variable	(1) CoD	(2) CoD	(3) CoD	(4) CoD	(5) CoD	(6) CoD	(7) CoD	(8) CoD
PCIDs	-1.992*** (-5.781)				-2.237*** (-5.873)
PCIDs*own					1.167 (1.568)
TPCIDs		-1.058*** (-4.391)				-1.250*** (-4.692)
TPCIDs*own						0.907* (1.698)
RPCIDs1			-5.090*** (-5.615)				-5.529*** (-5.618)
RPCIDs1*own							2.373 (1.102)
RPCIDs2				-6.174*** (-3.295)				-6.896*** (-3.407)
RPCIDs2*own								4.095 (0.894)
Ownership					-0.722** (-2.292)	-0.741** (-2.384)	-0.688** (-2.222)	-0.657** (-2.122)
LEV	-0.457 (-0.551)	-0.435 (-0.523)	-0.400 (-0.481)	-0.463 (-0.556)	-0.467 (-0.563)	-0.429 (-0.516)	-0.390 (-0.469)	-0.434 (-0.521)
SIZE	0.914*** (10.05)	0.918*** (10.06)	0.911*** (10.02)	0.905*** (9.914)	0.938*** (10.20)	0.944*** (10.23)	0.939*** (10.21)	0.931*** (10.09)
CFlow	-2.219 (-1.566)	-2.264 (-1.594)	-2.298 (-1.621)	-2.252 (-1.584)	-2.300 (-1.623)	-2.353* (-1.657)	-2.369* (-1.671)	-2.325 (-1.635)
CRat	-0.450*** (-4.467)	-0.453*** (-4.481)	-0.450*** (-4.462)	-0.453*** (-4.478)	-0.459*** (-4.556)	-0.462*** (-4.575)	-0.458*** (-4.542)	-0.461*** (-4.557)
SGRTH	-0.00131 (-1.027)	-0.00140 (-1.096)	-0.00140 (-1.095)	-0.00138 (-1.084)	-0.00131 (-1.028)	-0.00141 (-1.106)	-0.00142 (-1.114)	-0.00141 (-1.102)
Duality	2.395*** (8.612)	2.413*** (8.664)	2.388*** (8.585)	2.420*** (8.678)	2.445*** (8.749)	2.468*** (8.815)	2.442*** (8.735)	2.478*** (8.837)
Y1	0.818*** (3.247)	0.608** (2.458)	0.713*** (2.905)	0.470* (1.924)	0.824*** (3.273)	0.615** (2.491)	0.719*** (2.930)	0.474* (1.940)
Y2	-	-	-	-	-	-	-	-
Constant	-16.76*** (-8.269)	-16.88*** (-8.300)	-16.70*** (-8.239)	-16.60*** (-8.160)	-17.23*** (-8.409)	-17.39*** (-8.462)	-17.27*** (-8.428)	-17.15*** (-8.342)
Observations	3,578	3,578	3,578	3,578	3,578	3,578	3,578	3,578
R-squared	0.079	0.075	0.078	0.073	0.080	0.077	0.080	0.074
Adjusted R-squared	0.0767	0.0731	0.0762	0.0709	0.0777	0.0742	0.0770	0.0715

Note:

t-statistics are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. PCIDs = Politically connected independent directors; PCIDs*own = The interaction of ownership; TPCIDs = Total politically connected independent directors; TPCIDs*own = The interaction of ownership; RPCIDs = Ratio of politically connected independent directors; RPCIDs*own = The interaction of ownership; RPCIDs1 = Total PCIDs/total independent directors; RPCIDs1*own = The interaction of ownership; RPCIDs2 = Total PCIDs/board size; RPCIDs2*own = The interaction of ownership; Ownership = Dummy variable with a value of 1 if the government is the ultimate controlling shareholder, and 0 otherwise; LEV = Leverage; SIZE = Total assets; CFlow = Cash flow; CRat = Current ratio; SGRTH = Sales growth; Duality = Executive directors’ duality; Y = A dummy variable for years, with a value of 1 for a particular year and 0 for the others (Y1 to Y6 = year 1 to year 6).

Variable	2011–2012				2013–2014
	(1) CoD	(2) CoD	(3) CoD	(4) CoD	(1) CoD	(2) CoD	(3) CoD	(4) CoD
PCIDs	-0.664 (-1.167)				-0.951 (-1.228)
TPCIDs		-0.0762 (-0.203)				-0.240 (-0.441)
RPCIDs1			-2.834* (-1.834)				-3.072 (-1.381)
RPCIDs2				-0.529 (-0.158)				-2.687 (-0.567)
LEV	-1.775 (-1.169)	-1.876 (-1.235)	-1.750 (-1.155)	-1.876 (-1.233)	0.0268 (0.0170)	-0.0932 (-0.0590)	0.0790 (0.0499)	-0.0748 (-0.0473)
SIZE	0.621*** (3.010)	0.591*** (2.858)	0.624*** (3.048)	0.588*** (2.859)	0.745*** (3.698)	0.733*** (3.630)	0.738*** (3.673)	0.733*** (3.638)
CFlow	-7.890** (-2.309)	-7.783** (-2.276)	-8.111** (-2.375)	-7.785** (-2.275)	-7.330** (-1.999)	-7.357** (-2.005)	-7.415** (-2.023)	-7.354** (-2.004)
CRat	-0.811** (-2.407)	-0.812** (-2.409)	-0.808** (-2.401)	-0.812** (-2.407)	-0.00571 (-0.0197)	-0.0148 (-0.0512)	-0.00668 (-0.0231)	-0.0117 (-0.0403)
SGRTH	0.00214 (0.962)	0.00222 (0.999)	0.00211 (0.951)	0.00223 (1.001)	-0.0176 (-1.023)	-0.0169 (-0.983)	-0.0176 (-1.021)	-0.0170 (-0.988)
Duality	2.960*** (3.334)	2.936*** (3.304)	2.964*** (3.343)	2.932*** (3.300)	2.653*** (3.068)	2.662*** (3.076)	2.659*** (3.076)	2.653*** (3.064)
Y1	-0.0988 (-0.183)	-0.111 (-0.205)	-0.0886 (-0.164)	-0.112 (-0.208)	0.648 (1.094)	0.417 (0.720)	0.633 (1.104)	0.441 (0.769)
Y2	-	-	-	-	-	-	-	-
Constant	-9.923** (-2.039)	-9.386* (-1.923)	-9.931** (-2.052)	-9.331* (-1.920)	-14.44*** (-2.949)	-14.14*** (-2.883)	-14.30*** (-2.927)	-14.14*** (-2.887)
Observations	695	695	695	695	714	714	714	714
R-squared	0.049	0.047	0.052	0.047	0.041	0.039	0.042	0.040

Note:

t-statistics are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. PCIDs = Politically connected independent directors; PCIDs*own = The interaction of ownership; TPCIDs = Total politically connected independent directors; TPCIDs*own = The interaction of ownership; RPCIDs = Ratio of politically connected independent directors; RPCIDs*own = The interaction of ownership; RPCIDs1 = Total PCIDs/total independent directors; RPCIDs1*own = The interaction of ownership; RPCIDs2 = Total PCIDs/board size; RPCIDs2*own = The interaction of ownership; Ownership = Dummy variable with a value of 1 if the government is the ultimate controlling shareholder, and 0 otherwise; LEV = Leverage; SIZE = Total assets; CFlow = Cash flow; CRat = Current ratio; SGRTH = Sales growth; Duality = Executive directors’ duality; Y = A dummy variable for years, with a value of 1 for a particular year and 0 for the others (Y1 to Y6 = year 1 to year 6).

Table 13 illustrates the robustness test results for the two periods in non-SOEs, which investigates the association between PCIDs and the CoD. In the first interval, the results report that politically connected independent directors have a significantly negative impact on the cost of debt in all model tests. This means that politically connected independent directors can significantly reduce the cost of debt in non-SOEs compared to SOEs. In the second interval, the results also show that politically connected independent directors have a significantly negative influence on the cost of debt. Therefore, the results in Tables 12 and 13 support our third hypothesis that PCIDs in non-SOEs play a more significant role in reducing the cost of debt compared to SOEs.

Variable	2011–2012				2013–2014
	(1) CoD	(2) CoD	(3) CoD	(4) CoD	(1) CoD	(2) CoD	(3) CoD	(4) CoD
PCIDs	-2.300*** (-7.726)				-2.268*** (-5.893)
TPCIDs		-1.309*** (-6.326)				-1.281*** (-4.771)
RPCIDs1			-5.283*** (-6.954)				-5.580*** (-5.634)
RPCIDs2				-6.512*** (-4.154)				-6.988*** (-3.435)
LEV	-0.885 (-1.014)	-0.788 (-0.899)	-0.802 (-0.917)	-0.759 (-0.862)	-0.459 (-0.466)	-0.350 (-0.354)	-0.367 (-0.372)	-0.365 (-0.368)
SIZE	0.976*** (8.743)	0.990*** (8.805)	0.970*** (8.670)	0.953*** (8.451)	1.004*** (9.693)	1.014*** (9.758)	1.008*** (9.725)	0.999*** (9.604)
CFlow	-0.555 (-0.341)	-0.532 (-0.326)	-0.454 (-0.279)	-0.363 (-0.221)	-1.133 (-0.737)	-1.176 (-0.763)	-1.195 (-0.777)	-1.147 (-0.743)
CRat	-0.469*** (-4.983)	-0.476*** (-5.034)	-0.462*** (-4.893)	-0.475*** (-5.009)	-0.525*** (-4.883)	-0.526*** (-4.881)	-0.523*** (-4.862)	-0.525*** (-4.867)
SGRTH	0.000218 (0.647)	0.000187 (0.553)	0.000203 (0.599)	0.000178 (0.523)	-0.00120 (-0.946)	-0.00131 (-1.030)	-0.00131 (-1.033)	-0.00130 (-1.023)
Duality	2.630*** (7.686)	2.682*** (7.812)	2.641*** (7.703)	2.704*** (7.840)	2.380*** (8.079)	2.401*** (8.135)	2.374*** (8.055)	2.415*** (8.166)
Y1	0.190 (0.683)	0.194 (0.695)	0.195 (0.702)	0.195 (0.697)	0.863*** (3.104)	0.661** (2.421)	0.740*** (2.725)	0.480* (1.781)
o.Y2	-	-	-	-	-	-	-	-
Constant	-17.03*** (-6.917)	-17.65*** (-7.122)	-17.12*** (-6.936)	-17.10*** (-6.875)	-18.51*** (-8.103)	-18.80*** (-8.200)	-18.62*** (-8.143)	-18.49*** (-8.056)
Observations	2,526	2,526	2,526	2,526	2,864	2,864	2,864	2,864
R-squared	0.097	0.090	0.093	0.082	0.093	0.089	0.092	0.085

Note:

t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. PCIDs = Politically connected independent directors; PCIDs*own = The interaction of ownership; TPCIDs = Total politically connected independent directors; TPCIDs*own = The interaction of ownership; RPCIDs = Ratio of politically connected independent directors; RPCIDs*own = The interaction of ownership; RPCIDs1 = Total PCIDs/total independent directors; RPCIDs1*own = The interaction of ownership; RPCIDs2 = Total PCIDs/board size; RPCIDs2*own = The interaction of ownership; Ownership = Dummy variable with a value of 1 if the government is the ultimate controlling shareholder and 0 otherwise; LEV = Leverage; SIZE = Total assets; CFlow = Cash flow; CRat = Current ratio; SGRTH = Sales growth; Duality = Executive directors’ duality; Y = A dummy variable for years, with a value of 1 for a particular year and 0 for the others (Y1 to Y6 = year 1 to year 6).

5. DISCUSSION

This study makes several additions to the existing body of knowledge. It provides more information on the link between political ties and the CoD, particularly in China and developing countries, and the results are consistent with previous research (e.g., Harjan et al., 2019). Debt finance across the globe has shown conflicting results when it comes to the influence of political ties. Connected enterprises in Indonesia were shown to be able to acquire funding at reduced prices by Leuz and Oberholzer-Gee (2006), which was corroborated by research in various countries such as the United States, Taiwan, Pakistan and Brazil. According to Bliss and Gul (2012), politically connected firms (PCFs) in Malaysia have to pay exorbitant borrowing rates. This link between PCFs and the cost of debt has not yet been studied in China. This research specifically examines the influence that PCIDs have on the CoD. The outcomes of this investigation show a robust negative correlation between PCFs and the CoD.

In addition, several studies document that PCIDs pay political resources to obtain a competitive edge (Francis et al., 2009; Goldman et al., 2013), while few studies focus on the value of PCIDs in financial decision making in Chinese firms (Shi et al., 2018). This research found that PCIDs impact the cost of debt by playing a resourcing role in businesses by sharing their expertise in law, finance, and holding positions on other boards. PCIDs also facilitate access to a wide range of financial resources, including government procurement contracts, bank loans, and equity financing for small businesses. Firms are able to profit from government programmes, such as lower tax rates, thanks to PCIDs.

Because this study considers the regulatory change as an exogenous event and focuses on two separate periods, the empirical results are new and arguably free from endogeneity concerns. The reason for this concern is because board independence and the selection of independent directors are exogenous, so the result of the value of independent directors could be biased (Hermalin & Weisbach, 1998; Wang, 2015). Hence, this paper follows several studies in the literature that use an exogenous event to avoid endogeneity problems by comparing the results before and after the regulatory change (Nguyen & Nielsen, 2010; Shi et al., 2018) .

Finally, we complement the literature by present the nature of firm ownership as moderating variable on the causal relationship between PCIDs and the cost of debt by considering the distinctions between SOEs and non-SOEs. This shows that, compared with SOEs, the resignation of PCIDs significantly increased the CoD in non-SOEs.

6. CONCLUSION

The primary goal of this research was to look at the association between politically connected independent directors (PCIDs) and the cost of debt (CoD). According to the results, politically connected firms (PCFs) are more risky than non-PCFs in developing markets, particularly in China, in terms of the CoD (see Bliss and Gul (2012)). In this research, it was shown that PCFs had a lower CoD than non-PCFs.

This study's conclusions are unfavorable, indicating a substantial link between the CoD and PCFs. In China, a negative link was found between the CoD and PCIDs. This shows that PCIDs have an influence by sharing their legal and financial expertise and by sitting on other boards. PCIDs assist businesses in obtaining a high degree of financial resources, such as bank loans, equity financing, and government procurement contracts. PCIDs allow businesses to take advantage of government policies, such as lower tax rates. Because of these functions, lenders believe that PCFs have a lower risk than non-PCFs. Lenders provide cheaper borrowing rates to businesses with PCIDs than to firms without PCIDs as a result of this perception.

For the natural ownership structure, the relationship between PCFs and PCIDs and the CoD is moderated positively and significantly by the type of ownership. In this study, we divided our sample into two types depending on the natural ownership – state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs). In this study, we compared the results based on the ownership type to determine how this regulation change and the resignation of PCIDs impacted the CoD. The results reported that, in SOEs, there was no change in the CoD, while in non-SOEs, the CoD increased after the regulatory change. This means that PCIDs played a significant role in determining the CoD level, especially in non-SOEs.

This research has two drawbacks. First, we identified PCFs and PCIDs by referring to Fan, Wong, and Zhang (2007); Wang (2015) and Shi et al. (2018). Non-PCFs and non-PCID companies and directors may appear in our data, but they might be politically connected. PCFs and PCIDs may have different levels of influence on the CoD due to their different levels of political ties. In PCFs, only one corporate governance trait, the influence of CEO duality on the cost of debt, was studied. This association between PCFs, PCIDs and the CoD may have been helped by the effectiveness of corporate governance.

Funding: This study received no specific financial support.

Competing Interests: The authors declare that they have no competing interests.

Authors’ Contributions: All authors contributed equally to the conception and design of the study.

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