Do State Laws Matter for Bondholders?

Transcription

1 Do State Laws Matter for Bondholders? Sattar A. Mansi, William F. Maxwell, and John K. Wald February 20, 2007 Abstract We examine the impact of state payout restrictions on firm credit ratings and bond yields. Using publicly traded bond data for a sample of large firms, we find that firms incorporated in states with more restrictive payout statutes (e.g., New York), have better credit ratings and significantly lower yield spreads (about 7.6%) relative to firms incorporated in less restrictive states (e.g., Delaware). These results suggest that incorporation in a more restrictive state provides a credible commitment mechanism for avoiding some of the moral hazard problems associated with long-term debt. This commitment corresponds to an economically and statistically significant difference in market yields and firm financing costs and is robust to controls for ownership, governance, debt type, Delaware versus non-delaware incorporation, and covenant usage. Overall, our results are consistent with the notion that Delaware incorporation may have hidden costs for some firms. JEL Classification: G32, G34 Keywords: state payout restrictions, antitakeover provisions, agency cost, corporate governance Mansi is at Virginia Tech, Maxwell is at University of Arizona, and Wald is at University of Texas, San Antonio. The authors would like to thank Thomas Bates, Jens Dammann, David Reeb, and seminar participants at the Conference on Empirical Legal Studies (UT Austin), University of Arizona, University of South Carolina, and Virginia Tech for comments and suggestions. We are also indebted to Lehman Brothers for providing debt data. The remaining errors are ours.

2 I. Introduction A company is subject to the laws of the state in which it is incorporated. Whether the state of incorporation and therefore state law has an impact on firm value is a subject of recent debate. The literature provides ample empirical evidence with mixed results. For example, Romano (1985), in an event study of corporations changing their domicile by reincorporating in Delaware, finds that such firms experienced statistically significant positive cumulative abnormal returns. She interprets this finding as evidence consistent with a race to the top wherein competition among states fosters improvements in state laws and increases in shareholder value. 1 Similarly, Daines (2001) argues that firms incorporated in Delaware are more likely to attract takeover bids and have higher values as measured by Tobin s Q than firms incorporated in other states. On the other hand, Subramanian (2004) finds that the benefit of Delaware incorporation is of a smaller magnitude than provided by Daines during the early 1990s and nonexistent by the late 1990s. Bebchuk and Ferrell (2001) note that the greater value for Delaware firms reported by Daines may be the result of a selection bias due to lack of adequate controls for potential endogeneity problems. Bebchuk, Cohen, and Ferrell (2002) suggest that the empirical results are consistent with a race to the bottom wherein state laws create environments supporting managerial entrenchment and reducing shareholder value. 2 In this paper, we take a different view and assess a subset of state laws (restrictions on payouts and hostile takeovers) from the perspective of bondholders. 3 In doing so, we attempt to provide a more comprehensive view of the impact of these state law on overall firm value by augmenting the stockholder evidence documented in prior research with our bondholder evidence. Specifically, we examine the impact of state payout restrictions on credit ratings and bond yields while controlling for antitakeover laws. We provide evidence for the following two main questions: (i) Are there advantages to firms incorporating in jurisdictions with more restrictive payout constraints, and (ii) what is the impact of payout restrictions and state antitakeover laws on firm credit ratings and bond yields? Our results suggest that after 1 Corporate law is said to be the product of a race among states where laws are chosen so as to attract or keep firm incorporations. 2 The debate on whether competition improves state laws dates back to Cary (1974). Recently, Bar-Gill, Barzuza, and Bebchuk (2006) provide a formal model of a race to the bottom, while Romano and Spiegel (2006) provide an alternative model of a race to the top. Additionally, Chen, Yee, and Yoo (2006) show that the greater value of firms incorporated in Delaware is not robust to controlling for either financial analyst or accounting biases. 3 Both of these types of restrictions are designed to protect creditors from expropriation by shareholders. 2

3 controlling for firm and debt characteristics, firms incorporated in states with more restrictive payout statutes, such as New York or California, have higher credit ratings and lower yield spreads, relative to firms incorporated in less restrictive states such as Delaware. This indicates that state laws provide a commitment device to avoid expropriation of bondholders through dividend payments or share repurchases. As bankruptcy laws and courts are federal, and SEC regulations and differences in U.S. financial markets apply equally to all U.S. firms, focusing on state payout restrictions and controlling for state antitakeover laws captures much of the difference in the legal environment for different firm incorporations. 4 Our results on antitakeover laws are consistent with the view of Bebchuk et al. (2002) that competition among states can be inimical to firm value (Heron and Lewellen (1998) show that state antitakeover laws decrease stock value and we find that it has no impact on bond value). However, the results on payout restrictions suggest a third alternative, between the race to the top and the race to the bottom approaches wherein firms sort themselves either away from binding payout restrictions that reduce financial flexibility and value, or towards greater restrictions that reduce debt financing costs. This sorting by firms, similar to that described by Tiebout (1956) for individuals, suggests that not all jurisdictions need or should converge either to the single best or worst alternative. Instead, by providing a variety of jurisdictions, states may allow firms to maximize value by choosing a set of laws most appropriate to their situation. States differ in their restrictions on distributions to shareholders and on the degree to which they restrict hostile takeovers. Stricter payout laws place an upper limit on the maximum debt to asset ratio in order for a payout to be made. Wald and Long (2005) show that limits on payouts in state laws are related to firm leverage. They find that while some firms sort themselves away from binding constraints, others appear to be impacted by these restrictions and thus choose lower leverage ratios than similar firms incorporated in less restrictive states. Our analyses differ from that of Wald and Long (2005) in that while they examine the impact of 4 Although bankruptcy laws are federal, questions arise whether there is a venue choice in reorganization cases. That is, petitioners have the right to choose the district where they want to file for bankruptcy based on a number of factors including the location of the firm s headquarters, assets, or incorporation. LoPucki and Whitford (1991) in an extensive study find that the state of incorporation is not a significant choice of bankruptcy venue. They also find that in situations where firms chose to transfer cases to different districts, the forum most commonly selected is New York City. 3

4 state laws on leverage, a firm-level decision, we instead analyze how these decisions impact the cost of debt financing, i.e., how they are valued by the credit markets. 5 The literature provides possible explanations for why some firms remain incorporated in more restrictive jurisdictions. One possibility is that costs of reincorporation may be too high, or there may be other regulatory advantages for certain firms to remain in particular states. 6 An alternative possibility is that payout restrictions are similar to covenants in that they reduce the agency cost to bondholders by restricting financial or investment policy (see Smith and Warner (1979) for a discussion of the role of covenants), but with reduced costs and less precision. That is, laws restricting payouts may help reduce the moral hazard problems described by Jensen and Meckling (1976), but because they apply to all firms within the jurisdiction, there are no negotiation costs associated with these laws and they do not necessarily fit the particular situation of every firm. Under this interpretation, more restrictive state laws would be associated with reduced debt financing costs for firms. Firms incorporated in more restrictive jurisdictions would be committing to lower future leverage ratios, and these firms would enjoy lower costs of debt financing. For this relation to hold, the costs of reincorporation must be significant enough that firms initially incorporated in a restrictive jurisdiction cannot reincorporate costlessly to a less restrictive state. Otherwise, these laws would not act as credible precommitments against increasing the leverage ratio. We test our hypotheses using a panel of publicly traded bond data from Lehman Brothers for the period from 1987 to Our analysis controls for a variety of institutional factors including an index of state antitakeover laws, Gompers, Ishii, and Metrick (2003) index of antitakeover measures (GIndex), and a measure of debt covenants used by the firm, as well as firm and security specific factors. We find that debt issued by firms incorporated in states with stricter payout restrictions has higher credit ratings and significantly lower yield spreads. Specifically, bonds from firms incorporated in more restrictive payout jurisdictions have spreads which are 7.6% lower, relative to firms from jurisdictions with no constraints. As a result, these restrictions appear to be valued by the market, and firms subject to these restrictions are rewarded with lower financing costs. Thus, these lower financing costs provide 5 Unlike leverage ratios, a Heckman self-selection analysis applied to yield spreads suggests that these results are not significantly impacted by the firm s decision of whether to incorporate in its home state. We detail these results in the empirical section of the analysis. 6 For a complete discussion on this issue, see Bebchuk and Cohen (2003). 4

5 an indirect benefit to equity which may more than offset the implicit costs from more restrictive statutes for some firms. This research contributes to the literature in two important ways. First, our analysis provides the first empirical evidence that state payout restriction laws have a significant economic impact on the cost of debt financing. The results suggest that bondholders are concerned with restrictions that better protects their interests, and that firms are rewarded with lower debt financing costs for committing to constrain their payouts. Second, this research strongly suggests that it is important to look at the effects of state laws on all classes of securities before concluding that certain laws are necessarily desirable for all firms. Instead, a variety of state legal environments may provide an opportunity for firms to differentially maximize their value, and thus differences in state laws may lead to neither a race to the bottom nor a race to the top. The remainder of the paper is organized as follows. Section II discusses the theoretical background and literature for this study. Section III highlights the data and methods used in the analysis. Section IV provides a discussion of our empirical results regarding the impact of state laws on the cost of debt, and section V concludes. II. Payout Restrictions, State Antitakeover Laws, and the Cost of Debt A. State Laws and Bondholder Value There are several reasons why the bond market represents an important natural setting for examining whether a subset of state laws has an impact on bond prices and yields. First, the bond market represents one of the world s largest securities markets $845 billion in new corporate bonds were issued in the U.S. in and the bond market remains the main source of new external financing for firms. Second, changes in firm value, reported in earlier studies, focus primarily on changes in equity prices. Since payout restrictions may have a greater impact on firms with significant liabilities where agency costs may be severe (over 85% of our sample is in the S&P 500 with median leverage ratios comprised of short plus long term debt of about 59%); not accounting for the impact on bond prices may produce inaccurate results regarding the effect of state laws on overall firm value. Third, studies that consider Tobin's Q as 5

6 a measure of firm value use the book rather than the market value of debt and, therefore, do not adequately capture the effect of state laws on total firm value. Book value of debt can significantly deviate from market value of debt when there are fluctuations in the bond market (large swings in interest rates) or when a firm s credit ratings change. Thus, a full accounting of the impact of state laws must consider the additional impact of laws on the market value of debt. Finally, bond prices tend to be more accurate than equity prices since bonds have shorter durations than equity, and their valuations are well specified and less subject to the criticism that the results might be driven by misspecification of the equilibrium asset pricing model. B. Agency Costs of Debt The agency costs of debt are typically described in terms of underinvestment or asset substitution. Jensen and Meckling (1976), and Myers (1977) detail how the existence of outstanding debt can create a moral hazard problem, where stockholders interests diverge from the interests of creditors and the firm as a whole. The potential conflict between equity and debt claimants is such that shareholders can expropriate wealth from bondholders by investing in new projects that are riskier than those presently held in the firm s portfolio. In this case, bondholders bear most of the cost (Jensen and Meckling (1976)), while shareholders capture most of the gains when high-risk projects pay off. Another agency conflict between bondholders and stockholders centers on the firm s payout policy. While stockholders benefit from on the announcement of large dividend changes and repurchases, bondholders are negatively impacted by these events, and the larger the payout the greater the loss. For example, Dhillon and Johnson (1994) document that large dividend increases negatively impact bondholders, and Maxwell and Stephens (2003) document the loss to bondholders around the announcement of open-market repurchases. Thus, state laws restricting payouts can provide additional moral hazard protection to bondholders. Restrictive state antitakeover provisions can also provide protection to bondholders. While Billet, King, and Mauer (2004) find that target firm bondholders benefit from a takeover, their finding is driven by firms with noninvestment grade bond ratings. For the larger sample of firms with investment grade bonds, bondholders suffer significant losses. There is also a potential selection bias that mitigates the observed effect to bondholders. As noted by Klock, 6

7 Mansi, and Maxwell (2005) when a firm is exposed to a takeover threat, managers take actions that are value decreasing to bondholders. For example, firms commonly use defensive restructurings to fend off takeovers. These restructurings include changes in firm payout policy and increases in firm leverage (for example, see Dann and DeAngelo, 1988). As a reaction to being targeted, managers will sometimes undertake a management led leveraged buyout, which is detrimental to bondholders (Asquith and Wizman, 1990 and Warga and Welch, 1993). Safieddine and Titman (1999) establish that even when targeted firms successfully fend off a takeover attempt, these firms significantly increase their leverage after being targeted. Thus, if the presence of antitakeover provisions reduces the probability of being targeted or acquired, then bondholders would benefit by lowering the probability of these events occurring. This suggests that the presence of antitakeover provisions may prevent shareholder or manager opportunism, and thus lead to a reduction in the bondholder-shareholder conflict and therefore lower costs of debt financing. Due to the shareholder-incentive problem arising from outside debt, bondholders can include protective covenants and monitoring devices to insulate themselves from risk shifting. However, the costs of writing and enforcing such contracts, as well as contracting for all future contingencies, are not trivial. Smith and Warner (1979) in their costly contracting hypothesis describe how covenants can mitigate these agency problems, how negotiating and writing specific covenants for debt contracts can be costly, and how optimal contracting is a function of this trade-off. 7 The empirical evidence implies that the costs of writing the restrictive dividend contract are high since relatively few firms have restrictive covenants on distributions. Smith and Warner (1979) find that only 23 percent of firms have restrictive dividend covenants, and the percentage of bonds including restrictive debt covenants has been decreasing over time. Begley and Freedman (2004) find only 10 percent of publicly issued debentures and notes in 1999 and 2000 include restrictive covenants on debt issuance and/or dividends. Nash, Netter, and Poulsen (2003) and Billet, King, and Mauer (2006) find a similar fraction of firms use these covenants, and their rate of occurrence has declined to 17 percent in the 2000 to 2003 period. Qi and Wald (2007) analyze the occurrence of various covenants and find some evidence of substitution between state laws and debt covenants; that is, firms incorporated in states with fewer restrictions use more covenants after controlling for firm-specific factors. As some debt 7 Jensen and Meckling (1976) and Myers (1977) also discuss the potential tradeoffs of covenants. 7

8 issues may have covenant protection similar to state payout laws, regressions without covenant controls provide a lower bound estimate of the full impact of state laws on bond yields. We provide analyses both with and without controls for covenants below. While debt covenants provide a possible alternative to state laws, an analysis of covenants has the disadvantage that the costs of such covenants is lower than the cost of reincorporation, and thus the covenant decision is part of the negotiation of the debt agreement. While the choice of incorporation, and thus of state laws, may also be seen as endogenous with the cost of debt, in practice there is no evidence that it affects the bond s yields. We detail our econometric tests of this issue below. Several papers attempt to examine the relation between covenant choice and bond yield. Bradley and Roberts (2004) study a sample of bank loan agreements and find a negative correlation between yields and covenants. Reisel (2004) uses a self-selection model for covenant use and finds that covenants which restrict financing activities reduce the cost of debt, whereas those that restrict payouts have no significant impact on debt yields. Wei (2005) finds that the relation between spreads and covenants is positive at issuance, but negative thereafter. In contrast to these studies, an analysis of the impact of payout restrictions in state laws on yield spreads is more straightforward as the decision to reincorporate out-of-state is not significantly correlated with the determination of the firm s cost of debt financing after controlling for leverage. Overall, bondholders should charge a higher risk premium in those cases where the agency costs of debt are higher (or the likelihood of takeover are greater) in order to compensate themselves for bearing this additional risk. As such, any costs arising from the conflicts of interest between shareholders and bondholders would lead to higher debt costs. Alternatively, state laws may provide a substitute to negotiated contracts by incorporating many of the more common features desired by both parties in a loan agreement. C. Payout Restrictions and State Antitakeover Laws After 1975 California modernized its laws on corporate distributions, and after 1979 the ABA Model Act was revised to provide more real economic restrictions on payouts. All states have some form of payout restrictions, with Delaware having some of the least binding 8

9 constraints and California having the most (see, e.g., Eisenberg (1983) and Gevurtz (2000)). Recently, Wald and Long (2005) study the impact of these payout constraints on firm financing and find that firms which use higher debt ratios appear to reincorporate in less restrictive jurisdictions thus creating a negative correlation between the strictness of payout restriction and firm leverage. Firms rarely change their state of incorporation, probably due to the costs associated with the process. Heron and Lewellen (1998) find that firms often reincorporate in states with more antitakeover rules, although these reincorporations can be detrimental to shareholders. Bebchuk and Cohen (2003) discuss some of the costs associated with reincorporating. They find that firms incorporated in their home state (i.e., where their headquarters is located) are less likely to change their state of incorporation when the home state has more antitakeover laws. LoPucki and Whitford (1991) note that while firms could change state of incorporation as part of bankruptcy venue choice, in practice the need for a vote by shareholders and other barriers to reincorporation makes other methods of bankruptcy venue choice easier. Firms incorporated in restrictive jurisdictions will have their shareholder payouts restricted if they get close to the statutory limits on payouts. These limits are typically a function of the debt to asset ratio for states with greater restrictions. In practice, firms prefer to have financial flexibility and thus remain far from the actual payout restriction. 8 Thus, being incorporated in a restrictive jurisdiction may act as a commitment device, with the cost of reincorporation acting as a disincentive from moving the firm to a state with laxer payout restrictions. Unless they change their state of incorporation, firms issuing debt in more restrictive states limit their future debt financing (as long as they make distributions) and thus take on less financial risk. This reduced future leverage may be reflected in lower financing costs for the firm s bonds. 9 III. Data Description We utilize six databases in our analysis of the impact of state laws on the cost of debt financing. These include: (i) Lehman Brothers Fixed Income (LBFI) database, (ii) Investor 8 For a discussion of the benefits of financial flexibility in terms of covenants, see Kalay (1982) 9 Smith (1993) provides a summary of how covenants can lower yield spreads and how this can be beneficial to shareholders. 9

10 Responsibility Research Center (IRRC) corporate governance database, (iii) Compustat Industrial Annual database, (iv) executive compensation (Execucomp) database, (v) Thomson Financial Institutional Ownership database, and (vi) Mergent s Fixed Income Securities database (FISD). For a firm-year observation to be included in our analysis, firms must have a debt issue that is present in the LBFI dataset. Financial information must also be available in the Compustat database. Additional information on institutional and insider ownership is collected from the Execucomp and Thomson Financial databases for a subsample. The firm must also be incorporated in one of the 50 U.S. states or the District of Columbia. Merging the databases and applying these requirements yields a data set of 8,531 firm-year observations on 1,625 firms for the years from 1987 to ,11 A. Measuring the Cost of Debt Financing We use the LBFI database to measure a firms cost of debt. The data contain month-end security specific information such as bid price, coupon, yield, credit ratings from Moody s and S&P, duration, and quote, issue, and maturity dates on nonconvertible bonds that are used in the Lehman Brothers Bond Indexes. Securities are included in various Lehman Brother Bond Indexes based on firm size, liquidity, credit ratings, maturity, and trading frequency. The LBFI covers the period from January, 1973 to the present, and is commonly used in the fixed income literature (Billet, King, and Mauer (2004) and Klock, Mansi, and Maxwell (2005)). While the LBFI does not contain the universe of fixed income securities, we have no reason to suspect any systematic bias in the database. The dependent variable, the log of the bond yield spread or risk premium, is used to measure the cost of debt financing. The yield spread is defined as the difference between the yield to maturity on a corporate bond and the yield to maturity on its duration equivalent Treasury security. The yield to maturity on a corporate bond is the discount rate that equates the present value of its future cash flows to its current price. The yield to maturity on a 10 To minimize survivorship bias, we allow firms to exit and reenter the data set. 11 Note that merged file of 8,531 observations corresponds to data that are available in the LBFI, Compustat, and Thomson Financial datasets for the period from 1987 to The other two datasets: Execucomp and IRRC have data that starts from 1992 and 1990 and onward, respectively. For both credit ratings and yield spreads, we provide the analysis with the entire file in the primary specification and with a subset in alternative specifications. 10

11 Treasury security is the yield on the constant maturity series obtained from the Federal Reserve Bank in its H15 release based on a par bond. For firms with multiple observations in the sample, a weighted average yield spread is computed, with the weight being the amount outstanding for each security divided by the total amount outstanding for all available publicly traded debt. In the cases where no corresponding Treasury yield is available for a given maturity, the yield spread is calculated using interpolation based on the Nelson and Siegel (1987) exponential functional form. 12 We also remove 25 observations where yield spreads are implausibly large for an ongoing firm (+100%), and the results are similar regardless of how the upper tail is trimmed. B. Measuring State Law and Antitakeover Variables We use the payout restrictions described in Wald and Long (2005) and the antitakeover laws described in Bebchuk and Cohen (2003). 13 Whereas Bebchuk and Cohen (2003) use an index of antitakeover laws and greenmail laws separately, we redefine our antitakeover index as equal to their antitakeover index plus one if greenmail laws exist in that state and year. 14 Where available, we adjust the firm s decision to opt-out of these laws according to data from the IRRC. We also include the Gompers et al. (2003) governance index (GIndex) from IRRC data, and the Bebchuk, Cohen, and Ferrell (2005) entrenchment index based on six antitakeover provisions: blank check preferred stock, classified board, limits to amend charter, limits to amend bylaws, supermajority, and poison pill. Although Compustat has variables for the firm s main headquarters and for the current state of incorporation, we gather details manually on historical reincorporation decisions using Mergent online. In addition, our variable for total asset constraint equals the minimum asset to debt ratio for a payout to be made. In states like New York and Texas, this constraint equals 1, in California this constraint equals 1.25, and in Delaware this constraint equals zero. 15 Excerpts 12 Jordan and Mansi (2003) provide evidence that Nelson and Siegel (1987) functional form produces the least pricing errors when compared to other interpolation functions. 13 The antitakeover index of Bebchuk and Cohen (2003) increases by one if the state includes control share statute, fair-price statute, no-freeze-out statute, poison pill endorsement statute, or constituencies statute. 14 Laws restricting greenmail apply to firms incorporated in Ohio and Pennsylvania after We also examined our results using these variables individually and find similar results. 15 Delaware has some constraints on payouts coming from either surplus or profit, but these constraints are usually seen as not very binding. See Gevurtz (2000) for further discussion. 11

12 on payout restrictions laws from Delaware, New York, and California laws are included in Appendix I. The IRRC database provides annual data for the years 1990, 1993, 1995, 1998, 2000, 2002, and 2004 on corporate antitakeover provisions for about 1,500 firms primarily drawn from the S&P 500 and other large corporations, derived from proxy statements, annual reports, SEC filings such as 10-Ks and 10-Qs. The IRRC database also includes the Gompers, Ishii, and Metrick (2003) shareholder rights index (GIndex) used to gauge the balance of power between shareholders and managers. Prior to 1998, the IRRC reported data on antitakeover provisions every two to three years and thereafter every two years. A number of studies fill in the missing years by assuming that the provisions in any given year were in place in the years preceding the publication date (see e.g., Bebchuk and Cohen (2005)). We follow their methodology and fill in the missing years using the initial base year (e.g., the year 1990 data is used for the years 1991 and 1992 and so on). We follow Bebchuk and Cohen (2003) and include the index of antitakeover statutes from Gartman (2000). 16 Firms in certain states, such as Pennsylvania, are able to opt-out of some of the antitakeover laws. We collect additional data on individual firm decisions from the IRRC database and gather data on state statutes restricting payouts from Lexus/Nexus as in Wald and Long (2005). We check to see whether firms in our sample reincorporated using the Mergent database and correct the historical Compustat data for such reincorporations. C. Control Variables The remaining variables are firm and security specific controls (motivated by Wald and Long (2005) and Klock, Mansi, and Maxwell (2005)). Firm-specific controls include firm size, leverage, profitability, intangibility, and firm risk. Firm size, a proxy for takeover deterrent and economies of scale, is measured as the natural log of total assets. Firm leverage, a proxy for financial health, is measured as the ratio of total debt (short and long term debt) to assets. 17 Firm profitability, a proxy for current performance, is measured as the ratio of earnings before 16 The full set of yearly antitakeover statutes is available from Professor Bebchuk s web page at law.harvard.edu. 17 Defining leverage as debt to total market value reduces our sample size by approximately 37% but produces similar results. In unreported regressions, we also include the market to book ratio, a proxy for growth opportunities, in our analysis and find this variable to be insignificant. 12

13 interest, taxes, depreciation, and amortization divided by assets. Intangibles, a proxy for the firm s long term success, are computed as the ratio of patents, trademarks, copyrights, and business methodologies to total assets. Firm risk, a proxy for business conditions, is computed as the firm s standard deviation of the cash flow ratio for the past five years. Given a small number of extreme observations and to ensure that outliers are not driving any of our results, we trim the variables size, leverage, market to book, profitability, and intangibles as in Wald and Long (2005). 18 Security specific variables include: credit ratings, debt duration, debt convexity, and debt liquidity. Firm credit rating (Credit) is the average of both Moody s and S&P bond ratings and represents the average firm credit rating at the date of the yield observation (i.e., credit ratings as the bond seasons). Mansi and Reeb (2002) suggest that using the average of both Moody s and S&P provides the most efficient measure of the default risk premium. Bond ratings are computed using a conversion process in which AAA rated bonds are assigned a value of 22 and D rated bonds receive a value of 1. For example, a firm with an A1 rating from Moody s and an A+ from S&P would receive an average score of 18. The conversion numbers for both Moody s and S&P firm bond ratings used in this study are provided in Appendix II. An alternative methodology used in the literature allows for the fact that the credit rating variable may incorporate part or all of the state law variables, i.e., whether rating agencies incorporate state laws in their analyses of firms. As such, we estimate credit rating without the impact of state laws. That is, we regress the credit ratings (Credit) on the state law variables, and the error term in this case incorporates the credit rating information without the influence or impact of state laws. The error term from this regression is labeled (Rating) and is our primary measure of credit ratings in the multivariate analysis. We also allow for a non-linear relation between bond yield spreads and credit ratings. When examining the entire LBFI dataset, we find that as firm credit ratings move from investment (debt with rating greater than 12) to non-investment grade debt (debt ratings less than or equal to 12), the increase in yield spread for the non-investment categories becomes non-linear. Therefore, we use a binary variable (HighYield) that takes a value of 1 when the debt is non-investment grade and zero otherwise. 18 Though not reported, we also estimate all reported models using the unadjusted variables and find similar results. 13

14 Duration, or the weighted average duration of all public debt outstanding for the firm, is computed as a summation of the weighted durations of all bonds for each firm, with the weight being the amount outstanding for each debt issue divided by the total amount outstanding for all publicly traded debt for the firm. For an individual security, duration (DUR) refers to Macaulay duration and represents a security's effective maturity. DUR is defined as the discounted time weighted cash flow of the security divided by its price. That is DUR = k t CF t P Y t t= 1 (1 + ) (1) where CF t is the security cash flows at time t, t is the number of periods until the cash flow, P is the security price, Y is the yield to maturity, and K is the number of cash flows. Overall, duration measures the rate of change in the price-yield relation and represents the bond s risk. An additional component of debt risk, convexity (Convexity), measures the rate of change in the slope of the price-yield relation, which accounts for the non-linearities present in the term structure of interest rates. This measure is important because of its impact on yield spreads in terms of a convexity premium. For each firm, we compute the weighted average convexity, with the weight being the amount outstanding for each debt issue divided by the total amount outstanding for all publicly traded debt for the firm. Overall, convexity measures the nonlinear relation between yield and price. For liquidity, we use the log of debt age (Age), where the age of the bond is the length of time (in years) that a bond has been outstanding. Beim (1992) finds that liquidity is positively priced in the debt market as more recently issued bonds are more liquid than older bonds. Beim also find that debt securities lose about a third of their liquidity in the first three years of issuance. Therefore, we use the log of the weighted age of bonds (Age) for each firm for each year as a measure of debt liquidity. We compute the weight as the amount outstanding for each debt issue divided by the total amount outstanding for all publicly traded debt for the firm. We also control for the agency problems that arise as a result of various governance structures that firms employ to maximize firm value. These include: insider ownership, institutional holdings, and debt covenants. Insider ownership (Insider) is measured as the ratio of top five insider holdings of common stocks to the total common stocks outstanding. To 14

15 account for the differences in market valuations for firms with different insider ownership, we report insider ownership and the square of that term (Insidersq) to capture any non-linearities observed (see e.g., McConnell and Servaes (1990)). We compute institutional ownership (Inst- Own) as the ratio of shares that institutions owned for a firm divided by the number of shares outstanding. For a subsample, we also control for the restrictions in debt covenants in the firm s outstanding debt using covenant information from Mergent FISD. We control for event risk covenants using poison puts, restrictions on consolidations and mergers, and puts caused by rating declines. We control for payout covenants using restrictions on payments, dividend restrictions, and restrictions on subsidiary dividend related payments. We control for financing covenants such as a negative pledge, cross-acceleration, indebtedness, subsidiary indebtedness, subsidiary stock or preferred stock issuance, stock transfers and sales, subordinated debt issuance, liens, and other infrequently used financing covenants. 19 Finally, given that our variables are sensitive to both time periods and industry effects, we control for both effects using two-digits SIC code and year dummies. D. Descriptive Statistics Approximately 55% of our observations are for firms incorporated in Delaware, with New York, Ohio, New Jersey, Pennsylvania, Virginia, and Texas the next most frequently chosen states for incorporation (4.6%, 4.1%, 3.0%, 2.13%, and 2.1% of the sample), respectively. Table 1 presents the percentage of firms in our sample incorporated in each state. As Delaware firms are more likely to use debt financing, more firms in our sample of debt-issuing firms are located in Delaware than in the overall Compustat data. The opposite holds for firms incorporated in states such as Oregon, Florida, and California. Also included in the table is the total asset constraint variable for each state. In general, with the exception of Delaware (TA constraint = 0), and California (TA constraint = 1.25), most states have a total asset constraint of 1.0. [Insert Table 1 about here] 19 See the FISD Data dictionary for details on covenant definitions. 15

16 Panel A of Table 2 presents descriptive statistics for the variables used in the analysis. Since the large majority of the sample (about 56%) represents firms incorporated in Delaware with total asset constraint of zero, we provide sample analysis segmented by this constraint (TA constraint = 0, or 1, or 1.25). 20 Included are the mean, median, and standard deviation values for yield spread, antitakeover index, GIndex, firm size, firm leverage, firm profitability (return on assets), firm risk, firm credit rating, debt age, debt duration, debt convexity, institutional ownership, insider ownership, and payout, financing, and event risk covenants. Finally, because of the limited amount of observations in the total asset constraint category of 1.25 (less than 1.5% of the sample), we present comparisons below based on firms either incorporated in Delaware or those incorporated in states other than Delaware with TA constraint = 1 (referred to hereafter as Delaware vs non-delaware firms). 21 Overall, the results suggest that Delaware firms have larger yield spreads, lower credit ratings, less antitakeover provisions, higher leverage ratios, lower return on assets, and higher firm risks than non-delaware firms. Delaware firms use slightly more payout, financing, or event risk covenants; however, this difference is relatively small. Both samples, however, have similar GIndex structures, firm size, debt duration, debt age, and institutional and insider holdings. In terms of the subset of the GIndex equal to the state antitakeover provisions, we find that while the GIndex of both Delaware and non-delaware are similar (both have median values = 10), the antitakeover index for non-delaware firms are about three times larger than Delaware firms. This is expected since Delaware has only one antitakeover statue, the business combination statue (also known as a no freeze-out statute), which restricts bidders from merging assets for a specified number of years. For our main variables in the analysis, Delaware firms have mean (median) yield spreads of about 439 (273) basis points vs 269 (155) basis points for non-delaware firms. Because of the skewness of this variable, we use the log of the yield spread, rather than the level, in our analysis to provide a better fit and to insure that any fitted values remain positive The 1.25 (1.0) payout constraint would be binding for approximately 25% (10%) of the non-investment grade firms in our sample. 21 Wald and Long find that approximately 5.5% of public industrial firms are incorporated in states with a total asset constraint of 1.25 (California and Alaska). Our smaller fraction of 1.5% is due to the requirement that the firm s debt be available in the LBFI dataset. 22 Using the yield spread, rather than the log of the spread, does not change the statistical or economic significance of the results. 16

17 For the overall sample (both Delaware and non-delaware firms), firm size has a mean of $2 billion, a standard deviation of $420 million, and 25 th and 75 th percentile values of $748 million and $5.2 billion, respectively. The median leverage (short term plus long term debt) ratio is 52% with a standard deviation of 26%, which suggest that a large portion of the sample consist of firms that have significant liabilities in their capital structure. The firms are on average profitable with a mean and median ROA of 13%. Firms in the sample have average variability of cash flows in the past five years of 40%. Institutions, on average, owned about 53% of the shares outstanding with a standard deviation of 21%, while insiders owned about 2.7% on average, with a standard deviation of 7%. In terms of debt variables, the mean and median bond rating variable roughly equates to S&P ratings of BB+ and BBB-, respectively, which indicates a large portion of the sample has slightly below average quality debt. This low average rating is expected because of the high debt ratios for firms in the sample. The payout, financing, and event risk covenants in the sample have mean values of 0.44, 1.94, and 1.19, respectively. The mean traded debt has duration and convexity of about 6 years and 0.58, respectively. Debt has a 75 th percentile duration of 7.2 years, and on average, has been outstanding for 4.7 years. [Insert Table 2 about here] Panel B of Table 2 describes the industry distribution of the sample (in absolute number and in percentage) using the standard Security Industry Classification (SIC) codes segmented by Delaware and non-delaware firms. Although we use two digit SIC codes to control for industry effects in our empirical analysis, for brevity we only report one digit SIC codes in our descriptive analysis. Industries include: agriculture, forestry, and fishing, mining and construction, manufacturing (food-petroleum and plastics-electronics), transportation and communications excluding utilities, wholesale and retail trade, finance, insurance, real estate, services, and public administration. Based on our segmentation, it seems that Delaware vs non- Delaware firms differ slightly in their concentration of industries. For Delaware firms, a large portion of the sample is concentrated in manufacturing (about 48%), transportation and communication (20%), wholesale and retail trade (13%), services (9%), and mining and construction (7%). For non-delaware firms, the majority of the sample is concentrated in 17

18 transportation and communication (47%), manufacturing (32%), wholesale and retail trade (10%), and services (6%). Panel C of Table 2 provides the Pearson correlation coefficients between the state law variables, yield spreads, and various selected control measures. In general, the yield spread is negatively correlated with the total asset constraint variable, antitakeover index, GIndex, firm size, profitability, credit ratings, and institutional ownership, and positively related to the sum of payout and financing covenants, leverage, firm risk, and managerial ownership. Consistent with our expectations, the analysis indicates that firms with more asset constraints have higher credit ratings and lower costs of debt financing. However, because of possible confounding effects by other variables, we use a multivariate framework to explore our hypotheses. IV. Empirical Results A. Evidence on Credit Ratings and State Laws We test the cross-sectional relation between credit ratings and various antitakeover provisions proxied by the state law variables, while controlling for firm specific measures motivated by Mansi, Miller, and Maxwell (2004), and present our results in Table 3. Our primary estimation method is a pooled panel regression. We report a White heteroskedasticconsistent estimator adjusted for clustering of standard errors across firms (see Wooldridge (2001) or Petersen (2006) for further discussion). The primary specification is Ratings i,t = β 0 + B 1 (TA Constraint i,t ) + B 2 (Antitakeover i,t ) + B 3-7 (FirmFactors i,t ) +B 8-23 (Time_Dum t ) + B (Ind_Dum i,t ) + ε i,t (2) where TA constraint is the total asset constraint depending on state of incorporation, and Antitakeover is the state antitakeover index. The remaining variables include controls such as firm size, firm leverage, ROA, market-to-book, firm risk, and Time_Dum and Ind_dum represent time and industry dummies, respectively. We include the GIndex, the square of leverage (Leveragesq), the square of firm risk (FirmRisksq), insider ownership and its square (Insider and Insidersq), and institutional ownership (Inst-own) in alternative specifications. 18

19 [Insert Table 3 about here] Table 3 provides our results. Model 1 presents our primary regression using the TA constraint and the Antitakeover Index. Model 2 substitutes the GIndex for the Antitakeover Index. Models 3 and 4 add the squared terms of leverage and firm risk, respectively. Model 5 adds governance variables including insider ownership, the squared term of insider ownership, and institutional ownership. In all specifications, a larger (i.e., more strict) total asset constraint is associated with a significantly higher credit rating. For the largest sample in Model 1, a total asset constraint of 1 is associated with a rating increase of 0.552, more than half a rating step. For the smaller samples where we also include the GIndex as an additional control, a total asset constraint of 1 is associated with an increase in ratings of approximately one third of a rating step. In unreported regressions, we check whether this smaller increase is due to the smaller sample of firms or to the impact of additional controls such as the GIndex. We find that the change between Models 1 and the remaining specifications is due principally to the smaller samples with smaller firms used in the latter models. The control variables in all specifications have their expected signs. While payout constraints are associated with significantly higher ratings, no such impact is visible for the antitakeover index. On the other hand, the total GIndex is associated with higher credit ratings, suggesting that components of the GIndex other than state laws are associated with significantly higher credit ratings. B. Evidence on the Cost of Debt and State Laws We next test the cross-sectional relation between various total asset constraints and the cost of debt financing while controlling for antitakeover provisions measured by the state law antitakeover index and firm and security specific variables. Again, our primary estimation method is pooled panel regressions with standard errors corrected for clustering by firms. The primary specification is LSpread i,t = β 0 + β 1 (TA Constraint i,t ) + β 2 (Antitakeover i,t ) + β 3-8 (FirmFactors i,t ) + β 9-13 (Debtfactors i,t ) + β (GovernanceFactors i,t ) 19

20 + β (Time_Dum t ) + β (Ind_Dum i,t ) + ε i,t (3) where the variables LSpread is the log of the yield spread, TA constraint is the total asset constraint depending on state of incorporation, and Antitakeover is the antitakeover index. The remaining variables are controls discussed above and Time_Dum and Ind_dum represent time and industry dummies, respectively. In this specification, we follow Klock, Mansi, and Maxwell (2005) and use the residual from regressing the credit rating on state laws (TA Constraint and either Antitakeover Index or GIndex) rather than the raw credit rating to capture the impact of state laws. That is, we are interested in the overall effect of these laws on yield spreads and examine what proportion of the laws impact is captured by the credit ratings below. Our principal concern in the analysis is the total asset constraint coefficient, B 1. Formally, our primary null hypothesis is: H 0,1 : There is no significant relation between the total asset constraint and bond yields, and thus B 1 = 0 A significant coefficient on TA Constraint would allow us to reject our null hypothesis. We expect the coefficient on the TA Constraint to be negative. One possible issue with this specification is that there may be a self-selection bias (see e.g., Wald and Long (2005)). That is, the firm s decision of where to incorporate may be endogenous with the yield spread, and this may bias our estimated coefficients. In order to test for selfselection, we run our primary regression with a Heckman self-selection correction, using the same specification for whether the firm incorporates in its home state as in Wald and Long. We find that the self-selection correction, the inverse mills ratio from the first-pass probit regression, is not significant in any of our specifications for bond yield. This result suggests that self-selection is not an issue for these regressions. Intuitively, whereas Wald and Long find that firms decisions about state of incorporation and debt ratio are jointly determined, our dependent variable, the yield spread (conditional on leverage), is instead a function of the market s perception of the bond s value and not a choice variable for the firm. 20