IPO pricing deregulation and corporate governance: Theory and evidence from Chinese public firms

Journal of Banking and Finance 107 (2019) 105606

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IPO pricing deregulation and corporate governance: Theory and evidence from Chinese public firms ✩ Ping He a,∗, Lin Ma b, Kun Wang c, Xing Xiao c a

Department of Finance, School of Economics and Management, Tsinghua University, Beijing 100084, China Department of Investment, School of Finance, University of International Business and Economics, Beijing 100029, China c Department of Accounting, School of Economics and Management, Tsinghua University, Beijing 100084, China b

a r t i c l e

i n f o

Article history: Received 31 December 2014 Accepted 5 August 2019 Available online 8 August 2019 JEL classification: G32 G14 G18 Keywords: Initial public offering Deregulation Corporate governance Pricing efficiency Market discipline

a b s t r a c t The disciplinary role of the financial market could interact with a firm’s choice of internal corporate governance. We prove that when the efficiency of the initial public offering (IPO) pricing improves, entrepreneurs choose stronger corporate governance structures as way of committing to extract fewer private benefits in exchange for higher prices. Using a difference-in-difference method that exploits the asymmetric impacts of the IPO pricing deregulation on the Chinese mainland and Hong Kong markets, we find that improving the efficiency of IPO pricing has a positive impact on a firm’s corporate governance quality. This impact is more pronounced for firms with lower tangibility, for firms with higher market-to-book ratios, and for state-owned enterprises. Our findings demonstrate that the development of the financial market can promote economic development through improving corporate governance. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Corporate governance is often a contested ground among stakeholders, either between shareholders and the top management or among shareholders; governance structures and principles are designed to resolve the possible conflicts of interests between these groups. Many forces can influence the structure and quality of corporate governance, as reviewed by Shleifer and Vishny (1997) or Young et al. (2008).1 In this paper, we propose a view based

✩ An earlier version of the paper was circulated under the title “Beyond Capital Allocation Efficiency.” We would like to thank the seminar participants at The Twelfth NBER-CCER Conference on China and the World Economy, Beijing Five-Star Meeting in Finance, Beijing University of Science and Technology, Renmin University. The authors acknowledge funding from the National Natural Science Foundation of China (Project 71172005, Project 70872055, Project 71372048, Project 70902004, Project 71472101 and Project 71703022). ∗ Corresponding author. E-mail addresses: [email protected] (P. He), [email protected] (L. Ma), [email protected] (K. Wang), [email protected] (X. Xiao). 1 Some authors empirically examined these determinants from the perspective of legal protection (La Porta et al. 20 0 0, 20 02), firm characteristics (Boone et al., 2007; Hutchinson et al., 2015; Lehn et al., 2009; Linck et al., 2008), internal negotiations and trade-offs (Baldenius et al., 2014; Hermalin and Weisbach, 1998; Katolnik et al., 2015; Pathan and Skully, 2010; Raheja, 2005 ), or government intervention and regulation (Becher and Frye, 2011; Chen and Al-Najjar, 2012; Li and Song, 2013).

https://doi.org/10.1016/j.jbankfin.2019.08.004 0378-4266/© 2019 Elsevier B.V. All rights reserved.

on the disciplinary role of the financial market. For shareholders, voting-by-foot represents an alternative way to discipline management or other insiders, which is why governance-sensitive pricing could help shareholders gain some bargaining power in corporategovernance negotiation. Specifically, we examine the impact of improving the efficiency of the initial public offering (IPO) pricing on corporate governance quality, and we make a connection between the development of the financial market and a firm’s internal operations. We first establish a theoretical model to illustrate how IPO pricing efficiency interacts with the quality of corporate governance in a firm. When IPO pricing is efficient, firms with better corporate governance will be priced higher because the shareholders in control are less likely to extract private benefits at the cost of other shareholders. We show that the deregulation of IPO pricing will increase the incentive for an entrepreneur to choose high-quality governance structures as a commitment device to reduce expropriation. This is because, for IPO firms, deregulation increases the sensitivity of the equity price to firm-specific characteristics that are related to corporate governance. In return, firms with high-quality corporate governance will be priced higher. We then empirically test the impact of IPO pricing efficiency on a firm’s choice of corporate governance by utilizing the IPO pricing-deregulation policy from the stock market of China. This

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P. He, L. Ma and K. Wang et al. / Journal of Banking and Finance 107 (2019) 105606

IPO pricing-deregulation policy was enacted in August 2004, and has since changed the IPO pricing mechanism from a governmentcontrolled to a market-oriented method. In the early years after the establishment of the Chinese stock market, the IPO offering price was simply a value of a firm’s earnings per share (EPS) multiplied by the fixed price-to-earnings (P/E) ratio that was predetermined by the China Securities Regulatory Commission (CSRC). During this period, IPO pricing could barely reflect the fundamentals of a specific firm. After the deregulation policy, brokerage firms were required to seek bids from institutional investors in a bookbuilding process and use the negotiated price for final offerings; as such, IPO pricing started reflecting heterogeneous firm-level characteristics to a greater extent. Therefore, in our empirical tests, we define the time period after August 2004 as the deregulated period and employ a difference-in-difference (DID) regression model to mitigate possible endogeneity concerns due to omitted variables. Our sample consists of 3375 Chinese firms (treatment sample) that went public on the Shanghai and Shenzhen stock exchanges from 1992 to 2017 and 710 Chinese mainland-based firms (control sample) that were listed on the Hong Kong stock exchange from 20 0 0 to 2017.2 We use five board characteristics to measure the quality of internal corporate governance, as in practice corporate boards are at the center of all governance mechanisms around the world (Bernile et al., 2018; Fauver et al., 2017; Giannetti and Zhao, 2019; Hauser, 2018). We find evidence that board professionalism improved significantly after deregulation, as indicated by a higher proportion of directors with business backgrounds or industry-related work experience, a higher educational level, and more academic experience. Moreover, we find that deregulation was associated with more board members being socially connected and with boards being more independent, as seen in the percentage of directors unaffiliated with the business groups to which the listed companies belonged. The overall governance measure based on all five board characteristics also improved significantly in the deregulated period. The improvement in corporate governance was both statistically significant and economically meaningful. For example, the overall governance quality of the treatment firms (compared to that of the control firms) increased by, on average, 41.52 percentage points (representing 15% of the mean) after deregulation. We then test the validity of our empirical strategy by checking the key assumption that the efficiency of IPO pricing did indeed improve after deregulation. First, we show that IPO underpricing significantly decreased after the enactment of the IPO deregulation policy, which is consistent with prior studies on China’s IPOs (e.g., Cheung et al., 2009). Second, we demonstrate that the positive correlation between firm value and corporate governance is stronger after deregulation. The empirical results are consistent with our conjectures. We next study the cross-sectional differences in the influence of deregulation on corporate governance. Specifically, we investigate how the impact of IPO pricing efficiency on corporate governance can vary with a firm’s asset tangibility, market-to-book ratio, and state ownership. Firms with more tangible assets have an installed physical capacity for economic shocks; as tangibility can explain part of a firm’s value, these firms are relatively easier to price in the IPO market (Chung et al., 20 05; Cooper, 20 06; Zhang, 20 05). Thus, firm insiders have weaker incentives to build high-quality governance after deregulation. The market-to-book ratio represents a proxy for growth opportunity and future external financing demand. Firms with higher market-to-book ratios are more likely to 2 The dataset includes mainland companies listed on the Hong Kong stock exchange in 20 0 0 at the earliest. Some firms are listed on both the mainland stock exchanges and the Hong Kong stock exchange. We describe how we treated those firms during the DID analysis in Section 4.

raise capital from the stock market in the future; therefore, they have a greater incentive to choose for stronger corporate governance in order to get a higher valuation. Compared with nonstate-owned firms (non-SOEs), state-owned firms (SOEs) are more likely to improve corporate governance after deregulation because the shareholders controlling SOEs, namely the government agencies, have less incentive for diversion (Chen et al., 2012). As such, they have more incentive to improve corporate governance when a firm’s valuation becomes more sensitive to the quality of corporate governance. Consistent with our conjectures, we find that the effect of deregulation on governance quality is more pronounced for firms with fewer tangible assets, for firms with higher marketto-book ratios, and for SOEs. Finally, we conduct a series of robustness tests. First, we reexamine our main hypothesis with a matched sample based on the Propensity Score Matching (PSM) method to control for the difference between the treatment and control samples. Second, we remove the data from before 20 0 0 (i.e., the earliest year our dataset included mainland companies listed on the Hong Kong stock exchange) as well as the data for the samples coming from financial sectors. The major results remain robust. In addition, we test the validity of the no pre-trend assumption of our DID method. The results demonstrate that the full policy impact occurred one to two years after the IPO deregulation and that the impact remained large and significant throughout the 2010s. Our study makes several contributions to the existing literature. First, our study builds a link between financial market development and the corporate governance of firms. Relatedly, some studies suggest firms will operate more efficiently in order to obtain resources from the capital market (Fan et al., 2011; Morck et al., 2005). Other studies document the effect of capital market development on stock pricing efficiency and on the management of earnings (Beatty and Kadiyala, 2003; Cheung et al., 2009; Ekkayokkaya and Pengniti, 2012; Lau, 2004; Ma and Faff, 2007; Tian, 2011). Our work complements these studies by showing that financial market development in the context of IPO pricing efficiency could play an important role in improving a firm’s corporate governance quality. Furthermore, the results demonstrate that the development of the financial market can enhance economic growth not only through more efficient capital allocation but also through its disciplinary role (e.g., Guisoet al., 2004; Pang and Wu, 2009; Rajan and Zingales, 1998). Second, the paper contributes to the corporate governance literature by identifying a significant determinant of corporate governance. Despite several studies showing how corporate governance can affect a firm’s behaviors such as their external financing, their cost of capital, or their operating performance (Becht et al., 2003; Claessens and Fan, 2002; Shleifer and Vishny, 1997), there is limited evidence on the determinants of corporate governance. We have proposed that the choice of corporate governance is endogenously determined as part of an IPO decision and have illustrated both theoretically and empirically that the efficiency of IPO pricing has a significant impact on a firm’s corporate governance quality. The remainder of this paper is organized as follows. Section 2 presents the theoretical analysis and the development of the empirical hypothesis. Section 3 describes the institutional background of the IPO pricing deregulation in China. Section 4 describes the data and the variables’ construction. The empirical model and results are presented and discussed in Section 5. Section 6 concludes this paper.

2. IPO model and hypotheses development We construct a simple model to analyze a firm’s decision regarding corporate governance before going public. There are two

P. He, L. Ma and K. Wang et al. / Journal of Banking and Finance 107 (2019) 105606

types of players, namely the controlling shareholder (i.e., an entrepreneur) and a series of outside investors. In making the decision to go public, the entrepreneur must choose a governance structure for the company; the quality of governance is denoted by b, with 0 < b < b¯ . When the value of b is higher, the governance structure is stronger. When the entrepreneur chooses for a stronger governance structure (i.e., a higher value of b), the entrepreneur finds it more difficult to extract private benefits from the firm. We call the behavior of extracting private benefits diversion; therefore we can also interpret b as the cost of such diversion. By choosing a stronger governance structure, the entrepreneur will and credibly commits to conduct less diversion. However, a stronger governance structure costs more and the firm value before the diversion, as denoted by V(b), represents a decreasing function of b. We also assume that the firm value is concave in b or, equivalently, that the cost of governance is convex in b. Moreover, we assume that V  (0 ) = 0, and V  (b¯ ) = −∞. Investors will bid competitively for IPO shares based on their expectations about the level of diversion, denoted as , which is in turn affected by the governance structure chosen before the firm going public. Thus, we can calculate the firm market value (in expectation of a diversion) as V (b) − . We assume that the IPO firm will need to raise I units of capital for the new investment and that the entrepreneur will retain the αe = 1 − I/(V (b) − ) fraction of the firm, where the subscript e represents the entrepreneur.3 After the firm goes public, given the retained fraction of the firm, the entrepreneur will reap a private benefit from the firm, as denoted by . Let G() be the private value of  to the entrepreneur with a quadratic form for tractability, G() = γ γ 2 2 2 0 − 2 (0 − ) , which is increasing and concave in , for any  < 0 , with 0 being a positive constant. We can now compute the payoff to the entrepreneur as follows:

Ve = [V (b) − ]αe − b +

γ 2

20 −

γ 2

(0 − )2

(1)

Now we define a sequential equilibrium for this game between the entrepreneur and the investors. Definition. In a sequential equilibrium, (i) the entrepreneur chooses an optimal governance structure which maximizes his expected payoff; (ii) the investors bid competitively, and the bidding price is equal to the firm’s value and conditional both on the firm’s governance structure and on the investors’ expected diversion level as chosen by the entrepreneur; and (iii) the entrepreneur chooses a diversion level based both on his choice of the firm’s governance structure and on the quantity of retained shares to maximizes his payoff. We use backward induction to solve this game. We first solve for the equilibrium diversion level . We aim to show that, when both 0 and the firm value V(b) are sufficiently large, the equilibrium diversion level  has the following expression:

=

V (b) + 0 − (1 + b)/γ −



[V (b) − 0 + (1 + b)/γ ] − 4I/γ 2 2

(2) At the beginning of the game, the entrepreneur chooses a governance structure associated with the cost of diversion b in order to maximize his own benefit Ve , as defined in Eq. (1). The governance structure chosen by the entrepreneur can be interpreted as 3 Here, a higher IPO price leads to a larger fraction of equity retained by the entrepreneur after the IPO. In reality, favorable market reactions could also benefit the entrepreneur in a different way, for example, if the firm has refinancing needs or if the entrepreneur needs to reduce his shareholdings in the secondary market.

3

a commitment device. The following theorem proves the existence of an optimal governance structure. Theorem 1. When the investment level is sufficiently large, an optimal governance structure exists under which the entrepreneur’s payoff is maximized. Proof. See Appendix B.  In our model, building a stronger governance structure implies a higher cost of diversion, which can be interpreted as the entrepreneur having weaker control. Therefore, there is a trade-off between control rights and cash-flow rights. However, as there is a misalignment between the firm’s value and the entrepreneur’s payoff, the entrepreneur will likely not choose a governance structure that maximizes the market value of the firm. Theorem 2 confirms this conjecture. Theorem 2. Given that the entrepreneur chooses an optimal governance structure to maximize his own payoff, if a stronger governance structure were to be chosen, the diversion level would decrease, and the market value of the firm would increase. Proof. See Appendix B.  Theorem 2 states that the value of a firm can be further improved by choosing a stronger governance structure. However, the entrepreneur will not choose a stronger governance structure because he or she receives all the private benefits from diversion despite owning only a fraction of the firm. The above analysis relies on the assumption of market efficiency. In the case of mainland China, market efficiency represents a strong assumption, because the development of the financial market is still in its early stages. We slightly extend the pricing model to incorporate market-inefficiency. We assume that the IPO price is defined as follows:

p = (V (b) − )β + (1 − β ) p0

(3)

in which p0 is a positive constant. The interpretation of the above assumption is that β measures the market efficiency of pricing. If the market is fully efficient at pricing, then β = 1 and p = V (b) − . In addition, if the market is inefficient, then β = 0 and p = p0 , which is in turn not affected by the entrepreneur’s decision on governance or its impact on diversion.4 We can solve for the equilibrium diversion :

=

b) V + 0 + (1−ββ ) p0 − (1+ γ −



2 4 I V − 0 + (1−ββ ) p0 + (1 + b)/γ − βγ 2 (4)

Finally, we hypothesized that, if the market becomes more efficient, then the entrepreneur will choose a stronger governance structure for the firm. In order to test this, we check the effect of market efficiency on the optimal governance structure of the firm. Theorem 3. When the market is with poor pricing efficiency, if there is an increase in the degree of market efficiency, then the entrepreneur will choose a stronger governance structure, and the firm’s market value will also increase. Proof. See Appendix B.  Therefore, we conclude that, in an inefficient financial market, an improvement in market efficiency provides incentives for the entrepreneur to choose a stronger governance structure; this choice can improve the operating efficiency of the firm as well as 4 Before 1999, a fixed P/E ratio for the offering price was used for all IPOs in China. This ratio corresponds to a constant p0 in our model.

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P. He, L. Ma and K. Wang et al. / Journal of Banking and Finance 107 (2019) 105606

its market value. The rapidly changing Chinese financial market offers a perfect natural experiment to examine this issue; in particular, many efficiency-related regulatory changes have taken place, thus making our empirical tests possible. Based on our theoretical analysis, we aim to test the following empirical hypothesis: Hypothesis. As IPO pricing becomes more efficient, IPO firms will choose a stronger governance structure. 3. Institutional background of the Chinese IPO market China’s mainland stock market was a vital part of the economic reform of 1978 and has since undergone much development. The Shanghai and Shenzhen stock exchanges were established in 1990 and 1991, respectively. The offering and listing procedures distinguish the IPO market of China from the markets of other countries. A key feature of the Chinese market is that, in the early stages of the stock market development, the IPO pricing procedure used to be highly regulated; since then, there has been a gradual process of deregulation. When the stock market was first established in the early 1990s, the Chinese government played a dominant role in nearly all major aspects of the IPO process in order to prevent speculative behavior. Before March 2001, the China Securities Regulatory Commission (CSRC) controlled the number of shares issued in the stock market each year, which was also referred to as the quota system. Companies had to obtain quotas from local governments or central industry-supervisory commissions before the launch of their IPOs. In early stage of the development of stock market, IPO pricing was rigorously controlled by the government and barely reflected a firm’s fundamentals. The consequences of such a highly regulated IPO pricing system included great underpricing (Cheung et al., 2009), an extensive management of earnings to inflate the IPO price (Shen et al., 2014) and, most importantly, poor corporate governance. These past experiences are in line with the predictions of our model. Starting from the late 1990s, the CSRC initiated several reforms of the IPO pricing policies. By transforming the IPO market from a highly regulated one into a market-oriented one, the CSRC aimed to solve the problems associated with a highly regulated mechanism of pricing. Originally, the price of an IPO was defined as the product between the earnings per share (EPS) and the P/E ratio, the latter of which was greatly influenced by the CSRC.5 Because the number of IPO applications was small until 1996, the CSRC determined the P/E ratio on a case-by-case basis. Since 1996, however, the number of IPO applications has increased so dramatically, that a case-bycase determination of P/E ratios became infeasible. Beginning in January 1996, the CSRC began to use, allowing for some slight adjustments, a standardized P/E ratio of 15 for all firms, regardless of a firm’s specific characteristics. Consequently, the P/E ratio used in IPO pricing varied only a little from this standard level, leading to a low variance in IPO pricing which could not reflect firm-specific characteristics. Aware of this problem, the CSRC enacted a number of reforms related to IPO pricing.6

5 The definition of EPS used for IPO pricing has also evolved over the years. For example, from January 1, 1996 to December 25, 1996, EPS was defined as the simple average of the forecasted EPS for the IPO year and the realized EPS for the fiscal year immediately preceding the IPO year; it was afterwards redefined as the average of the three-year pre-IPO realized EPS, and this definition was maintained from December 26, 1996 to March 16, 1998. 6 In June 1999, the CSRC introduced a cumulative auction method for IPO pricing. Under this method, underwriters set a price range and sought investor bids within that range, similar to the method used in the developed markets. However, the obvious overpricing that occurred suggested that a fully market-based pricing mechanism was premature in China because the participants in the stock market

In August 2004, the CSRC significantly modified the IPO pricing mechanism by introducing a P/E ratio which was based on cumulative price inquiries from institutional investors. Its goal was to reduce government intervention in IPO pricing and to shift the pricing power to institutional investors, as opposed to the lesssophisticated individual investors. This approach was appropriate in the context of the rapid development of institutional investors in China since 1999.7 Furthermore, the CSRC had removed the upper limit on the P/E ratio for the IPO. Therefore, underwriters could now seek bids from institutional investors and use the final negotiated price in the retail offering. The IPO pricing procedure has been transformed from the regulated mechanism before August 2004 to the market-oriented (i.e., deregulated) mechanism thereafter. In contrast to its initial stage, the IPO pricing of the deregulated stage corresponds more related to a firm’s fundamentals, particularly due to the involvement of institutional investors. Regarding the choice of governance structure, in a typical IPO process, the common practice is to carve out one or more divisions or business units from an existing company in order to form a new economic entity, which is then publicly listed in the stock market (Kao et al., 2009; Xu and Wang, 1999). During this process, the controlling shareholder determines the quantities of assets to be carved out in order to form a listed company, as well as the ownership structure. This is based on the number of tradable shares allocated to the company. At the same time, the board structure is formed and the board members are chosen, the latter taking place largely at the discretion of the controlling shareholder. In general, tradable shares owned by institutional and individual investors are a small fraction of the total shares, while nontradable shares owned by the controlling shareholders and other legal entities represent the majority (Hovey and Naughton, 2007). 4. Sample and variables’ definition We obtain all financial and corporate governance data from the China Stock Market and Accounting Research (CSMAR) and the WIND databases. Our sampling period ranges from 1992 to 2017. We create a dummy variable, DEREG, which equals one after August 2004 (i.e., the deregulation time point) and zero otherwise. Our treatment sample comprises all IPOs listed on the Shanghai and Shenzhen Stock Exchanges from 1992 until the end of 2017, excluding those firms which first went public on the Hong Kong market and were only subsequently listed on the mainland market. The control group sample comprises the Chinese mainland-based companies listed on the Hong Kong Stock Exchange from 20 0 0 to 2017, excluding those firms which first went public on the mainland market and were only subsequently listed on the Hong Kong market. Since all the chosen firms from the two markets are mainlandoperated, they share a similar operating environment, culture, and corporate governance environment. They also change and grow in a similar manner, as based on the economic growth experienced were not yet sufficiently sophisticated. To cool the overheated IPO market which had at that point reached excessively high P/E ratios, the CSRC placed an upper limit on the P/E ratio in September 2001. The upper limit applied to all firms and ignored differences between industries or other fundamentals. On the one hand, this method may have resulted in inefficient pricing for firms that would have otherwise achieved a P/E ratio higher than the upper bound. On the other hand, the upper limit of the P/E ratio reduced the inefficiency which had resulted from the overpricing of the prior period. 7 According to the China Securities Depository and Clearing Corporation Statistical Yearbook and the Wind dataset, China had only 24,500 institutional investors in the mainland market at the end of 1993; at the end of 1998, it had 109,800 such that the increase was on average only 17,0 0 0 every year. However, at the end of 1999, the same market had 161,300 institutional investors, with about 968,900 institutional investors at the end of 2017. The increase between 1999 and 2017 was of approximately 45,216 per year.

P. He, L. Ma and K. Wang et al. / Journal of Banking and Finance 107 (2019) 105606 Table 1 Descriptive statistics for the P/E ratio of the IPO in the stock market from mainland China and Hong Kong. This table presents the summary statistics of the P/E ratios for IPOs in different regulatory stages. We divide the whole sampling period into two regulatory stages of IPO pricing. In the first stage, the IPO pricing is highly regulated (from 1992 to July 2004). In the second stage, the IPO pricing is market-oriented; in this latter case, cumulative price inquiry from institutional investors was used, allowing underwriters to seek bids from institutional investors. The final negotiated price was used for the retail offering (from August 2004 until the end of 2017). The data for Hshare companies are only available from the year 20 0 0. Panel A: A-share companies No. of Observations

Mean

Median

Min

Max

Regulated stage: From 1992 to July 2004 1046 18.32 16.23 5.56 71.45 Deregulated stage: From August 2004 to the end of 2017 2097 25.80 19.15 3.28 111.79 Overall: From 1992 to the end of 2017 3145 23.31 17.49 3.28 111.79 Panel B: H-share companies No. of Observations Mean

Median

Min

Max

Regulated stage: From 2000 to July 2004 117 36.81 14.78 5.72 233.52 Deregulated stage: From August 2004 to the end of 2017 561 22.47 16.52 5.89 74.74 Overall: From 2000 to the end of 2017 678 24.94 16.45 5.72 233.52

STD 7.51 14.27 12.92

STD 56.55 17.92 29.03

by China. However, they were listed in different stock exchanges. The firms listed in the mainland market have experienced a major change in their IPO pricing policy since 2004, while the firms listed in the Hong Kong market have not been affected by this institutional change. For simplicity, we call the firms in the treatment group A-share companies and we call the firms in the control group H-share companies. The sample is comprised of 3484 A-share companies and 1021 H-share companies.8 Table 1 provides descriptive statistics on the P/E ratio of IPOs for the mainland stock market and the Hong Kong stock market in the regulated and deregulated stages, respectively. As shown in Table 1 Panel A, over the entire sampling period of the mainland markets, the mean and median of the P/E ratios used in IPO pricing were 23.31 and 17.49, respectively, with a minimum value of 3.28 and a maximum value of 111.79. When we split the sampling period into two regulatory stages, the average P/E ratio increased from 18.32 in the regulated stage to 25.80 in the deregulated stage, consistent with results from prior studies (e.g., Cheung et al., 2009). In addition, the standard deviation of the P/E ratio in the deregulated stage was much higher than that in the regulated stage (14.27 as compared to 7.51). This variation supports our conjecture that the P/E ratio could be more sensitive to heterogeneous firm-level fundamentals for the A-share companies when it is determined by market mechanisms. In Panel B of Table 1, we provide summary statistics for the P/E ratio in the Hong Kong market during the same time period. In contrast to the mainland market, the median of the P/E ratio in the Hong Kong market before and after August 2004 does not differ significantly, suggesting that the pricing regulation enacted in the mainland market did not affect the H-share companies. To measure corporate governance and based on prior studies (e.g., Fan et al., 2007), we hand-collect board characteristics in professionalism, capability and independence from IPO prospectuses. In particular, we include three proxy variables for board 8 The sample sizes were calculated based on our definition of the treatment and control groups. The sample sizes in the tables from the appendix may be smaller than the full sample size because data for some variables are missing.

5

professionalism, as follows: (i) The PROF variable is calculated as the proportion of board directors with accounting, law, finance, or industry-related working experience obtained in firms other than the listed company. The directors’ knowledge as gained through relevant work experience can increase both the efficacy and the vigilance of a board (Faleye et al., 2018; Kroll et al., 2008; Wang et al., 2015); (ii) EDU represents the average educational level of all board directors, with five levels—high school or below, junior college, bachelor’s degree, master’s degree, and doctoral degree— which are coded as zero, one, two, three, or four, respectively; (iii) ACAD represents the proportion of directors who have academic experience, for example by working or having worked as professors or researchers. Directors with an academic background are generally expected to possess systematic knowledge of one industry; thus, they can provide more effective advice to a firm.9 Consistent with prior literature, we assume that professionallyexperienced, better educated, and academia-related directors could play a stronger monitoring role while also providing superior advice to the listed companies (Berger et al., 2014; Fan et al., 2007; Wincent et al., 2010). We also use a proxy to measure how socially connected a board of directors is. (iv) COMM is calculated as the proportion of directors who have worked as a government officer, as a leader in the Communist Party, or in public institutions or industrial associations. In addition to technical and professional ability, community support or the ability to create and maintain prestigious social connections can help the firm absorb additional resources or subsidies, which are both also important in China. We also include a board characteristic representing independence. (v) UNAFF is the proportion of directors who are not affiliated with the business group to which the listed company belongs (i.e., unaffiliated directors). Affiliated firms include the largest corporate shareholders and their subsidiaries, other corporate block shareholders and their related corporate parties, and the subsidiaries of the listed firms. Finally, (vi) we construct a composite corporate governance index, denoted as INDEX, which is calculated as the equally weighted average of all five proxies normalized using their standard deviation. Table 2 reports descriptive statistics for the selected board characteristics of the A-share and H-share companies. As Panel A shows, all six measures of corporate governance quality for the Ashare companies significantly improved after deregulation. For example, the mean (median) of EDU increased from 1.64 (1.60) to 2.42 (2.44). These descriptive statistics are consistent with our expectation that firms which went public in the deregulated stage had better corporate governance. In comparison, Panel B shows that the values of the board characteristics of mainland companies listed in Hong Kong barely changed, even experiencing a slight decrease after the enactment of the deregulation policy; the mean (median) of INDEX was 3.08 (3.16) in the regulated stage, while the mean (median) was 3.08 (3.05) in the deregulated stage; this change was not statistically significant. 5. Empirical evidence 5.1. Effects of deregulation on governance structure We first investigate the impact of deregulation on corporate governance quality with the following regression model, control9 Some researchers found that academic directors were more likely to be independent (Arioglu, 2014) and that firms with such directors had a better overall performance (Francis et al., 2015; Jiang and Murphy, 2007) or a better CRS performance (Cho et al., 2017; Owen, 2005). They attributed their findings to professors having high standards when it comes to social responsibility. In our setting, we mainly focus on directors’ professional backgrounds; however, we do not eliminate the alternative explanation in terms of having higher moral standards.

6

P. He, L. Ma and K. Wang et al. / Journal of Banking and Finance 107 (2019) 105606 Table 2 Descriptive statistics for the board characteristics. This table presents the number of observations, means, and medians for the board characteristics at the moment of the IPO. The definitions of the variables are in Appendix A. These observations refer to Chinese A-share companies between 1992 and 2017, and to mainland-based Hshare companies between 20 0 0 and 2017. Panel A contains the descriptive statistics for the board characteristics of the A-share companies, while Panel B contains the descriptive statistics for the board characteristics of the mainland-based H-share companies. Panel A: A-share companies PROF

EDU

ACAD

COMM

UNAFF

Regulated stage: From 1992 to July 2004 Obs. 1192 1175 1192 1192 1192 Mean 0.62 1.64 0.11 0.47 0.22 Median 0.64 1.60 0.00 0.44 0.18 Deregulated stage: From August 2004 to the end of 2017 N 2104 2039 2104 2104 2104 Mean 0.68 2.42 0.33 0.53 0.45 Median 0.67 2.44 0.33 0.56 0.43 Overall: From 1992 to the end of 2017 N 3298 3216 3298 3298 3298 Mean 0.66 2.14 0.25 0.51 0.37 Median 0.67 2.18 0.22 0.55 0.37 Panel B: H-share companies PROF EDU ACAD

COMM

UNAFF

Regulated stage: From 2000 to July 2004 Obs. 83 83 83 83 83 Mean 0.71 2.57 0.28 0.52 0.53 Median 0.71 2.57 0.25 0.50 0.44 Deregulated stage: From August 2004 to the end of 2017 N 579 576 579 579 579 Mean 0.69 2.56 0.34 0.51 0.48 Median 0.67 2.60 0.33 0.50 0.43 Overall: From 2000 to the end of 2017 N 662 659 662 662 662 Mean 0.69 2.56 0.33 0.51 0.49 Median 0.67 2.60 0.33 0.50 0.43

INDEX 1175 1.82 1.77 2039 2.63 2.62 3216 2.33 2.36

INDEX 83 3.08 3.16 576 3.08 3.05 659 3.08 3.06

ling for other factors which could potentially affect board characteristics.

C Gi =

α0 + α1 ASHAREi + α2 DE RE Gt + β1 (ASHAREi · DE RE Gt ) + γ1 Xi + κt + f j + εi (5)

Here, the dependent variable CGi represents the corporate governance quality of firm i, the variable ASHAREi is an indicator variable which equals one for any firm i listed on the mainland market (treatment sample) and zero for any firm listed on the Hong Kong stock market (control sample). DEREGt represents an indicator variable which equals one for the post-deregulation period (from August 2004 to the end of 2017) and zero for the pre-deregulation period (from 1992 to July 2004). Xi represents any control variables that might affect the firm’s corporate governance quality, and these variables were chosen based on prior studies on corporate governance (Boone et al., 2007; Booth et al., 2002; Cicero et al., 2013; Germain et al., 2014; Lehn et al., 2009; Linck et al., 2008). Among these control variables, at firm level, we include the natural logarithm of total assets (SIZE) in the IPO year, since larger companies tend to have better corporate governance than small firms; financial leverage (LEV) calculated as the total liability in the IPO year divided by the total assets in the same year; the fraction of tradable A-shares and/or H-shares for a listed firm (TDSH); the firms’ age (AGE), which could affect the monitoring complexity, thus affecting a firm’s corporate governance (Boone et al., 2007; Eisenberg et al., 1998); and the amount of capital raised in the IPOs normalized by the value of the total assets (CAP). Since the firms appearing in the stock exchanges of mainland China could choose to be listed on different boards, we control for the fixed effect of the board with the BOARD variable, which equals one if the IPO

Table 3 Descriptive statistics for the control variables. This table presents the number of observations, means, medians, and the 25% and 75% quantile values of firm- or market-characteristics for all mainland-based firms listed between 1992 and 2017 in the two mainland stock exchanges or in the Hong Kong stock exchange. The definitions of the variables are in Appendix A. Variable

N

Mean

Median

STD

p25

p75

SIZE LEV TDSH MSYN AGE IECD CAP BOARD BULL TYPE GUID TANG MB STATE

3401 3401 3401 3401 3401 3401 3401 3401 3401 3401 3401 3401 3401 3401

20.87 0.35 0.80 0.72 8.56 0.71 0.47 0.53 0.46 0.31 0.31 0.13 2.86 0.24

20.70 0.33 1.00 0.73 7.00 1.00 0.41 1.00 0.00 0.00 0.00 0.09 2.55 0.00

1.27 0.21 0.32 0.05 6.27 0.45 0.32 0.50 0.50 0.46 0.46 0.14 2.68 0.43

20.14 0.17 0.39 0.70 3.00 0.00 0.28 0.00 0.00 0.00 0.00 0.01 1.91 0.00

21.37 0.52 1.00 0.77 13.00 1.00 0.60 1.00 1.00 1.00 1.00 0.20 3.22 0.00

firm is listed on the main board and zero otherwise. We also control for the issuing method with the TYPE variable, which equals one if the issuing method is rationing and zero otherwise. We also control for market conditions. We include the synchronicity of the stock market (MSYN) to control for the impacts of the secondary market pricing efficiency, as constructed by Morck et al. (20 0 0).10 In addition, we control for BULL, a dummy variable equaling one if the market is in the bull period and is thus characterized by periods of increasing prices, and zero if the market is in the bear period and is thus characterized by periods of decreasing prices.11 In May 2006, the CSRC established the Issuance Examination Committee (IEC). As this development took place during out sampling period, it could have had an influence on a firm’s governance choice. To control for it, we include a dummy variable (IECD) which equals one after May 2006 and zero otherwise. Starting in January 2014, the CSRC introduced a non-compulsory standard for the P/E ratio, which suggested the P/E ratio of an IPO should be no greater than twenty-three or no greater than the industry average. Because of this, we include a dummy variable (GUID) which equals one if the year is after 2013 and zero otherwise. Lastly, our regressions include two-digit fixed effects for the industry (fj ) and for the year (κ t ).12 The data for the control variables are obtained from the CSMAR and the WIND databases. Table 3 provides descriptive statistics for the control variables used in our DID analyses. The mean (median) of financial lever-

10 By including the MSYN variable in the regression, we control for the effects of the secondary-market efficiency on a firm’s corporate governance decisions, as firms with future financing needs may also be concerned with the secondary-market efficiency. In accordance with Morck et al. (20 0 0) and for all included firms, we calculate the number of stocks whose prices rose and of stocks whose prices decreased during each week. Using the larger of the two numbers divided by their sum, we obtain a ratio for each week in a given year. The MSYN in a given year is calculated as the average ratio for all weeks during that specific year. We calculate the MSYN for the mainland and for the Hong Kong markets separately. 11 We compute the BULL variable for the mainland and the Hong Kong markets separately. We use the method of Pagan and Sossounov (2003) and utilize the closing index of the A-share and H-share markets per each month to identify the bull and the bear markets. Because China’s capital market has a relatively short history and the stock prices fluctuate greatly, we use a window length of three months instead of eight months when choosing the local peaks and troughs. We also control for the issuing method, which could affect the accessibility of individual or institutional investors to IPO shares, and which could also affect the ownership structure and thus the board composition of IPO firms. Since most of the IPO shares are issued through subscription and rationing, two methods which are highly correlated in our sample, we only control for whether a firm is issued through rationing. 12 Year fixed effects do not include the year of deregulation.

P. He, L. Ma and K. Wang et al. / Journal of Banking and Finance 107 (2019) 105606

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Table 4 The effect of the deregulation of IPO pricing on the board composition of firms when using the DID method. The alternative dependent variables in this table are PROF, EDU, ACAD, COMM, UNAFF, and INDEX. The definitions of the variables are in Appendix A. T-statistics are reported in parentheses. ∗ ∗ ∗ , ∗ ∗ and ∗ denote significance at the 0.01, 0.05, and 0.1 levels, respectively. All of the regressions used robust standard errors.

DEREG ASHARE ASHARE∗ DEREG SIZE LEV TDSH MSYN AGE IECD CAP BOARD BULL TYPE GUID Constant Industry Year Observations Adjusted R2

PROF (I)

EDU (II)

ACAD (III)

COMM (IV)

UNAFF (V)

INDEX (VI)

−0.0124 (−0.37) −0.0256 (−0.78) 0.0396∗ (1.79) −0.0139∗ ∗ ∗ (−5.66) 0.0630∗ ∗ ∗ (4.08) 0.0511∗ ∗ ∗ (3.40) −0.0953 (−0.41) 0.0018∗ ∗ ∗ (3.59) 0.0733∗ (1.81) −0.0243∗ ∗ (−2.30) −0.0076 (−1.13) −0.0135∗ ∗ ∗ (−2.81) 0.0074 (0.76) 0.0053 (0.08) 0.9498∗ ∗ ∗ (5.47) YES YES 3608 0.08

−0.0912 (−0.71) −0.4545∗ ∗ ∗ (−4.09) 0.3084∗ ∗ ∗ (3.93) 0.0825∗ ∗ ∗ (9.37) −0.1517∗ ∗ ∗ (−2.70) 0.0069 (0.12) 0.3176 (0.39) −0.0048∗ ∗ (−2.50) −0.1236 (−0.82) 0.0608 (1.47) −0.0693∗ ∗ ∗ (−2.74) −0.0087 (−0.50) 0.0508 (1.35) 0.1983∗ ∗ ∗ (2.86) 0.4288 (0.76) YES YES 3528 0.41

0.0210 (0.46) −0.1252∗ ∗ (−2.19) 0.0716∗ ∗ (2.22) 0.0085∗ ∗ (2.40) 0.0228 (1.10) 0.0343 (1.50) 0.4737 (1.19) −0.0003 (−0.37) 0.0153 (0.25) 0.0505∗ ∗ ∗ (3.34) −0.0170∗ (−1.77) 0.0025 (0.37) 0.0102 (0.88) −0.0045 (−0.15) −0.1128 (−0.44) YES YES 3608 0.27

0.0123 (0.29) −0.0217 (−0.46) 0.0601∗ (1.89) 0.0139∗ ∗ ∗ (3.84) 0.0561∗ ∗ (2.54) −0.0464∗ (−1.83) 0.2431 (0.72) 0.0010 (1.45) 0.0901 (1.43) 0.0358∗ ∗ (2.37) −0.0141 (−1.54) −0.0019 (−0.28) −0.0019 (−0.14) −0.2398∗ ∗ (−2.49) 0.3481 (1.41) YES YES 3608 0.11

−0.1022∗ (−1.68) −0.2437∗ ∗ ∗ (−3.67) 0.1251∗ ∗ ∗ (2.89) −0.0156∗ ∗ ∗ (−3.59) −0.0255 (−1.05) 0.1145∗ ∗ ∗ (3.73) 0.6002 (1.19) 0.0005 (0.60) 0.1061 (1.27) −0.0480∗ ∗ ∗ (−2.98) −0.0097 (−0.91) −0.0442∗ ∗ ∗ (−5.70) −0.0022 (−0.14) −0.0098 (−0.24) 0.3508 (1.08) YES YES 3608 0.31

−0.1382 (−0.84) −0.6588∗ ∗ ∗ (−3.81) 0.4152∗ ∗ ∗ (3.57) 0.0199∗ (1.71) 0.0846 (1.24) 0.1073 (1.37) 1.3774 (1.13) 0.0023 (1.08) 0.1793 (1.10) 0.0453 (0.86) −0.0754∗ ∗ ∗ (−2.61) −0.0453∗ ∗ (−2.16) 0.0415 (0.94) −0.1804∗ ∗ (−2.00) 1.5798∗ ∗ (1.99) YES YES 3528 0.37

age (LEV) is 35% (33%), with a standard deviation of 21%. On average, approximately 80% of all issued shares in our sample comprise tradable shares (TDSH). The average firm age as measured in the IPO year is 8.56 years. Approximately 53% of firms belong to the main board of the mainland stock market. The synchronicity of the stock market (MSYN) was on average 0.72. Compared with stock markets in the United States and other developed countries, the Chinese mainland stock market has one of the highest synchronicities. On average, approximately 46% of firms issued equities in a bull-market period and 31% of firms issued equities using rationing. Table 4 reports the results of the regression model based on Eq. (5). We can observe that the DID coefficient β 1 is positive and statistically significant, indicating that the corporate governance quality of A-share companies significantly improved after deregulation. The coefficient of the interaction term in column (I) is 0.0396, suggesting that the board professionalism of A-share companies improved on average by 3.96% because of the deregulation of IPO pricing. Thus, the effect we document is both statistically and economically significant. The coefficients of DEREGt are nonsignificant, which means that the difference between the corporate governance from before and after the deregulation is not significant in the full sample. The coefficient of ASHAREi is negative and statistically significant in most of the columns, which means that, in general, the governance quality is better for H-share than for Ashare companies. The signs of the coefficients for the other control variables are also sensible.

5.2. IPO underpricing after deregulation Having established the relationship between deregulation and the quality of corporate governance, we next validate the key assumption that the IPO pricing efficiency improves in the deregulated stage. We first test this assumption by replacing CGi in Eq. (5) with the proxies for IPO pricing efficiency (EFCi ). The new equation is as follows:

EF Ci =

α0 + α1 ASHAREi + α2 DE RE Gt + β1 (ASHAREi · DE RE Gt ) + γ1 Xi + κt + f j + εi (6)

The IPO pricing efficiency of firm i (EFCi ) is measured by the level of underpricing of the IPO, with higher underpricing signifying a lower pricing efficiency. We use four underpricing variables to measure the IPO pricing efficiency as follows: (i) EFC1 represents the difference between the IPO price and the intrinsic value normalized by the IPO price. Here, the intrinsic value is calculated by dividing the firm’s earnings per share by the appropriate P/E ratio, the latter of which represents the average value of comparable firms that have been listed for at least three years; (ii) EFC2 represents the first-day return; (iii) EFC3 represents the difference between the IPO price and the intrinsic value normalized by the IPO price. Here, the intrinsic price is calculated using the Ohlson (1995) model, which is estimated using data from comparable firms which have been listed for at least three years; (iv) EINDEX represents the equally-weighted average value of the three proxies normalized with respect to their standard deviations.

8

P. He, L. Ma and K. Wang et al. / Journal of Banking and Finance 107 (2019) 105606 Table 5 The effects of the deregulation of IPO pricing on the efficiency of IPO pricing. The alternative dependent variables presented in this table are EFC1, EFC2, EFC3, and EINDEX. The definitions of the variables are in Appendix A. T-statistics are reported in parentheses. ∗ ∗ ∗ , ∗ ∗ and ∗ denote significance at the 0.01, 0.05, and 0.1 levels, respectively. All of the regressions used robust standard errors.

DEREG ASHARE ASHARE∗ DEREG SIZE LEV AGE SH1 IPE ISSUVOL CAPITAL MB ROA BULL TYPE Constant Industry Year Observations Adjusted R2

EFC1 (I)

EFC2 (II)

EFC3 (III)

EINDEX (IV)

−0.9324∗ ∗ ∗ (−3.34) 1.4622 (0.91) −0.7246∗ ∗ ∗ (−3.59) 0.0315 (0.41) 0.0137∗ ∗ ∗ (2.84) −0.0016 (−0.28) −0.0012 (−0.70) −0.0306∗ ∗ ∗ (−9.10) −0.0764 (−1.22) −0.1252 (−1.34) −0.0480∗ ∗ ∗ (−2.85) 1.6092∗ ∗ ∗ (2.66) 0.0599 (1.01) 0.0015 (0.01) 5.4512∗ ∗ ∗ (2.95) YES YES 3098 0.38

−0.4503∗ ∗ ∗ (−6.70) 0.3333 (1.07) −0.4266∗ ∗ ∗ (−7.42) 0.0256∗ (1.77) −0.0005 (−0.46) −0.0013 (−1.08) −0.0008∗ ∗ (−2.05) −0.0002 (−0.27) 0.0324∗ ∗ (2.25) −0.1503∗ ∗ ∗ (−8.87) −0.0031 (−1.29) 0.0268 (0.28) −0.0087 (−0.69) −0.0534∗ (−1.68) 3.0683∗ ∗ ∗ (8.03) YES YES 3139 0.60

0.2433∗ (1.86) −3.6157∗ ∗ ∗ (−4.50) −0.8879∗ ∗ ∗ (−8.18) 0.3503∗ ∗ ∗ (9.34) 0.0027∗ (1.66) −0.0043 (−1.44) 0.0019∗ ∗ (2.41) −0.0062∗ ∗ ∗ (−2.98) 0.0977∗ ∗ ∗ (2.67) −0.4574∗ ∗ ∗ (−10.18) −0.0168∗ ∗ ∗ (−3.33) −0.4335∗ (−1.83) 0.0105 (0.39) 0.1100∗ ∗ ∗ (3.34) 3.6556∗ ∗ ∗ (3.89) YES YES 3080 0.75

−0.3754∗ ∗ ∗ (−3.20) −0.6845 (−1.44) −0.4317∗ ∗ ∗ (−5.66) 0.1165∗ ∗ ∗ (5.16) 0.0034∗ ∗ (2.40) −0.0021 (−1.21) −0.0006 (−1.19) −0.0078∗ ∗ ∗ (−6.62) 0.0544∗ ∗ (2.55) −0.2660∗ ∗ ∗ (−8.96) −0.0132∗ ∗ (−2.15) 0.3625∗ (1.94) −0.0065 (−0.38) 0.0540 (1.51) 4.2884∗ ∗ ∗ (7.86) YES YES 3039 0.72

We expect the estimated coefficient for the DID term to be negative and significant. The results presented in Table 5 are consistent with our conjecture. The coefficients of the interaction term are negative and statistically significant for all alternative measures. For example, the coefficient of the interaction term in column (II) is −0.4266, suggesting that mispricing decreased by approximately 43% after deregulation. These results suggest an improvement in the IPO pricing efficiency of A-share companies in the deregulated stage. 5.3. Effects of deregulation on firm valuation We now further validate the assumption that the IPO pricing efficiency improves in the deregulated stage by testing whether a firm’s value around IPO can become more sensitive to the quality of governance during the deregulated stage. Specifically, we regress firm value, as measured by the Tobin’s Q at the end of the IPO year or by the P/E ratio of the IPO, on the triple interaction term of CGi , DEREGt , and ASHAREi .13 In this regression, we control for each individual term and for the interaction term between any two of the three variables. The equation can be written as follows: 13 In our theoretical model, we did not differentiate between the IPO and the secondary-market prices. In our empirical analysis, the Tobin’s Q reflects that the secondary market price after an IPO is subject to less distortion than the P/E ratio of the IPO due to the underpricing of the IPO and the management of earnings related to the IPO. This is also a concern of the controlling shareholder. We also use the P/E ratio of the secondary market at the end of the listed year in our tests, and the results are similar to the results using Tobin’s Q. We do not provide these results in the text.

T obinQi (P Ei ) =

α0 + α1 ASHAREi + α2 DE RE Gt + α3C Gi + β1 (ASHAREi · DE RE Gt ) + β2 ASHAREi · C Gi + β3 DE RE Gt · C Gi + β4 DE RE Gt · C Gi · ASHAREi + γ1 Xi + κt + f j + εi (7)

Control variables (Xi ) include firm size (SIZE), financial leverage (LEV), and other variables that may be related to the value of a firm, as documented in prior studies (Su and Fleisher, 1999) and described in Table 3. The variables κ t and fj represent year and industry fixed effects, respectively. The results are shown in Panels A and B of Table 6. For all but one governance measure, we find evidence supporting the hypothesis that a firm’s value around the time of the IPO experienced a greater increase for firms which had chosen for a higher governance quality in the deregulated stage.14 For example, the coefficient of DEREGt · CGi · ASHAREi in column (VI) of Panel A is 0.3907 (t = 2.73), which is significant at the 0.01 level. This suggests that, when the average level of governance quality is increased by one, then the Tobin’s Q increases by an additional 0.39 in the deregulated stage when compared to the regulated stage. Panel B presents the regression results using the P/E ratio of the IPO as the dependent variable; these results are consistent with those in Panel A. The coefficient in column (VI) of Panel B is 7.6628, indicating that an increase in the overall corporate governance quality can lead to a further price increase in the deregulated stage. To summarize, our findings suggest that the stock market price of IPO firms is more sensitive to governance quality in the deregulated stage as opposed to the regulated stage. 5.4. Cross-sectional analyses Although all A-share companies listed in the mainland stock market have experienced the same deregulation reform, the impact could have varied for different types of firms. We conduct three tests to examine how the impact of deregulation on corporate governance quality can vary with asset tangibility (TANGi , as calculated by dividing the net fixed assets by the total assets from the year before the IPO), market-to-book ratio (MBi ), and state ownership (STATEi ). We regress the quality of corporate governance (CGi ) on the triple interaction term of TANGi /MBi /STATEi , DEREGt and ASHAREi . In this regression, we control for each individual term and for the interaction terms between any two of the three variables. We first examine the degree of asset tangibility. Compared with intangible assets, tangible assets are easier to price (e.g., Chung et al., 2005; Cooper, 2006; Zhang, 2005). Accordingly, firms with more intangible assets have stronger incentives to choose for a higher quality of corporate governance. Panel A of Table 7 reports the results of this regression. Consistent with our conjecture, the results show that firms with more tangible assets do not improve their governance quality as much as those with fewer tangible assets. For example, the coefficient of the triple-interaction term in column (III) is −0.5673, suggesting that firms with a higher degree of tangibility have fewer academically-related directors in the deregulated stage. In comparison, firms with a lower degree of tangibility (i.e., those with more intangible assets, which are also more difficult to price) prefer to build a stronger board after experiencing an improvement in the IPO pricing efficiency. In Panel B of Table 7, we use MB as a proxy for the demand for external finance. Firms with a greater demand for external financing are more likely to choose for stronger corporate governance in order to get a higher pricing both of the IPO and of fu14 The board characteristic which only weakly affected the value of an IPO firm is UNAFF. This variable is a measure of the independence of a board.

P. He, L. Ma and K. Wang et al. / Journal of Banking and Finance 107 (2019) 105606

9

Table 6 The effect of the deregulation of IPO pricing on a firm’s value through corporate governance. The dependent variables in this table are Tobin’s Q and the P/E ratio for the IPO; they are presented in Panel A and Panel B, respectively. The independent variables included in this table are PROF, EDU, ACAD, COMM, UNAFF, and INDEX (all denoted as “CG”); their interaction terms. The interaction terms of DEREG and ASHARE, DEREG and CG, and CG and ASHAR are included as well. The definitions of the variables are in Appendix A. Panel A and Panel B report the effects of the deregulation of IPO pricing on a firm’s value, as represented by Tobin’s Q and P/E ratio of the IPO, respectively. T-statistics are reported in parentheses. ∗ ∗ ∗ , ∗ ∗ and ∗ denote significance at the 0.01, 0.05, and 0.1 levels, respectively. All of the regressions used robust standard errors. Panel A: The Effect of IPO Pricing Deregulation on Tobin’s Q. Dependent variable: Tobin’ Q

ASHARE∗ DEREG ASHARE∗ CG∗ DEREG CG∗ DEREG CG∗ ASHARE DEREG ASHARE CG SIZE LEV TDSH MSYN AGE IECD CAP BOARD BULL TYPE GUID Constant Industry Year Observations Adjusted R2

CG = PROF (I)

CG = EDU (II)

CG = ACAD (III)

CG = COMM (IV)

CG = UNAFF (V)

CG = INDEX (VI)

−1.4530∗ ∗ ∗ (−6.54) 0.5753∗ (1.85) −0.6072∗ ∗ (−1.97) 0.0449 (0.17) 1.1237∗ ∗ ∗ (4.66) −0.2012 (−0.97) −0.0125 (−0.05) −0.0164∗ ∗ ∗ (−2.65) −1.1894∗ ∗ ∗ (−36.75) 1.1703∗ ∗ ∗ (24.71) 2.0504∗ ∗ ∗ (3.06) −0.0016∗ ∗ (−2.23) 0.2149∗ ∗ ∗ (3.25) 0.5157∗ ∗ ∗ (21.62) 0.0249∗ ∗ (2.33) −0.0058 (−0.71) 0.0261∗ (1.65) −0.5343∗ ∗ ∗ (−3.18) −0.3013 (−0.61) YES YES 3526 0.77

−1.4220∗ ∗ ∗ (−11.35) 0.1135∗ ∗ (2.44) −0.1265∗ ∗ ∗ (−2.73) −0.0172 (−0.67) 1.2122∗ ∗ ∗ (7.33) −0.2975∗ ∗ ∗ (−2.75) 0.0275 (0.92) −0.0222∗ ∗ ∗ (−4.26) −1.1200∗ ∗ ∗ (−45.54) 1.2831∗ ∗ ∗ (31.09) 2.7138∗ ∗ ∗ (4.16) −0.0018∗ ∗ ∗ (−3.21) 0.2348∗ ∗ ∗ (3.19) 0.9289∗ ∗ ∗ (56.99) 0.0063 (0.72) 0.0047 (0.73) 0.0111 (0.86) 0.1094 (1.61) −1.4967∗ ∗ ∗ (−3.57) YES YES 3446 0.48

−1.2452∗ ∗ ∗ (−14.75) 0.5112∗ ∗ ∗ (3.17) −0.4524∗ ∗ ∗ (−2.98) −0.1646 (−1.53) 0.7635∗ ∗ ∗ (5.34) −0.2886∗ ∗ ∗ (−2.78) 0.1170 (1.07) −0.0191∗ ∗ (−2.32) −1.2977∗ ∗ ∗ (−31.29) 1.0267∗ ∗ ∗ (13.92) 2.3594∗ ∗ ∗ (3.03) −0.0027∗ ∗ ∗ (−2.77) 0.2641∗ (1.91) 0.9933∗ ∗ ∗ (34.96) 0.0395∗ ∗ ∗ (2.69) 0.0042 (0.40) 0.0208 (1.22) 0.0166 (0.20) −0.7261 (−1.38) YES YES 3526 0.49

−1.2885∗ ∗ ∗ (−16.19) 0.4259∗ ∗ ∗ (3.62) −0.3839∗ ∗ ∗ (−3.39) 0.0221 (0.30) 0.9511∗ ∗ ∗ (7.49) −0.1597∗ (−1.74) −0.0525 (−0.66) −0.0113∗ (−1.78) −1.1915∗ ∗ ∗ (−36.31) 1.1419∗ ∗ ∗ (24.17) 1.9455∗ ∗ ∗ (2.95) −0.0017∗ ∗ (−2.54) 0.2597∗ ∗ ∗ (4.19) 0.5222∗ ∗ ∗ (21.75) 0.0239∗ ∗ (2.23) −0.0056 (−0.68) 0.0283∗ (1.78) 0.0288 (0.41) −0.9431∗ ∗ (−2.19) YES YES 3526 0.77

−1.1331∗ ∗ ∗ (−13.15) 0.2038 (1.44) −0.2941∗ ∗ (−2.45) −0.1858 (−1.53) 0.6981∗ ∗ ∗ (7.10) −0.2001∗ ∗ (−2.20) 0.2279∗ ∗ (2.09) −0.0177∗ ∗ ∗ (−3.14) −1.2668∗ ∗ ∗ (−37.51) 1.0038∗ ∗ ∗ (27.86) 2.4044∗ ∗ ∗ (4.88) −0.0035∗ ∗ ∗ (−3.19) 0.2486∗ ∗ ∗ (2.70) 0.5556∗ ∗ ∗ (25.36) 0.0236 (1.58) −0.0062 (−0.59) 0.0548∗ ∗ ∗ (2.68) −0.0284 (−0.68) −0.7256∗ ∗ (−2.09) YES YES 3526 0.77

−2.1473∗ ∗ ∗ (−4.90) 0.3907∗ ∗ ∗ (2.73) −0.3311∗ ∗ (−2.50) −0.1107 (−1.16) 1.5155∗ ∗ ∗ (3.68) −0.2152 (−0.58) 0.0597 (0.86) −0.0247∗ (−1.73) −1.2525∗ ∗ ∗ (−16.65) 1.1462∗ ∗ ∗ (9.89) 2.6393 (1.33) −0.0041∗ ∗ (−2.00) 0.2677∗ (1.68) 1.0277∗ ∗ ∗ (20.30) 0.0273 (1.18) 0.0289 (1.15) 0.0322 (0.52) 0.0623 (0.58) −0.9740 (−0.84) YES YES 3446 0.49

Panel B: The Effect of IPO Pricing Deregulation on the P/E ratio of the IPO Dependent variable: IPE

ASHARE∗ DEREG ASHARE∗ CG∗ DEREG CG∗ DEREG CG∗ ASHARE DEREG ASHARE

CG = PROF (I)

CG = EDU (II)

CG = ACAD (III)

CG = COMM (IV)

CG = UNAFF (V)

CG = INDEX (VI)

−11.9611∗ ∗ (−2.49) 14.4491∗ ∗ (2.10) −17.2723∗ ∗ (−2.53) −9.8936 (−1.57) 12.3733∗ ∗ ∗ (2.60) 20.9555∗ ∗ ∗ (4.91)

−31.1412∗ (−1.95) 13.5499∗ ∗ (2.06) −11.2867∗ (−1.74) −9.5842 (−1.56) 23.6080 (1.50) 33.4576∗ ∗ (2.16)

−3.9726∗ (−1.75) 17.6745∗ ∗ (2.43) −16.1937∗ ∗ (−2.27) −13.7010∗ ∗ (−2.05) 2.1182 (0.92) 12.6492∗ ∗ ∗ (4.30)

−4.2897∗ ∗ (−2.14) 7.9755∗ ∗ (2.33) −8.0970∗ ∗ (−2.55) −3.1423 (−1.19) 4.4474∗ ∗ (2.30) 17.3643∗ ∗ ∗ (7.30)

0.2506 (0.11) −1.3472 (−0.36) −1.1890 (−0.33) −0.1279 (−0.04) −0.9468 (−0.41) 12.4291∗ ∗ ∗ (4.35)

−19.0822∗ ∗ ∗ (−2.66) 7.6628∗ ∗ ∗ (2.93) −7.1699∗ ∗ ∗ (−3.07) −3.9646∗ ∗ (−2.03) 15.6194∗ ∗ (2.32) 21.2096∗ ∗ ∗ (3.26)

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P. He, L. Ma and K. Wang et al. / Journal of Banking and Finance 107 (2019) 105606 Table 6 (continued) CG SIZE LEV TDSH MSYN AGE IECD CAP BOARD BULL TYPE GUID Constant Industry Year Observations Adjusted R2

11.1793∗ (1.79) 0.6452∗ ∗ ∗ (4.80) −4.0237∗ ∗ ∗ (−5.58) −2.5974∗ ∗ ∗ (−3.30) −53.1709∗ ∗ ∗ (−3.58) −0.0560∗ ∗ (−2.52) −1.2987 (−0.69) 4.8258∗ ∗ ∗ (10.54) −1.0738∗ ∗ ∗ (−3.90) −1.5088∗ ∗ ∗ (−6.76) −1.2268∗ ∗ ∗ (−3.45) −0.8949 (−0.94) 28.4955∗ ∗ (2.46) YES YES 3443 0.50

8.9940 (1.48) 0.8317∗ ∗ ∗ (2.80) −4.1634∗ ∗ ∗ (−3.17) −2.4250 (−1.39) −74.6758∗ ∗ (−2.08) −0.1048∗ ∗ ∗ (−2.91) −1.0022 (−0.45) 8.4401∗ ∗ ∗ (9.39) −2.6301∗ ∗ ∗ (−4.70) −1.1432∗ ∗ ∗ (−2.80) −1.3146∗ ∗ (−2.35) 1.0478 (0.36) 35.8371 (1.41) YES YES 3363 0.51

13.1496∗ ∗ (2.01) 0.4691∗ ∗ ∗ (2.69) −1.7378∗ ∗ (−2.08) −1.5904 (−1.56) −59.5095∗ ∗ ∗ (−3.28) −0.0779∗ ∗ ∗ (−3.41) −0.9384 (−0.53) 5.5181∗ ∗ ∗ (10.19) −1.8948∗ ∗ ∗ (−5.55) −1.1076∗ ∗ ∗ (−4.57) −1.3910∗ ∗ ∗ (−3.55) 6.7192∗ ∗ (2.31) 38.6778∗ ∗ ∗ (2.98) YES YES 3443 0.61

ture equity offerings. Although they could adjust their board structure after the IPO, hiring new or firing existing board directors is a costly process. The coefficients for the triple interaction term of MB, DEREG, and ASHARE are positive and statistically significant across all columns. For example, the coefficient of the interaction term in column (II) is 0.1519. This suggests that firms with a market-to-book ratio that is higher than average by one standard deviation (here, 2.68) had a board of directors with a greater educational level by approximately 0.41 points when compared to firms with a lower market-to-book ratio in the deregulated stage. We obtain similar results when using other governance measures as the dependent variable. Lastly, we predict that SOEs are more likely than non-SOEs to build an effective corporate governance structure in the deregulated stage. According to Chen et al. (2012), a government agency’s incentive and opportunity to regard the finances of an SOE as a resource to fund its own needs is diminished, because the agency is not a specific individual. Consequently, government agencies, which represent the controlling shareholders of SOEs, have fewer incentives for diversion. Moreover, the governance quality of SOEs is bound to be lower in the regulated stage due to a lack of competition in the product market, which implies that SOEs have both more space for improvement and more pressure to improve once the stock market becomes more efficient. Therefore, the deregulation of the IPO pricing system should have a stronger effect on the governance mechanism of SOEs when compared to those of nonSOEs. Panel C of Table 7 reports the results of SOEs and non-SOE comparisons. The coefficient of the triple interaction term of STATEi , ASHAREi , and DEREGt is positive and statistically significant across most of the columns. These results are consistent with our conjecture that, when compared to non-SOEs, SOEs’ choice of corporate governance is more sensitive to deregulation. For example, the coefficient of the interaction term in column (VI) is 0.3856, indicating that, for SOEs listed in the deregulated stage, the corporate governance index increased by an approximately 0.39 more points than that for non-SOEs.

2.0884 (0.98) 0.8967∗ ∗ ∗ (5.63) −4.1737∗ ∗ ∗ (−4.76) −4.8691∗ ∗ ∗ (−5.03) −69.0564∗ ∗ ∗ (−4.61) −0.0510∗ (−1.84) −2.0021 (−0.93) 6.4358∗ ∗ ∗ (11.01) −1.5027∗ ∗ ∗ (−4.42) −1.5233∗ ∗ ∗ (−5.52) −1.3134∗ ∗ ∗ (−3.15) 12.3833∗ ∗ ∗ (3.83) 27.7920∗ ∗ (2.55) YES YES 3443 0.50

1.8613 (0.57) 0.6545∗ ∗ ∗ (4.03) −2.4891∗ ∗ ∗ (−3.03) −2.3263∗ ∗ (−2.40) −54.4799∗ ∗ ∗ (−3.21) −0.0775∗ ∗ ∗ (−3.37) −1.2693 (−0.66) 5.6274∗ ∗ ∗ (10.79) −1.8048∗ ∗ ∗ (−5.56) −1.1105∗ ∗ ∗ (−4.63) −1.3183∗ ∗ ∗ (−3.47) 4.7663∗ (1.67) 34.4386∗ ∗ ∗ (2.79) YES YES 3443 0.65

3.3861∗ ∗ (2.15) 0.8744∗ ∗ ∗ (2.99) −4.0811∗ ∗ ∗ (−3.07) −2.2625 (−1.31) −76.5745∗ ∗ (−2.15) −0.1130∗ ∗ ∗ (−3.16) −0.9749 (−0.42) 8.5344∗ ∗ ∗ (9.50) −2.6418∗ ∗ ∗ (−4.78) −1.2161∗ ∗ ∗ (−2.97) −1.2610∗ ∗ (−2.29) 0.9870 (0.34) 48.6613∗ ∗ (2.21) YES YES 3363 0.51

5.5. Robustness tests We have conducted several robustness tests, the results of which are reported in Table 8.15 A potential concern is that, if treatment and control firms do differ along observable dimensions, then they are likely to differ along unobservable dimensions. If this is the case, the unobservable heterogeneity between the two samples may not be controlled for adequately. We use the Propensity Score Matching (PSM) method to identify a matched sample of control firms listed in the Hong Kong stock market; in our calculations, we adopt a nearest-neighbor PSM scheme. In the first step, we run a logit regression with an indicator variable equaling one if the IPO is in the mainland market and zero otherwise.16 Based on this regression, we obtain estimated coefficients predicting the probability of treatment, which are then used to perform a nearest-neighbor match in order to identify the matched control sample for all IPO firms in the Hong Kong market. We repeat the main test with the matched sample and report the results in column (I) of Table 8. The coefficient for the interaction term between ASHARE and DEREG remains positive and statistically significant, indicating that our results are robust. We then exclude the A-share samples from before 20 0 0 so as to obtain an equivalent IPO sampling period across all markets, since the H-share companies in our sample only had available data from after 20 0 0. Finally, we exclude firms from the finance industry, because their pricing mechanism may be different than that of non-financial firms. Columns (II) and (III) of Table 8 present the results for this alternative sampling period and without the financialindustry firms. The major conclusions remain unchanged.

15 For brevity reasons, we only present results using the comprehensive measure of corporate governance quality (INDEX). The results remain consistent when using other specific measures of board quality. 16 Due to a limited H-share sample size, we only used the variables related to major firm-level characteristics (which are SIZE, LEV, TDSH, AGE, CAP, BOARD, TYPE, and the industry dummy) as covariates in the logic regression. We also used one-to-one matching without replacement to ensure more similarity between the treatment and control group samples in the PSM analysis.

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Table 7 Cross-sectional evidence for the effect of the IPO pricing deregulation on board characteristics. The alternative dependent variables in this table are PROF, EDU, ACAD, COMM, UNAFF, and INDEX. The definitions of the variables are in Appendix A. Variable TANG in Panel A measures the tangibility of an IPO firm’s assets. Variable MB in Panel B measures the external financing needs of IPO firms. Variable STATE in Panel C is a dummy variable which equals one if the firm is controlled by the government or an SOE, and zero otherwise. T-statistics are reported in parentheses. ∗ ∗ ∗ , ∗ ∗ and ∗ denote significance at the 0.01, 0.05, and 0.1 levels, respectively. All of the regressions used robust standard errors. Panel A: Asset Tangibility and the Effect of IPO Pricing Deregulation on Board Characteristics

DEREG ASHARE TANG TANG∗ DEREG TANG∗ ASHARE ASHARE∗ DEREG TANG∗ ASHARE∗ DEREG SIZE LEV TDSH MSYN AGE IECD CAP BOARD BULL TYPE GUID Constant Industry Year Observations Adjusted R2

PROF (I)

EDU (II)

ACAD (III)

COMM (IV)

UNAFF (V)

INDEX (VI)

−0.1351∗ (−1.82) −0.1553∗ ∗ (−2.11) −0.5945∗ ∗ (−2.29) 0.6515∗ ∗ (2.39) 0.8148∗ ∗ ∗ (2.76) 0.1742∗ ∗ ∗ (2.72) −0.7868∗ ∗ (−2.53) −0.0105∗ ∗ (−2.49) 0.0556∗ ∗ (2.09) 0.0533∗ (1.89) −0.1252 (−0.31) 0.0017∗ ∗ (2.20) 0.0692 (1.04) −0.0087 (−0.46) −0.0039 (−0.37) −0.0238∗ ∗ ∗ (−3.11) 0.0195 (1.13) −0.0004 (−0.01) 1.0280∗ ∗ ∗ (3.74) YES YES 3554 0.09

−0.1651 (−1.13) −0.6124∗ ∗ ∗ (−4.85) −0.4990 (−1.63) 0.3074 (0.94) 1.1674∗ ∗ (2.49) 0.5154∗ ∗ ∗ (4.82) −1.5175∗ ∗ ∗ (−3.04) 0.0780∗ ∗ ∗ (8.63) −0.1237∗ ∗ (−2.11) −0.0039 (−0.06) 0.3886 (0.48) −0.0051∗ ∗ (−2.57) −0.1273 (−0.82) 0.0561 (1.31) −0.0571∗ ∗ (−2.17) −0.0065 (−0.37) 0.0438 (1.16) 0.1255∗ (1.79) 0.5908 (1.03) YES YES 3474 0.41

−0.0530 (−0.86) −0.2039∗ ∗ ∗ (−2.99) −0.2144 (−1.56) 0.3274∗ ∗ (2.22) 0.4851∗ ∗ ∗ (2.86) 0.1632∗ ∗ ∗ (3.20) −0.5673∗ ∗ ∗ (−3.10) 0.0093∗ ∗ (2.52) 0.0281 (1.29) 0.0359 (1.52) 0.6086 (1.55) 0.0000 (0.04) 0.0161 (0.27) 0.0578∗ ∗ ∗ (3.67) −0.0097 (−0.97) 0.0007 (0.10) 0.0108 (0.91) −0.0338 (−1.14) −0.1614 (−0.64) YES YES 3554 0.27

−0.0468 (−0.77) −0.0636 (−1.09) −0.0563 (−0.42) 0.2268 (1.55) 0.1454 (0.73) 0.0888∗ (1.78) −0.1542 (−0.72) 0.0146∗ ∗ ∗ (3.71) 0.0447∗ (1.89) −0.0440∗ (−1.66) 0.4694 (1.34) 0.0012∗ (1.72) 0.0729 (1.21) 0.0353∗ ∗ (2.17) −0.0068 (−0.69) −0.0024 (−0.34) −0.0000 (−0.00) −0.0221 (−0.80) 0.0102 (0.04) YES YES 3554 0.12

−0.1490∗ ∗ ∗ (−3.78) −0.1848∗ ∗ ∗ (−4.52) −0.1832∗ ∗ ∗ (−2.82) 0.1101∗ (1.75) 0.4330∗ ∗ ∗ (3.01) 0.1719∗ ∗ ∗ (4.85) −0.4025∗ ∗ ∗ (−2.63) −0.0120∗ ∗ ∗ (−4.43) −0.0431∗ ∗ ∗ (−2.72) 0.0826∗ ∗ ∗ (4.49) −0.0948 (−0.34) −0.0003 (−0.59) 0.0501 (0.88) −0.0515∗ ∗ ∗ (−5.22) −0.0195∗ ∗ ∗ (−2.81) −0.0177∗ ∗ ∗ (−3.78) 0.0088 (0.95) −0.0516∗ ∗ (−2.08) 0.8887∗ ∗ ∗ (4.50) YES YES 3554 0.39

−0.5284∗ ∗ (−2.50) −1.0849∗ ∗ ∗ (−5.16) −1.7889∗ ∗ ∗ (−2.62) 2.0820∗ ∗ ∗ (2.88) 2.8292∗ ∗ ∗ (3.63) 0.8747∗ ∗ ∗ (4.99) −2.9985∗ ∗ ∗ (−3.63) 0.0206∗ (1.72) 0.1035 (1.45) 0.0901 (1.12) 1.6421 (1.39) 0.0025 (1.18) 0.1806 (1.11) 0.0688 (1.26) −0.0523∗ (−1.74) −0.0490∗ ∗ (−2.33) 0.0403 (0.91) 0.0076 (0.08) 1.7544∗ ∗ (2.22) YES YES 3474 0.38

Panel B: External Financing Need and the Effect of IPO Pricing Deregulation on Board Characteristics

DEREG ASHARE MB MB∗ DEREG MB∗ ASHARE ASHARE∗ DEREG MB∗ ASHARE∗ DEREG SIZE LEV

PROF (I)

EDU (II)

ACAD (III)

COMM (IV)

UNAFF (V)

INDEX (VI)

0.1739∗ ∗ (2.23) 0.1526∗ (1.88) 0.0695∗ ∗ (2.56) −0.0938∗ ∗ ∗ (−3.29) −0.0820∗ ∗ ∗ (−2.89) −0.1600∗ ∗ (−2.11) 0.0978∗ ∗ ∗ (3.23) −0.0089∗ ∗ (−2.29) 0.0675∗ ∗ ∗ (2.70)

0.2312 (1.22) −0.2018 (−1.04) 0.1565∗ ∗ (2.31) −0.1800∗ ∗ (−2.56) −0.1379∗ ∗ (−1.98) 0.0613 (0.35) 0.1519∗ ∗ (2.06) 0.0825∗ ∗ ∗ (9.25) −0.1901∗ ∗ ∗ (−3.02)

0.2033∗ ∗ (2.54) −0.0059 (−0.06) 0.0639∗ ∗ (2.05) −0.0890∗ ∗ ∗ (−2.75) −0.0562∗ (−1.78) −0.0930 (−1.20) 0.0797∗ ∗ (2.40) 0.0091∗ ∗ (2.49) 0.0166 (0.75)

0.1723∗ ∗ ∗ (2.62) 0.0688 (0.95) 0.0377∗ (1.67) −0.0743∗ ∗ ∗ (−3.08) −0.0389 (−1.63) −0.0625 (−0.93) 0.0546∗ ∗ (2.10) 0.0181∗ ∗ ∗ (4.86) 0.0463∗ (1.89)

0.1106 (1.11) −0.0604 (−0.58) 0.0924∗ ∗ (2.24) −0.1041∗ ∗ (−2.48) −0.0894∗ ∗ (−2.12) −0.0906 (−0.97) 0.1067∗ ∗ (2.48) −0.0169∗ ∗ ∗ (−4.25) −0.0224 (−0.95)

0.5563∗ ∗ ∗ (2.65) 0.0112 (0.05) 0.2632∗ ∗ ∗ (3.81) −0.3483∗ ∗ ∗ (−4.79) −0.2472∗ ∗ ∗ (−3.45) −0.2460 (−1.20) 0.2993∗ ∗ ∗ (3.91) 0.0257∗ ∗ (2.52) 0.0822 (1.26)

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P. He, L. Ma and K. Wang et al. / Journal of Banking and Finance 107 (2019) 105606 Table 7 (continued) TDSH MSYN AGE IECD CAP BOARD BULL TYPE GUID Constant Industry Year Observations Adjusted R2

0.0479∗ (1.88) −0.1659 (−0.45) 0.0017∗ ∗ (2.35) 0.0741 (1.21) 0.0015 (0.08) −0.0008 (−0.08) −0.0238∗ ∗ ∗ (−3.27) 0.0189 (1.22) −0.0193 (−0.19) 0.7832∗ ∗ ∗ (2.77) YES YES 3366 0.08

0.0051 (0.08) 0.2020 (0.25) −0.0045∗ ∗ (−2.29) −0.1066 (−0.70) 0.0645 (1.33) −0.0521∗ ∗ (−1.97) −0.0118 (−0.65) 0.0600 (1.54) 0.6088∗ ∗ ∗ (2.63) −0.2601 (−0.43) YES YES 3296 0.39

0.0436∗ (1.83) 0.4469 (1.13) −0.0005 (−0.68) 0.0257 (0.39) 0.0447∗ ∗ ∗ (2.59) −0.0168∗ (−1.68) 0.0009 (0.12) 0.0155 (1.24) 0.1580∗ (1.65) −0.4257 (−1.52) YES YES 3366 0.25

−0.0372 (−1.47) 0.2669 (0.82) 0.0007 (1.01) 0.1016 (1.49) 0.0529∗ ∗ ∗ (2.77) −0.0186∗ (−1.95) −0.0026 (−0.37) 0.0055 (0.38) −0.1076 (−0.60) −0.0068 (−0.02) YES YES 3366 0.12

0.0949∗ ∗ ∗ (3.32) 0.3609 (0.82) 0.0006 (0.99) 0.0844 (1.21) −0.0495∗ ∗ ∗ (−2.84) −0.0015 (−0.17) −0.0247∗ ∗ ∗ (−3.73) 0.0005 (0.04) 0.4033∗ ∗ ∗ (3.60) −0.0006 (−0.00) YES YES 3366 0.37

0.0951 (1.40) 0.7046 (0.71) 0.0010 (0.50) 0.1815 (1.26) 0.0850 (1.63) −0.0706∗ ∗ (−2.56) −0.0521∗ ∗ ∗ (−2.70) 0.0440 (1.14) 0.5545∗ ∗ (2.49) 0.7604 (1.02) YES YES 3296 0.35

Panel C: State-ownership and the Effect of IPO Pricing Deregulation on Board Characteristics

DEREG ASHARE STATE STATE∗ DEREG STATE∗ ASHARE ASHARE∗ DEREG STATE∗ ASHARE∗ DEREG SIZE LEV TDSH MSYN AGE IECD CAP BOARD BULL TYPE GUID Constant Industry Year Observations Adjusted R2

PROF (I)

EDU (II)

ACAD (III)

COMM (IV)

UNAFF (V)

INDEX (VI)

−0.0250 (−0.62) −0.0090 (−0.23) −0.0032 (−0.09) −0.0105 (−0.27) −0.0172 (−0.46) 0.0114 (0.40) 0.0783∗ (1.83) −0.0065∗ ∗ (−2.13) 0.0364∗ (1.90) 0.0232 (1.21) −0.1483 (−0.56) 0.0013∗ ∗ (2.11) 0.0342 (0.78) −0.0126 (−0.91) −0.0094 (−1.10) −0.0195∗ ∗ ∗ (−3.22) 0.0129 (1.00) −0.0509∗ ∗ ∗ (−2.60) 0.9291∗ ∗ ∗ (5.13) YES YES 3608 0.09

−0.0424 (−0.51) −0.2912∗ ∗ ∗ (−2.81) 0.1526 (1.24) −0.0534 (−0.41) −0.1227 (−0.98) 0.1689∗ ∗ (2.18) 0.2491∗ (1.83) 0.0818∗ ∗ ∗ (10.07) −0.2052∗ ∗ ∗ (−4.08) 0.0260 (0.48) −0.6972 (−0.99) −0.0039∗ ∗ (−2.24) −0.2501∗ ∗ (−2.35) 0.0818∗ ∗ (2.34) −0.0786∗ ∗ ∗ (−3.47) −0.0112 (−0.72) 0.0283 (0.90) 0.2072∗ ∗ ∗ (3.28) 1.1577∗ ∗ (2.41) YES YES 3528 0.42

0.0516 (1.26) −0.0461 (−0.91) 0.0798 (1.64) −0.0914∗ (−1.73) −0.0913∗ (−1.86) 0.0112 (0.34) 0.1255∗ ∗ (2.29) 0.0078∗ ∗ (2.54) 0.0107 (0.62) 0.0307 (1.50) 0.3094 (0.93) −0.0002 (−0.38) 0.0043 (0.07) 0.0290∗ ∗ ∗ (2.69) −0.0192∗ ∗ (−2.26) 0.0010 (0.18) 0.0135 (1.60) 0.1719∗ ∗ (2.08) −0.2331 (−0.96) YES YES 3608 0.30

0.0019 (0.04) −0.0021 (−0.04) 0.0742 (1.53) −0.0297 (−0.54) −0.0740 (−1.47) 0.0073 (0.20) 0.1315∗ ∗ (2.25) 0.0095∗ ∗ (2.47) 0.0397∗ (1.76) −0.0378 (−1.39) 0.2087 (0.59) 0.0010 (1.46) 0.0698 (1.14) 0.0293∗ (1.87) −0.0177∗ (−1.89) −0.0008 (−0.11) −0.0032 (−0.22) −0.1115∗ ∗ ∗ (−4.14) 0.2601 (1.12) YES YES 3608 0.13

−0.0654 (−1.48) −0.1489∗ ∗ ∗ (−2.80) 0.0252 (0.43) −0.0436 (−0.69) −0.0545 (−0.91) 0.0601∗ (1.70) 0.0862 (1.32) −0.0103∗ ∗ ∗ (−2.96) −0.0284 (−1.40) 0.0826∗ ∗ ∗ (3.40) 0.6142 (1.62) 0.0002 (0.37) 0.0768 (1.05) −0.0376∗ ∗ ∗ (−2.62) −0.0174∗ ∗ (−2.05) −0.0314∗ ∗ ∗ (−5.10) −0.0014 (−0.11) 0.0018 (0.06) 0.2063 (0.81) YES YES 3608 0.37

−0.1498 (−0.85) −0.5742∗ ∗ ∗ (−3.15) 0.1184 (0.62) −0.0554 (−0.27) −0.1768 (−0.91) 0.2859∗ ∗ (2.22) 0.3856∗ (1.81) 0.0145 (1.24) 0.0456 (0.67) 0.1145 (1.40) 1.2594 (1.03) 0.0024 (1.16) 0.1614 (1.00) 0.0444 (0.85) −0.0860∗ ∗ ∗ (−3.00) −0.0491∗ ∗ (−2.35) 0.0364 (0.83) −0.1684∗ (−1.86) 1.8101∗ ∗ (2.29) YES YES 3528 0.38

5.6. Validity of the DID method The validity of our DID approach relies on the assumption of parallel trends, which assumes that the corporate governance quality prior to the deregulation year is comparable between treatment

and control firms; it also assumes that there was no observable trend in the quality before deregulation (Finkelstein, 2007; Focke et al., 2017; Tanaka, 2015). We confirm the validity of our strategy by performing two sets of empirical tests. First, we test whether a common pre-existing trend existed using the empirical model be-

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low: Table 8 Robustness checks on the effect of the deregulation of IPO pricing on board characteristics. In this table, the only dependent variable is the comprehensive board governance quality variable, INDEX, as the results of the other board characteristics are similar and consistent. The definitions of the variables are in Appendix A. Column (I) contains the results for the DID regression testing the deregulation effect on INDEX and using PSM to match the A-share and H-share companies. Column (II) contains the results of the DID regression testing the deregulation effect on INDEX and using only samples from 20 0 0 to 2017. Column (III) contains the results of the DID regression testing the deregulation effect on INDEX while excluding all data from the financial sector. Dependent variable: INDEX

DEREG ASHARE ASHARE∗ DEREG SIZE LEV TDSH MSYN AGE IECD CAP BOARD BULL TYPE GUID Constant Industry Year Observations Adjusted R2

(I)

(II)

(III)

−0.1473 (−1.17) −1.0804∗ ∗ ∗ (−6.00) 0.3810∗ ∗ ∗ (2.98) 0.0192 (1.48) 0.0099 (0.10) 0.0972 (1.29) 5.7996∗ ∗ ∗ (4.12) 0.0088∗ ∗ ∗ (2.81) 0.0863 (0.52) 0.0600 (0.72) −0.0661 (−1.44) −0.0111 (−0.36) 0.1506 (1.41) 1.3501∗ ∗ ∗ (4.48) −3.7642∗ ∗ ∗ (−3.53) YES YES 1261 0.33

−0.1375 (−0.83) −0.6450∗ ∗ ∗ (−3.67) 0.4182∗ ∗ ∗ (3.26) 0.0167 (1.33) 0.1264 (1.60) 0.1231 (1.52) 1.3359 (1.10) 0.0013 (0.63) 0.1636 (0.99) 0.0322 (0.50) −0.0750∗ ∗ (−2.57) −0.0557∗ ∗ (−2.50) 0.0516 (0.72) 0.1537 (0.60) 1.6870∗ ∗ (2.01) YES YES 3008 0.17

−0.1255 (−0.76) −0.5825∗ ∗ ∗ (−3.30) 0.3908∗ ∗ ∗ (3.30) 0.0078 (0.63) 0.0536 (0.76) 0.1126 (1.31) 0.9247 (0.75) 0.0004 (0.20) 0.1800 (1.09) 0.0358 (0.67) −0.0724∗ ∗ (−2.46) −0.0454∗ ∗ (−2.13) 0.0372 (0.83) 0.0519 (0.51) 2.1111∗ ∗ ∗ (2.61) YES YES 3425 0.37



C Gi = α0 + α1 ASHAREi + βt Y EARt · ASHAREi + γ1 Xi + κt + f j + εi (8) Here, YEARt represents a vector of dummy variables for year, including the variables from 2001 to 2003, the variables of 2004 and 2005, the variables from 2006 to 2009, and the dummy variable POST2009. We treat the year 20 0 0 as the benchmark year and compare all subsequent years to it. The dummy variable YEAR0405 equals one if the IPO occurred between August 2004 and the end of 2005. Moreover, we include dummy variables for the three years before and the four years after deregulation. The variable POST2009 equals one if the IPO occurred after 2009, while the interaction term between the ASHARE and the POST2009 variables is also included in the analyses as a way to control for a possible later-year effect. We also included dummy variables for the industry and the year among our control variables. The parameter vector β t captures the difference between A-share and H-share companies in how their corporate governance changed from one year to another. If the pre-trend assumption holds, the β t parameter for before the IPO deregulation should be non-significant, and the β t parameters for during and after the IPO deregulation should be positive and statistically significant. Table 9 shows the results of these analyses. The coefficients for the interaction terms between the year dummies before IPO deregulation and the ASHARE variable are non-significant, which suggest that the observable trend before deregulation did not significantly differ between A-share and H-share companies. In contrast, most of the coefficients for the interaction terms between the variables for the post-deregulation years and the ASHARE variable are positive and statistically significant. This suggests that deregulation has had a positive effect on the quality of a firm’s board. Fig. 1 plots the coefficients for β t and their associated 90% confidence interval as obtained from the regression results. In Fig. 1, the solid line contains the coefficients for the interaction terms between the year dummies and the ASHARE dummy, while the two dotted lines represent the upper and the lower confidence interval. We can observe that the post-2004 coefficients are positive and statistically significant, while those before 2004 are not significantly different from zero. Therefore, the differences between

Fig. 1. Coefficients for the pre-trend assumption test. The solid line contains the coefficients of the interaction terms between the year dummies and the ASHARE dummy; the two dotted lines represent the upper and the lower confidence interval (CI).

14

P. He, L. Ma and K. Wang et al. / Journal of Banking and Finance 107 (2019) 105606

Table 9 Results for the common pre-trend test. The alternative dependent variables in this table are PROF, EDU, ACAD, COMM, UNAFF, and INDEX. The definitions of the variables are in Appendix A. T-statistics are reported in parentheses. ∗ ∗ ∗ , ∗ ∗ and ∗ denote significance at the 0.01, 0.05, and 0.1 levels, respectively. All of the regressions used robust standard errors.

YEAR2001∗ ASHARE YEAR2002∗ ASHARE YEAR2003∗ ASHARE YEAR0405∗ ASHARE YEAR2006∗ ASHARE YEAR 2007∗ ASHARE YEAR2008∗ ASHARE YEAR2009 ASHARE

∗

POST2009∗ ASHARE Constant Control+ Industry+ Year Observations Adjusted R2

PROF (I)

EDU (II)

ACAD (III)

COMM (IV)

UNAFF (V)

INDEX (VI)

0.0962

0.0545

0.1265

0.1546

0.0027

0.3137

(0.84) 0.1699

(0.25) 0.1961

(1.44) 0.1345

(1.40) 0.1350

(0.02) 0.0635

(1.03) 0.2724

(1.49) 0.1438

(0.89) 0.1923

(1.44) 0.0857

(1.24) 0.1284

(0.43) 0.1353

(0.92) 0.3250

(1.53) 0.1507∗

(1.04) (1.06) (1.48) (1.14) 0.6397∗ ∗ ∗ 0.3777∗ ∗ ∗ 0.2997∗ ∗ ∗ 0.0738

(1.34) 0.9634∗ ∗ ∗

(1.83) (3.49) (4.67) (3.38) (0.65) 0.2557∗ ∗ ∗ 0.6820∗ ∗ ∗ 0.3030∗ ∗ ∗ 0.3389∗ ∗ ∗ 0.1923

(4.11) 1.1692∗ ∗ ∗

Table 10 Results from testing for confounding effects. In Table 10, the only presented dependent variable is the comprehensive board governance quality variable, INDEX, as the results for the other board characteristics are similar and consistent. The definitions of the variables are in Appendix A. In Column (I), we separate the full sampling period into two parts: before 2010 and after 2010. Therefore, we replace the DEREG dummy with the POST2010 dummy using the full sample and report the results of the DID regression. In Column (II), we separate the sample into treatment and control group according to a randomly chosen industry, represented by a dummy variable IND; therefore, we replace the ASHARE dummy with the IND dummy using the full sample and report the results of the DID regression. In Column (III), we separate the full sampling period into two parts, namely the bull and the bear markets; therefore, we replace the DEREG dummy with the BULL dummy, and include the interaction term between the ASHARE and the BULL dummies in the regression. Dependent variable: Board Composition INDEX (I)

(2.79) (3.27) (3.26) (3.41) (1.55) (4.38) 0.3351∗ ∗ ∗ 0.8651∗ ∗ ∗ 0.3629∗ ∗ ∗ 0.4133∗ ∗ ∗ 0.2980∗ ∗ ∗ 1.5117∗ ∗ ∗ (4.23) (5.02) (4.85) (4.91) (2.88) (6.96) 0.3402∗ ∗ ∗ 0.7266∗ ∗ ∗ 0.3964∗ ∗ ∗ 0.4483∗ ∗ ∗ 0.2983∗ ∗ ∗ 1.5306∗ ∗ ∗ (4.00) (3.98) (4.34) (4.92) (2.82) 0.2721∗ ∗ ∗ 0.7887∗ ∗ ∗ 0.3373∗ ∗ ∗ 0.2858∗ ∗ ∗ 0.2483∗ ∗

(5.95) 1.2045∗ ∗ ∗

(3.37) 0.1565∗ ∗

(4.47) (4.32) (3.22) (2.33) 0.7238∗ ∗ ∗ 0.2887∗ ∗ ∗ 0.2259∗ ∗ ∗ 0.1901∗

(5.33) 0.9613∗ ∗ ∗

(2.07) 1.8724∗ ∗ ∗ (2.65) YES

(4.46) 1.0382 (0.69) YES

(3.99) 1.1535∗ (1.82) YES

(2.81) 0.7815 (1.12) YES

(1.86) 0.6634 (0.75) YES

(4.47) 3.7571∗ (1.94) YES

3523 0.10

3447 0.42

3527 0.30

3525 0.13

3523 0.31

3447 0.37

POST2010 DEREG ASHARE

ASHARE∗ POST2010 IND∗ DEREG

−0.2875∗ (−1.87)

−0.2202∗ ∗ ∗ (−3.65)

ASHARE∗ BULL SIZE LEV TDSH MSYN AGE

CAP BOARD BULL TYPE GUID Constant Industry Year Observations Adjusted R2

(III)

0.1886 (1.38)

IND

IECD

A-share and H-share companies only start being significant after the IPO deregulation stage, which proves the absence of a preexisting trend. We then perform three additional tests to control for other confounding factors which could have led to a change in board composition during the deregulation stage. First, we repeat our main analysis but mechanically shift the deregulation year to 2010; we hypothesize that, if the coefficients remain positive and statistically significant, then other factors may have influenced our initial results. Second, we divide the sample into a treatment and a control group in a random manner; again, we hypothesize that if the coefficients remain positive and statistically significant, then other factors may have influenced our initial results. Third, we know that when the market is in a bull period, then firms will also have an incentive to improve board composition as a way to increase their value. We therefore include an interaction term between ASHARE and BULL in our regression model and hypothesize that if the coefficient of this interaction term is positive and statistically significant, there may be no real effect of deregulation. Table 10 shows the results of the three tests using the variable INDEX to denote the quality of corporate governance. The coefficients of the interaction terms in these three tests are either statistically significant and negative or non-significant. This suggests that the current research design is unlikely to have been biased by unobservable factors.

0.8147 (3.60)

(II) ∗∗∗

0.0361∗ ∗ ∗ (3.11) −0.0409 (−0.60) 0.2749∗ ∗ ∗ (3.47) 1.2252 (0.96) 0.0052∗ ∗ (2.48) 0.0999 (0.59) −0.0189 (−0.36) −0.1057∗ ∗ ∗ (−3.67) −0.0454∗ ∗ (−2.15) −0.0359 (−0.85) 0.1391 (1.29) 0.4559 (0.49) YES YES 3528 0.37

0.1487∗ ∗ (2.04)

−0.3316∗ ∗ (−2.17)

−0.1399∗ ∗ ∗ (−2.61)

0.0306∗ ∗ ∗ (2.61) 0.0292 (0.42) 0.2061∗ ∗ ∗ (2.65) −1.4767∗ ∗ ∗ (−3.14) 0.0027 (1.31) 0.2257 (1.38) 0.0071 (0.14) −0.0966∗ ∗ ∗ (−3.38) −0.0494∗ ∗ (−2.34) 0.0381 (0.95) −0.1375 (−1.52) 2.6864∗ ∗ ∗ (5.45) YES YES 3528 0.37

−0.0667 (−1.12) 0.0304∗ ∗ ∗ (2.62) 0.0031 (0.05) 0.2159∗ ∗ ∗ (2.83) 1.2102 (0.95) 0.0037∗ (1.80) 0.2136 (1.29) −0.0029 (−0.06) −0.1126∗ ∗ ∗ (−3.93) 0.0064 (0.12) −0.0138 (−0.33) −0.1575∗ (−1.69) 1.3056 (1.58) YES YES 3528 0.37

6. Conclusion This paper uses the deregulation of IPO pricing in China’s stock market as a natural experiment to investigate how such deregulation can affect a firm’s choice of corporate governance quality. Firms aim for more capable and independent board directors who can protect minority shareholders when the efficiency of IPO pricing mechanisms increases and when the prices are more correlated with firm-level characteristics. We conducted empirical analyses using data on the board structures of A-share and H-share companies and confirmed that the deregulation of corporate IPO pricing has a persistent, strong, and positive effect on a firm’s corporate

P. He, L. Ma and K. Wang et al. / Journal of Banking and Finance 107 (2019) 105606

governance. This effect is stronger for firms with higher funding demands, for firms with lower levels of tangible assets, and for SOEs. Results remained robust after a variety of alternative tests. The data also allowed us to estimate the significance of the annual treatment effects in the common pre-trend test, which confirmed that the full impact of the IPO deregulation occurred with a lag of one to two years and that the impact remained large and significant throughout the 2010s. We have also shown that the positive effect of corporate governance on a firm’s value increased after deregulation. This is because the improvements brought to the efficiency of the pricing mechanism in the financial market can help a firm mitigate the moral hazard problem associated with the controlling shareholder. In the long run, this leads to an increased firm value and to a decreased cost of external funding. Accordingly, our findings have practical implications for policy-makers. Our results show that the regulation of the IPO market has a significant impact on the incentive of controlling shareholders to expropriate minority investors. Thus, they suggest that market-based discipline can be a complementary channel of investor protection, which would be a valuable addition to the existing legal protections of minority shareholders in emerging markets. Therefore, the Chinese government could strengthen investor protection by improving the pricing efficiency in the stock market. Appendix A. Variables’ definition This table provides the definitions of the variables employed in the paper. PROF

EDU

ACAD COMM

UNAFF

INDEX

EFC1

EFC2 EFC3

EINDEX

DEREG ASHARE

Calculated as the proportion of board directors with accounting, law, finance, or industry-related working experience in firms other than the listed companies. The average level of education for all board directors, as divided into five categories: high school or below, junior college, bachelor’s degree, master’s degree, and doctoral degree. The scores are zero, one, two, three, and four, respectively. The proportion of directors with academic experience, such as from having worked as professors or researchers. The community influence, which is calculated as the proportion of directors who have worked as a government officer, as a leader or presenter of the Communist Party, or who have worked in public institutions, schools, or industrial associations. The proportion of directors who are not affiliated with the business group to which the listed company belongs, defined as unaffiliated directors. Affiliated firms include the largest corporate shareholders and their subsidiaries, other block corporate shareholders and their related corporate parties, and the subsidiaries of the listed firms. Calculated as the equally-weighted average of all the five proxies, PROF, EDU, ACAD, COMM and UNAFF, normalized with respect to their standard deviations. The difference between the IPO price and the intrinsic value as normalized with respect to the IPO price. The intrinsic value is calculated using the firm’s earnings per share multiplied by the appropriate P/E ratio, the latter of which represents the average value of comparable firms which had been listed for at least three years. The first day return (i.e., underpricing). The difference between the IPO price and the intrinsic value as normalized with respect to the IPO price. The intrinsic price is calculated using the Ohlson (1995) model, which is estimated using data from comparable firms which had been listed for at least three years. Calculated as the equally-weighted average value of the three pricing-efficiency proxies as normalized with respect to their standard deviations. Dummy variable which equals one if the IPO occurs in the deregulated stage (i.e., after August 2004) and zero otherwise. Dummy variable which equals one if the firm is traded in one of the mainland stock exchanges and zero if the firm is traded in the Hong Kong stock exchange.

15

Tobin’s Q IPE SIZE

Tobin’s Q ratio. The price-to-earnings ratio of the IPO. Firm size, calculated as the natural logarithm of total assets in the IPO year. LEV The financial leverage, calculated as the total debt divided by the total assets in the IPO year. TDSH The fraction of tradable A-shares and/or H-shares for a listed firm. MSYN The synchronicity of the stock market. AGE The age of a firm in the IPO year. IECD Dummy variable which equals one after May 2006 (i.e., the establishment date of the Issuance Examination Committee) and zero otherwise. BOARD Dummy variable which equals one if the IPO firm is listed on the main board and zero otherwise. CAP The capital raised in the IPO as deflated by the total assets. BULL Dummy variable which equals one if the market is in the bull period and zero if the market is in the bear period. Dummy variable which equals one if the issuing method is TYPE rationing and zero otherwise. GUID Dummy variable which equals one if the year is after 2013 and zero otherwise. STATE Dummy variable which equals one if the firm is controlled by the government or is a SOEs and zero otherwise. MB The market-to-book ratio of the IPO. TANG The tangibility of each firm, calculated by dividing the net fixed assets by the total assets from the year before the IPO year. SH1 The percentage of shares owned by the largest shareholder. ISSUVOL The natural logarithm of the issue volume. CAPITAL The natural logarithm of the total amount of market capitalization. ROA The return on assets. YEAR2001- Year dummy variables which equal one if the IPO occurs in that YEAR2009 respective year, and zero otherwise. YEAR0405 Dummy variable which equals one if the IPO occurs during the period between the beginning of 2004 and the end of 2005 and zero otherwise. POST2009 Dummy variable which equals one if the IPO year is after 2009 and zero otherwise. POST2010 Dummy variable which equals one if the IPO year is after 2010 and zero otherwise. IND Dummy variable which equals one if the firm belongs to the industry we have randomly chosen as the treatment group industry and zero otherwise.

Appendix B. Proofs

Proof of Theorem 1. We need only to prove that the following first-order condition has an interior solution.

dVe = db

∂ Ve ∂ αe ∂ Ve ∂  ∂ Ve ∂ Ve ∂ V + + + = 0. ∂ αe ∂ b ∂  ∂ b ∂ b ∂V ∂ b

Define δ = V – 0 + (1 + b)/γ , and since we also have αe = γ (0 − ) − b, we obtain:

  ∂ ∂ ∂ + 1 − (αe + b) + γ (0 − ) γ ∂b ∂b ∂b   ∂V ∂V ∂ −  + αe − (V − ) γ + 1 −  + αe ∂b ∂b ∂b    δ γ ∂V = −(V − ) 1− √ 2∂ b δ 2 − 4I/γ

 1 δ ∂V − 1+  + 1 −  + αe 2 ∂b 2 δ − 4I/γ

  δ ∂V γ ∂V 1 = −(V − ) + 1−  −  + αe . 2∂ b 2 ∂b 2 δ − 4I/γ

dVe = (V − ) db

16

us:

P. He, L. Ma and K. Wang et al. / Journal of Banking and Finance 107 (2019) 105606

Based on the assumption limb→0 ∂∂Vb = 0, our computations give



dVe 1 = −(V − ) 1− db 2







δ

b) V (b) + 0 − (1+ γ − = − V (b ) − 2



β is small. By writing σ = V − 0 +  δ 2 − 4I/γ , we obtain:

−

δ 2 − 4I/γ



δ 2 − 4I/γ

1 2



δ

1−

2

that

∂ 2 (V −) ∂ β∂ b

∂2

= − ∂ β∂ b > 0.

∂ b > 0 when β is very small. We Next, we want to show that ∂β ∂ b from the first-order condition of b can obtain the expression of ∂β

for the entrepreneur’s optimization problem, which is:

dVe = db

∂ Ve ∂ αe ∂ Ve ∂  ∂ Ve ∂ Ve ∂ V + + + = 0. ∂ αe ∂ b ∂  ∂ b ∂ b ∂V ∂ b

This can be rewritten as:



γ ∂V 1 − (V (b) − (b) ) + 2 ∂b 2

give us:

− (b) + αe

Based on the assumption limb→b¯ ∂∂Vb = −∞, our computations

  ∂ Ve δ ∂V γ ∂V 1 = −(V − ) + 1−  −  + αe ∂b 2∂ b 2 ∂b δ 2 − 4I/γ   I/γ ∂V ∂V =  + 1 −  + αe γ ∂b ∂b δ 2 − 4I/γ

γ I/γ ∂V I/γ = αe +  −+  ∂ b 2 2 δ − 4I/γ δ − 4I/γ As the other terms are of finite value, we obtain limb→b¯ ∂∂Vbe =

Ve −∞. Therefore, ddb = 0 must have an interior solution, which means that an optimal governance structure does exist for the entrepreneur. 

Proof of Theorem 2. We first prove that, at the governance structure under which the entrepreneur’s payoff is maximized, we have ∂∂Vb > − γ1 . The first-order condition with respect to b gives

γ ∂V us: −(V − )( 2∂ b + 12 )(1 − √ 2 δ ) −  + αe ∂∂Vb = 0. As − + δ −4I/γ γ ∂V αe ∂∂Vb < 0 and 1 − √ 2 δ < 0, we must obtain either 2∂ b + 12 > δ −4I/γ 0, or ∂∂Vb > − γ1 . d (V −) d

We then only need to show that db < 0 and writing δ = V –0 + (1 + b)/γ , we obtain:





δ



δ 2 − 4I/γ



1 − 2γ

1+



db

> 0. By



δ

δ 2 − 4I/γ

∂V δ δ ∂V < 1−  +1+  = < 0. 2∂ b ∂b 2 2 δ − 4I/γ δ − 4I/γ



1 ∂V + 2∂ b 2γ



1+

 1−



δ



δ 2 − 4I/γ

∂V = 0. ∂b

The first-order condition for β gives:

  ∂V ∂ b ∂  ∂ b ∂  δ γ ∂V 1 − − + 1−  ∂ b ∂β ∂ b ∂β ∂β 2 ∂b 2 δ 2 − 4I/βγ

γ ∂ 2V ∂ b δ 1−  (V − ) 2 ∂ b2 ∂β 2 δ − 4I/βγ    ∂V 2 I α0 2I ∂b ∂ ∂b ∂ +1 + − − (V − ) γ ∂b ∂ b ∂β ∂β β 3 γ η3 βγ 2 η3 ∂β   ∂ ∂b ∂ ∂ b ∂V ∂ 2V ∂ b γ− −γ − + αe 2 = 0. ∂ b ∂β ∂β ∂β ∂ b ∂ b ∂β 

−

− − +

Let a = γ ∂∂Vb + 1, k = 1 − √

δ ; after rearranging these δ 2 −4I/βγ

terms, we obtain:



∂ 2V ∂V ∂  −a − ∂b ∂b βγ η ∂ b2   γ ∂ 2V ∂ b ∂V ∂  ∂b − (V − ) k − − ak 2 ∂ b2 ∂β ∂b ∂b ∂β   ak ∂  2Iα0 ∂  ∂V = 1− + (V − )a 3 + γ. 2 ∂β β γ η3 ∂β ∂ b (V − )a

2I 2

3

+ αe

We can prove that, as β approaches 0, the term (V − )a β 3 γ η0 3 2I p

dominates all other terms on the right-hand side of the equation above. As such, the term (V − )a βγ22Iη3 dominates all the other

terms in the bracket on the left-hand side of the equation above. ∂ b > 0.  Since they are both positive, we obtain ∂β References

Some further computations give us:

∂ [V (b) − (b)] = ∂b

η=

When β is close to zero, we obtain ∂∂β∂b < 0, which implies

We assume that the investment I is sufficiently large; because, as we have previously described,  is relatively small, we obtain dVe > 0 and b → 0. db

d ∂V = 1− db 2∂ b

(1−β ) p0 + (1 + b)/γ and β

  ∂ 2 1 ∂V 2I =− + p . ∂ β∂ b γ ∂ b 0 β 3 γ η3



− δ 2 − 4I/γ

  δ + δ 2 − 4I/γ δ 2 − 4I/γ − δ =− −  4 δ 2 − 4I/γ I/γ =  − . 2 δ − 4I/γ ×

Proof of Theorem 3. We have the firm value as measured by V(b) 2 – , and we therefore only need to show that ∂∂β∂b < 0 when



δ δ 2 − 4I/γ

> 0.

Therefore, at the optimal governance structure as chosen by the entrepreneur to maximize his own payoff, a firm’s value increases with each increase in the quality of governance, while the equilibrium diversion decreases with each increase in the quality of governance. 

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