Agency Conflicts around the World

Agency Conflicts around the World Abstract We construct firm-level indexes for agency conflicts between controlling shareholders and outside investors by estimating a dynamic model of financing decisions. Our estimates for 12,652 firms from 14 countries show that agency conflicts are large and highly variable across firms and countries. Differences in agency conflicts are largely due to differences in firm-level governance, ownership concentration, and other firm characteristics. The origin of law is more relevant for curtailing governance excesses than for guarding the typical firm. Agency costs split about equally between wealth transfers and value losses from policy distortions. Recent governance reforms in Europe have significantly reduced agency costs. Received April 19, 2016; editorial decision November 18, 2017 by Received April 19, 2016; editorial decision November 18, 2017 by Editor Francesca Cornelli. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online. The scope of expropriation of minority shareholders and creditors by controlling shareholders, managers, and other corporate insiders is extensive in many countries because of the separation between ownership and control in public firms (La Porta et al. 1998, 1999, 2000, 2002). Such conflicts of interests are severe impediments to the efficient allocation of capital. They distort financing and securities issuance decisions and depress market valuations. Despite their prevalence, the major difficulty in measuring agency conflicts and their effects on policy choices is that these conflicts are not directly observable and good empirical proxies for their severity are difficult to construct. Empirical researchers have developed a number of indexes for corporate governance and investor protection that count the number of governance provisions addressing the expropriation of outside investors.1 Governance provisions, however, do not measure the extent of agency frictions or the distortions caused by them. Instead, they capture the response of the institutional and legal environment to their prevalence. Very few studies directly quantify agency conflicts using unique data for specific countries and in special settings (Nenova 2003; Mironov 2013). In this paper, we follow a radically different approach and infer agency conflicts from the distortions they cause in corporate policy choices. We construct theory-grounded indexes of agency conflicts at the firm level for a broad set of 12,652 firms from 14 OECD countries by backing out the incentive problems within the firm from the consequences they have on observed policy choices in the data, rather than by counting governance provisions. Complementing the prior literature that focuses on corporate governance mechanisms (the cure), we quantify the severity of the underlying agency frictions (the disease). We do so by developing a dynamic model of financing choices with agency conflicts and using data on observable variables—corporate leverage decisions—to infer properties of unobserved variables—agency conflicts. Our structural estimation uses novel data on international ownership structures and a simulated maximum likelihood (SML) method that captures cross-sectional firm heterogeneity, rather than assuming a representative firm. Our structural estimation approach requires a tractable, yet credible, model to benchmark actual corporate behavior to the model-implied first-best and second-best behaviors under varying degrees of agency conflicts.2 A large theoretical literature has shown that conflicts of interest can cause distortions in financial policies (Jensen and Meckling 1976; Jensen 1986; Stulz 1990; Hart and Moore 1995; Zwiebel 1996; Leland 1998; DeMarzo and Sannikov 2006). For this reason, we augment a workhorse dynamic capital structure model in the spirit of Fischer, Heinkel, and Zechner (1989) and Strebulaev (2007) with agency conflicts. We then use the model to infer agency conflicts from the distortions they induce in both the levels and time series of corporate leverage. A simplified version of the model has been shown to capture the main stylized facts about U.S. corporate capital structures (Strebulaev 2007; Morellec, Nikolov, and Schürhoff 2012; Danis, Rettl, and Whited 2014). We broaden and advance this literature by the cross-country nature of the estimation, which is necessary for comparing legal environments, and the addition of two layers of agency conflicts, which we show is key for capturing international variation in capital structures. In our model, financing choices reflect two types of conflicts of interest among stakeholders, on top of the standard trade-off between the tax advantage of debt, the costs of issuing securities, and default costs. First, controlling shareholders can pursue private benefits at the expense of minority shareholders like in, for example, Hart and Moore (1995), Zwiebel (1996), or Lambrecht and Myers (2008). Second, shareholders can extract concessions from creditors by renegotiating debt contracts in default like in, for example, Fan and Sundaresan (2000), Garlappi, Shu, and Yan (2008), or Garlappi and Yan (2011). While the real economy is arguably more complex, we consider that each firm is run by a controlling shareholder who sets the firm’s investment, financing, and default policies.3 The controlling shareholder owns a fraction of equity and can capture part of free cash flows as private benefits. Debt constrains the controlling shareholder by reducing free cash flow available for cash diversion. The policy choices are such that firms that perform well releverage to a target to exploit the debt tax shields embedded in the controlling shareholders’ equity stake. Firms that perform poorly deleverage by renegotiating existing debt contracts. But since debt is not ex ante contractible (like in Zwiebel 1996), agency conflicts distort these financing choices. The intuition for how our structural estimation identifies the agency conflict parameters is directly related to these policy distortions. The model implies specific leverage dynamics for a firm given the model parameters for the technological, tax, and legal environments and agency conflicts. Corporate and personal taxes, bankruptcy costs, refinancing costs, and agency conflicts prescribe not only the mean, median, and mode for leverage but also the speed of mean reversion, variability over time, and the magnitude and frequency of capital structure readjustments. Absent agency conflicts, realistic values for taxes, bankruptcy costs, and refinancing costs predict large target leverage and infrequent and large capital restructurings. Increasing refinancing costs reduces mean leverage and the frequency of readjustments, but still produces large target leverage. The trade-off theory without agency conflicts is therefore unable to match the data for realistic parameter values. Introducing agency conflicts allows matching both leverage levels and readjustment dynamics. Benefits of control diminish the incentives by controlling shareholders to undertake large debt issuances, which lowers the mean of leverage by both reducing target leverage and lowering the speed of mean reversion. Control benefits beyond a critical threshold generate zero leverage. Shareholder advantage in default operates differently by making debt renegotiation more likely and rendering debt more costly, thereby lowering mean and target leverage. Building on this intuition, our identification strategy uses data on leverage dynamics to infer the unobserved control benefits and bargaining power of shareholders in default, similar to the path-breaking GMM approach in asset pricing by Hansen and Singleton (1982). In a first step, we obtain closed-form expressions for the model-implied time-series distribution of leverage ratios. In a second step, we use a simulated maximum likelihood procedure that allows for both observed and unobserved cross-sectional firm heterogeneity to estimate from panel data the firm-specific levels of agency conflicts—that is, both the control advantage of majority shareholders (CADV) and the shareholder bargaining advantage in default (SADV)—that best explain observed financing behavior. Our empirical analysis delivers several novel results. First, we find that moderate agency conflicts are sufficient to explain firms’ financing behavior across a variety of institutional and legal settings. Agency theory has thus the potential to resolve several stylized capital structure puzzles. Our estimates for private control benefits CADV represent 2.6% (4.4%) of free cash flows for the median (average) firm, and shareholder advantage in default represents 45% (42%) of the renegotiation surplus. Median private benefits of control CADV are lower than the mean, suggesting that they are of moderate importance for the typical firm but large for some firms in all countries. Shareholders’ renegotiation power SADV is distributed more symmetrically, but much larger than stipulated by the absolute priority rule. Shareholders can extract substantial concessions from creditors when firms approach financial distress. Second, agency conflicts systematically vary across firms both between and within countries. The variation across countries in agency conflicts is small for the average firm, but it is large in the tails. A maximum of 2% of the variation in control benefits across firms and 1% in shareholders’ renegotiation power in default can be attributed to the country of origin. The effect of legal provisions is very pronounced in the right tail. Notably, the legal environment seems less effective in civil law countries at curtailing governance excesses that in common law countries, as evidenced by the larger fraction of firms with large amounts of resources diverted. The origin of law and other country determinants are thus more relevant for curtailing governance excesses than for guarding the typical firm, consistent with limited enforcement. Differences in agency conflicts are largely due to differences in firm-level governance, ownership concentration, and other firm characteristics. Agency costs relate to firm characteristics in the same way in all countries. Firms with more cash, higher market-to-book ratio, and more intangible assets are those with the largest agency costs. Ownership concentration by family and other individuals is, consistent with agency theory, one of the single most important determinants of control benefits. Overall, firm-specific factors explain variation in agency conflicts better than country factors. Evidence from governance and bankruptcy code reforms in Europe during the 2000s suggests the relation between governance, institutions, and agency is causal. Specifically, estimating agency frictions before and after regulatory and bankruptcy code reforms in France and Italy, we find that these reforms had a strong impact on the magnitude of agency conflicts. Notably, the “greater voice” to minority shareholders reforms lead to a significant reduction in the private benefits of the controlling shareholders. In parallel, the change in debt enforcement due to the easing of debt renegotiability led to a significant increase in shareholders’ bargaining power in default. By contrast, our estimates show that agency frictions in Great Britain, Japan, and Poland did not exhibit any variation around these French and Italian reforms. Third, what matters for financial policies is the mix between direct ownership and indirect compensation, or control benefits. In civil law countries, in which the legal environment is weaker, ownership concentration is much higher than in common law countries. As a result, even though control advantage is larger under civil law, shareholder incentives are better aligned in civil than common law countries, resulting in smaller financial distortions. This is consistent with firms responding to a weaker legal environment by increasing the ownership stake of controlling shareholders. Fourth, the value losses from agency conflicts are large. The total loss to minority shareholders has two parts, a part due to rent extraction and another due to financial distortions. While rents are a transfer from one class of shareholders (minority) to another (controlling), financial distortions destroy overall value. We find agency conflicts reduce firm value by 5.4% on average, with about equal shares coming from net transfers between stakeholders (57%) and net losses due to financial distortions (43%). The composition of agency costs varies strongly across countries. In countries where incentives are less aligned due to more dispersed ownership, such as the United States, financial distortions constitute a larger portion (63%) of agency costs, with wealth transfers (37%) making up the remainder. Counterfactual policy experiments show that agency costs mostly arise from control benefits and the financial frictions that they cause. That is, improving corporate governance to diminish private benefits of control has a larger effect than strengthening creditor rights alone. Our paper relates to the large literature initiated by Jensen and Meckling (1976) on the relation between agency conflicts, corporate policies, and firm performance.4 As relevant as it is for regulation and policy evaluation to quantify these conflicts, there are surprisingly few papers addressing this problem. Nenova (2003) estimates that the value of control ranges between 1% and 10% for a sample 661 dual-class firms in 18 countries in 1997, based on the value of voting rights. Dyck and Zingales (2004) use a sample of 393 control transactions across 39 countries from 1990 to 2000 and find an average control value of 14%. The methodology used in these studies implies that these estimates are based on very small country samples. Albuquerque and Schroth (2010) use a model of block trades to estimate private benefits of control of 3% to 4% of equity value in the United States, a number close to our own estimates. Morellec, Nikolov, and Schürhoff (2012) use a capital structure model to estimate private benefits of control of 1% to 2% of equity value in the United States. In contrast to these last two studies, we base our analysis on a large cross-section of countries that differ in their legal tradition and enforcement environment. This allows us to disentangle the effects of country-wide factors on agency conflicts from those of firm characteristics.5 Another difference is that these papers do not incorporate conflicts of interests between shareholders and creditors. We show that both control benefits and deviations from the absolute priority rule are important in explaining variation in capital structure. Lastly, and more importantly, a key benefit of our approach is that we can accommodate firm heterogeneity and determine how firm characteristics relate to agency conflicts. The standard approach in the structural estimation literature is instead to estimate one parameter that applies to all firms, independently of their characteristics. While the empirical approach developed in this paper is applicable to any theory of financial policy, a prerequisite for our analysis is a model that captures the dynamics of firms’ financing behavior. Among the many existing explanations of capital structure choice, only the trade-off argument has a fully worked out dynamic theory that produces quantitative predictions about leverage ratios in dynamics.6 In addition, and as discussed above, this theory has been shown to perform well at explaining the financing patterns of U.S. firms. Also, while other factors, such as illiquid debt markets or opaque equity markets, may help explain financing behavior in some countries (see, e.g., Cornelli, Portes, and Schaeffer 1998), we focus in our analysis on a set of developed countries in which these factors should be less important. Lastly, we focus in our study on capital structure choices and two types of agency conflicts because a large body of theoretical work has argued that these conflicts should affect firms’ financing decisions and both types of conflicts have received empirical support in the reduced-form empirical literature.7 This specific focus leads to a sharper characterization but forces us to look at a limited set of economic questions, in that we do not consider all agency conflicts or corporate policies. While it is not clear that other conflicts will bias our results in any particular way, rather than adding noise that is filtered by the firm characteristics, future research could build on our setup to incorporate explicitly other determinants of financing decisions. Similarly, while our study provides many robustness tests, incorporating additional corporate policies in the estimation could prove useful in understanding the impact of agency conflicts of corporate behavior and valuations. 1. Agency Conflicts and Financing Dynamics This section develops a dynamic model of the firm based on Morellec, Nikolov, and Schürhoff (2012) in which financing and default decisions reflect personal and corporate taxes, refinancing costs, bankruptcy costs, and agency conflicts between controlling shareholders, minority shareholders, and creditors. The next sections use this model to estimate agency costs at the firm level. 1.1 Model assumptions Throughout the model, we operate under the risk-neutral probability measure $$Q$$ and assume that the risk-free rate $$r>0$$ is constant. Firms $$i=1,\ldots ,N$$ are infinitely lived and rent capital at the rental rate $$R$$ to produce output with the production function $$G:\mathbb{R}_{+}\rightarrow \mathbb{R}_{+}$$, $$G(k_{t})=k_{t}^{\gamma }$$, where $$\gamma \in (0,1)$$. Capital depreciates at a constant rate $$\delta >0$$ so that the rental rate is $$R \equiv r+\delta$$. The goods produced by the firms are not storable so that output equals demand. Output is sold at a unit price. To keep the setup tractable, there are no costs of adjusting capital so that the optimal capital stock maximizes static profits, like in Miao (2005) or Abel and Eberly (2011).8 Firms are heterogeneous in their productivity, ownership, taxation, and exposure to agency conflicts. While their productivity shocks are drawn from the same ex ante distribution, they differ ex post in the shock realizations. Specifically, we consider that the firm-specific state variable is its technology shock process, denoted by $$X_{i}$$ and governed by   \begin{equation} dX_{it}\,=\,\mu _{Xi}\,X_{it}\,dt+\sigma _{Xi}\,X_{it}\,dZ_{it}\,, \quad X_{i0}\,=\,x_{i0}\,>\,0, \label{gbm} \end{equation} (1) where $$\mu _{Xi}<r$$ and $$\sigma _{Xi}>0$$ are constants and $$\left(Z_{it}\right) _{t\geq 0}$$ is a standard Brownian motion under $$Q$$. Given a realization $$x_{i}$$ of $$X_{i}$$ and a size $$k_{i}$$, the operating profit of firm $$i$$ is $$x_{i}G(k_{i})-\delta k_{i} \equiv x_{i}k^{\gamma}_{i}-\delta k_{i}$$ and is taxed at the corporate tax rate $$\tau ^{c}$$. The personal tax rate on dividends $$\tau^{e}$$ and coupon payments $$\tau^{d}$$ are identical for all investors in a given country. Because of corporate taxation and the deductibility of interest payments, firms have an incentive to issue debt. To stay in a simple time-homogeneous setting, we consider debt contracts that are characterized by a perpetual flow of coupon payments $$c$$ and a principal $$P$$. Debt is callable and issued at par. The proceeds from the debt issue are distributed on a pro rata basis to shareholders at the time of flotation. Firms can adjust their capital structure upwards at any point in time by incurring a proportional refinancing cost $$\lambda$$, but they can only reduce their indebtedness in default.9 Under this assumption, any given firm’s debt structure remains fixed until either the firm goes into default or the firm calls its debt and restructures with newly issued debt. Firms that perform well may releverage to exploit the tax benefits of debt. Firms whose conditions deteriorate may default. Default can lead either to liquidation or to renegotiation. A fraction of assets are lost as a frictional cost at the time of default so that if the instant of default is $$T$$, then $$X_{iT}=(1-\alpha_{i} )X_{iT^{-}}$$ in case of liquidation and $$X_{iT}=(1-\kappa_{i})X_{iT^{-}}$$ in case of reorganization, where $$0\leq \kappa_{i} <\alpha_{i}$$. Because liquidation is more costly than reorganization, there exists a surplus associated with renegotiation. Like Fan and Sundaresan (2000), François and Morellec (2004), and Broadie, Chernov, and Sundaresan (2007), we consider a Nash bargaining game in default that leads to a debt-equity swap. Denoting the bargaining power of shareholders by $$\eta_{i} \in \left[ 0,1\right]$$, the generalized Nash bargaining solution implies that shareholders get a fraction $$\eta_{i} \left( \alpha_{i} -\kappa_{i} \right)$$ of assets in default.10 Greater $$\eta_{i}$$ yields larger cash flows to shareholders and thus stronger incentives to default. Private benefits of control are introduced by considering that each firm $$i$$ is run by a controlling shareholder who can capture a fraction $$\phi_{i} \in \lbrack 0,1)$$ of the free cash flow to equity, like in La Porta et al. (2002), Lambrecht and Myers (2008), or Albuquerque and Wang (2008). The controlling shareholder owns a fraction $$\varphi_{i}$$ of the firm’s equity and has discretion over firm size, the firm’s initial capital structure, as well as its restructuring and default policies. When making policy choices, the controlling shareholder maximizes the present value of the cash flows from its equity stake and private benefits. Like in Leland (1998), Strebulaev (2007), and Morellec, Nikolov, and Schürhoff (2012), we focus on barrier restructuring and default policies whereby the firm’s initial capital structure remains fixed until cash flows reach a low level $$x_D$$ (the default barrier) and the firm goes into default or cash flows rise to a high level $$x_U$$ (the restructuring barrier) and the firm calls the debt and restructures with newly issued debt.11 We can thus view the controlling shareholder’s policy choices, and hence agency conflicts, as determining firm size, the initial coupon payment, the restructuring barrier, and the default barrier. 1.2 Shareholders’ objective function To solve for the controlling shareholders’ optimization problem, we start by determining firm size. To save on notation, we omit the firm index $$i$$ whenever possible. When choosing firm size $$k$$, the objective of controlling shareholders is to maximize12  \begin{equation}\label{profit} \pi _{c}(x,c) \equiv \max_{k \geq 0}\{(1-\tau ^{e})\left[ \phi +\left( 1-\phi \right) \varphi \right] [(1-\tau ^{c})(xk^{\gamma }-\delta k-c)-rk]\}. \end{equation} (2) The solution to this static problem is given by $$k^{\ast}=\{ \frac{ (1-\tau )\gamma}{(1-\tau)\delta +(1-\tau ^{e})r}\} ^{\xi }x^{\xi }$$, with $$\xi \equiv \frac{1}{1-\gamma }>1$$, where the effective tax rate $$\tau \equiv 1-\left( 1-\tau ^{c}\right) \left( 1-\tau^{e}\right)$$ reflects corporate and personal taxes. Replacing the expression for $$k^*$$ in (2) yields a cash flow to controlling shareholders over each time interval of length $$dt$$ given by   \begin{equation} \pi_{c}(x,c)dt = (1-\tau)\left[ \phi +\left( 1-\phi \right)\varphi \right] (\Sigma \, x^\xi-c)dt, \end{equation} (3) where   \begin{equation} \Sigma \equiv \frac{(1-\tau )\delta +(1-\tau ^{e})r }{\gamma (1-\gamma)^{-1}(1-\tau)}\left\{ \frac{\gamma (1-\tau )^{\gamma }}{ (1-\tau )\delta +(1-\tau ^{e})r}\right\} ^{\frac{1}{1-\gamma }}. \end{equation} (4) In our analysis of financing and default policies, it will be more convenient to work with the capacity-adjusted technology shock $$Y\equiv X^{\xi }$$ with realizations denoted by $$y$$ and dynamics given by   \begin{equation} dY_{t}=\mu Y_{t}dt+\sigma Y_{t}dZ_{t},\,\,\text{with}\,\,Y_{0}=\Sigma X_{0}^{\xi }>0, \end{equation} (5) where $$\mu =\xi \mu _{X}+\xi (\xi -1)\sigma _{X}^{2}/2$$ and $$\sigma =\xi\sigma _{X}$$. Using this change of variable shows that the after-tax cash flows to minority and controlling shareholders satisfy, respectively,   \begin{equation} \begin{array}{ll} \text{Minority shareholders: } & \pi _{m}(y,c) = (1-\varphi)\left( 1-\phi \right)(1-\tau)(y-c), \\ \text{Controlling shareholders: } & \pi _{c}(y,c) = \left[ \phi +\left( 1-\phi \right) \varphi \right] (1-\tau)(y-c). \end{array} \label{controlling} \end{equation} (6) The expression for $$\pi _{m}(y,c)$$ in (6) shows that minority shareholders receive a fraction $$(1-\varphi)$$ of the cash flows from operations $$y$$ net of the coupon payment $$c$$, the fraction $$\phi$$ of cash flows captured by the controlling shareholder, and the taxes paid on corporate and personal income. The expression for $$\pi _{c}(y,c)$$ shows that controlling shareholders get the rents that they extract from the firm, given by $$\phi(1-\tau)(y-c)$$, in addition to a fraction $$\varphi$$ of the dividend payments.13 Considering next financing decisions, the objective of controlling shareholders is to determine the initial coupon payment $$c$$ and the default and restructuring boundaries $$y_D$$ and $$y_U$$ that maximize the present value of their claim to cash flows. Denote the present value of controlling shareholders’ cash flows by $$\mathbf{CS}(y,c)$$. This value is the sum of controlling shareholders’ equity stake and the value of their private benefits. The value of equity at the time of debt issuance is equal to total firm value because debt is competitively priced. Since controlling shareholders own a fraction $$\varphi$$ of equity and can divert a fraction $$\phi$$ of net income, we can express the total value of controlling shareholders’ claims as   \begin{equation} \begin{array}{cccc} \mathbf{CS}(y_0,c)\,= & \underbrace{\varphi \,\mathbf{V}(y_0,c)} & + & \underbrace{\phi \,\mathbf{N}(y_0,c)} \\ & \text{Equity stake} & & \text{Cash diversion} \end{array} \label{ObjFun} \end{equation} (7) where $$\mathbf{N}\left( y_0,c\right)$$ is the total value of a claim to net income absent agency conflicts (i.e., a claim to $$(1-\tau)(y-c)$$) given in Appendix A by Equation (A6) and $$\mathbf{V}(y_0,c)$$ is the value of the firm given in Appendix A by Equation (A11) and defined as the sum of the present value of a claim on net income plus the value of all debt issues minus the present value of issuance costs and the present value of private benefits of control. The objective of controlling shareholders, $$\sup_{c,\rho }\;\mathbf{CS}(y_0,c)$$, is to maximize the ex ante value of their claims by selecting the coupon payment $$c$$ and the restructuring factor $$\rho \equiv \frac{y_{U}}{y_{0}}$$. Since net income decreases with $$c$$, so does $$\mathbf{N}(y_0,c)$$. Equation (7) therefore implies that the debt level selected by controlling shareholders is lower than the debt level that maximizes firm value whenever $$\phi >0$$. Denote by $$\mathbf{D}(y_0,c)$$ the value of total debt claims, defined in Appendix A by Equation (A9), and by $$\mathbf{I}(y_0,c)$$ the total value of issuance costs, defined in Appendix A by Equation (A10). Plugging the expression for $$\mathbf{V}\left( y_{0},c\right)$$ in Equation (7) also shows that the objective function of controlling shareholders can be written as   \begin{equation} \eqalign{ & \mathop {\sup }\limits_{c,\rho } \;{\bf{CS}}({y_0},c) = [\varphi {\mkern 1mu} (1 - \phi ) + \phi ] \times \cr & \,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\mathop {\sup }\limits_{c,\rho } \{ {\bf{N}}({y_0},c) + \mathop {\underbrace {{\varphi \over {\varphi {\mkern 1mu} (1 - \phi ) + \phi }}}_{{\rm{Weighting\,\,factor}}}}\limits_{} {\mkern 1mu} [{\bf{D}}({y_0},c) - {\bf{I}}({y_0},c)]\} . \cr} \end{equation} (8) Equation (8) demonstrates that what matters for financial policies is the mix of direct and indirect compensation, as captured by the weighting factor $$\frac{\varphi}{\varphi \,(1-\phi)+\phi}$$. Everything else equal, the bigger direct compensation, the bigger this factor, and the lower the distortions in financial policies. The larger the private benefits of control, the larger these distortions. As $$\varphi$$ tends to one, conflicts between controlling and minority shareholders decrease and the financing policy selected by controlling shareholders converges to the financing policy that maximizes shareholder wealth, as derived for example in Leland (1998). In this case, the selected debt policy balances tax benefits of debt against default and refinancing costs. When $$\varphi$$ tends to zero, conflicts between controlling and minority shareholders increase and the firm converges to an all-equity financed firm. The solution to problem (8) reflects the fact that, following the issuance of corporate debt, controlling shareholders choose a default trigger that maximizes the value of their claim. Like in Fan and Sundaresan (2000), the selected default threshold results from a trade-off between the continuation value of equity, defined by $$\mathbf{E}(y,c)=\mathbf{V}(y,c)-d\left( y,c\right)$$ where $$d\left( y,c\right)$$ is the current value of corporate debt given in Appendix A by Equation (A7), and the value of shareholders’ claim in default. Since all claims are scaled down by the same factor in default, controlling and minority shareholders agree on the firm’s default policy. Since shareholders recover a fraction $$\eta$$ of the renegotiation surplus in default, the value of equity satisfies the value-matching condition:   \begin{equation} \mathbf{E}(y_D,c) =\eta \left( \alpha -\kappa \right) \mathbf{V}(y_D,c). \end{equation} (9) The default threshold can therefore be determined using the smooth-pasting condition:   \begin{equation} \left. \frac{\partial \mathbf{E}(y,c)}{ \partial y}\right\vert _{y=y_{D}}=\eta \left( \alpha -\kappa \right) \left. \frac{\partial \mathbf{V}(y,c)}{\partial y}\right\vert _{y=y_{D}}. \label{objective 2} \end{equation} (10) Hugonnier, Malamud, and Morellec (2015) demonstrate that there exists a unique solution to this optimization problem. The full problem consists of solving (8) subject to (10). 1.3 Leverage dynamics with agency conflicts Under policy choices that maximize the controlling shareholders’ total claim to cash flows, the firm’s interest coverage ratio $$z_{t}\equiv Y_{t}/c_{t}$$ follows a geometric Brownian motion with drift $$\mu$$ and volatility $$\sigma$$, that is reset to the target level $$z_{T}\in \left( z_{D},z_{U}\right)$$ whenever it reaches either the endogenous lower default barrier $$z_{D} \equiv \frac{y_D}{c}$$ or the endogenous higher restructuring barrier $$z_{U}\equiv \frac{y_U}{c}$$. Figure 1, panel A, shows two trajectories for the interest coverage ratio $$z_{t}$$ that lead to a reset of capital structure, following either an improvement in the firm’s fortunes or a default. Consider for example trajectory 1 that leads controlling shareholders to restructure debt upward. In our model, debt provides a tax benefit so that firms that perform well may seek to releverage. Because changing the capital structure is costly, the optimal policy is to relever only when the interest coverage ratio exceeds the endogenously determined threshold $$z_U$$. At that point, the firm issues additional debt and increases its coupon payment to reset its interest coverage ratio to the target level $$z_T$$. Figure 1 View largeDownload slide View largeDownload slide Leverage dynamics in the model The figure illustrates the firm’s optimal policy with two trajectories for interest coverage (panel A) and leverage (panel B). Panel C shows the endogenous mapping between interest coverage $$z$$ and leverage $$L(z)$$. Panel D illustrates the resultant statistical distribution for leverage. Both trajectories lead to a reset in the firm’s capital structure, following either an improvement in the firm’s fortunes at time $$T_U(\phi,\eta)$$ (Trajectory 1) or a default at time $$T_D(\phi,\eta)$$ (Trajectory 2). The firm optimally relevers when interest coverage exceeds threshold $$z_U$$ (i.e., leverage falls below $$L(z_U)$$), and it renegotiates outstanding debt when interest coverage drops below threshold $$z_D$$ (i.e., leverage rises above $$L(z_D)$$). In both cases, the firm resets its interest coverage (leverage) ratio to the target level $$z_T$$ ($$L(z_T)$$). Figure 1 View largeDownload slide View largeDownload slide Leverage dynamics in the model The figure illustrates the firm’s optimal policy with two trajectories for interest coverage (panel A) and leverage (panel B). Panel C shows the endogenous mapping between interest coverage $$z$$ and leverage $$L(z)$$. Panel D illustrates the resultant statistical distribution for leverage. Both trajectories lead to a reset in the firm’s capital structure, following either an improvement in the firm’s fortunes at time $$T_U(\phi,\eta)$$ (Trajectory 1) or a default at time $$T_D(\phi,\eta)$$ (Trajectory 2). The firm optimally relevers when interest coverage exceeds threshold $$z_U$$ (i.e., leverage falls below $$L(z_U)$$), and it renegotiates outstanding debt when interest coverage drops below threshold $$z_D$$ (i.e., leverage rises above $$L(z_D)$$). In both cases, the firm resets its interest coverage (leverage) ratio to the target level $$z_T$$ ($$L(z_T)$$). A key implication of our analysis is that the target debt level and the restructuring and default barriers reflect the interaction between market frictions and agency conflicts, so that one can write for each firm $$i$$: $$z_{T}(\phi_{i},\eta_{i})$$, $$z_{D}(\phi_{i},\eta_{i})$$, and $$z_{U}(\phi_{i},\eta_{i})$$. Notably, Equation (6) shows that control rents decrease with debt, so that the controlling shareholder’s choice of debt differs from the efficient choice of debt (optimal for shareholders when there are no controlling-minority shareholder conflicts) whenever $$\phi_{i} >0$$. By increasing the cost of debt, deviations from the absolute priority rule in default lead to further distortions in debt policies (like in, e.g., Mella-Barral and Perraudin 1997; Fan and Sundaresan (2000); François and Morellec (2004); or Broadie, Chernov, and Sundaresan (2007)). The leverage ratio $$\ell_{it}$$ being a monotonically decreasing function of the interest coverage ratio (see the top-right panel of Figure 1), we can write $$\ell_{it}=L\left( z_{it}\right)$$ with $$L:\mathbb{R}^{+}\rightarrow \mathbb{R}^{+}$$ and $$L^{\prime }<0$$. The relation between the interest coverage ratio and financial leverage implies that the leverage ratio of each firm in the economy evolves freely between $$L\left( z_{U}(\phi_{i},\eta_{i})\right)$$ (its lowest value at the time of a restructuring) and $$L\left( z_{D}(\phi_{i},\eta_{i})\right)$$ (its highest value at the time of default) and is reset to its target level $$L\left( z_{T}(\phi_{i},\eta_{i})\right)$$ at the time of a restructuring or a default (bottom-left panel in Figure 1). The model-based distribution of financial leverage (bottom-right panel in Figure 1) thus depends on both the policy choices of the controlling shareholder and on the distributional characteristics of the interest coverage ratio. In particular, let $$f_{z}(z_{i})$$ be the density of the interest coverage ratio. The density of the leverage ratio can then be written in terms of $$f_{z}$$ and the Jacobian of $$L^{-1}$$ as   \begin{equation} f_{\ell}\left( \ell_{i}\right) =f_{z}\left( L^{-1}\left( \ell_{i}\right) \right) \left\vert \left( \frac{\partial \ell_{i}}{\partial L^{-1}\left( \ell_{i}\right) }\right) ^{-1}\right\vert . \label{density_qml1} \end{equation} (11) Equation (11) shows that to compute the time-series distribution of leverage implied by agency conflicts, we need to know the distribution of the interest coverage ratio $$f_{z}$$. Appendix B derives closed-form solutions for both the stationary and conditional distributions of the interest coverage ratio, given the target interest coverage ratio $$z_{T}(\phi_{i},\eta_{i})$$ and the restructuring and default policies (thresholds) $$z_{U}(\phi_{i},\eta_{i})$$ and $$z_{D}(\phi_{i},\eta_{i})$$ selected by controlling shareholders. 2. Data and Estimation Approach Our goal in the empirical analysis is to back out from observed leverage dynamics the firm-specific levels of agency conflicts that best explain corporate financing behavior. Korteweg and Strebulaev (2015) show that leverage data can be used to determine firms’ target leverage zone. We go one step further and link the policies to the underlying incentive problems. To do so, we derive the model predictions for how leverage dynamics change with the agency parameters $$(\phi,\eta)$$. Simulated maximum likelihood (SML) then backs out estimates for the agency parameters from panel data on leverage ratios. 2.1 Data Our empirical analysis combines a number of data sources. We obtain financial statements from Compustat U.S. and Global, stock prices from CRSP and Datastream, and tax rates from the OECD. Ownership data are provided to us through a data feed by Thomson Reuters.14 We collect proxies for the legal environment and other institutional determinants used in the law and finance literature from Andrei Shleifer’s website.15 We remove all regulated (SIC 4900–4999) and financial firms (SIC 6000-6999). Observations with missing total assets, market value, long-term debt, debt in current liabilities, and SIC code are deleted. We obtain a panel data set with 74,855 observations for 12,652 firms and 14 countries between 1997 and 2011. The distribution of the firms in our sample is Austria (AUT; 61 firms, 0.5% of total), Denmark (DNK; 107, 0.8%), France (FRA; 588, 4.6%), Germany (DEU; 595, 4.7%), Great Britain (GBR; 1,459, 11.5%), Ireland (IRL; 42, 0.3%), Italy (ITA; 204, 1.6%), Japan (JPN; 3,274, 25.9%), the Netherlands (NLD; 138, 1.1%), Poland (POL; 236, 1.9%), Portugal (PRT; 37, 0.3%), Spain (ESP; 102, 0.8%), Switzerland (CHE; 178, 1.4%), and the United States (USA; 5,631, 44.5%). We split the model parameters into two groups. The nonagency parameters are standard and can be directly estimated from stock prices and other publicly available sources. The focus of our estimation is thus on the deep parameters $$(\phi_i,\eta_i)$$ describing the effects of agency conflicts. The basic model parameters that we can estimate directly from public data include the risk-free rate $${r}$$, the corporate tax rate $${\tau}^c$$, the personal tax rates on interest income and dividends $${\tau}^d$$ and $${\tau}^e$$, the expected profitability $$\mu_{i}$$, volatility $$\sigma_{i}$$, the systematic exposure $$\beta_{i}$$, the controlling shareholder ownership $$\varphi_{i}$$, liquidation costs $$\alpha_{i}$$, renegotiation costs $$\kappa$$, and debt issuance costs $${\lambda}$$. The risk-free rate, tax rates, market risk premium, and issuance costs are country specific, with the risk-free rate $${r}$$ corresponding to the 3-year Treasury rate. The rest of the parameters are firm specific. Table 1 reports the country means for the parameters. We specify firm-level values for the model parameters as follows. We estimate the growth rate of cash flows, $${\mu}^P_{it}$$, indexed by firm $$i$$ and time $$t$$, as the industry average of the least-squares growth rate of EBIT where industries are defined at the SIC level 2. We estimate the risk-neutral growth rate of cash flows, $${\mu}_{it}$$, using the Capital Asset Pricing Model (CAPM). We have $$\mu_{it}=\mu_{it}^P-{\beta}_{it}{\psi}$$, where $$\psi=6\%$$ is the market risk premium and $${\beta}_{it}$$ is the leverage-adjusted cash-flow beta. We estimate market betas based on equity returns (where we use the MSCI country index for each country in our sample) and unlever these betas based on model-implied relations. Similarly, we estimate cash-flow volatility, $${\sigma}_{it}$$, using the standard deviation of monthly equity returns and the relation $$\sigma _{it}^{E}= \frac{\partial \mathbf{E}(y,c)}{\partial y}\frac{y}{\mathbf{E}(y,c)}\sigma _{it}$$, where $$\sigma _{it}^{E}$$ is the stock return volatility and $$\mathbf{E}(y,c)\equiv \mathbf{V}(y,c)-d(y,c)$$ is the model-implied stock price.16 Table 1 Model parameters A. Country-level model parameters  Country  $${r}$$  $${\tau}^c$$  $${\tau}^d$$  $${\tau}^e$$  $${\tau}$$  AUT  0.031  0.298  0.429  0.250  0.045  CHE  0.016  0.235  0.376  0.360  0.134  DEU  0.030  0.407  0.482  0.272  0.086  DNK  0.033  0.289  0.536  0.423  0.053  ESP  0.035  0.335  0.452  0.246  0.047  FRA  0.031  0.364  0.378  0.369  0.221  GBR  0.041  0.295  0.417  0.270  0.069  IRL  0.041  0.179  0.430  0.410  0.086  ITL  0.037  0.341  0.423  0.150  0.018  JPN  0.005  0.411  0.468  0.212  0.069  NLD  0.030  0.311  0.521  0.341  0.026  POL  0.057  0.249  0.280  0.181  0.105  PRT  0.045  0.310  0.374  0.218  0.087  USA  0.033  0.393  0.426  0.310  0.155  A. Country-level model parameters  Country  $${r}$$  $${\tau}^c$$  $${\tau}^d$$  $${\tau}^e$$  $${\tau}$$  AUT  0.031  0.298  0.429  0.250  0.045  CHE  0.016  0.235  0.376  0.360  0.134  DEU  0.030  0.407  0.482  0.272  0.086  DNK  0.033  0.289  0.536  0.423  0.053  ESP  0.035  0.335  0.452  0.246  0.047  FRA  0.031  0.364  0.378  0.369  0.221  GBR  0.041  0.295  0.417  0.270  0.069  IRL  0.041  0.179  0.430  0.410  0.086  ITL  0.037  0.341  0.423  0.150  0.018  JPN  0.005  0.411  0.468  0.212  0.069  NLD  0.030  0.311  0.521  0.341  0.026  POL  0.057  0.249  0.280  0.181  0.105  PRT  0.045  0.310  0.374  0.218  0.087  USA  0.033  0.393  0.426  0.310  0.155  B. Firm-level model parameters  Country  Firms  $${\mu}_z$$ (SD)  $${\sigma_Y}$$ (SD)  $${\beta_Y}$$ (SD)  $${\varphi}$$ (SD)  $${\alpha}$$ (SD)  AUT  61  0.015  0.308  0.373  0.196  0.495        (0.121)  (0.111)  (0.242)  (0.223)  (0.101)  CHE  178  0.083  0.283  0.607  0.166  0.489        (0.046)  (0.111)  (0.354)  (0.203)  (0.108)  DEU  595  0.089  0.389  0.407  0.207  0.525        (0.064)  (0.175)  (0.294)  (0.250)  (0.127)  DNK  107  0.085  0.316  0.464  0.185  0.492        (0.062)  (0.195)  (0.343)  (0.195)  (0.113)  ESP  102  –0.099  0.271  0.403  0.204  0.540        (0.103)  (0.126)  (0.291)  (0.223)  (0.118)  FRA  588  –0.011  0.348  0.510  0.263  0.531        (0.059)  (0.205)  (0.372)  (0.267)  (0.127)  GBR  1,459  0.137  0.398  0.618  0.132  0.509        (0.058)  (0.180)  (0.352)  (0.128)  (0.157)  IRL  42  0.135  0.353  0.532  0.101  0.494        (0.072)  (0.163)  (0.462)  (0.110)  (0.147)  ITL  204  –0.145  0.281  0.387  0.277  0.550        (0.135)  (0.085)  (0.284)  (0.262)  (0.120)  JPN  3,274  0.035  0.330  0.447  0.188  0.468        (0.024)  (0.137)  (0.308)  (0.161)  (0.087)  NLD  138  0.039  0.323  0.490  0.159  0.500        (0.050)  (0.153)  (0.322)  (0.175)  (0.135)  POL  236  0.171  0.460  0.594  0.317  0.481        (0.108)  (0.186)  (0.281)  (0.220)  (0.099)  PRT  37  –0.132  0.256  0.272  0.345  0.596        (0.122)  (0.119)  (0.250)  (0.257)  (0.117)  USA  5,631  0.152  0.473  0.731  0.101  0.522        (0.040)  (0.216)  (0.567)  (0.100)  (0.147)  B. Firm-level model parameters  Country  Firms  $${\mu}_z$$ (SD)  $${\sigma_Y}$$ (SD)  $${\beta_Y}$$ (SD)  $${\varphi}$$ (SD)  $${\alpha}$$ (SD)  AUT  61  0.015  0.308  0.373  0.196  0.495        (0.121)  (0.111)  (0.242)  (0.223)  (0.101)  CHE  178  0.083  0.283  0.607  0.166  0.489        (0.046)  (0.111)  (0.354)  (0.203)  (0.108)  DEU  595  0.089  0.389  0.407  0.207  0.525        (0.064)  (0.175)  (0.294)  (0.250)  (0.127)  DNK  107  0.085  0.316  0.464  0.185  0.492        (0.062)  (0.195)  (0.343)  (0.195)  (0.113)  ESP  102  –0.099  0.271  0.403  0.204  0.540        (0.103)  (0.126)  (0.291)  (0.223)  (0.118)  FRA  588  –0.011  0.348  0.510  0.263  0.531        (0.059)  (0.205)  (0.372)  (0.267)  (0.127)  GBR  1,459  0.137  0.398  0.618  0.132  0.509        (0.058)  (0.180)  (0.352)  (0.128)  (0.157)  IRL  42  0.135  0.353  0.532  0.101  0.494        (0.072)  (0.163)  (0.462)  (0.110)  (0.147)  ITL  204  –0.145  0.281  0.387  0.277  0.550        (0.135)  (0.085)  (0.284)  (0.262)  (0.120)  JPN  3,274  0.035  0.330  0.447  0.188  0.468        (0.024)  (0.137)  (0.308)  (0.161)  (0.087)  NLD  138  0.039  0.323  0.490  0.159  0.500        (0.050)  (0.153)  (0.322)  (0.175)  (0.135)  POL  236  0.171  0.460  0.594  0.317  0.481        (0.108)  (0.186)  (0.281)  (0.220)  (0.099)  PRT  37  –0.132  0.256  0.272  0.345  0.596        (0.122)  (0.119)  (0.250)  (0.257)  (0.117)  USA  5,631  0.152  0.473  0.731  0.101  0.522        (0.040)  (0.216)  (0.567)  (0.100)  (0.147)  The table reports model parameters used for the estimation. $$r$$ is the risk-free rate. $$\tau^c$$ is the corporate tax rate. $$\tau^d$$ is the personal tax rate on coupons. $$\tau^e$$ is the personal tax rate on dividends. $$\tau$$ is the effective tax rate. $${\mu}_z$$ and $${\sigma_Y}$$ are the drift and standard deviation of the cash-flow process. $${\beta_Y}$$ is beta of cash flows. $${\varphi}$$ is the controlling shareholder ownership. $${\alpha}$$ is the liquidation cost. Panel A reports country-level parameters, and panel B reports firm-level parameters. In panel B, numbers represent the average per country with the standard deviation in parentheses. Table 1 Model parameters A. Country-level model parameters  Country  $${r}$$  $${\tau}^c$$  $${\tau}^d$$  $${\tau}^e$$  $${\tau}$$  AUT  0.031  0.298  0.429  0.250  0.045  CHE  0.016  0.235  0.376  0.360  0.134  DEU  0.030  0.407  0.482  0.272  0.086  DNK  0.033  0.289  0.536  0.423  0.053  ESP  0.035  0.335  0.452  0.246  0.047  FRA  0.031  0.364  0.378  0.369  0.221  GBR  0.041  0.295  0.417  0.270  0.069  IRL  0.041  0.179  0.430  0.410  0.086  ITL  0.037  0.341  0.423  0.150  0.018  JPN  0.005  0.411  0.468  0.212  0.069  NLD  0.030  0.311  0.521  0.341  0.026  POL  0.057  0.249  0.280  0.181  0.105  PRT  0.045  0.310  0.374  0.218  0.087  USA  0.033  0.393  0.426  0.310  0.155  A. Country-level model parameters  Country  $${r}$$  $${\tau}^c$$  $${\tau}^d$$  $${\tau}^e$$  $${\tau}$$  AUT  0.031  0.298  0.429  0.250  0.045  CHE  0.016  0.235  0.376  0.360  0.134  DEU  0.030  0.407  0.482  0.272  0.086  DNK  0.033  0.289  0.536  0.423  0.053  ESP  0.035  0.335  0.452  0.246  0.047  FRA  0.031  0.364  0.378  0.369  0.221  GBR  0.041  0.295  0.417  0.270  0.069  IRL  0.041  0.179  0.430  0.410  0.086  ITL  0.037  0.341  0.423  0.150  0.018  JPN  0.005  0.411  0.468  0.212  0.069  NLD  0.030  0.311  0.521  0.341  0.026  POL  0.057  0.249  0.280  0.181  0.105  PRT  0.045  0.310  0.374  0.218  0.087  USA  0.033  0.393  0.426  0.310  0.155  B. Firm-level model parameters  Country  Firms  $${\mu}_z$$ (SD)  $${\sigma_Y}$$ (SD)  $${\beta_Y}$$ (SD)  $${\varphi}$$ (SD)  $${\alpha}$$ (SD)  AUT  61  0.015  0.308  0.373  0.196  0.495        (0.121)  (0.111)  (0.242)  (0.223)  (0.101)  CHE  178  0.083  0.283  0.607  0.166  0.489        (0.046)  (0.111)  (0.354)  (0.203)  (0.108)  DEU  595  0.089  0.389  0.407  0.207  0.525        (0.064)  (0.175)  (0.294)  (0.250)  (0.127)  DNK  107  0.085  0.316  0.464  0.185  0.492        (0.062)  (0.195)  (0.343)  (0.195)  (0.113)  ESP  102  –0.099  0.271  0.403  0.204  0.540        (0.103)  (0.126)  (0.291)  (0.223)  (0.118)  FRA  588  –0.011  0.348  0.510  0.263  0.531        (0.059)  (0.205)  (0.372)  (0.267)  (0.127)  GBR  1,459  0.137  0.398  0.618  0.132  0.509        (0.058)  (0.180)  (0.352)  (0.128)  (0.157)  IRL  42  0.135  0.353  0.532  0.101  0.494        (0.072)  (0.163)  (0.462)  (0.110)  (0.147)  ITL  204  –0.145  0.281  0.387  0.277  0.550        (0.135)  (0.085)  (0.284)  (0.262)  (0.120)  JPN  3,274  0.035  0.330  0.447  0.188  0.468        (0.024)  (0.137)  (0.308)  (0.161)  (0.087)  NLD  138  0.039  0.323  0.490  0.159  0.500        (0.050)  (0.153)  (0.322)  (0.175)  (0.135)  POL  236  0.171  0.460  0.594  0.317  0.481        (0.108)  (0.186)  (0.281)  (0.220)  (0.099)  PRT  37  –0.132  0.256  0.272  0.345  0.596        (0.122)  (0.119)  (0.250)  (0.257)  (0.117)  USA  5,631  0.152  0.473  0.731  0.101  0.522        (0.040)  (0.216)  (0.567)  (0.100)  (0.147)  B. Firm-level model parameters  Country  Firms  $${\mu}_z$$ (SD)  $${\sigma_Y}$$ (SD)  $${\beta_Y}$$ (SD)  $${\varphi}$$ (SD)  $${\alpha}$$ (SD)  AUT  61  0.015  0.308  0.373  0.196  0.495        (0.121)  (0.111)  (0.242)  (0.223)  (0.101)  CHE  178  0.083  0.283  0.607  0.166  0.489        (0.046)  (0.111)  (0.354)  (0.203)  (0.108)  DEU  595  0.089  0.389  0.407  0.207  0.525        (0.064)  (0.175)  (0.294)  (0.250)  (0.127)  DNK  107  0.085  0.316  0.464  0.185  0.492        (0.062)  (0.195)  (0.343)  (0.195)  (0.113)  ESP  102  –0.099  0.271  0.403  0.204  0.540        (0.103)  (0.126)  (0.291)  (0.223)  (0.118)  FRA  588  –0.011  0.348  0.510  0.263  0.531        (0.059)  (0.205)  (0.372)  (0.267)  (0.127)  GBR  1,459  0.137  0.398  0.618  0.132  0.509        (0.058)  (0.180)  (0.352)  (0.128)  (0.157)  IRL  42  0.135  0.353  0.532  0.101  0.494        (0.072)  (0.163)  (0.462)  (0.110)  (0.147)  ITL  204  –0.145  0.281  0.387  0.277  0.550        (0.135)  (0.085)  (0.284)  (0.262)  (0.120)  JPN  3,274  0.035  0.330  0.447  0.188  0.468        (0.024)  (0.137)  (0.308)  (0.161)  (0.087)  NLD  138  0.039  0.323  0.490  0.159  0.500        (0.050)  (0.153)  (0.322)  (0.175)  (0.135)  POL  236  0.171  0.460  0.594  0.317  0.481        (0.108)  (0.186)  (0.281)  (0.220)  (0.099)  PRT  37  –0.132  0.256  0.272  0.345  0.596        (0.122)  (0.119)  (0.250)  (0.257)  (0.117)  USA  5,631  0.152  0.473  0.731  0.101  0.522        (0.040)  (0.216)  (0.567)  (0.100)  (0.147)  The table reports model parameters used for the estimation. $$r$$ is the risk-free rate. $$\tau^c$$ is the corporate tax rate. $$\tau^d$$ is the personal tax rate on coupons. $$\tau^e$$ is the personal tax rate on dividends. $$\tau$$ is the effective tax rate. $${\mu}_z$$ and $${\sigma_Y}$$ are the drift and standard deviation of the cash-flow process. $${\beta_Y}$$ is beta of cash flows. $${\varphi}$$ is the controlling shareholder ownership. $${\alpha}$$ is the liquidation cost. Panel A reports country-level parameters, and panel B reports firm-level parameters. In panel B, numbers represent the average per country with the standard deviation in parentheses. Our source for ownership data is the Thomson-Reuters Global Institution Ownership Feed. This is a commercial database compiling public records on the declarable ownership stakes in companies around the world that is updated quarterly. It allows separating between ownership by individuals, institutions, and mutual funds.17 We use these data to construct firm-specific measures of controlling shareholders’ ownership, $$\varphi_{it}$$. We define $$\varphi_{it}$$ as the ownership share of the largest shareholder. In robustness tests, we define $$\varphi_{it}$$ as the ownership share of the five largest shareholders. The literature has developed several methods to estimate liquidation costs. In this paper, we use the approach of Berger, Ofek, and Swary (1996) (also used in, e.g., Garlappi, Shu, and Yan 2008; Favara, Schroth, and Valta 2012) and estimate liquidation costs as: $$\alpha _{it}=1-(\text{Tangibility}_{it}+\text{Cash}_{it})/\text{Total Assets}_{it}$$, where $$\text{Tangibility}_{it}$$ equals $$0.715\ast \text{Receivables}_{it}+0.547\ast \text{Inventory}_{it}+0.535\ast \text{Capital}_{it}$$. Gilson, John, and Lang (1990) provide evidence that renegotiation costs are negligible. We thus set the renegotiation costs parameter, $$\kappa$$, to zero. The empirical literature provides estimates of debt issuance costs as a fraction of debt being issued. In the model, the cost of debt issuance, $$\lambda$$, is defined as a fraction of total debt outstanding. The cost of debt issuance as a fraction of the issue size is given in the model by $$\frac{\rho }{\rho -1}\lambda$$, where $$\rho \equiv \frac{z_U}{z_0}$$ is the restructuring threshold multiplier. We observe a median value of 2 for $$\rho$$ in our estimations, so we set $$\lambda=1\%$$. The implied cost as a fraction of debt issued of $$2\%$$ corresponds to the upper range of values reported by Altinkilic and Hansen (2000).18 2.2 From leverage dynamics to agency cost parameters The main objective of our empirical analysis is to estimate from panel data the parameters $$(\phi_i,\eta_i)$$ describing agency conflicts. The remaining nonagency parameters can be directly estimated or calibrated. With the parameter vector split into two parts, we proceed in two steps. We first estimate the nonagency parameters $$\theta _{i}^{\star} = (\mu _{i},\sigma _{i},\beta _{i},\alpha _{i},\varphi _{i},\kappa,\tau^{c},\tau^{e},\tau^{d},\lambda,r,\psi)$$ using the data sources described above. Even then, estimating the agency conflict parameters $$(\phi_i,\eta_i)$$ for each firm using solely data on financial leverage is infeasible, or at least noisy. To reduce the dimensionality of the estimation problem further, we treat $$(\phi_i, \eta_i)$$ as random coefficients, instead of separately estimating a value for each firm. We specify for all firms $$i=1,\ldots,N$$:   \begin{equation} \phi_{i}=h(\alpha_{\phi}+X_i' \beta_{\phi}+\epsilon_{i}^{\phi }), \text{ and } \eta_{i}=h(\alpha_{\eta}+X_i' \beta_{\eta}+\epsilon_{i}^{\eta }), \label{estim_cost} \end{equation} (12) with   \begin{equation} \left( \begin{array}{l} \epsilon _{i}^{\phi } \\ \epsilon _{i}^{\eta } \end{array} \right) \sim \mathcal{N}\left( 0,\left[ \begin{array}{cc} \sigma _{\phi }^{2} & \sigma _{\phi \eta } \\ \sigma _{\phi \eta } & \sigma _{\eta }^{2} \end{array} \right] \right) . \label{eps_dist} \end{equation} (13) This specification is sufficiently flexible to capture cross-sectional variation in the parameter values while imposing the model-implied structural restrictions on the domains. We use for $$h:\mathbb{R}\rightarrow [0,1]$$ the Normal cumulative density function and, alternatively, the inverse logit transformation to guarantee that the parameters stay in their natural domain. Table 2 provides data definitions for the $$X_i$$s and other variables used in the analysis. The variables $$X_i$$ include the firms’ market-to-book ratio and cash holdings as well as the ownership share of the largest shareholder. The market-to-book ratio captures growth opportunities and other intangibles and has been shown to be related to leverage (see, e.g., Smith and Watts 1992). Large cash holdings are a means to divert funds more easily from the firm and, hence, agency conflicts are likely to vary with the firm’s cash holdings (see, e.g., Nikolov and Whited 2014). Lastly, the size of the controlling stake of the largest shareholder is likely to relate to both private benefits (see, e.g., Nikolov and Whited 2014) and the bargaining power of shareholders in default (see, e.g., Davidenko and Strebulaev 1997). We capture unobserved heterogeneity and omitted variables among the $$X_i$$s by the bivariate random variables $$\epsilon_{i}=(\epsilon_{i}^{\phi },\epsilon_{i}^{\eta })$$. The $$\epsilon_{i}^{\phi }$$ and $$\epsilon_{i}^{\eta }$$ are normally distributed (skewed $$t$$ distribution in robustness tests), correlated with each other, and independent across firms like in random effects models. Table 2 Variable definitions Variable  Description  Financial indicators (Source: Compustat Global)  $$\quad$$ Book debt  Long-term debt (DLTT) + Debt in current liabilities (DLC)  $$\quad$$ Book debt (alternative)  Liabilities total (LT) + Preferred stock (PSTK) – Deferred taxes (TXDITC)  $$\quad$$ Book equity  Assets total (AT) – Book debt  $$\quad$$ Book equity (alternative)  Assets total – Book debt (alternative)  $$\quad$$ Leverage  Book debt/(Assets total – Book equity + Market value (CSHOC*abs(PRCCD)))  $$\quad$$ Leverage (alternative)  Book debt (alternative) / (Assets total – Book equity (alternative) + Market value))  $$\quad$$ EBIT growth rate  5-year least squares annual growth rate of EBIT  $$\quad$$ Market-to-book M/B  (Market value + Book debt) / Assets total  $$\quad$$ Cash  Cash and short-term Investments (CHE) / Assets total  $$\quad$$ Size  log(Sales net (SALE))  $$\quad$$ Return on assets ROA  (EBIT (EBIT) + Depreciation (DP)) / Assets total  $$\quad$$ Tangibility  Property, plant, and equipment total net (PPENT) / Assets total  Volatility and systematic risk (Source: Datastream and CRSP)  $$\quad$$ Equity volatility  SD of monthly equity returns, rolling over past 5 years  $$\quad$$ Market model beta  CAPM beta based on monthly equity returns, rolling over past 5 years  Ownership structure  (Source: Thomson-Reuters Equity Ownership Data Feed)  $$\quad$$ Controlling shareholders  Ownership of the 1 (5) largest shareholders, measured as a fraction of market capitalization.  $$\quad$$ Ownership individuals  Ownership of all reported individual shareholders, measured as a fraction of market capitalization  $$\quad$$ Ownership institutions  Ownership of all reported institutional shareholders, measured as a fraction of market capitalization  $$\quad$$ Ownership mutual funds  Ownership of all reported mutual fund shareholders, measured as a fraction of market capitalization  Origin of law (Source: Djankov et al. 2008)  $$\quad$$ Legal origin  Dummy variable that identifies the legal origin of the bankruptcy law of each country. The four origins are English common law and French, German, and Scandinavian civil law  Variable  Description  Financial indicators (Source: Compustat Global)  $$\quad$$ Book debt  Long-term debt (DLTT) + Debt in current liabilities (DLC)  $$\quad$$ Book debt (alternative)  Liabilities total (LT) + Preferred stock (PSTK) – Deferred taxes (TXDITC)  $$\quad$$ Book equity  Assets total (AT) – Book debt  $$\quad$$ Book equity (alternative)  Assets total – Book debt (alternative)  $$\quad$$ Leverage  Book debt/(Assets total – Book equity + Market value (CSHOC*abs(PRCCD)))  $$\quad$$ Leverage (alternative)  Book debt (alternative) / (Assets total – Book equity (alternative) + Market value))  $$\quad$$ EBIT growth rate  5-year least squares annual growth rate of EBIT  $$\quad$$ Market-to-book M/B  (Market value + Book debt) / Assets total  $$\quad$$ Cash  Cash and short-term Investments (CHE) / Assets total  $$\quad$$ Size  log(Sales net (SALE))  $$\quad$$ Return on assets ROA  (EBIT (EBIT) + Depreciation (DP)) / Assets total  $$\quad$$ Tangibility  Property, plant, and equipment total net (PPENT) / Assets total  Volatility and systematic risk (Source: Datastream and CRSP)  $$\quad$$ Equity volatility  SD of monthly equity returns, rolling over past 5 years  $$\quad$$ Market model beta  CAPM beta based on monthly equity returns, rolling over past 5 years  Ownership structure  (Source: Thomson-Reuters Equity Ownership Data Feed)  $$\quad$$ Controlling shareholders  Ownership of the 1 (5) largest shareholders, measured as a fraction of market capitalization.  $$\quad$$ Ownership individuals  Ownership of all reported individual shareholders, measured as a fraction of market capitalization  $$\quad$$ Ownership institutions  Ownership of all reported institutional shareholders, measured as a fraction of market capitalization  $$\quad$$ Ownership mutual funds  Ownership of all reported mutual fund shareholders, measured as a fraction of market capitalization  Origin of law (Source: Djankov et al. 2008)  $$\quad$$ Legal origin  Dummy variable that identifies the legal origin of the bankruptcy law of each country. The four origins are English common law and French, German, and Scandinavian civil law  Table 2 Variable definitions Variable  Description  Financial indicators (Source: Compustat Global)  $$\quad$$ Book debt  Long-term debt (DLTT) + Debt in current liabilities (DLC)  $$\quad$$ Book debt (alternative)  Liabilities total (LT) + Preferred stock (PSTK) – Deferred taxes (TXDITC)  $$\quad$$ Book equity  Assets total (AT) – Book debt  $$\quad$$ Book equity (alternative)  Assets total – Book debt (alternative)  $$\quad$$ Leverage  Book debt/(Assets total – Book equity + Market value (CSHOC*abs(PRCCD)))  $$\quad$$ Leverage (alternative)  Book debt (alternative) / (Assets total – Book equity (alternative) + Market value))  $$\quad$$ EBIT growth rate  5-year least squares annual growth rate of EBIT  $$\quad$$ Market-to-book M/B  (Market value + Book debt) / Assets total  $$\quad$$ Cash  Cash and short-term Investments (CHE) / Assets total  $$\quad$$ Size  log(Sales net (SALE))  $$\quad$$ Return on assets ROA  (EBIT (EBIT) + Depreciation (DP)) / Assets total  $$\quad$$ Tangibility  Property, plant, and equipment total net (PPENT) / Assets total  Volatility and systematic risk (Source: Datastream and CRSP)  $$\quad$$ Equity volatility  SD of monthly equity returns, rolling over past 5 years  $$\quad$$ Market model beta  CAPM beta based on monthly equity returns, rolling over past 5 years  Ownership structure  (Source: Thomson-Reuters Equity Ownership Data Feed)  $$\quad$$ Controlling shareholders  Ownership of the 1 (5) largest shareholders, measured as a fraction of market capitalization.  $$\quad$$ Ownership individuals  Ownership of all reported individual shareholders, measured as a fraction of market capitalization  $$\quad$$ Ownership institutions  Ownership of all reported institutional shareholders, measured as a fraction of market capitalization  $$\quad$$ Ownership mutual funds  Ownership of all reported mutual fund shareholders, measured as a fraction of market capitalization  Origin of law (Source: Djankov et al. 2008)  $$\quad$$ Legal origin  Dummy variable that identifies the legal origin of the bankruptcy law of each country. The four origins are English common law and French, German, and Scandinavian civil law  Variable  Description  Financial indicators (Source: Compustat Global)  $$\quad$$ Book debt  Long-term debt (DLTT) + Debt in current liabilities (DLC)  $$\quad$$ Book debt (alternative)  Liabilities total (LT) + Preferred stock (PSTK) – Deferred taxes (TXDITC)  $$\quad$$ Book equity  Assets total (AT) – Book debt  $$\quad$$ Book equity (alternative)  Assets total – Book debt (alternative)  $$\quad$$ Leverage  Book debt/(Assets total – Book equity + Market value (CSHOC*abs(PRCCD)))  $$\quad$$ Leverage (alternative)  Book debt (alternative) / (Assets total – Book equity (alternative) + Market value))  $$\quad$$ EBIT growth rate  5-year least squares annual growth rate of EBIT  $$\quad$$ Market-to-book M/B  (Market value + Book debt) / Assets total  $$\quad$$ Cash  Cash and short-term Investments (CHE) / Assets total  $$\quad$$ Size  log(Sales net (SALE))  $$\quad$$ Return on assets ROA  (EBIT (EBIT) + Depreciation (DP)) / Assets total  $$\quad$$ Tangibility  Property, plant, and equipment total net (PPENT) / Assets total  Volatility and systematic risk (Source: Datastream and CRSP)  $$\quad$$ Equity volatility  SD of monthly equity returns, rolling over past 5 years  $$\quad$$ Market model beta  CAPM beta based on monthly equity returns, rolling over past 5 years  Ownership structure  (Source: Thomson-Reuters Equity Ownership Data Feed)  $$\quad$$ Controlling shareholders  Ownership of the 1 (5) largest shareholders, measured as a fraction of market capitalization.  $$\quad$$ Ownership individuals  Ownership of all reported individual shareholders, measured as a fraction of market capitalization  $$\quad$$ Ownership institutions  Ownership of all reported institutional shareholders, measured as a fraction of market capitalization  $$\quad$$ Ownership mutual funds  Ownership of all reported mutual fund shareholders, measured as a fraction of market capitalization  Origin of law (Source: Djankov et al. 2008)  $$\quad$$ Legal origin  Dummy variable that identifies the legal origin of the bankruptcy law of each country. The four origins are English common law and French, German, and Scandinavian civil law  In sum, $$\theta=(\alpha_{\phi},\alpha_{\eta},\beta_{\phi},\beta_{\eta},\sigma_{\phi},\sigma_{\eta},\sigma_{\phi \eta})$$ is the parameter vector that we estimate structurally for each country. The likelihood function $$\mathcal{L}$$ of the parameters $$\theta$$ given $$\theta ^{\star }$$ is based on the probability of observing the leverage ratio $$\ell _{it}$$ for firm $$i$$ at date $$t$$, where we define the leverage ratio as   \begin{equation} \ell \equiv \frac{\text{Book Debt}}{\text{Book Debt + Market Value Equity}}, \end{equation} (14) where Book Debt is the sum of Long-term debt (DLTT) and Debt in current liabilities (DLC). Table 3 provides summary statistics for the leverage ratio $$\ell _{it}$$ and the firm attributes $$X_i$$ used throughout the analysis. The numbers represent sample averages with standard deviations in parentheses. Table 3 Firm characteristics Country  Leverage  Leverage alternative  M/B  Cash  Size  ROA  Tangibility  Individuals  Institutions  Mutual funds  All firms  0.271  0.454  1.466  0.131  7.209  0.070  0.275  0.260  0.142  0.060     (0.247)  (0.233)  (0.902)  (0.131)  (1.974)  (0.126)  (0.198)  (0.197)  (0.121)  (0.062)  Civil law  0.324  0.529  1.189  0.130  9.258  0.082  0.289  0.370  0.043  0.038     (0.262)  (0.232)  (0.606)  (0.110)  (1.768)  (0.075)  (0.176)  (0.234)  (0.055)  (0.046)  Common law  0.203  0.362  1.803  0.136  4.705  0.053  0.264  0.117  0.264  0.087     (0.229)  (0.236)  (1.271)  (0.162)  (2.218)  (0.193)  (0.227)  (0.149)  (0.203)  (0.083)  AUT  0.336  0.535  1.227  0.100  6.249  0.104  0.327  0.230  0.075  0.067     (0.259)  (0.228)  (0.478)  (0.115)  (1.899)  (0.070)  (0.175)  (0.277)  (0.084)  (0.077)  CHE  0.246  0.409  1.575  0.124  6.323  0.107  0.308  0.201  0.095  0.096     (0.214)  (0.215)  (1.020)  (0.120)  (1.753)  (0.097)  (0.195)  (0.257)  (0.092)  (0.090)  DEU  0.285  0.504  1.364  0.119  5.623  0.086  0.231  0.245  0.080  0.072     (0.258)  (0.239)  (0.790)  (0.131)  (2.201)  (0.129)  (0.177)  (0.303)  (0.089)  (0.085)  DNK  0.298  0.443  1.576  0.089  7.097  0.091  0.334  0.285  0.045  0.052     (0.236)  (0.235)  (1.144)  (0.123)  (1.765)  (0.120)  (0.213)  (0.284)  (0.057)  (0.063)  ESP  0.331  0.492  1.542  0.053  7.211  0.115  0.315  0.301  0.090  0.087     (0.279)  (0.255)  (0.983)  (0.073)  (2.504)  (0.083)  (0.197)  (0.298)  (0.075)  (0.070)  FRA  0.312  0.523  1.377  0.087  5.849  0.102  0.188  0.328  0.068  0.066     (0.247)  (0.232)  (0.733)  (0.086)  (2.249)  (0.089)  (0.155)  (0.324)  (0.078)  (0.072)  GBR  0.201  0.403  1.596  0.126  4.269  0.077  0.272  0.323  0.090  0.083     (0.205)  (0.218)  (1.048)  (0.154)  (2.252)  (0.163)  (0.237)  (0.221)  (0.104)  (0.078)  IRL  0.232  0.406  1.501  0.173  5.519  0.090  0.292  0.161  0.063  0.073     (0.213)  (0.213)  (0.927)  (0.166)  (2.325)  (0.117)  (0.229)  (0.183)  (0.084)  (0.080)  ITL  0.399  0.573  1.208  0.050  6.768  0.083  0.242  0.354  0.052  0.057     (0.285)  (0.243)  (0.519)  (0.073)  (3.112)  (0.081)  (0.183)  (0.321)  (0.059)  (0.059)  JPN  0.338  0.547  1.087  0.142  10.771  0.077  0.301  0.417  0.026  0.020     (0.267)  (0.231)  (0.506)  (0.107)  (1.570)  (0.057)  (0.172)  (0.206)  (0.041)  (0.030)  NLD  0.251  0.446  1.552  0.101  6.352  0.117  0.251  0.216  0.101  0.127     (0.213)  (0.210)  (0.878)  (0.124)  (2.084)  (0.104)  (0.186)  (0.263)  (0.101)  (0.103)  POL  0.223  0.397  1.502  0.074  5.771  0.094  0.341  0.467  0.084  0.078     (0.216)  (0.224)  (0.965)  (0.083)  (1.635)  (0.092)  (0.196)  (0.268)  (0.085)  (0.079)  PRT  0.516  0.641  1.188  0.028  6.866  0.098  0.358  0.506  0.074  0.056     (0.270)  (0.223)  (0.493)  (0.047)  (2.358)  (0.056)  (0.190)  (0.313)  (0.078)  (0.053)  USA  0.204  0.348  1.874  0.139  4.837  0.045  0.261  0.049  0.323  0.089     (0.237)  (0.242)  (1.347)  (0.165)  (2.206)  (0.204)  (0.224)  (0.125)  (0.236)  (0.084)  Country  Leverage  Leverage alternative  M/B  Cash  Size  ROA  Tangibility  Individuals  Institutions  Mutual funds  All firms  0.271  0.454  1.466  0.131  7.209  0.070  0.275  0.260  0.142  0.060     (0.247)  (0.233)  (0.902)  (0.131)  (1.974)  (0.126)  (0.198)  (0.197)  (0.121)  (0.062)  Civil law  0.324  0.529  1.189  0.130  9.258  0.082  0.289  0.370  0.043  0.038     (0.262)  (0.232)  (0.606)  (0.110)  (1.768)  (0.075)  (0.176)  (0.234)  (0.055)  (0.046)  Common law  0.203  0.362  1.803  0.136  4.705  0.053  0.264  0.117  0.264  0.087     (0.229)  (0.236)  (1.271)  (0.162)  (2.218)  (0.193)  (0.227)  (0.149)  (0.203)  (0.083)  AUT  0.336  0.535  1.227  0.100  6.249  0.104  0.327  0.230  0.075  0.067     (0.259)  (0.228)  (0.478)  (0.115)  (1.899)  (0.070)  (0.175)  (0.277)  (0.084)  (0.077)  CHE  0.246  0.409  1.575  0.124  6.323  0.107  0.308  0.201  0.095  0.096     (0.214)  (0.215)  (1.020)  (0.120)  (1.753)  (0.097)  (0.195)  (0.257)  (0.092)  (0.090)  DEU  0.285  0.504  1.364  0.119  5.623  0.086  0.231  0.245  0.080  0.072     (0.258)  (0.239)  (0.790)  (0.131)  (2.201)  (0.129)  (0.177)  (0.303)  (0.089)  (0.085)  DNK  0.298  0.443  1.576  0.089  7.097  0.091  0.334  0.285  0.045  0.052     (0.236)  (0.235)  (1.144)  (0.123)  (1.765)  (0.120)  (0.213)  (0.284)  (0.057)  (0.063)  ESP  0.331  0.492  1.542  0.053  7.211  0.115  0.315  0.301  0.090  0.087     (0.279)  (0.255)  (0.983)  (0.073)  (2.504)  (0.083)  (0.197)  (0.298)  (0.075)  (0.070)  FRA  0.312  0.523  1.377  0.087  5.849  0.102  0.188  0.328  0.068  0.066     (0.247)  (0.232)  (0.733)  (0.086)  (2.249)  (0.089)  (0.155)  (0.324)  (0.078)  (0.072)  GBR  0.201  0.403  1.596  0.126  4.269  0.077  0.272  0.323  0.090  0.083     (0.205)  (0.218)  (1.048)  (0.154)  (2.252)  (0.163)  (0.237)  (0.221)  (0.104)  (0.078)  IRL  0.232  0.406  1.501  0.173  5.519  0.090  0.292  0.161  0.063  0.073     (0.213)  (0.213)  (0.927)  (0.166)  (2.325)  (0.117)  (0.229)  (0.183)  (0.084)  (0.080)  ITL  0.399  0.573  1.208  0.050  6.768  0.083  0.242  0.354  0.052  0.057     (0.285)  (0.243)  (0.519)  (0.073)  (3.112)  (0.081)  (0.183)  (0.321)  (0.059)  (0.059)  JPN  0.338  0.547  1.087  0.142  10.771  0.077  0.301  0.417  0.026  0.020     (0.267)  (0.231)  (0.506)  (0.107)  (1.570)  (0.057)  (0.172)  (0.206)  (0.041)  (0.030)  NLD  0.251  0.446  1.552  0.101  6.352  0.117  0.251  0.216  0.101  0.127     (0.213)  (0.210)  (0.878)  (0.124)  (2.084)  (0.104)  (0.186)  (0.263)  (0.101)  (0.103)  POL  0.223  0.397  1.502  0.074  5.771  0.094  0.341  0.467  0.084  0.078     (0.216)  (0.224)  (0.965)  (0.083)  (1.635)  (0.092)  (0.196)  (0.268)  (0.085)  (0.079)  PRT  0.516  0.641  1.188  0.028  6.866  0.098  0.358  0.506  0.074  0.056     (0.270)  (0.223)  (0.493)  (0.047)  (2.358)  (0.056)  (0.190)  (0.313)  (0.078)  (0.053)  USA  0.204  0.348  1.874  0.139  4.837  0.045  0.261  0.049  0.323  0.089     (0.237)  (0.242)  (1.347)  (0.165)  (2.206)  (0.204)  (0.224)  (0.125)  (0.236)  (0.084)  The table reports descriptive statistics of firm characteristics. Numbers represent country-level averages with standard deviations in parentheses. Table 2 provides the variable definitions. Table 3 Firm characteristics Country  Leverage  Leverage alternative  M/B  Cash  Size  ROA  Tangibility  Individuals  Institutions  Mutual funds  All firms  0.271  0.454  1.466  0.131  7.209  0.070  0.275  0.260  0.142  0.060     (0.247)  (0.233)  (0.902)  (0.131)  (1.974)  (0.126)  (0.198)  (0.197)  (0.121)  (0.062)  Civil law  0.324  0.529  1.189  0.130  9.258  0.082  0.289  0.370  0.043  0.038     (0.262)  (0.232)  (0.606)  (0.110)  (1.768)  (0.075)  (0.176)  (0.234)  (0.055)  (0.046)  Common law  0.203  0.362  1.803  0.136  4.705  0.053  0.264  0.117  0.264  0.087     (0.229)  (0.236)  (1.271)  (0.162)  (2.218)  (0.193)  (0.227)  (0.149)  (0.203)  (0.083)  AUT  0.336  0.535  1.227  0.100  6.249  0.104  0.327  0.230  0.075  0.067     (0.259)  (0.228)  (0.478)  (0.115)  (1.899)  (0.070)  (0.175)  (0.277)  (0.084)  (0.077)  CHE  0.246  0.409  1.575  0.124  6.323  0.107  0.308  0.201  0.095  0.096     (0.214)  (0.215)  (1.020)  (0.120)  (1.753)  (0.097)  (0.195)  (0.257)  (0.092)  (0.090)  DEU  0.285  0.504  1.364  0.119  5.623  0.086  0.231  0.245  0.080  0.072     (0.258)  (0.239)  (0.790)  (0.131)  (2.201)  (0.129)  (0.177)  (0.303)  (0.089)  (0.085)  DNK  0.298  0.443  1.576  0.089  7.097  0.091  0.334  0.285  0.045  0.052     (0.236)  (0.235)  (1.144)  (0.123)  (1.765)  (0.120)  (0.213)  (0.284)  (0.057)  (0.063)  ESP  0.331  0.492  1.542  0.053  7.211  0.115  0.315  0.301  0.090  0.087     (0.279)  (0.255)  (0.983)  (0.073)  (2.504)  (0.083)  (0.197)  (0.298)  (0.075)  (0.070)  FRA  0.312  0.523  1.377  0.087  5.849  0.102  0.188  0.328  0.068  0.066     (0.247)  (0.232)  (0.733)  (0.086)  (2.249)  (0.089)  (0.155)  (0.324)  (0.078)  (0.072)  GBR  0.201  0.403  1.596  0.126  4.269  0.077  0.272  0.323  0.090  0.083     (0.205)  (0.218)  (1.048)  (0.154)  (2.252)  (0.163)  (0.237)  (0.221)  (0.104)  (0.078)  IRL  0.232  0.406  1.501  0.173  5.519  0.090  0.292  0.161  0.063  0.073     (0.213)  (0.213)  (0.927)  (0.166)  (2.325)  (0.117)  (0.229)  (0.183)  (0.084)  (0.080)  ITL  0.399  0.573  1.208  0.050  6.768  0.083  0.242  0.354  0.052  0.057     (0.285)  (0.243)  (0.519)  (0.073)  (3.112)  (0.081)  (0.183)  (0.321)  (0.059)  (0.059)  JPN  0.338  0.547  1.087  0.142  10.771  0.077  0.301  0.417  0.026  0.020     (0.267)  (0.231)  (0.506)  (0.107)  (1.570)  (0.057)  (0.172)  (0.206)  (0.041)  (0.030)  NLD  0.251  0.446  1.552  0.101  6.352  0.117  0.251  0.216  0.101  0.127     (0.213)  (0.210)  (0.878)  (0.124)  (2.084)  (0.104)  (0.186)  (0.263)  (0.101)  (0.103)  POL  0.223  0.397  1.502  0.074  5.771  0.094  0.341  0.467  0.084  0.078     (0.216)  (0.224)  (0.965)  (0.083)  (1.635)  (0.092)  (0.196)  (0.268)  (0.085)  (0.079)  PRT  0.516  0.641  1.188  0.028  6.866  0.098  0.358  0.506  0.074  0.056     (0.270)  (0.223)  (0.493)  (0.047)  (2.358)  (0.056)  (0.190)  (0.313)  (0.078)  (0.053)  USA  0.204  0.348  1.874  0.139  4.837  0.045  0.261  0.049  0.323  0.089     (0.237)  (0.242)  (1.347)  (0.165)  (2.206)  (0.204)  (0.224)  (0.125)  (0.236)  (0.084)  Country  Leverage  Leverage alternative  M/B  Cash  Size  ROA  Tangibility  Individuals  Institutions  Mutual funds  All firms  0.271  0.454  1.466  0.131  7.209  0.070  0.275  0.260  0.142  0.060