Family control and cost of debt: Evidence from China

Pacific-Basin Finance Journal 60 (2020) 101286

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Family control and cost of debt: Evidence from China Hao Gaoa, Jing Heb, Yong Lic,1, Yuanyu Quc,2,

⁎

T

a

PBC School of Finance, Tsinghua University, Beijing 100084, China Business School, University of International Business and Economics, Beijing 100029, China c School of Banking and Finance, University of International Business and Economics, Beijing 100029, China b

ARTICLE INFO

ABSTRACT

Keywords: Family firms Costs of debt Investor protection Chinese listed firms

This paper studies how family ownership influences the cost of debt. Using a sample of Chinese listed firms, we find that family control leads to a higher bond yield-spread. This evidence contradicts the findings in developed markets. We document that the risk of expropriation and financial reporting quality are plausible mechanisms. Besides, Protection of debtholders' rights can mitigate the concern of family expropriation and information asymmetry, and reduce the cost of debt. We also show consistent evidence that family firms generally take less debt and have lower debt maturity due to the high cost. Overall, our results shed light on how family control affects financing costs in the capital market with less protection for creditor rights.

JEL classification: G32 G34

1. Introduction The control of founding family ownership has elicited considerable academic attention. On the one hand, the reputation concern argues that family firms are long-term investors who are concerned with survival, inheritance, and a lasting relationship with creditors (Anderson et al., 2003; Ellul et al., 2007; Bertrand and Schoar, 2006; Gómez-Mejía et al., 2007). To mitigate agency conflicts, family firms will adopt risk-averse policies (Shleifer and Vishny, 1986), take on less debt (McConaughy et al., 2001; González et al., 2013; Chen et al., 2014), and being less opaque (Ma et al., 2017). Thus, a negative relationship exists between debt yields and founding family ownership. On the other hand, the entrenchment view contends that family control aggravates the agency conflicts between shareholders and creditors through excessive family control rights, family representation in management and corporate boards, and heirs of the founders in management (Claessens et al., 2002; Boubakri and Ghouma, 2010; Lin et al., 2011; Bennedsen et al., 2015; Pan and Tian, 2016). To prevent outsiders from taking control of the firms, family firms are reluctant to hire professional managers from outside (Cao et al., 2015; Xu et al., 2015). In addition, family firms, particularly those with severe agency conflicts, are more likely to suffer from expropriation and engage in tunneling activities (Boubakri and Ghouma, 2010; Lin et al., 2011; Pan and Tian, 2016). Therefore, creditors will incorporate these costs into their lending decisions and require a higher premium for family firms. The unique governance features of founding families indicate interesting implications on the pricing and contracting of corporate debt. In this study, we investigate how the Chinese bond market responds to family ownership. We find a significantly positive association between family ownership and the bond spread in China.

Corresponding author. E-mail addresses: [email protected] (H. Gao), [email protected] (J. He), [email protected] (Y. Li), [email protected] (Y. Qu). 1 Yong Li thanks financial support by "the National Law and Legal Theory Research Project of the Ministry of Justice 2019: A study on the legal system of corporate dividend distribution" (No.19SFB3032). 2 Yuanyu Qu thanks financial support by the Fundamental Research Funds for the Central Universities" in UIBE (grant ID: 19QN08). ⁎

https://doi.org/10.1016/j.pacfin.2020.101286 Received 17 December 2019; Received in revised form 3 February 2020; Accepted 9 February 2020 Available online 11 February 2020 0927-538X/ © 2020 Elsevier B.V. All rights reserved.

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China offers an excellent setting for our study. It is well known that China's rapid economic development has been accompanied by a poor market environment and weak legal protection for investors. Moreover, the market and legislation environment could affect activities of both shareholders and outside investors (Esty and Megginson, 2003; Qian and Strahan, 2007; Houston et al., 2010; Lin et al., 2011; Pan and Tian, 2016). In the US, the developed institutional and legislation environment offers well protection for investors and meanwhile acts as an external monitoring mechanism for shareholders and managers. Anderson et al. (2003) find that the cost of debt for family firms is lower than for non-family firms in the US. In China, however, the poor institutional environment, together with weak legal or regulatory protections for minority shareholders and creditors, makes the Chinese listed firms highly conducive for tunneling activities (Peng et al., 2011, Jiang et al., 2010; Qian and Yeung, 2015). If bondholders anticipate such behavior, they will require a higher return, leading to a higher bond spread. In this paper, we examine the relationship between family ownership and the cost of debt in China during the period of 2009 to 2017. Our univariate analysis shows that the bond spread for family firms is 0.69% higher than non-family firms. By conducting multivariate analysis, we further document that bond spreads of family firms are significantly higher than that of non-family firms. We also use a propensity score matching (PSM) method to control potential confounding factors. Besides, we use the industry competition level as an instrument variable for family ownership to address potential endogeneity issues. PSM and IV regressions confirm our results. Taken together, our results support the proposition that family firms have a higher cost of debt than non-family firms in China. We further explore the mechanisms behind the relation between family ownership and the cost of debt from three perspectives. The first draws on the risk of expropriation. Expropriation in firms includes outright theft and selling (buying) assets or products at lower (higher) than market price to (from) a firm in which the controlling shareholder has a high stake. A large body of empirical evidence has shown that controlling shareholders may take advantage of other investors through related party transactions, especially in emerging markets where legal protection of other investors is weak (Cheung et al., 2006; Jian and Wong, 2010; Peng et al., 2011). Another common form for controlling shareholders to tunnel is to borrow on company assets or cash, which in the Chinese case, is largely captured by an accounting item “other receivables” (Jiang et al., 2010). We follow prior studies to use related party transactions and other receivables respectively to measure the level of tunneling activities in firms. We provide evidence that both related party transactions and other receivables, as indicators of controlling shareholders' expropriation incentive, are mechanisms behind the relationship between family control and the cost of debt. Bondholders will require a higher premium to compensate for the high expropriation risk in family firms, resulting in a higher debt cost. The second draws on financial reporting quality. Existing studies document that conservative accounting is an important characteristic of high-quality financial reporting. It can provide creditors with more timely information, influence the lending decisions of creditors, and benefit firms with lower debt financing costs (Ahmed et al., 2002; Ball and Shivakumar, 2005; Ball et al., 2000; Watts, 2003; Zhang, 2008). Later on, Chen and Zhu (2013) show that conservative accounting and high financial reporting quality can reduce the costs of longer-maturity debt including bank loans and bonds in China, an emerging market where banks are dominators in financial sectors. Following Chen and Zhu (2013), we investigate whether family firms with poor financial reporting quality will suffer from high bond spreads. Our results show that for family firms with high financial reporting quality, bondholders will charge less premium on bonds because there is less information asymmetry and bondholders' interests are better protected; in contrast, family firms with poor financial reporting quality will bear relatively higher bond spreads. The third mechanism draws on the market environmental factors. Institutional and legislation development affect creditors lending behaviors (Esty and Megginson, 2003; Qian and Strahan, 2007; Houston et al., 2010; Lin et al., 2011; Pan and Tian, 2016; Ni and Yin, 2018) as well as firms' accounting properties (Ball et al., 2000; DeFond et al., 1999; Leuz et al., 2003). In the presence of robust legal and market institutions, lenders are better positioned to force repayment or take control of a firm in the event of default, and therefore more willing to provide credit at favorable terms (lower spreads, longer maturities) ex-ante (Qian and Strahan, 2007). However, when shareholder litigation right weakens, the cost of debt goes up because of deteriorated corporate governance, increased information asymmetry, and heightened managerial risk-taking incentives (Ni and Yin, 2018). China offers a good setting to test the influence of market development on investor protection because levels of market development and legislation vary significantly across the 32 provinces, autonomous regions, and municipalities. We use Market, an index on the development of the regional market, and Legal, an index on the development of the market intermediary organization and legislation environment, to measure the development of marketization and legislation in the regions respectively (Fan 2016 Index, Wang et al., 2016). We show that market development and legislation environment are key factors moderating the relation between family ownership and the cost of debt. Bondholders value development of marketization and legislation in the regions and the cost of debt for family firms will be lower if the firms are located in provinces where creditors are under better protection. Our main findings are robust to the alternative estimation of bank loan interest rates and the application of the firm-level dataset. Our further analysis shows that family firms take on less debt and have lower debt maturity than non-family firms. In contrast, Chen et al. (2014) show that family firms have a higher debt ratio than non-family firms in the US. Our results indicate that family firms in China, an emerging market with a low level of marketization and poor legislation environment, are facing more difficulties in the availability of debt financing than non-family firms. Our study contributes to the literature in three ways. First, we contribute to the studies on influencing factors of debt financing cost. Prior studies show that corporate governance, such as board characteristics, ownership structure and political connection (Anderson et al., 2004; Boubakri and Ghouma, 2010; Bradley and Chen, 2011; Lin et al., 2011; Bliss and Gul, 2012), corporate disclosure quality and information transparency (Sengupta, 1998; Andrade et al., 2014; Ma et al., 2017; Bonsall and Miller, 2017), corporate liquidity (Diamond and Verrecchia, 1991; Morellec, 2001), and corporate social performance (Chava, 2014; Du et al., 2017; Eichholtz et al., 2019; Goss and Roberts, 2011) could affect corporate debt financing cost. In this study, we shed light on the 2

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influence of family ownership. Our results show a positive relationship between family ownership and the bond spread in China. Second, we contribute to the ongoing debate on the relation between family ownership and the cost of debt. Prior studies hold mixed arguments on this issue. Anderson et al. (2003) show that family firms in the US have a lower cost of debt and lower the agency problem between shareholders and creditors. Later on, studies outside the US do not provide consistent results, especially those concerning China. Some argue that family firms are overall significantly less opaque, more concerned with survival, and have better monitoring ability (Ma et al., 2017) and therefore have lower debt financing costs. However, in these studies, they use interest expense scaled by debt to measure the cost of debt for listed firms. This measurement of cost of debt can be easily influenced by firms' capital structure and thus can't reflect the firms' true financing costs. In our study, we base our study on a 9-year bond level panel and use bond spreads to proxy the cost of debt. Using bond spreads to measure the cost of debt allows us to get rid of the interference of firms' capital structure. Also, bond spreads are more market-oriented, less regulated, and thus more efficient in reflecting the risks faced by borrowers. We provide evidence that family firms in China have higher bond spreads than non-family firms. Third, we contribute to studies on the factors affecting family firms' financing costs. On the one hand, some prior studies examine the impact of firm characteristics, such as corporate governance, dividend policy, engagement in management, family founders' religious belief and political connections, and information environment (Anderson et al., 2003; Mansi et al., 2004; Ashbaugh-Skaife et al., 2006; Chen et al., 2009; Boubakri and Ghouma, 2010; Chen et al., 2014; Xu et al., 2015; Jiang et al., 2015). In our study, we provide evidence that potential tunneling activities in family firms could significantly increase their debt financing costs. Bondholders will require a higher premium to compensate for the high expropriation risk in family firms. Besides, existing studies show that financial reporting quality can influence the debt financing costs (Ahmed et al., 2002; Ball and Shivakumar, 2005; Ball et al., 2000; Chen and Zhu, 2013; Watts, 2003; Zhang, 2008). In this study, we find that the financial reporting quality of family firms is relatively poorer than non-family firms. Family firms with poor-quality financial reporting will bear higher bond spreads because of information asymmetry. Last, prior studies document that the investor protection environment and credit market competition have an impact on the cost of debt (Hainz, 2003; Ellul et al., 2007; Boubakri and Ghouma, 2010; Pan and Tian, 2016). In our study, we investigate the impact of the market environment on family firms' cost of debt in China, an emerging market where the level of marketization and legislation vary significantly across regions. We argue that family firms in China will suffer from an even higher cost of debt when located in provinces with weak protection for investors. Our study sheds light on how family control affects financing costs in the capital market with less protection for creditor rights. The remainder of this paper is organized as follows. Section 2 discusses the data, key variables and descriptive statistics. Section 3 reports the results of baseline regressions, PSM regression and IV approach. Section 4 analyzes possible mechanisms for the identified effect. Section 5 reports the robustness tests. Section 6 concludes the paper. 2. Data and summary statistics Our initial sample begins with all Corporate Bonds, Enterprise Bonds and Medium-term Notes issued by Chinese listed firms from 2009 to 2017. The information on bonds is collected from Wind and CSMAR Database. The firms' accounting and ownership data are collected from the CSMAR Database. We exclude bonds issued by financial firms from our sample and require that bonds have nonmissing value on transaction data, credit ratings, and financial information of issuers. All continuous variables are winsorized at 1% and 99%. Finally, our sample has 5377 bond-year observations. In Table 1 summarizes sample distribution. 2.1. Variables definitions The key dependent variable in our analysis is the yield spread (Spread), which is measured as the difference between the annual yield to maturity on the firm's outstanding traded bond and yield to maturity on a Treasury bond of comparable maturity. In the cases where there is no equivalent Treasury maturity, the yield is computed using the interpolation method. The main independent variable of the family firm, Family Dummy, is a dummy variable that is equal to one if the firm is a family Table 1 Sample distribution. Year

Enterprise Bond

Corporate Bond

Medium-term Notes

Total

2009 2010 2011 2012 2013 2014 2015 2016 2017 Total

18 19 20 22 18 20 18 12 9 156

43 60 134 265 370 423 501 685 708 3189

37 51 112 166 208 253 360 400 445 2032

98 130 266 453 596 696 879 1097 1162 5377

This table summarizes the bond sample distribution from 2009 to 2017. We include Enterprise Bond, Corporate Bond, and Medium-term Notes in our sample. Bonds issued by financial firms or with missing value on transaction data, credit ratings, and financial information of issuers are excluded. 3

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Table 2 Univariate tests. Variables

Spread Size Lev ROE OR RPT FRQ1 FRQ2 Duration Proceeds RMaturity Rating

Mean

Median

family

non-family

diff

t-statistics

family

non-family

diff

z-statistics

2.76% 23.17 0.558 0.089 0.020 1.221 −0.033 0.007 2.366 1.881 0.851 1.434

2.07% 24.2 0.601 0.068 0.019 1.02 0.037 0.033 2.647 2.387 0.920 1.596

0.69%*** −1.03*** −0.043*** 0.021 0.002* 0.201*** −0.070*** −0.026*** −0.281*** −0.506*** −0.069*** −0.162***

15.44 −21.02 −8.10 1.15 1.69 3.77 −15.04 −11.50 −6.02 −16.76 −2.664 −26.1977

2.57% 23.08 0.563 0.081 0.012 0.566 −0.038 0.006 2.357 1.946 1.012 1.386

1.84% 24 0.622 0.075 0.009 0.514 0.039 0.036 2.532 2.303 1.072 1.609

0.73%*** −0.92*** −0.059*** 0.006*** 0.003*** 0.052*** −0.077*** −0.030*** −0.175*** −0.357*** −0.060*** −0.223***

17.65 −20.44 −8.40 5.90 4.47 3.89 −15.86 −12.80 −4.814 −16.158 −4.033 −25.48

In this table, we report the results of univariate tests of key variables for family firms and non-family firms. The sample period covers the period 2009 to 2017. Bonds issued by financial firms or with missing value on transaction data, credit ratings, and financial information of issuers are excluded. The definitions of all of the variables are provided in Appendix A1. ***, and * correspond to statistical significance at the 1%, and 10% levels, respectively.

firm in the previous year and zero otherwise. We use the definition of the family firm in CSMAR as one in which at least one family member, except the controlling shareholders, holds shares, manages and controls the listed firms or the firms of controlling shareholders. We also use the proportion of family ownership, Family Ownership, as the independent variable for robustness checks. We also include a set of well-documented control variables that might affect the bond spread, and the inclusion of these variables is in line with existing studies (Anderson et al., 2003). First, we control for borrower characteristics, including Size, Lev, ROE, and SG. We also control for ownership structure and corporate governance, including Own, the shareholdings of the largest shareholder, and Board Size. Second, we control for bond-level factors including Duration, Proceeds, RMaturity and Rating. The detailed definitions for these variables, as well as all the other variables used in this paper, are reported in the Appendix. 2.2. Univariate tests Table 2 reports the results of univariate tests between family firms and non-family firms. The mean and median values of the bond spread are both significantly higher for family firms than for non-family firms, implying that bondholders charge a higher premium on family firms. Besides, family firms are with smaller size and take on less debt than non-family firms. However, family firms have a higher level of other receivables and related party transactions, indicating a higher incentive for family firms to engage in tunneling activities. Besides, family firms have significantly lower financial reporting quality than non-family firms. The results of univariate tests also indicate that bonds issued by family firms are generally in smaller proceeds, shorter maturity, lower ratings, and smaller durations. 3. Family firm control and bond spread 3.1. Baseline regressions We begin by presenting panel regressions of the bond spread on Family and a range of control variables and fixed effects. This approach maximizes the generalizability of our results by covering all firms with outstanding bonds. Including a range of fixed effects and a large set of control variables allows us to control for unobserved heterogeneity. However, this approach relies critically on the assumption that family firms in our sample are comparable to the non-family firms after removing the influence of these controls through regression analysis. The main dependent variable is the Spread and the independent variable of the family firm, Family, is a dummy variable that is equal to one if firmi is a family firm in the previous yeart−1 and zero otherwise. We also use family ownership as the independent variable for robustness checks. Controlsi,t-1 is a vector of variables controlling firm-level characteristics, including Size, Lev, ROE, SG, Own, and Board Size, and controlling bond-level characteristics, including Duration, Proceeds, RMaturity, and Rating. The variables' definitions are given in the Appendix. We include industry and year fixed effects to control for unobserved factors affecting the results, and we use robust standard errors. Specifically, the baseline regression is established as follows:

Spreadi, t =

+ Familyi, t

1

+

Controlsi, t

1

+ Fixed Effects +

i, t

(1)

Table 3 reports the results for the full sample. We use the Family Dummy as the independent variable in Column (1) and (2), and Family Ownership in Column (3) and (4) for robustness checks. Meanwhile, Column (1) and (3) report the results with firm-level 4

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Table 3 Family control and the bond spread: full sample. Variables

(1)

(2)

(3)

Family Dummy Family Size Lev ROE SG Own Board Size Duration

0.004*** (8.74) −0.003*** (−22.15) 0.025*** (15.64) −0.001 (−1.03) 0.018*** (3.12) −0.001 (−1.37) −0.003*** (−4.09)

Proceeds RMaturity Rating Industry Year N Adj.R2

YES YES 5377 0.260

(4)

Family Ownership 0.002*** (5.59) −0.002*** (−7.39) 0.020*** (12.81) −0.001 (−1.00) 0.015*** (3.34) 0.001 (1.34) −0.004*** (−4.78) −0.002*** (−6.15) −0.000 (−0.50) 0.003*** (3.83) −0.018*** (−13.84) YES YES 5377 0.312

0.009*** (8.48) −0.003*** (−21.89) 0.025*** (15.50) −0.001 (−1.04) 0.018*** (3.12) −0.002** (−2.36) −0.004*** (−4.50)

YES YES 5377 0.260

0.006*** (5.05) −0.002*** (−7.37) 0.020*** (12.70) −0.001 (−1.01) 0.015*** (3.38) 0.001 (0.75) −0.004*** (−5.09) −0.002*** (−6.12) −0.000 (−0.46) 0.003*** (3.82) −0.018*** (−13.85) YES YES 5377 0.312

This table reports the results of Eq. (1) for the full sample. The dependent variable is the bond spread, Spread. The key independent variable is Family Dummy in Column (1) and (2), and Family Ownership in Column (3) and (4). All of the control variables are lagged by one period. Appendix A1 presents the definitions of all of the variables. Robust t-statistics are reported in parentheses. *** correspond to statistical significance at the 1% levels.

controls and fixed effects, Column (2) and (4) report the results with both firm-level and bond-level controls and fixed effects. Across all four columns, the coefficients on Family are significantly positive. Our results indicate that family firms suffer from higher bond spreads. Taking Column (2) as an example, the bond spread of family firms is 0.2% higher than that of non-family firms when controlling for firm-specific and bond-specific characteristics and fixed effects, consistent with the notion that bondholders suspect the governance of family firms and charge higher premium on bonds issued by family firms. 3.2. Regression based on PSM While results in Section 3.1 hold in a large sample of firms and generalizability of our results should be high, there could still be unobserved heterogeneity that can be taken care of only if the set of control firms is narrowed down to a more comparable subsample. Therefore, in this subsection, we construct a set of control groups to which we apply several matching estimators. Matching improves on the panel regressions above by not assuming the global validity of the regression function. To construct the matched sample, we require the matched bonds issued by non-family firms to have the closest Size, ROE, Lev, and Rating, and be in the same industry as bonds issued by family firms every year. Table 4 reports the results using the PSM-matched sample. We use Family Dummy as the independent variable in Column (1) to (3), and Family Ownership in Column (4) to (6) for robustness checks. We report the results using 1-to-1 (Columns (1) and (4)), 1-to-3 (Columns (2) and (5)), and 1-to-5 (Columns (3) and (6)) matched samples. All the results are with both firm-level and bond-level controls and fixed effects. Consistent with our baseline results, the coefficients of Family are still significantly positive. Taken together, our baseline and PSM-matched regressions show a positive relationship between family control and the bond spread. The bondholders require a higher premium on bonds issued by family firms, resulting in a higher cost of debt for family firms than their counterparts. 3.3. Two-stage instrumental approach The positive relation between family control and the bond spread may be suspicious due to the potential endogeneity issues. In this sub-section, we use industry-level competition (the Herfindahl index, Herfindahl) as an instrument variable to run 2SLS regressions (Chen et al., 2014). As founding family members are less likely to retain their control if the firms operate in relatively more 5

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Table 4 Family control and the bond spread: matched sample Variables

(1)

(2)

(3)

(4)

Family Dummy

Family Size Lev ROE SG Own Board Size Duration Proceeds RMaturity Rating Industry Year N Adj.R2

(5)

(6)

Family Ownership

1 on 1

1 on 3

1 on 5

1 on 1

1 on 3

1 on 5

0.004*** (5.42) −0.003*** (−6.10) 0.021*** (7.18) −0.019* (−1.74) 0.049 (0.96) 0.001 (0.23) −0.005*** (−2.80) −0.004*** (−4.86) −0.001 (−0.86) 0.005*** (3.96) −0.018*** (−6.40) YES YES 1491 0.285

0.004*** (7.42) −0.003*** (−7.15) 0.021*** (8.87) −0.026*** (−3.56) 0.023** (2.44) 0.001 (0.52) −0.004*** (−2.65) −0.003*** (−4.34) −0.001 (−1.63) 0.003*** (3.39) −0.018*** (−7.64) YES YES 2036 0.302

0.004*** (8.24) −0.003*** (−6.57) 0.021*** (8.62) −0.028*** (−3.26) 0.018 (1.54) 0.002 (1.00) −0.003* (−1.83) −0.003*** (−5.13) −0.001** (−2.10) 0.004*** (3.94) −0.017*** (−8.24) YES YES 2349 0.299

0.008*** (5.23) −0.003*** (−5.81) 0.021*** (7.28) −0.019* (−1.70) 0.054 (1.04) −0.002 (−0.71) −0.005*** (−3.13) −0.003*** (−4.81) −0.001 (−0.92) 0.005*** (3.96) −0.017*** (−6.24) YES YES 1491 0.282

0.009*** (6.91) −0.003*** (−6.83) 0.021*** (9.04) −0.026*** (−3.49) 0.024** (2.30) −0.001 (−0.80) −0.005*** (−3.16) −0.003*** (−4.30) −0.001* (−1.68) 0.003*** (3.38) −0.017*** (−7.44) YES YES 2036 0.297

0.009*** (7.60) −0.003*** (−6.31) 0.021*** (8.83) −0.028*** (−3.19) 0.019 (1.53) −0.001 (−0.46) −0.003** (−2.38) −0.003*** (−5.06) −0.001** (−2.07) 0.004*** (3.90) −0.016*** (−8.04) YES YES 2349 0.295

This table reports the results of Eq. (1) for the 1-on-1, 1-on-3 and 1-on-5 PSM samples. The dependent variable is the bond spread, Spread. The key independent variable is Family Dummy in Column (1) to (3), and Family Ownership in Column (4) to (6). All of the control variables are lagged by one period. Appendix A presents the definitions of all of the variables. Robust t-statistics are reported in parentheses. ***, **, and * correspond to statistical significance at the 1%, 5%, and 10% levels, respectively.

competitive industries, we expect that industry-level competition, HHI, should be negatively related to Family.3 However, industrylevel competition is less likely to determine the bond spread of a firm. To validate the effectiveness of HHI as an instrumental variable, we regress Family and HHI and the control variables as the first-stage regression:

Familyi, t

1

=

+

1 HHIi,t 1

+

Controlsi, t

1

+ Fixed Effects +

i, t

(2)

We obtain the fitted value of the dependent variable of Eq. (2) denoted by Family and thereby construct the second-stage regression as follows:

Spreadi, t =

+

1 Familyi, t 1

+

Controlsi, t

1

+ Fixed Effects +

i, t

(3)

In Eq. (2)–(3), all the control variables and fixed effects are the same as in Eq. (1). The results of the first-stage treatment models are presented in Column (1) and (2) of Table 5, using the Family Dummy and Family Ownership as the dependent variable respectively. The coefficients of HHI are statistically significant with the predicted signs, indicating that the higher HHI is (lower level of industry competition), the more likely family firms will retain their control. Column (3) and (4) report the results of the second-stage regression in Eq. (3). The coefficient of the predicted Family Dummy and Family Ownership are all significantly positive. Thus, the 2SLS regressions show that family firms on average bear higher bond spreads than non-family firms. Overall, the endogeneity tests—PSM matched regression and IV approach—show consistent results with our baseline regressions. Our results indicate a positive relationship between family control and the cost of debt in China. 4. Mechanisms analysis In this section, we investigate three channels through which family control in firms could affect bond spread: risk of expropriation, financial reporting quality, and regional environment. 3

HHI value should vary from 0 to 1. When the market is in a complete monopoly, HHI equals to 1. 6

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Table 5 The effect of family control on the bond spread: 2SLS regression. (1)

(2)

(3)

First Stage

HHI Family Dummy

Second Stage

Family Dummy

Family Ownership

0.130*** (4.30)

0.050*** (3.94)

Spread

0.016* (1.88)

Family Ownership Size

−0.037*** (−5.84) −0.244*** (−5.69) 0.011 (0.75) 0.015 (0.46) −0.053 (−0.22) −0.204*** (−8.12) −0.018** (−2.44) 0.027*** (3.19) 0.049*** (3.53) −0.485*** (−11.31) YES YES 5377 0.204 195.8

Lev ROE SG Own Board Size Duration Proceeds RMaturity Rating Industry Year N Adj.R2 F-Statistics

(4)

−0.016*** (−6.78) −0.080*** (−4.52) 0.005 (0.89) 0.118*** (8.18) −0.055 (−0.65) −0.055*** (−4.99) −0.010*** (−3.53) 0.010*** (2.97) 0.024*** (4.43) −0.200*** (−11.49) YES YES 5377 0.189 59.52

0.041* (1.85) −0.001*** (−2.90) 0.023*** (8.49) −0.002 (−1.01) −0.004 (−1.18) 0.016*** (2.82) −0.002 (−1.29) −0.002*** (−4.10) −0.000 (−1.21) 0.002** (2.01) −0.011** (−2.36) YES YES 5377 0.172

−0.001*** (−3.49) 0.024*** (7.99) −0.001 (−0.98) 0.001 (0.83) 0.015*** (2.58) −0.001 (−0.49) −0.002*** (−4.78) −0.001 (−1.27) 0.002** (2.35) −0.012*** (−2.65) YES YES 5377 0.188

This table reports the results of IV regressions (Eqs. (2) and (3)). The instrument variable is HHI, which measures the industry-level competition. Column (1) and (2) reports the results of the first stage regressions and column (3) and (4) reports the results of the second stage regressions. The dependent variable is the bond spread, Spread. All the control variables are lagged by one period. Appendix A presents the definitions of variables. Robust t-statistics are reported in parentheses. ***, **, and * correspond to statistical significance at the 1%, 5%, and 10% levels, respectively.

4.1. Risk of Expropriation Different from the case of the US, the institutional feature, together with weak legal or regulatory protections for minority shareholders, makes the Chinese listed firms highly conducive for tunneling activities (Jiang et al., 2010; Peng et al., 2011). We conjecture that the higher the expropriation risk is, the higher the bond spread of family firms will be. To be specific, we measure the risk of expropriation from the perspectives of related party transactions and other receivables respectively. First, we use related party transactions, RPT, as a proxy for expropriation risk. Related party transactions are conducted with other parties with which an entity has a close association, such as its parent company, subsidiaries, affiliates, any firms that hold substantial shares of the listed firm, shareholders' affiliates, and shareholders' relatives (e.g. spouses, parents, step-parents, brothers/ sisters, step-brothers/sisters, and in-laws), etc. Prior empirical evidence has shown that controlling shareholders may take advantage of other stakeholders through related party transactions, especially in emerging markets where legal protection of investors is weak (Cheung et al., 2006; Jian and Wong, 2010; Peng et al., 2011). Hence, the related party transaction, RPT, could act as a good proxy for expropriation risk. RPT is calculated as the total amount of the firm's related party transactions scaled by total sales of the year. Greater RPT indicates a higher incentive to expropriation. We examine the relation of family control, RPT and the bond spread as follow:

Spreadi, t

1

=

+ Familyi, t

1

+ Familyi, t

1

RPTi, t

1

+ RPTi, t

1

+

Controlsi, t

1

+ Fixed Effects +

i, t

(4)

In Eq. (4), all the control variables and fixed effects are the same as in Eq. (1). In Eq. (4), if γ is significantly positive, the results will suggest that family firms with a high level of RPT will bear even higher bond spreads than other family firms. Column (1) and Column (3) of Table 6 report the results of regression in Eq. (4). We use the family dummy as the independent variable in Column (1) and family ownership in Column (3). Consistent with our expectation, γ is significantly positive. Thus, family firms with a higher level of related party transactions will bear higher bond spreads. 7

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Table 6 Family control, expropriation risk, and the bond spread. Variables

(1)

Family*RPT Family*OR Family

(2)

OR Size

Family Ownership

0.719*** (3.12)

0.014*** (2.75)

0.059*** (3.84) 0.001** (2.37)

ROE SG Own Board Size Duration Proceeds RMaturity Rating Industry Year N Adj.R2

0.001*** (2.64) 0.004*** (2.74)

0.004*** (2.81) 0.054** (2.29)

−0.013** (−2.26) −0.002*** (−7.22) 0.020*** (12.62) −0.001 (−0.99) 0.015*** (3.40) 0.001 (1.11) −0.004*** (−4.91) −0.002*** (−6.12) −0.000 (−0.52) 0.003*** (3.82) −0.019*** (−13.93) YES YES 5377 0.314

−0.002*** (−7.37) 0.019*** (12.30) −0.001 (−0.99) 0.016*** (3.55) 0.001 (1.08) −0.004*** (−4.88) −0.002*** (−6.14) −0.000 (−0.63) 0.003*** (3.80) −0.018*** (−13.68) YES YES 5377 0.315

Lev

(4)

Family Dummy

0.002*** (3.10) 0.054** (2.29)

RPT

(3)

−0.009 (−1.60) −0.002*** (−7.25) 0.020*** (12.60) −0.001 (−1.00) 0.015*** (3.41) 0.001 (0.67) −0.004*** (−5.16) −0.002*** (−6.12) −0.000 (−0.45) 0.003*** (3.82) −0.019*** (−13.91) YES YES 5377 0.312

−0.002*** (−7.35) 0.019*** (12.23) −0.001 (−1.00) 0.016*** (3.54) 0.001 (0.54) −0.004*** (−5.26) −0.002*** (−6.11) −0.000 (−0.59) 0.003*** (3.80) −0.018*** (−13.71) YES YES 5377 0.314

This table reports the results of Eq. (4) and Eq. (5). The dependent variable is the bond spread, Spread. Family*RPT is an interaction term of Family Dummy and RPT in Column (1), and an interaction term of Family Ownership and RPT in Column (3). Family*OR is an interaction term of Family Dummy and OR in Column (2), and an interaction term of Family Ownership and OR in Column (4). All the control variables are lagged by one period. Appendix A presents the definitions of variables. Robust t-statistics are reported in parentheses. *** and ** correspond to statistical significance at the 1% and 5%, levels, respectively.

Apart from related party transactions, another common form for controlling shareholders to tunnel is to borrow on company assets or cash, which in the Chinese case, is largely captured by an accounting item “other receivables” (Jiang et al., 2010). Next, we use other receivables, OR, as another proxy for expropriation risk. OR is calculated as the firm's net other receivables scaled by the total asset of the year. The controlling shareholders, usually family members of family firms, will be suspicious if OR is high. Greater OR means higher expropriation risk. We examine the relation of family control, OR and the bond spread as follow:

Spreadi, t

1

=

+ Familyi, t

1

+ Familyi, t

1

ORi, t

1

+ ORi, t

1

+

Controlsi, t

1

+ Fixed Effects +

i, t

(5)

In Eq. (5), all the control variables and fixed effects are the same as in Eq. (1). In Eq. (5), if δ is significantly positive, the results will suggest that high OR leads to high bond spreads of family firms. Column (2) and Column (4) of Table 6 report the results of regression in Eq. (5). We use the Family Dummy as the independent variable in Column (2) and Family Ownership in Column (4). Consistent with our expectation, δ is significantly positive. Thus, family firms with a higher level of other receivables will bear higher bond spreads. Taking these results together, we conclude that family firms with expropriation risk will have higher debt financing costs. Bondholders sense the high expropriation risk through a large number of related party transactions and other receivables in family firms and therefore charge higher premiums to compensate for the risk. 4.2. Financial reporting quality Financial reporting quality is an important influence factor for debt financing costs, as it helps to reduce information asymmetry and protect the interest of debt holders (Chen and Zhu, 2013). In this section, we examine whether financial reporting quality is a key mechanism that affects family firms' debt financing costs. 8

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Following Ahmed and Duellman (2007), Qiang (2007) and Chen and Zhu (2013), we use three-year cumulative accruals to measure the financial reporting quality (FRQ).4 In specific, we use two kinds of measurements for FRQ. First, FRQ1 represents threeyear cumulative accruals multiplied by −1; accruals for each year are equal to earnings minus cash flow from operations, divided by total assets. Second, to get rid of the inference of extraordinary items, we also measure financial reporting quality using earnings before extraordinary items. FRQ2 represents three-year cumulative accruals multiplied by −1; accruals for each year are equal to earnings before extraordinary items minus cash flow from operations, divided by total assets. We examine the relation of family control, FRQ and the bond spread as follow:

Spreadi, t

1

=

+ Familyi, t

1

+ Familyi, t

1

FRQi, t

+ FRQi, t

1

1

+

Controlsi, t

1

+ Fixed Effects +

(6)

i, t

In Eq. (6), all the control variables and fixed effects are the same as in Eq. (1). In Eq. (6), if γ is significantly negative, the results will suggest that family firms with higher FRQ will have lower bond spreads; and those with lower FRQ will suffer from higher bond spreads. Table 7 reports the results of the regression in Eq. (6). Column (1) and (3) presents the results of regressions using FRQ1 as a proxy for financial reporting quality, and Column (2) and (4) using FRQ2. We still use the Family Dummy as the independent variable in Column (1) and (2) and Family Ownership in Column (3) and (4). Consistent with our expectation, γ is significantly negative. Thus, family firms with a higher level of financial reporting quality will bear lower bond spreads. For those with poor financial reporting quality, bondholders will charge higher premiums. 4.3. Regional environment In this section, we discuss the influence of market development and legislation on debt financing costs of family firms. Market is an index measuring the development of the regional market in which the firm is registered, and Legal is an index measuring the development of the market intermediary organization and legislation environment (Fan 2016 Index, Wang et al., 2016). First, we examine whether family firms bear higher debt financing costs to a greater extent in high- or low-marketization provinces. To identify the effect of marketization difference, we add Market and the interaction term, Family*Market, to explore the marginal effect of Market on family firms' bond spread. We examine the relation of family control, Market and the bond spread as follow:

Spreadi, t

1

=

+ Familyi, t

1

+ Familyi, t

1

Marketi, t

1

+ Marketi, t

1

+

Controlsi, t

1

+ Fixed Effects +

i, t

(7)

In Eq. (7), all the control variables and fixed effects are the same as in Eq. (1). The results of regression in Eq. (7) are reported in Column (1) and (2) in Table 8. We control for Market in Column (1) and further add Family*Market into the regression in Column (2). In Column (1), the coefficient of Market is significantly negative (−0.002) at the 1% level, revealing that the market development in the region helps to reduce the agency cost of debt for all listed firms in our sample. In Column (2), the coefficient of Family*Market is significantly negative (−0.003) at 10% level, indicating that the market development has a marginal effect on the cost of debt for family firms. Family firms located in provinces with a high level of marketization will have a lower cost of debt, while those located in less developed regions will bear a higher cost of debt. The results suggest that market development could mitigate creditors' concern and reduce the bond spreads for family firms. Next, we use Legal, which represents the development of market intermediary organization and legislation, to re-run the regressions. The results of regression in Eq. (8) are reported in Column (3) and (4) in Table 8. We control for Legal in Column (3) and further add Family*Legal into the regression in Column (4). The coefficient of Legal is significantly negative, indicating that the development of market intermediary and legislation has a negative effect on the cost of debt. The coefficient of Family*Legal is also significantly negative, suggesting that this effect is more pronounced in family firms. The results show that the development of market intermediary and legislation could mitigate creditors' concern and reduce the bond spread for family firms.

S preadi, t

1

=

+ Familyi, t

1

+ Familyi, t

1

Legali, t

1

+ Legali, t

1

+

Controlsi, t

1

+ Fixed Effects +

i, t

(8)

Taking these findings together, we show that creditors will charge family firms with higher financing costs due to higher expropriation risk and poor financial reporting quality. However, in more developed regions, the market and legislation efficiency could act as a substitute for corporate governance. Meanwhile, creditors will be better protected in more developed regions, and thus exposed to lower risk. Prior studies document that the protection for shareholders and debtholders will influence the debt financing cost (Boubakri and Ghouma, 2010; Ni and Yin, 2018). In our study, we show that bondholders will charge less premium on family firms' bonds if the firms are located in regions with more developed marketization or market intermediary organization and legislation environment, which offer better protection for creditors. In contrast, family firms located in regions with lower protection of debtholders' rights will suffer from even higher debt financing costs.

4 We follow prior studies to multiply the cumulative accruals by −1; thus, the higher this measure, the higher the financial reporting quality (Ahmed and Duellman, 2007; Xia and Zhu, 2009).

9

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Table 7 Family control, financial reporting quality, and the bond spread. Variables

(1)

Family*FRQ1 Family*FRQ2 Family

(2)

Family Dummy

Family Ownership

−0.014*** (−3.91)

−0.026*** (−2.84)

−0.020*** (−2.99) 0.003*** (5.85)

0.002*** (4.40) 0.006*** (2.86)

FRQ1 FRQ2 Size

0.011*** (3.24) −0.002*** (−7.35) 0.020*** (12.84) −0.001 (−1.01) 0.015*** (3.56) 0.001 (1.21) −0.004*** (−4.75) −0.002*** (−5.93) −0.000 (−0.38) 0.003*** (3.65) −0.019*** (−13.77) YES YES 5243 0.310

−0.002*** (−7.48) 0.020*** (12.78) −0.001 (−1.04) 0.015*** (3.38) 0.001 (1.02) −0.004*** (−5.09) −0.002*** (−5.85) −0.000 (−0.33) 0.003*** (3.58) −0.019*** (−13.71) YES YES 5246 0.312

Lev ROE SG Own Board Size Duration Proceeds RMaturity Rating Industry Year N Adj.R2

(3)

0.004*** (3.58) 0.005** (2.33) −0.002*** (−7.44) 0.020*** (12.71) −0.001 (−1.04) 0.015*** (3.44) 0.001 (0.59) −0.004*** (−5.33) −0.002*** (−5.89) −0.000 (−0.34) 0.003*** (3.65) −0.019*** (−13.71) YES YES 5246 0.310

(4)

−0.037** (−2.28) 0.006*** (4.91) 0.010*** (2.79) −0.002*** (−7.35) 0.020*** (12.76) −0.001 (−1.01) 0.016*** (3.59) 0.001 (0.71) −0.004*** (−5.04) −0.002*** (−5.93) −0.000 (−0.36) 0.003*** (3.68) −0.019*** (−13.74) YES YES 5243 0.309

This table reports the results of Eq. (6). The dependent variable is the bond spread, Spread. Family*FRQ1 is an interaction term of Family Dummy and FRQ1 in Column (1), and an interaction term of Family Ownership and FRQ1 in Column (3). Family*FRQ2 is an interaction term of Family Dummy and FRQ2 in Column (2), and an interaction term of Family Ownership and FRQ2 in Column (4). All the control variables are lagged by one period. Appendix A presents the definitions of variables. Robust t-statistics are reported in parentheses. *** and ** correspond to statistical significance at the 1% and 5%, levels, respectively.

5. Robustness tests 5.1. Other measurement In our main analysis, we use bond spreads to proxy for the cost of debt and narrow our sample to firms with outstanding bonds. However, there are still a large proportion of firms rely on bank loans, but not on bonds, for debt financing source. Thus, we expand our sample to all non-financial A-share listed firms and use interest rates, Interest, as another proxy for the cost of debt to improve robustness. Interest is defined as interest expenses plus capitalized interest expense, divided by total liabilities. Specifically, the regression is established as follows:

Interesti, t =

+ Familyi, t

1

+

Controlsi, t

1

+ Fixed Effects +

i, t

(9)

In Eq. (9), the control variables only include firm-level characteristics. Besides, we control for both industry and year fixed effects. Apart from the full sample regressions, we also use the 1-on-1 PSM-matched sample to re-run Eq. (9). We require the matched nonfamily firms to have the closest Size, ROE, and Lev, and be in the same industry as family firms every year. Table 9 reports the results using the full sample (in Column (1) and (3)) and PSM sample (in Column (2) and (4)). We use the Family Dummy as the independent variable in Column (1) and (2) and Family Ownership in Column (3) and (4). Our results are consistent. The coefficients of Family are all significantly positive when we expand our sample to all non-financial listed firms and use Interest to proxy for the cost of debt. Table 9 shows that our baseline results are robust and not biased. A positive relationship exists between family firms and the cost of debt when we use either bond spread or interest rate to proxy for the financing cost. Creditors will charge family firms higher costs of debt compared to non-family firms in China. 10

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Table 8 Family control, market environment, and the bond spread. Variables

(1)

(2)

(3)

(4)

Family

0.003*** (6.48) −0.002*** (−3.10)

0.014** (2.27) −0.002** (−2.20) −0.003* (−1.79)

0.003*** (6.61)

0.013** (2.25)

−0.003*** (−3.98)

−0.002*** (−3.01) −0.003* (−1.72) −0.002*** (−7.18) 0.018*** (10.48) −0.001 (−0.96) 0.016*** (3.32) −0.001 (−1.22) −0.004*** (−5.10) −0.002*** (−5.67) −0.000 (−1.04) 0.002*** (3.42) −0.018*** (−13.47) YES YES 5377 0.348

Market Family*Market Legal Family*Legal Size

−0.002*** (−7.25) 0.018*** (10.49) −0.001 (−0.96) 0.016*** (3.37) −0.001 (−1.18) −0.004*** (−5.07) −0.002*** (−5.66) −0.000 (−1.08) 0.002*** (3.42) −0.018*** (−13.38) YES YES 5377 0.347

Lev ROE SG Own Board Size Duration Proceeds RMaturity Rating Industry Year N Adj.R2

−0.002*** (−7.34) 0.018*** (10.55) −0.001 (−0.96) 0.016*** (3.36) −0.001 (−1.26) −0.004*** (−5.04) −0.002*** (−5.67) −0.000 (−1.09) 0.002*** (3.43) −0.018*** (−13.45) YES YES 5377 0.347

−0.002*** (−7.10) 0.018*** (10.41) −0.001 (−0.97) 0.016*** (3.36) −0.001 (−1.14) −0.004*** (−5.16) −0.002*** (−5.67) −0.000 (−1.03) 0.002*** (3.42) −0.018*** (−13.38) YES YES 5377 0.347

This table reports the results of Eq. (7) and Eq. (8). The dependent variable is the bond spread, Spread. Family*Market is an interaction term of Family Dummy and Market. Family*Legal is an interaction term of Family Dummy and Legal. All the control variables are lagged by one period. Appendix A presents the definitions of variables. Robust t-statistics are reported in parentheses. ***, **, and * correspond to statistical significance at the 1%, 5%, and 10% levels, respectively.

5.2. Family firms and debt characteristic In this section, we investigate the debt characteristics of family firms. Our previous results suggest that family firms will be charged with a higher cost of debt because they are facing more severe agency problems and expropriation risk with creditors. However, whether the agency problems between family ownership and creditors will affect the capital structure and debt maturity remain unsolved. Chen et al. (2014) find that family firms in the US have lower debt maturity and higher leverage ratios for expropriation purposes. To testify whether the financing choices of Chinese family firms are the same as those of the US family firms, we run the regressions in Eq. (10) and Eq. (11). First, we aim to investigate the capital structure of Chinese family firms. Specifically, the regression is established as follows:

Levi, t =

+ Familyi, t

1

+

Controlsi, t

1

+ Fi xed Effects +

(10)

i, t

In Eq. (10), the dependent variable is Lev, which is calculated as total liabilities scaled by the total asset. The control variables only include firm-level characteristics. Besides, we control for both industry and year fixed effects. In Table 10, Column (1) and (3) report the results of Eq. (10) using the Family Dummy and Family Ownership as the independent variable respectively. The coefficient of Family is significantly negative, suggesting that family firms on average take on less debt than non-family firms. Next, we pay attention to the maturity of bonds issued by family firms. Specifically, the regression is established as follows:

Maturityi, t =

+ Familyi, t

1

+

Controlsi, t

1

+ Fixed Effects +

i, t

(11)

In Eq. (11), the control variables include both firm-level characteristics and bond-level characteristics. We still control for both industry and year fixed effects. In Table 10, Column (2) and (4) report the results of Eq. (11) using the Family Dummy and Family Ownership as the independent variable respectively. The coefficient of Family is also significantly negative, suggesting that family firms tend to issue bonds with 11

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Table 9 The effect of family control on the interest rate. Variables

(1)

(2)

(3)

Family Dummy

Family Size Lev ROE SG Own Board Size Industry Year N Adj.R2

(4)

Family Ownership

full sample

matched sample

full sample

matched sample

0.002*** (4.87) 0.001*** (3.56) 0.002 (1.05) −0.000 (−1.10) −0.028 (−0.74) −0.011*** (−10.22) −0.001 (−1.02) YES YES 16,908 0.0546

0.002*** (5.05) 0.001*** (5.04) 0.007 (1.04) −0.000*** (−4.14) −2.012*** (−11.82) −0.011*** (−6.73) −0.003** (−2.46) YES YES 8750 0.0488

0.002** (2.08) 0.000*** (3.18) 0.002 (1.02) −0.000 (−1.15) −0.030 (−0.80) −0.011*** (−10.29) −0.001 (−1.33) YES YES 16,908 0.0535

0.002* (1.76) 0.001*** (5.08) 0.007 (1.07) −0.000*** (−4.52) −1.977*** (−11.72) −0.011*** (−6.75) −0.003*** (−2.86) YES YES 8750 0.0463

This table reports the results of sample based on firm-year observations (Eq. (9)). The dependent variable is the interest rate, Interest. The key independent variable is Family Dummy in Column (1) and (2), and Family Ownership in Column (3) and (4). Column (1) and (3) report the regressions based on full sample of all non-financial A-share listed firms during the period of 2009 to 2017. Column (2) and (4) report the regressions for 1-on-1 PSM sample. All the control variables are lagged by one period. Appendix A presents the definitions of variables. Robust t-statistics are reported in parentheses. ***, **, and * correspond to statistical significance at the 1%, 5%, and 10% levels, respectively. Table 10 The effect of family control on capital structure and bond maturity. Variables

(1)

(2)

(3)

Family Dummy

Family Size ROE SG Own Board Size Duration

Family Ownership

Lev

Maturity

Lev

Maturity

−0.010*** (−3.32) −0.005* (−1.81) 0.000 (0.99) 0.094** (2.13) 0.001 (0.10) 0.003 (0.47)

−0.134** (−2.52) −0.063** (−2.11) −0.009 (−0.12) −1.895*** (−3.13) 0.525*** (3.53) 0.135 (1.13) 0.664*** (33.89) 0.299*** (7.11) 0.029 (0.80) YES YES 5377 0.309

−0.032*** (−4.77) −0.006* (−1.88) 0.000 (0.96) 0.083* (1.65) 0.007 (0.56) 0.001 (0.24)

−0.460*** (−3.59) −0.066** (−2.20) −0.009 (−0.11) −1.912*** (−3.14) 0.578*** (3.86) 0.136 (1.15) 0.664*** (33.90) 0.300*** (7.15) 0.023 (0.63) YES YES 5377 0.309

Proceeds Rating Industry Year N Adj.R2

(4)

YES YES 16,908 0.0126

YES YES 16,908 0.0138

This table reports the effect of family control on firms leverage ratio and bond maturity. In Column (1) and (3), the dependent variable is the leverage ratio, Lev, and the regressions are based on full sample of all non-financial A-share listed firms during the period of 2009 to 2017. In Column (2) and (4), the dependent variable is the Maturity, and the regressions are based on bond-year observations. The key independent variable is Family Dummy in Column (1) and (2), and Family Ownership in Column (3) and (4). All the control variables are lagged by one period. Appendix A1 presents the definitions of variables. Robust t-statistics are reported in parentheses. ***, **, and * correspond to statistical significance at the 1%, 5%, and 10% levels, respectively.

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shorter maturity than non-family firms. Overall, the results in Table 10 indicate that family control still exhibits significant effects on maturity and capital structure in China. Our results suggest that family firms in China also tend to issue bonds with shorter maturity. However, different from family firms in the US, family firms in China take on less debt than non-family firms. As family firms in the US prefer debt to equity for expropriation purposes (Chen et al., 2014), our results indicate that family firms in China are facing difficulties in the availability of debt financing. 6. Conclusion We examine the relation between family control and the cost of debt using bond-level dataset in China. We show that the bond spread is significantly higher for family firms compared to non-family firms. For robustness tests, we also expand our sample to all non-financial listed firms and use interest rates as another proxy for the cost of debt. The results are still consistent. Thus, a significantly positive relation exists between family control and the cost of debt. Our results are robust when we implement two endogeneity examination. First, we use a PSM-matched sample to control potential confounding factors. Next, we use the level of industry competition as an instrument variable. The results of PSM-matched sample and 2SLS regressions both show a positive relation between family control and the bond spread. We further provide evidence that the cost of debt for family firms will be even higher when they have larger amount of related party transactions or other receivables, which indicate the incentive to expropriation, and when they have poor-quality financial reporting, which suggests information asymmetry. In addition, the developed market and legislation environment could mitigate creditors' concern and reduce the cost of debt for family firms. Last, we find that family firms in China tend to issue debt with shorter maturity and take on less debt than non-family firms. Our findings complement the existing debate on family ownership. In countries with better creditor protection and institutional environments, family firms are concerned with survival and reputation, and thus have incentive to build long-term relationship with outside investors, mitigating the agency problems (Anderson et al., 2003). However, we argue that in an emerging market like China, with low marketization and poor legislation environment, family firms have more incentive to tunnel rather than build the reputation. Therefore, creditors will charge higher cost of debt for family firms in response to the high credit risks and severe agency problems. Appendix A. Appendix Table A1

Variable definitions. Variable Name

Variable Definition

Spread

The difference between the annual yield to maturity on the firm's outstanding traded bond and yield to maturity on a Treasury bond of comparable maturity. (Interest Expense+ Capitalized Interest Expense)/Total Liabilities. A dummy variable that is equal to one if the bond is issued by a family firm and zero otherwise. A variable that is equal to proportion of founding family members' ownership in the firm if the bond is issued by a family firm and zero otherwise. Logarithm of total asset. Net Profit/Equity. Total Liabilities/Total Asset. Annual sales growth rate for the firm. Shareholdings of the largest shareholder/total shares outstanding. Logarithm of number of people in the board of director. A dummy variable that is equal to one if there is a duality between CEO and Chairman in the firm and zero otherwise. Logarithm of net other receivables. Logarithm of total related party transactions amount. Negative three-year cumulative accruals; For FRQ1, Accruals = (Net Profits-Cash Flow from Operating Activities)/Total Asset; For FRQ2, Accruals = (Net Profits before extraordinary items -Cash Flow from Operating Activities)/Total Asset. Adjusted Bond Duration. Logarithm of bond issue size. Bond credit ratings are computed using a conversion process in which AAA rated bonds are assigned a value of 6 and A- rated bonds receive a value of 1, then take the logarithm. Logarithm of bond residual maturity. Logarithm of bond maturity. The Herfindahl-Hirschman Index (HHI) is measured by firms' revenue. HHI equals to 1 when the market is in complete monopoly, indicating low level of industry competition. Logarithm of the province's ranking of the Comprehensive Marketization Index (Wang et al., 2016). Logarithm of the province's ranking of the Market Intermediary Organization and Legislation Index (Wang et al., 2016).

Interest Family Dummy Family Ownership Size ROE Lev SG Own Board Size Duality OR RPT FRQ Duration Proceeds Rating RMaturity Maturity HHI Market Legal

This table provides construction details for our key variables. The financial data and stock returns data of listed firms are from the China Stock Market and Accounting Research (CSMAR) Database. The bonds' information and transaction data are from Wind and CSMAR. All variables are winsorized at 1% level in each period in regressions with accounting variables.

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