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  • 1. Debt Structure and Debt Specialization* Paolo Colla Università Bocconi† Filippo Ippolito Università Bocconi‡ Kai Li University of British Columbia§ This version: March, 2010 First version: October, 2009 Abstract This paper provides the first large sample evidence on the patterns and determinants of debt structure using a new database of publicly listed U.S. firms. Within what is generally referred to as debt financing, we are able to distinguish between commercial paper, revolving credit facilities, term loans, senior and subordinated bonds, and capital leases. We show that small and unrated firms rely exclusively on either capital leases or bank debt for financing. In contrast, financing through multiple types of debt is only observed for large firms with very high credit quality. Most sample firms concentrate their borrowing in only one of these debt types, and this debt specialization persists over time. Finally, we show that firm characteristics that are known to be associated with leverage, such as profitability and tangibility of assets, have very different effects on different types of debt. Our paper suggests that debt structure is an important part of capital structure decisions. Keywords: debt specialization, debt structure, revolving credit facilities, senior bonds, term loans JEL classification: G32 * We thank Miguel Ferreira, Mark Flannery, Michael Meloche, and seminar participants at the New University of Lisbon (Nova), and Universitat Pompeu Fabra for helpful comments. We thank Milka Dimitrova and Huasheng Gao for excellent research assistance. Li acknowledges the financial support from the Social Sciences and Humanities Research Council of Canada. All remaining errors are our own. † Department of Finance-2-D2-08, Università Bocconi, Via G. Röntgen, 20136 Milano, Italy, (+39) 02.5836.5346, paolo.colla@unibocconi.it. ‡ Department of Finance-2-D2-02, Università Bocconi, Via G. Röntgen, 20136 Milano, Italy, (+39) 02.5836.5918, filippo.ippolito@unibocconi.it. § Sauder School of Business, University of British Columbia, 2053 Main Mall, Vancouver, BC V6T 1Z2, 604.822.8353, kai.li@sauder.ubc.ca.
  • 2. Debt Structure and Debt Specialization Abstract This paper provides the first large sample evidence on the patterns and determinants of debt structure using a new database of publicly listed U.S. firms. Within what is generally referred to as debt financing, we are able to distinguish between commercial paper, revolving credit facilities, term loans, senior and subordinated bonds, and capital leases. We show that small and unrated firms rely exclusively on either capital leases or bank debt for financing. In contrast, financing through multiple types of debt is only observed for large firms with very high credit quality. Most sample firms concentrate their borrowing in only one of these debt types, and this debt specialization persists over time. Finally, we show that firm characteristics that are known to be associated with leverage, such as profitability and tangibility of assets, have very different effects on different types of debt. Our paper suggests that debt structure is an important part of capital structure decisions. Keywords: debt specialization, debt structure, revolving credit facilities, senior bonds, term loans JEL classification: G32 1
  • 3. I. Introduction Much attention has been devoted to the issues of why firms choose between equity and debt, and how optimal capital structure is designed to minimize the cost of financing (see the survey by Frank and Goyal (2007) of the voluminous literature on capital structure). In this paper, we focus on a much less studied topic in capital structure, namely debt structure. Our goals are to explore the types of debt commonly employed by U.S. public companies, and to examine their determinants. To our knowledge, our paper is the first to provide large sample evidence on the subject. The existing literature suggests a number of important and as of yet unanswered questions concerning the patterns and determinants of debt structure: How are different types of debt used in practice to meet corporate funding needs? Do firms tend to specialize in one or two debt instruments, or do they borrow simultaneously from a variety of sources? How do these choices vary with firm characteristics, including their access to the public bond markets and their funding needs? To answer these questions, we take advantage of a new database available through Capital IQ, an affiliate of Standard and Poor’s, to examine debt structure of publicly listed U.S. firms.1 Within what is generally referred to as debt financing, we are able to distinguish between commercial paper, revolving credit facilities, term loans, senior and subordinated bonds, and capital leases. After merging the Capital IQ database with the Compustat database, we end up with a large panel data set that comprises 14,242 firm-year observations involving 3,332 unique firms for the period 2001-2007. In addition to information on debt structure, the sample also contains leverage and other firm characteristics (e.g., firm size and profitability). Our first main finding is that firms specialize in borrowing: Most firms concentrate their borrowing in only one of the above types of debt. As primarily an exploratory exercise, we use 1 The SEC mandated electronic submission of SEC filings in 1996. Capital IQ has been collecting information about debt structure since then. The coverage has much improved since 2001 and hence the starting point of our sample period. 1
  • 4. cluster analysis to search for patterns of debt structure. We identify seven different groups of firms: Six of them are users of a single type of debt, while only one group relies simultaneously on more than one debt instrument. This last group is mainly composed of the largest firms with the highest credit ratings. The evidence is suggestive of the idea that an average U.S. public firm specializes in borrowing one type of debt to meet its funding needs. To further corroborate the above finding, we analyze conditional debt structure. We first require firms to allocate a significant fraction of their debt to a given type of debt, and we then examine the composition of their debt structure under this condition. We find that the majority of firms rely overwhelmingly on only one type of debt; specifically, the one which we have conditioned upon. For example, conditioning for firms to have more than 30% of their debt in term loans, we find that among the selected firms term loans represent approximately 70% of their debt. We show that this result is robust to different specifications of the conditioning threshold, including using the debt structure in the prior year. For example, over 70% of the firms that have at least 30% of their debt in term loans in any given year continue to have at least 30% of their debt in term loans in the following year. This finding supports the idea that debt specialization is not only a widespread phenomenon, but also a persistent one. Second, we show that a key factor for understanding debt structure is credit quality. We find that debt structure varies substantially between not only unrated and rated firms but also across firms with different credit ratings: Large and high credit quality firms tend to have access to different sources of financing, while small and unrated firms rely exclusively on either capital leases or bank debt for financing. Faulkender and Petersen (2006) show that firms that do not have access to the public debt markets, as measured by not having a debt rating, tend to have lower debt ratios. Our finding highlights that the actual credit rating, a comprehensive measure of firm credit worthiness, affects firm access to different sources of financing. Third, we find that there are asymmetric changes in debt structure in response to rating downgrades versus upgrades. In particular, downgrades are associated with decreases in bank 2
  • 5. debt and increases in senior bonds, while upgrades are only associated with increases in revolving credit facilities. Sufi (2009) finds that firms with high cash flows are more likely to obtain bank lines of credit. Our finding further shows that firms with improved credit quality increase their usage of revolving credit facilities, suggesting that credit ratings are an important metric in banks’ lending decisions. Finally, we address the question of how choices of different types of debt vary with firm characteristics, including their access to the public bond markets and their funding needs. We rely on some recent papers in capital structure (see for example, Fama and French (2002) and Lemmon, Roberts and Zender (2008)) to identify firm characteristics that are known to be associated with cross-sectional variations in leverage, including profitability, asset tangibility, market to book ratios, firm size, cash flow volatility, and the dividend payer dummy variable. We find that the effects these firm characteristics have on leverage and on types of debt can vary substantially. For example, the previous literature has shown profitability to be negatively and significantly associated with leverage as predicted by the pecking order theory. Our analysis corroborates this finding, and further shows that this negative association is mainly driven by two types of debt: senior bonds and capital leases. In contrast, profitability is positively and significantly associated with revolving credit and term loans. These results indicate that using a gross measure of leverage such as total debt can be misleading, as it hides heterogeneity across various types of debt. Further, there are some significant non-linear relations between actual credit ratings and types of debt beyond the usual categorization of firms being rated or not. For example, the amount of senior bonds is increasing in credit quality, peaks at the rating of A, and then is decreasing in credit quality as the latter further improves. Importantly, we show that the presence of a financing gap is mainly met by firms issuing senior and subordinated bonds, complementing prior work by Frank and Goyal (2003) and Lemmon and Zender (2009) that focuses on the role of financing gap in testing capital structure theories. We conclude that the choice of using different types of debt instruments is also an important financing decision. 3
  • 6. Our paper is most related to Rauh and Sufi (2010) who examine types, sources, and priorities of debt using a sample of 305 rated public firms. Our work differs from theirs in the following aspects. First, our much larger sample allows us to examine the financing patterns of unrated firms as well, while the Rauh and Sufi sample is limited to rated firms. Second, as a result of the broader sample, we are able to uncover the phenomenon of debt specialization in firm financing behavior, which is otherwise unobservable. Indeed, Rauh and Sufi conclude that financing through multiple sources of debt is the norm among large and rated firms in their sample, which we confirm only among a subsample of our firms. We are also able to show that smaller firms with no or poor credit ratings focus on only one type of debt. Finally, we present new evidence on how the presence of a financing gap affects choices of debt instruments. The outline of the paper is as follows. Section II reviews the related literature that motivates the current study. Section III describes our data and provides an overview of debt structure. Section IV presents evidence that suggests specialization in debt structure. Section V provides our explanations for the observed financing pattern. Section VI carries out various robustness checks on our main findings and some additional investigations. Finally, Section VII summarizes our findings and concludes. II. Literature Review The literature on capital structure is vast and we only selectively review empirical papers that primarily examine the structure of debt rather than its level.2 The first strand of the literature examines the role of bank relationships, sources of debt, and growth opportunities in capital structure decisions. Houston and James (1996) show that reliance on bank borrowing depends on firm size, the importance of growth opportunities and intangible assets, leverage, the number of bank relationships, and the firm’s access to public debt 2 As such, we also bypass a strand of the literature that examines debt maturity (e.g., Barclay, and Smith (1995), Guedes and Opler (1996), Stohs and Mauer (1996), and Billett, King and Mauer (2007)). 4
  • 7. markets. They find that reliance on bank borrowing is decreasing in firm size and leverage, suggesting that the banks specialize in lending to smaller, less risky firms. They also find that the relation between bank borrowing and the importance of growth opportunities is negative for firms with a single bank relationship, and positive for firms with multiple banking relationships and public debt. Johnson (1997) focuses on the relation between sources of debt (bank debt, non-bank private debt, and public debt) and firm characteristics. He finds that firms use more public debt if they have lower information and monitoring costs, lower likelihood and costs of inefficient liquidation, and weaker incentive to harm the lenders. Goyal, Lehn, and Racic (2002) examine the relation between growth opportunities and corporate debt policy using the case of the U.S. defense industry during the late 1980s to early 1990s as a natural experiment. They show that when growth opportunities decline, firms increase their debt level, lengthen debt maturity, decrease private debt relative to public debt, and decrease senior debt. Using a sample of 305 randomly selected non-financial firms for the period 1996-2006 (2,453 firm-year observations), Rauh and Sufi (2010) examine the determinants of debt structure. They first show that almost three quarters of their sample firm-year observations have more than two types of debt, and that a quarter of the sample firms has no significant one-year change in their total debt but significant change in their composition in debt. Further, high credit quality firms (BBB and higher) primarily use two tiers of capital: senior unsecured debt and equity. Low credit quality firms (BB and lower) tend to use several tiers of debt including secured, senior unsecured, and subordinated issues. Finally, they establish the causal relation between changes in credit quality and choices of different debt instruments by focusing on a sample of “fallen angels.” 5
  • 8. The second strand of the literature focuses on the new issue decisions. Hadlock and James (2002) ask why some firms borrow from public sources while others borrow from banks. They find that undervalued firms tend to borrow from banks, because banks have the ability to accurately price financial claims and thus alleviate any information asymmetry problem. Also, the sensitivity of choosing bank debt to solve information problem is greater for firms with no public debt, which is the authors’ proxy for the low contracting costs of bank debt. Denis and Mihov (2003) examine the choice among bank debt, non-bank private debt, and public debt using evidence from new corporate borrowings. They find that new debt choices are linked with prior financing decisions: Firms with public debt outstanding are likely to issue public debt, while firms with no reputation in the credit markets resort predominantly to bank debt. Controlling for the existing mix of debt claims, they show that firms with the highest credit quality use public debt; firms with medium credit quality borrow from banks; and firms with the lowest credit quality use non-bank private debt. Gomes and Phillips (2009) examine why public firms issue different types of securities. They show that asymmetric information is a major determinant of security issuance decisions within private and public markets as well as across markets. Further, firms that switch from issuing public securities to private equity and convertibles experience increases in the extent of asymmetric information, while firms that switch from issuing private securities to public equity experience decreases in the extent of asymmetric information. In summary, most prior studies have examined the cross-sectional determinants of different sources of debt financing in a piecemeal fashion: choices between public versus private debt (including differentiating between bank and non-bank private debt), choices between public versus private issues, with Rauh and Sufi (2010) as the notable exception. Our paper focuses on the patterns and determinants of debt structure using a more detailed and comprehensive dataset on types of debt, thus complementing prior studies of the determinants of new security issues. 6
  • 9. III. Data Overview A. Sample Description We start with U.S. public firms traded on AMEX, NASDAQ, and NYSE covered by both Capital IQ (CIQ) and Compustat from 2001 to 2007. CIQ compiles capital structure details by going through financial footnotes contained in firms’ 10K SEC filings.3 We remove utilities (SIC codes 4900-4949) and financials firms (SIC codes 6000-6999) and end up with 34,923 firm-year observations. We further remove 1) firm-years with missing data for any of the Compustat variables listed in Table A1 (21,857 observations remaining); 2) firm-years with market or book leverage outside the unit interval (as in Lemmon, Roberts and Zender (2008), 21,674 observations remaining); 3) firm-years with zero total debt (17,232 observations remaining); 4) firm-years with missing data for any of the CIQ variables listed in Table A2 (15,717 observations remaining); and 5) firm-years for which the difference between total debt as reported in Compustat and the sum of debt types as reported in CIQ exceeds 5% of total debt. Our final sample has 14,242 firm-year observations involving 3,332 unique firms. In constructing our variables we use the same definitions as in Lemmon, Roberts and Zender (2008). The appendix provides a detailed description of the variables used in our analysis. Total assets, book value (BV) equity, and total debt are expressed in millions of 2001 dollars deflated by the consumer price index. All variables are winsorized at the upper and lower 0.5th percentiles to mitigate the effect of outliers and data errors. Table 1 presents descriptive statistics. Panel A contains means and medians of key firm characteristics aggregated across all years for our sample firms (columns (1) and (2)). We show that over the sample period, the mean (median) market leverage as measured by the ratio of total 3 Regulation S-X requires firms to detail their long-term debt instruments. Regulation S-K requires firms to discuss their liquidity, capital resources, and operating results. As a result of these regulations, firms detail their long-term debt issues and bank revolving credit facilities. Firms often also provide information on notes payable within a year (Rauh and Sufi (2009)). 7
  • 10. debt to the sum of total debt and market value of equity is 0.215 (0.159). In Rauh and Sufi (2010), the sample mean (median) market leverage is 0.263 (0.238) for a sample of 305 firms with credit ratings in at least one year from 1996 to 2006. Using a sample of non-financial, non- utility firms from Compustat over the period 1986-2000, Faulkender and Petersen (2006) report that the mean (median) market leverage is 0.222 (0.183) for leveraged firms. These comparisons suggest that the leverage ratio of our sample firms is similar to that reported in prior studies. In comparison to the sample in Rauh and Sufi, firms in our sample are less profitable, (as measured by operating income before depreciation over assets), have less tangible assets as a percentage of total assets, and have a similar market to book ratio of assets. The sample mean (median) firm size in terms of total assets is $2.35 billion ($392.2 million) in 2001 constant dollars. In contrast, the sample mean (median) firm size is $6.19 billion ($1.31 billion) in 1996 constant dollars for the Rauh and Sufi sample. About 30% of our sample firms pay out dividends in any given year, in contrast with the evidence presented in Fama and French (2001) where the proportion of firms paying cash dividends in 1999 is only 20.8%. Panel A of Table 1 also presents means and medians for key firm characteristics for the entire Compustat population (columns (3)–(4)), and for Compustat leveraged firms, i.e., firms with positive debt, (columns (7)–(8)) over the same period of our sample. The two Compustat samples are formed by imposing similar filters as to our sample except filters 4) and 5). Our sample on average covers two-thirds of the Compustat population, and close to 85% of the Compustat leveraged firms. Columns (5)–(6), and (9)–(10) test whether our sample is different from the Compustat population, and from the Compustat leveraged firms, respectively. We show that our sample firms are larger, more leveraged, more profitable, have more tangible assets, and pay out dividends more often, while firms in the Compustat population have higher market-to-book ratios and higher cash flow volatility. Compared to the Compustat leveraged firms, our sample firms are significantly more profitable and more likely to make dividend payments, although the 8
  • 11. economic significance of these differences is small. We conclude that our sample is representative of the Compustat leveraged firms. Table 1 Panel B presents summary statistics of key firm characteristics over time. Our sample firms experience a gradual decline in their market leverage, a small, gradual increase in their size, and a gradual increase in their propensity to pay dividends. Overall, however, we find that most firm characteristics have little variation over the sample period. B. Debt Structure Overview CIQ decomposes total debt into seven mutually exclusive debt types: commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt.4 Our appendix provides an example illustrating how CIQ collects and constructs the various debt types. Table 2 provides detailed summary statistics for debt types (normalized by total debt). We first show that the majority of sample firms rely on senior bonds for financing. The sample mean (median) ratio of senior bonds to total debt is 0.361 (0.130). Second, the median ratios of both revolving credit and term loans to total debt are zero, while the 75th percentiles are above zero, suggesting that between a quarter and a half of the sample firms rely on revolving credit facilities or term loans, both provided by banks. When adding up both debt types to obtain total bank debt, we find that more than half of the sample firms employ bank debt, with the sample mean (median) at 0.390 (0.229) (untabulated). Third, more than a quarter of sample firms have capital leases, and less than a quarter of the sample firms use subordinated bonds. Lastly, we show that the 95th percentile of commercial paper is zero, suggesting that less than 5% of the sample firms use commercial paper for financing. Total adjustment is the difference between the total debt variable obtained from Compustat and the sum of CIQ seven debt types. When forming our sample, we have imposed 4 Other debt mostly consists of short-term borrowings. Occasionally, it takes other forms such as deferred credits, fair value adjustments related to hedging contracts, or trust-preferred securities. 9
  • 12. the filter that the total adjustment for firms in the sample be less than 5% of total debt. After this filter, there is little discrepancy between the sum of debt types from CIQ and total debt from Compustat: Both mean and median values of total adjustment are zero, and only the 99th percentile of this variable is slightly above 3% of total debt. This result, together with our wide coverage of Compustat leveraged firms, is reassuring about the CIQ’s data quality. It is interesting to compare the composition of debt in our sample firms with that reported by Rauh and Sufi (2010). Defining bank debt as the sum of revolving credit and term loans, they report a mean total debt to capital ratio of 0.502 for their sample firms, with a mean bank debt to total debt ratio at 0.263, and a mean bonds (the sum of senior and subordinated bonds) to total debt ratio at 0.382. About two thirds of their sample firms use bonds and bank debt. Given that Rauh and Sufi have a sample of larger firms with better ratings than ours, bank debt appears to be less important for their sample than for our own. About 15% of their firms use commercial paper, a third uses capital leases, and about a quarter uses convertible debt. As we discuss in more detail below, it appears that our sample firms employ fewer types of debt instruments at any point in time, as compared to the larger firms in the Rauh and Sufi sample. In summary, although there are many different types of debt, for our sample of firms, senior bonds are the most commonly employed debt instrument, followed by revolving credit and term loans. In the rest of the paper, we provide a more detailed investigation of patterns and determinants of debt structure. C. Credit Ratings and Debt Structure The literature has previously examined the relation between credit ratings and leverage. Diamond (1991), Chemmanurs and Fulghieri (1994), and Bolton and Freixas (2000) have shown that credit quality is the primary source of variation driving a firm’s optimal choices of different types of debt instruments. Faulkender and Petersen (2006) examine the role of the source of capital in firms’ financing decisions. Using a dummy variable for being rated as a proxy for firm 10
  • 13. access to public bond markets, they find that firms with access have substantially more debt. Kisgen (2006) finds that firm credit ratings affect capital structure decisions: Firms near a credit rating upgrade or downgrade issue less debt. Lemmon and Zender (2009) use the likelihood of being rated as a proxy for debt capacity. They show that after accounting for debt capacity, the pecking order appears to be a good description of a firm’s financing behavior. Rauh and Sufi (2010) find that high credit quality firms (BBB and higher) rely almost exclusively on two tiers of capital—senior unsecured debt and equity—while lower credit quality firms (BB and lower) use multiple tiers of debt including secured, senior unsecured, and subordinated issues. Table 3 presents an overview of the relation between credit ratings and debt structure. We consider a firm-year to be rated if the firm has at least one monthly Standard & Poor’s long-term issuer rating, as recorded in Compustat (data item 280). About a third of our sample firms are rated. In untabulated analysis, we find that there is little temporal variation in the fraction of firms being rated over time.5 Panel A presents differences in the use of various debt types between unrated and rated firms in our sample. We show that revolving credit facilities and term loans together, on average, account for about half of unrated firms’ total debt, while senior bonds account for about 30% of their total debt. Unrated firms are also the heaviest users of capital leases. Overall, unrated firms use significantly less commercial paper and senior and subordinated bonds, and significantly more revolving credit and capital leases than their rated counterparts, suggesting that both banks and lessors have a comparative advantage in dealing with information asymmetry associated with unrated firms. At the bottom of Panel A, we also present sample mean (median) market leverage of unrated and rated firms. We show that consistent with Faulkender and Petersen 5 Using Compustat firms over the period 1986-2000, Faulkender and Petersen (2006) show that only 19% (21%) of firms (with positive debt) have debt ratings. They conclude that public debt is uncommon. In unreported analysis, we find that our sample firms are larger (both in terms of sales and book value of assets), and have a higher book leverage ratio than those in Faulkender and Petersen (2006). This is consistent with Lemmon and Zender’s (2009) finding that large firms with high leverage are more likely to be rated. Focusing on 305 randomly chosen Compustat firms with a long-term issuer rating in at least one year from 1996-2006, Rauh and Sufi (2009) show that three- quarters of their firm-year observations are rated. 11
  • 14. (2006), unrated firms tend to employ less debt with a mean (median) market leverage ratio of 17% (11%) than rated firms with a mean (median) ratio of 31% (26%). To examine the relation between credit ratings and debt structure, we first assign to each monthly S&P letter rating class an integer number ranging from 1 (AAA) to 22 (D). Then, for each rated firm-year we round the average monthly rating to the nearest integer, and refer to it as the firm rating in a given year. In unreported analysis, we find that 16.7% of our sample firms have a credit rating of A and higher. Close to 43% of our sample firms have investment grade ratings (equal to or higher than BBB-).6 Panel B provides differences in the use of various debt types (as a share of total debt) across a broad rating spectrum. We first show that there is a non-linear relation between credit quality and the amount of senior bonds used by our sample firms: The amount of senior bonds is increasing in credit quality, peaks at the rating of A, and then is decreasing in credit quality. Second, term loans and subordinated bonds are most heavily used by speculative grade (equal to or lower than BB) firms. Third, commercial paper is used almost exclusively by investment grade firms, especially AAA- and AA-rated firms. We conclude that credit quality affects both the composition and the level of debt, as shown by Faulkender and Petersen (2006) and Kisgen (2006) for the latter. Later in our multivariate analysis, we will include a dummy for each different rating class and for firms that are not rated to control for the complex relation between credit ratings and debt structure.7 IV. Debt Specialization and Persistence A. Cluster Analysis 6 Using Compustat firms from 1986-2001, Kisgen (2006) shows that 44.2% of his sample firms have a credit rating of A and higher, and 69.1% of his sample have investment grade ratings. Rauh and Sufi (2009) report 21.7% of their firms have a credit rating of A and higher, and close to half has investment grade ratings. The difference in rating distributions between the Kisgen’s sample and our sample is probably due to the fact that his sample includes financial and utilities which tend to have better ratings than industrial firms. 7 We assign an integer equal to 23 to the variable “Rating” for an unrated firm-year observation. 12
  • 15. Our first piece of evidence on specialization in borrowing comes from cluster analysis, which is commonly used to discover unknown structures in data by maximizing variance (in terms of the Euclidean distance) between clusters and minimizing it within clusters. Once we separate data in clusters, we effectively remove much of the variance in any of the debt types. Table 4 Panel A presents summary statistics for debt structure and key firm characteristics across the identified clusters using firm-year observations, sorted according to ascending median firm size.8 Cluster 1 includes the smallest firms in our sample. These firms have much lower profitability and asset tangibility, but much higher M/B ratios and cash flow volatility than the average firm in our sample. They are also much less likely to pay dividends and are essentially unrated. These firms tend to use predominantly capital leases for financing. The group mean (median) capital leases to total debt ratio is 0.945 (1.000). It is worth noting that this group of firms has the lowest leverage among the sample firms. The group mean (median) market leverage is 0.034 (0.003), compared to the full sample mean (median) at 0.215 (0.159). Cluster 2 includes the second smallest firms in our sample. These firms are less likely to make dividend payments and to be rated as compared to the average firm in our sample. They tend to use mostly term loans. The group mean (median) term loans to total debt ratio is 0.819 (0.875). Cluster 3 has the smallest number of observations. The group median firm size suggests that firms in this cluster actually are quite small in size. These firms tend to use predominantly other debt (see footnote 4 for a description). The group mean (median) other debt to total debt ratio is 0.913 (1.000). This group of firms has the second lowest leverage among the sample firms. The group mean (median) market leverage is 0.130 (0.044). 8 Firm characteristics are measured contemporaneously. Using lagged measures gives qualitatively the same results except that sample size is slightly smaller. 13
  • 16. Cluster 4 is the set of firms that have higher profitability and lower M/B ratios than the average firm in our sample. Less than a tenth of them are rated as compared to a third of our sample firms that are, and the credit quality of these firms is slightly better than that of cluster 1 firms. These firms tend to use mostly revolving credit. The group mean (median) revolving credit to total debt ratio is 0.829 (0.884). Firms in cluster 5 are considerably bigger: The median firm size is more than three times larger than that of the firms in cluster 4. These firms are significantly less likely to make dividend payments as compared to the average firm in the sample: Only 13.4% of them pay dividends. More than half of them are rated. These firms tend to use mostly subordinated bonds. The group mean (median) subordinated notes and bonds to total debt ratio is 0.799 (0.858). This set of firms appears to have the second highest leverage among the sample firms. The group mean (median) market leverage is 0.291 (0.241) as compared to an average mean (median) market leverage of 0.215 (0.159). Cluster 6 includes the second largest firms in the sample. They are more likely to make dividend payments as compared to the average firm in the sample. Close to half of them are rated, and they appear to have slightly better credit quality than the average firm in the sample. These firms tend to use predominantly senior bonds. The group mean (median) senior bonds to total debt ratio is 0.925 (0.965). Finally, cluster 7 includes the largest firms in the sample. These firms are more profitable, and have more tangible assets and lower cash flow volatility than the average firm in the sample. They are also more likely to make dividend payments. More than half of them are rated, and they have much better credit quality than the rest of the sample firms. These firms tend to use a mix of senior bonds, revolving credit, and term loans. The group mean (median) senior notes and bonds, revolving credit, and term loans to total debt ratio is 0.542 (0.565), 0.173 (0.118), and 0.116 (0.002), respectively. It is worth noting that this group of firms has the highest leverage among the sample firms. 14
  • 17. In summary, the evidence from cluster analysis suggests that firms specialize in borrowing from one type of debt. The smallest firms exclusively use capital leases for financing. This is probably due to the fact that they have no other source of capital available. Larger firms predominantly rely on only one major source of financing, which may be term loans, revolving credit, subordinated bonds, or senior bonds. Only the largest and least risky firms simultaneously employ multiple types of debt. Our findings on debt specialization are in stark contrast to Rauh and Sufi (2010), who show that their average sample firm employs multiple types of debt simultaneously. We attribute the difference in findings to the different samples examined in their paper and ours. They focus primarily on large and rated firms, while in our sample only a third of the firms are rated. From our findings we conclude that across public firms, specialization—not diversity—in types of debt is the dominant phenomenon. B. Conditional Debt Structure Our second piece of evidence on debt specialization comes from examining conditional debt structures. Table 5 Panel A presents the shares of firm-year observations conditional on a particular debt type exceeding 30% of total debt (significant user). Looking across the rows in Panel A, we find that significant users of one debt type are rarely significant users of any other debt types. This is true with the exception of significant users of commercial paper which are all very large firms that simultaneously employ a significant amount of senior bonds. In all other cases, the table indicates that if a firm’s use of a particular type of debt exceeds 30% of its debt, that type is likely to be its only source of debt financing. Furthermore, the last column shows that close to 45% of the sample firms are significant users of senior bonds, and about a quarter of the sample are significant users of either revolving credit or term loans. These results provide further support to the idea that firms specialize in borrowing from one type of debt. If firms were 15
  • 18. simultaneously employing multiple types of debt, we would have observed few firms exceeding 30% of their debt from a single source of financing. Panel B presents both the mean and median ratios of each debt type to total debt conditional on a particular debt type exceeding 30% of total debt. Specifically, we first impose the condition that a firm’s use of a particular debt type exceeds 30% of its debt, thus identifying a subset of firms. Then, for this subset we compute mean and median ratios of all debt types to total debt and test the null hypothesis that the mean (median) ratio is less than 30%. We also report the number of firm-year observations whose particular debt type is strictly greater than 30% of total debt. For example, in the first row we require that the amount of commercial paper exceeds 30% of debt. This leaves us with 129 observations. For these observations the mean (median) ratio of commercial paper to total debt is 0.413 (0.453), the mean (median) ratio of revolving credit to total debt is 0.033 (0.000), and so on for all other types. Examining the numbers in bold face along the diagonal line of the panel, we show that the ratio of a given debt type to total debt is between 70% and 80%, conditional on the ratio of that particular type of debt to total debt exceeding the threshold of 30% (again with the notable exception of commercial paper). Further, the t- and median tests strongly reject the null that the mean and median ratios of various debt types are below 30%. The off-diagonal numbers reveal that significant reliance on more than one debt type is rarely observed: The exception is that the significant users of commercial paper are also significant users of senior bonds. Indeed, these results highlight the general pattern that very few firms use other sources of debt over and beyond the one which we condition upon. This is strong evidence of firms borrowing primarily from a single type of debt. We conclude that once firms decide to employ a significant portion of a particular type of debt, they tend to overwhelmingly rely on that particular type of debt for financing. The next natural question to ask is whether specialization in borrowing is persistent for firms across time. 16
  • 19. C. Persistence in Debt Specialization Debt structure is likely to be influenced by firm characteristics, for example, both Johnson (1997), and Goyal, Lehn, and Racic (2002) show that firm size is positively related to public debt and negatively related to bank debt. Insofar as these firm characteristics are persistent over time (as shown in Table 1 Panel B), we expect to observe persistence in debt specialization. We examine this issue in Tables 6 and 7. Table 6 replicates the analysis in Table 5 with conditions imposed on debt types in the previous year. For example, in the first row of Table 6 Panel A we examine the percentage of firm-year observations for which the ratio of each debt type to total debt is strictly greater than 30%, conditional on the ratio of that particular debt type to total debt being strictly greater than 30% in the previous fiscal year. As we are now conditioning on previous year debt, the number of firms in each row is smaller than the parallel row of Table 5 Panel A. Table 6 Panel A shows that, with the exception of commercial paper, firms exhibit persistence in debt structure. For example, 73.3% of the firms with their ratio of revolving credit to total debt greater than 30% in the previous year have the same ratio greater than 30% in the current year. Panel B reports summary statistics for debt structure conditional on the ratio of a particular debt type to total debt being greater than 30% in the previous year. The first row shows that firms use commercial paper as a transition before resorting to senior bonds: 77.8% (= 81/104) of the firms that specialize in commercial paper borrowing in the previous year now have more than 50% of debt in senior bonds in the current year. In all other cases, the two statistical tests that we perform indicate significant persistence in firm debt structure: Only along the diagonal line of Panel B are the mean and median ratios of various debt types to total debt far greater than the threshold of 30%. 17
  • 20. Having established persistence in debt specialization, we then examine the possible explanations for persistence in a multivariate setting by estimating the following regression model: Debt Typei ,t = α + βC Debt Typei ,t i ,t −1 + β P Profitabilityi ,t −1 + βT Tangibilityi ,t −1 + β MB M / Bi ,t −1 + β S Sizei ,t −1 + βV CF Volatilityi ,t −1 + β D Dividend Payeri ,t −1 (1) + Industry FE + Rating FE + Year FE + ε i ,t The explanatory variables include known determinants of leverage such as profitability, asset tangibility, M/B, firm size, cash flow volatility, and the dividend payer dummy variable (for example, see Rajan and Zingales (1995), and Faulkender and Petersen (2006)), all measured in the previous year, together with the lagged measure of the particular debt type being examined. We also include industry (measured at the 2-digit SIC) fixed effects,9 rating fixed effects,10 and year fixed effects, and standard errors are clustered at the firm level following Sufi (2009) and Lemmon and Zender (2009). Equation (1) takes the lead-lag specification, and as a result, the sample size is slightly different from that for the summary statistics. Table 7 provides our multivariate results on persistence in specialization. Panel A reports OLS regression results, while Panel B reports probit regression results, where the dependent variable takes a value of one if the ratio of that particular debt type to total debt is strictly greater than 30% and zero otherwise.11 We use probit to account for the large number of zeros across different debt types (see Table 2). The main result of Table 7 is that debt structure persists over time: The coefficients on the lagged measures of each debt type are all positive and statistically significant at the 1% level. The results of the probit regressions are consistent with those of the OLS specification. Denis 9 In the literature, both the 4-digit SIC and 2-digit SIC are used to capture the industry effects. Our results are robust for both choices. 10 As we have shown in Table 3 Panel B, conditional on a particular rating class, there is substantial heterogeneity in debt structure. Therefore, we opt here for rating class dummies that are finer than the investment/speculative/unrated grid previously used in the literature (for example, Guedes and Opler (1996), and Billett et al. (2007)). 11 The sample sizes differ from Panel A due to the fact that some of our independent variables explain the binary dependent variable perfectly and Stata automatically drops them. 18
  • 21. and Mihov (2003) show that firms exhibit persistence in the type of securities to issue in new corporate borrowings. We further confirm that there is persistence in borrowing from one type of debt measured at the capital stock level. V. Explaining Debt Structure A. Rating Changes and Debt Structure In this section we examine more closely the relation between ratings and debt structure. Previously we have established the fact that ratings are a significant determinant of how firms raise debt capital. Firms with better rating are able to tap more diversified types of debt, while firms that are not rated rely more on bank debt. We now explore how ratings affect debt choices in a dynamic setting, as shown by Kisgen (2006) for capital structure. Table 8 examines the relation between changes in rating and changes in debt structure for the three year period centered on the year of a rating change.12 As a benchmark, in the first column of each table, we present the relation between changes in rating and changes in market leverage. A downgrade (upgrade) means that the rating of the firm has decreased (increased) during the current year of observation. 13 With downgrade(–1) and upgrade(–1) we refer to variables measured one year before the rating change. Similarly, downgrade(+1) and upgrade(+1) refers to variables measured one year after the rating change. We only consider events that are not contaminated by any other rating change in the opposite direction during the event window examined. We therefore identify 334 downgrades and 277 upgrades in our sample. Panel A shows that leverage increases significantly in the year prior to the downgrade and decreases significantly both at the time of the downgrade and in the year after. This suggests that credit quality deteriorates following an increase in leverage. Further, in the year prior to the 12 Rating changes that take place either during 2001 or 2007 automatically drop out of the analysis as we do not have data for changes in debt types for 2000 and 2008. 13 The addition of a plus or a minus modifier to the rating class, e.g., from A to A+, is considered as a rating change. 19
  • 22. downgrade, commercial paper experiences a significant decline, a trend that continues over the subsequent two years. In contrast, senior bonds significantly rise in the year before and the year of the rating change. These findings indicate that as their rating worsens firms issue less commercial paper, as it requires high credit quality, and shift to bond financing. Both Graham and Harvey (2001) and Kisgen (2006) observe that corporate managers place a high priority on maintaining their existing credit rating. We offer one explanation for the observed pattern: Downgrades limit firms’ access to multiple sources of financing. In terms of upgrades, we find that leverage decreases significantly in the year prior to and during the upgrade. Consistent with our findings about downgrades, a reduction in market leverage leads to higher credit quality and an upgrade. Accordingly, in the year prior to the upgrade, subordinated bonds experience a moderate decline that continues in the next two years. This shift is accompanied by an increase in senior bonds, thus suggesting a reallocation of debt from subordinated to senior bonds. We also observe a significant increase in the use of commercial paper. In summary, an upgrade is associated with a decrease in leverage, a decrease in the use of subordinated bonds in favor of senior bonds, and a contemporaneous increase in the use of commercial paper. To examine whether and how changes in credit quality affect a firm’s debt structure, we run the following regression on our samples of downgrades and upgrades: Debt Typei ,t = α + β BYear Beforei ,t + β C Current Yeari ,t + β AYear Afteri ,t (2) + Industry FE + Rating FE + Year FE + ε i ,t The coefficients of interest are βB, βC, and βA, which represent the change in the dependent variable for the year before, at, and after the rating change, respectively. Table 8 Panel B presents the downgrade regression results. There is a significant increase in market leverage in the year before the downgrade, which is consistent with the univariate evidence in Panel A. Moreover, we document that firms reduce bank debt (including both revolving credit and term loans) and increase senior bonds in the year after the downgrade. 20
  • 23. Panel C presents the upgrade regression results. We show that there is a significant decrease in market leverage before the upgrade, again in line with evidence in Panel A. There is some evidence of a significant drop in term loans in the year before the upgrade, and a significant increase in revolving credit after the upgrade. Sufi (2009) finds that firms with high cash flows are more likely to obtain bank lines of credit. We further show that firms with improved credit quality increase their usage of revolving credit facilities. We conclude that downgrades are associated with significantly reduced use of bank debt, and increased use of senior bonds. Upgrades are associated with significantly increased use of revolving credit. Our results confirm some of the findings in Rauh and Sufi (2010), and further highlight how debt structure reacts asymmetrically with respect to downgrades versus upgrades. B. Determinants of Capital and Debt Structures So far, we have focused on the composition of debt and accordingly we have expressed debt types as a percentage of total debt. We now compare and contrast the determinants of capital and debt structures. To this end we divide each debt type by total capital, i.e. the sum of book value of debt and market value of equity, which is typically used to compute market leverage (see Table A1 in the appendix). Scaling debt types by total capital enables us to decompose market leverage into its constituents. To examine determinants of capital and debt structures, we estimate the following regression model: Debt Typei ,t = α + β P Profitabilityi ,t −1 + βT Tangibilityi ,t −1 + β MB M / Bi ,t −1 + β S Sizei ,t −1 + βV CF Volatilityi ,t −1 + β D Dividend Payeri ,t −1 (3) + Industry FE + Rating FE + Year FE + ε i ,t The regressors largely overlap with those in Equation (1). To account for the fact that the decisions of employing different types of debt are not independent, we employ the Seemingly Unrelated Regression (SUR) specification when estimating all debt types. We further restrict the 21
  • 24. sum of coefficients on each control variable in the debt type regressions to be equal to the coefficient on the same control variable in the market leverage regression due to the fact that the sum of all debt types equals market leverage. Table 9 presents the regression results. To establish a benchmark, the first column presents estimation results when market leverage is the dependent variable. We find that consistent with prior research such as Frank and Goyal (2007) and Lemmon, Roberts, and Zender (2008), profitability, M/B, cash flow volatility, and the dividend payer dummy are negatively and significantly associated with market leverage, while asset tangibility and firm size are positively and significantly associated with market leverage. More importantly, we show that some of the significant relations between the above firm characteristics and leverage are driven by certain debt types but definitely not all. For example, the negative association between profitability and leverage is mainly driven by senior bonds, capital leases, and other debt. In contrast, profitability is positively and significantly associated with revolving credit and term loans. This suggests that banks are relatively more risk averse than other investors, thus confirming results previously shown by Houston and James (1996). The positive association between tangibility and leverage is mainly driven by revolving credit, term loans, senior bonds and capital leases, whereas there is a negative association between tangibility and subordinated bonds. These findings support the idea that subordinated bonds are likely to be unsecured, while term loans and senior bonds are generally secured. M/B ratios, which proxy for growth opportunities, are negatively and significantly associated with revolving credit and term loans, consistent with findings in Johnson (1997) but opposite to findings in Goyal, Lehn, and Racic (2002). Overall, our evidence suggests that banks specialize in lending to small and less risky firms. Finally, we demonstrate that to understand the exact relation between firm characteristics and debt types, it is important to separate bank debt into its two components: revolving credit and term loans; and bond debt into its two components: senior and subordinate bonds. For 22
  • 25. example, the effect of tangibility on term loans is twice the size of that on revolving credit, suggesting that banks put much more emphasis on collateral when providing long-term financing. Within bond financing, tangibility is negatively and significantly associated with subordinated bonds, while it is positively associated with senior bonds. Overall, our results capture several salient features of the choices of different types of debt within a firm’s capital structure. Small, profitable, low growth, and low risk firms tend to borrow primarily from banks. Large, low growth, and low risk firms use mainly senior bonds for financing. Smaller, less profitable firms with a more tangible asset base use capital leases. A direct comparison of our results with Rauh and Sufi (2010) is not possible, because our debt types do not exactly overlap with the Rauh and Sufi’s classification. In addition, in our regressions we control for more firm characteristics than they do. However, in general terms our results are consistent with Rauh and Sufi in that our findings show that profitability is positively and significantly associated with bank debt, while growth opportunities and firm size are negatively and significantly associated with bank debt. Compared to Rauh and Sufi, we find a more robust association between some firm characteristics and debt types, as shown by the high adjusted R-squares of our regressions. C. Financing Gap and Debt Structure One long standing debate in the capital structure literature is whether the trade-off or the pecking order theory is better at explaining financing decisions (see for example, Frank and Goyal (2003), and Lemmon and Zender (2009)). Following this strand of literature, we explore how the financing gap affects the composition of debt. As Frank and Goyal (2003) suggest, we define the financing gap as the sum of dividends, investment, and change in working capital; minus the cash flow after interest and taxes; and then we scale this sum by lagged total capital. This measure of financing gap is intended to capture a firm’s funding need. 23
  • 26. To examine the effect of financing gap on changes of debt structure we estimate the following regression: ∆Debt Typei = α + β P ∆Profitabil ityi + βT ∆Tangibilit yi + β MB ∆M / Bi (4) + β S ∆Sizei + β DEF GAPi + ε i ∆ denotes the change in debt type scaled by lagged total capital. GAP is the financing gap, defined as above. We employ the SUR specification and restrict the regression coefficients accordingly as in Equation (3) when estimating changes to debt types. Table 10 presents the regression results. We first show that the change in profitability is negatively and significantly associated with the change in leverage, while the changes in asset tangibility and firm size are positively and significantly associated with the change in leverage. These findings are consistent with Frank and Goyal (2003). Second, we document that the role of the financing gap in capital structure does not support predictions of the pecking order theory: The coefficient in front of the financing gap in the leverage regression is significantly different from unity, again consistent with Frank and Goyal (2003). Third and more importantly, we show that the financing gap is positively and significantly associated with senior and subordinated bonds. The economic interpretation of this result is that for every dollar of the financing gap, about 6 (3) cents are met by issuing senior (subordinated) bonds. Park (2000) predicts that senior debt holders have the strongest monitoring incentives given their priority to receive payoffs from their monitoring effort. Our evidence supports that prediction by showing that when firms are under financial duress and hence require external financing, senior debt is most frequently used because of their holders’ strong incentives to monitor. VI. Additional Investigation In this section, we implement various robustness checks on our main results and some additional analyses. First, to mitigate the fact that in our unbalanced panel some firms with 24
  • 27. complete time series observations from 2000 to 2007 receive more weight than other firms with fewer observations, we implement the cluster analysis by using the time series average of each debt type and hence firm-level observations. Table 11 Panel A presents the result. We still observe seven distinct clusters of firms with six of them engaged in debt specialization, even though the extent of specialization is somehow weakened based on firm-level observations as compared to that based on firm-year observations. This drop in the degree of specialization is expected if debt specialization is indeed a persistent phenomenon (as shown in Tables 6 and 7), while the time series averaging (as reported in Panel A) ignores the temporal persistence in debt specialization. Second, we also re-examine conditional debt structures and show that our results are robust to a different threshold. In Panels B and C we use 10% as the threshold. For example, in the first row of the table, we require firm-year observations with the ratio of commercial paper to total debt exceeding 10%. We find that the patterns of specialization in borrowing are qualitatively similar to what we observe in Table 5 Panels A and B where we use 30% as the threshold. Third, we further assess persistence in debt structures by replicating the analysis in Table 5 with conditions imposed on debt types observed three-years prior. Panels D and E contain the results. For example, in the first row of Panel D we report the percentage of firm-year observations for which the ratio of the amount of a particular debt type to total debt is strictly greater than 30%, conditional on the ratio of the amount of that debt type to total debt being strictly greater than 30% three fiscal-years ago. Not surprisingly, the number of firms in each row is much smaller than the parallel row of Table 5 Panel A once we go back by three years. Looking across the diagonal line of Panel E, we still observe persistence among five debt types: revolving credit, term loans, senior and subordinated bonds, and capital leases. Finally, we examine the effect of drastic changes in credit ratings on debt structure. Table 12 presents changes in debt structure associated with a major downgrade or an upgrade. We use 25
  • 28. the term fallen angel to refer to a downgrade from investment grade to speculative grade, and the term rising star to refer to an upgrade from speculative grade to investment grade following Rauh and Sufi (2010). As these major changes in rating are relatively rare, the sample size becomes relatively small. Differing from what we observe in the broader downgrade sample, fallen angels do not experience a reduction in their use of commercial paper, although they do increase term loans and capital leases in the year after the downgrade. Similar to the upgrade sample, rising stars significantly reduce leverage in the year before the upgrade, but not during the year of the rating change. Further differing from the upgrade sample, rising stars have increased their use of revolving credit in the year of the upgrade. For comparison, using a sample of fallen angels Rauh and Sufi (2010) find that after a downgrade, bank debt increases, senior unsecured debt (including program debt, bank debt, and bonds) decreases, and subordinated debt (including bonds and convertible) increases. VII. Conclusions This paper provides a comprehensive analysis of the patterns and determinants of debt structure. We first show that debt structure varies substantially between unrated and rated firms and across a wide spectrum of credit ratings. Large and high credit quality firms tend to have access to different sources of financing, while small and unrated firms rely exclusively on either capital leases or bank debt for financing. We then present fresh evidence on firms’ specialization in borrowing. Cluster analysis identifies seven distinct groups of firms, six of which concentrate their borrowing through one type of debt. Conditional on borrowing a significant fraction of their debt through a particular type of debt instrument, firms are found to specialize in borrowing by limiting themselves to a few debt instruments. 26
  • 29. We further show that debt structure exhibits a substantial degree of persistence: Controlling for standard firm characteristics known to affect capital structure, we find that each debt type is positively and significantly related to its prior level. Moreover, common factors that are known to explain capital structure, such as profitability, growth opportunities, and credit ratings, also tend to have very different influences on different types of debt. We also show that asymmetric changes in debt structure occur in response to rating downgrades and upgrades. Specifically, downgrades are significantly associated with reduced use of bank debt, and increased use of senior bonds. In contrast, upgrades are significantly associated with increased use of revolving credit. Finally, we find that bonds are most frequently used to meet the financing gap. Based on these results, we conclude that the choice of debt structure is an important part of capital structure decisions. 27
  • 30. References: Barclay, Michael J., and Clifford W. Smith, 1995, The priority structure of corporate liabilities, Journal of Finance 50, 899-917. Billett, Matthew T., Tao-Hsien D. King, and David C. Mauer, 2007, Growth opportunities and the choice of leverage, debt maturity, and covenants, Journal of Finance 62, 697-729. Bolton, Patrick, and Xavier Freixas, 2000, Equity, bonds and bank Debt: Capital structure and financial market equilibrium under asymmetric information, Journal of Political Economy 108, 324-351. Chemmanur, Thomas, and Paolo Fulghieri, 1994, Reputation, renegotiation, and the choice between bank loans and publicly traded debt, Review of Financial Studies 7, 475-506. Denis, David, and Vassil Mihov, 2003, The choice among bank debt, non-bank private debt and public debt: Evidence from new corporate borrowings, Journal of Financial Economics 70, 3-28. Diamond, Douglas, 1991, Monitoring and reputation: The choice between bank loans and privately placed debt, Journal of Political Economy 99, 689-721. Fama, Eugene F., and Kenneth R. French, 2001, Disappearing dividends: Changing firm characteristics or lower propensity to pay? Journal of Financial Economics 60, 3-43. Faulkender, Michael, and Mitchell Petersen, 2006, Does the source of capital affect capital structure? Review of Financial Studies 19, 45-79. Frank, Murray Z., and Vidhan Goyal, 2003, Testing the pecking order theory of capital structure, Journal of Financial Economics 67, 217-248. Frank, Murray Z., and Vidhan Goyal, 2008, “Tradeoff and pecking order theories of debt,” in: Handbook of Corporate Finance: Empirical Corporate Finance Vol. II (Elsevier/North- Holland), ed. B. E. Eckbo. Gomes, Armando, and Gordon Phillips, 2009, Private and public security issuance by public firms: The role of asymmetric information, University of Maryland working paper. Goyal, Vidhan, Kenneth Lehn, and Stanko Racic, 2002, Growth opportunities and corporate debt policy: the case of the U.S. defense industry, Journal of Financial Economics 64, 35-59. Graham, John, and Campbell Harvey, 2001, The theory and practice of corporate finance: evidence from the field, Journal of Financial Economics 60, 187–243. Hadlock, Charles, and Christopher James, 2002, Do banks provide financial slack? Journal of Finance 57, 1383-1419. 28
  • 31. Houston, Joel, and Christopher James, 1996, Bank information monopolies and the mix of private and public debt claims, Journal of Finance 51, 1863-1889. Jensen, Michael C., 1986, Agency costs of free cash flow, corporate finance and takeovers, American Economic Review 76, 323-339. Johnson, Shane, 1997, An empirical analysis of the determinants of corporate debt ownership structure, Journal of Financial and Quantitative Analysis 32, 47-69. Kisgen, Darren, 2006, Credit ratings and capital structure, Journal of Finance 61, 1035-1072. Lemmon, Michael, Michael R. Roberts, and Jamie Zender, 2008, Back to the beginning: Persistence and the cross-section of corporate capital structure, Journal of Finance 63, 1575-1608. Lemmon, Michael, and Jamie Zender, 2009, Debt capacity and tests of capital structure theories, Journal of Financial and Quantitative Analysis forthcoming. Myers, Stewart C., and Nicholas S. Majluf, 1984, Corporate financing and investment decisions when firms have information that investors do not have, Journal of Financial Economics 13,187-221. Park, Cheol, 2000, Monitoring and the structure of debt contracts, Journal of Finance 55, 2157- 2195. Rajan, Raghuram, and Luigi Zingales, 1995, What do we know about capital structure? Journal of Finance 50, 1421-1460. Rauh, Joshua D., and Sufi, Amir, 2010, Capital structure and debt structure, Review of Financial Studies forthcoming. Sufi, Amir, 2009, Bank lines of credit in corporate finance: An empirical analysis, Review of Financial Studies 22, 1057-1088. 29
  • 32. APPENDIX I. Variable Definitions Table A1. Description of Compustat Variables Variable Construction Firm Size Logarithm of Book Value of Total Assets (6) Preferred Stock Max[Preferred Stock Liquidating Value (10), Preferred Stock Redemption Value (56), Preferred Stock Carrying Value (130)] BV Equity Total Assets (6) – Total Liabilities (181) – Deferred Taxes and Investment Tax Credit (35) – Preferred Stock Total Debt Debt in Current Liabilities (34) + Long-Term Debt (9) Book Leverage Total Debt / Total Assets (6) MV Equity Stock Price (199) × Common Shares Used to Calculate EPS (54) Market Leverage Total Debt / (Total Debt + Market Value of Equity) Profitability Operating Income Before Depreciation (13) / Total Assets (6) Tangibility Net Property, Plant, and Equipment (8) / Total Assets (6) M/B (Market Value of Equity + Total Debt + Preferred Stock Liquidating Value (10) – Deferred Taxes and Investment Tax Credit (35)) / Total Assets (6) CF Volatility Standard Deviation of Operating Income (13) over Previous 12 Quarters Scaled by Total Assets (6) Dividend Payer A dummy variable that takes the value of one if common stock dividends (21) are positive, and zero otherwise Rated A dummy variable that takes the value of one if the firm is rated by the S&P, and zero otherwise Rating Monthly S&P ratings (280) Table A2. Description of Capital IQ Variables Variable Construction CP Commercial Paper RC Drawn Revolving Credit TL Term Loans SBN Senior Bonds SUB Subordinated Bonds CL Capital Leases Other Other Debt TPrinDue = CP + RC + TL + SBN + SUB + CL + Total Principal Due = Commercial Paper + Revolving Credit + Term Loans + Senior Bonds + Other Subordinated Bonds + Capital Leases + Other Debt TAdj = total_debt – TPrinDue Total Adjustment = Total Debt – Total Principal Due 30
  • 33. II. Example: CIQ’s Classification of Debt Types Using Form 10K AMR Corporation, Form 10K for the fiscal year ended December 31, 2003. Avalable at: http://www.sec.gov/Archives/edgar/data/6201/000095013404002668/d12953e10vk.htm The following table illustrates how CIQ calculates each item (in millions of USD): CIQ Source Calculation Capital Structure Data Total Debt 13,930 10K Item 8 Long-term debt, less current maturities (11,901) + Obligations under capital leases, less current obligations (1,225) + Current maturities of long-term debt (603) + Current obligations under capital leases (201) = 13,930 Total Equity 46 10K Item 8 Stockholders’ equity (46) Total Capital 13,976 10K Item 8 Total debt + Stockholders’ equity Debt Summary Data Total Revolving Credit 834 10K Item 8 Credit facility agreement due through 2005 (834) Total Term Loans 0 10K Item 8 Total Senior Bonds and 11,668 10K Item 8 Secured variable and fixed rate indebtedness due through 2021 Notes (6,041) + Enhanced equipment trust certificates due through 2011 (3,747) + Special facility revenue bonds due through 2036 (947) + Debentures due through 2021 (330) + Notes due through 2039 (303) + Senior convertible notes due through 2023 (300) Total Capital Leases 1,426 10K Item 8 Obligations under capital leases, less current obligations (1,225) + Current obligations under capital leases (201) Other Borrowings 2 10K Item 8 Other (2) Additional Totals Total Senior Debt 13,930 10K Item 8 = Total debt Total Convertible Debt 300 10K Item 8 Senior convertible notes due through 2023 (300) Curr. Port. of Long-Term 804 10K Item 8 Current maturities of long-term debt (603) + Current obligations Debt/Capital Leases under capital leases (201) Long-Term Debt (Incl. 13,126 10K Item 8 Long-term debt, less current maturities (11,901) + Obligations under Capital Leases) capital leases, less current obligations (1,225) Total Bank Debt 834 10K Item 8 Credit facility agreement due through 2005 (834) Total Secured Debt 11,214 10K Item 8 Secured variable and fixed rate indebtedness due through 2021 (6,041) + Enhanced equipment trust certificates due through 2011 (3,747) + Obligations under capital leases, less current obligations (1,225) + Current obligations under capital leases (201) Total Unsecured Debt 2,716 10K Item 8 Special facility revenue bonds due through 2036 (947) + Credit facility agreement due through 2005 (834) + Debentures due through 2021 (330) + Notes due through 2039 (303) + Senior convertible notes due through 2023 (300) + Other (2) 31
  • 34. Table 1. Sample Overview The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. We have removed 1) firm-years with missing data for any of the Compustat variables listed in Table A1, 2) firm-years with market or book leverage outside the unit interval, 3) firm-years with zero total debt, 4) firm-years with missing data for any of the CIQ variables listed in Table A2, and 5) firm-years whose difference between total debt reported in Compustat and the sum of debt types reported in CIQ exceeds 5% of total debt. After the above filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. Applying the same filters (except the data discrepancy filter #5) to the Compustat population over the same years as our sample, we obtain 21,674 firms of which 17,232 have positive leverage. See Table A1 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Total Assets are expressed in millions of 2001 dollars deflated by the consumer price index. Panel A presents means and medians aggregated across all years for our sample, the Compustat population, and Compustat leveraged firms. We also test for difference between our sample and the two Compustat samples using the t-test and the two-sample Wilcoxon rank-sum (Mann- Whitney) test. Panel B presents key firm characteristics year by year. Panel A: Comparing Our Sample with Compustat Firms Our Sample Compustat All Test of Difference Compustat Test of Difference Firms Comp. All Firms vs. Leveraged Firms Comp. Leveraged Our Sample Firms vs. Our Sample (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Mean Median Mean Median t-test MW-test Mean Median t-test MW-test (p-value) (p-value) (p-value) (p-value) Mkt Leverage 0.215 0.159 0.171 0.093 -19.884 -34.265 0.215 0.155 -0.071 -1.586 (0.000) (0.000) (0.944) (0.056) Profitability 0.071 0.111 0.043 0.102 -11.634 -10.228 0.061 0.107 -4.137 -4.184 (0.000) (0.000) (0.000) (0.000) Tangibility 0.272 0.200 0.242 0.164 -12.340 -16.111 0.271 0.197 -0.380 -1.219 (0.000) (0.000) (0.704) (0.111) M/B 1.678 1.250 1.878 1.322 11.356 8.431 1.679 1.236 0.073 -1.936 (0.000) (0.000) (0.942) (0.026) BV Assets 2347.9 392.2 1936.4 239.3 -5.767 -18.397 2329.6 341.5 -0.236 -5.048 (0.000) (0.000) (0.814) (0.000) CF Volatility 0.024 0.012 0.028 0.014 4.792 14.048 0.025 0.012 1.489 3.880 (0.000) (0.000) (0.136) (0.000) Dividend Payer 0.307 0.000 0.262 0.000 -9.100 -9.179 0.289 0.000 -3.359 -3.364 (0.000) (0.000) (0.001) (0.000) Panel B: Firm Characteristics Over Time Mkt Profitability Tangibility M/B BV Assets CF Dividend # Obs. Leverage Volatility Payer Mn Md Mn Md Mn Md Mn Md Mn Md Mn Md Mn Md 2001 0.256 0.054 0.295 1.680 2390.9 0.025 0.273 1654 0.186 0.103 0.228 1.174 355.5 0.012 0.000 2002 0.269 0.065 0.290 1.284 2233.6 0.029 0.265 2080 0.208 0.105 0.221 0.973 331.1 0.014 0.000 2003 0.210 0.077 0.275 1.782 2217.3 0.034 0.288 2045 0.159 0.107 0.204 1.272 360.7 0.013 0.000 2004 0.191 0.084 0.268 1.788 2311.3 0.022 0.319 2127 0.142 0.114 0.194 1.337 389.8 0.012 0.000 2005 0.189 0.087 0.260 1.754 2277.3 0.019 0.333 2143 0.138 0.118 0.190 1.331 412.8 0.011 0.000 2006 0.186 0.072 0.263 1.760 2457.3 0.019 0.334 2136 0.137 0.115 0.187 1.363 454.9 0.011 0.000 2007 0.216 0.057 0.263 1.693 2556.6 0.020 0.326 2057 0.161 0.109 0.177 1.272 470.3 0.011 0.000 32
  • 35. Table 2. Summary Statistics The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Table A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Total adjustment is calculated as the difference between total debt and the sum of the individual debt types. When total adjustment is zero, the sum of the debt types collected by Capital IQ is identical to total debt obtained from Compustat. We present mean, median, 75th, 95th, and 99th percentiles across all firm-years. Share of Total Debt Mean Median 75th 95th 99th Percentile Percentile Percentile Debt Types Commercial Paper 0.008 0.000 0.000 0.000 0.285 Revolving Credit 0.203 0.000 0.300 0.993 1.000 Term Loans 0.187 0.000 0.250 0.997 1.000 Senior Bonds 0.361 0.130 0.783 1.000 1.000 Sub. Bonds 0.102 0.000 0.000 0.876 1.000 Capital Leases 0.092 0.000 0.015 1.000 1.000 Other Debt 0.046 0.000 0.001 0.238 1.000 Total Adjustment 0.000 0.000 0.000 0.006 0.031 # Obs. 14242 33
  • 36. Table 3. Credit Ratings and Debt Structure The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Table A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Data on ratings ranging from AAA to D are from Compustat (280). Panel A presents debt structure in terms of mean and median debt ratios across unrated and rated groups. Panel B presents debt structure in terms of mean and median debt ratios across different rating classes. We test for difference between rated and unrated using the t-test and the two-sample Wilcoxon rank- sum (Mann-Whitney) test. Panel A: Being Rated and Debt Structure Unrated Rated Test of Difference Unrated/Rated (1) (2) (3) (4) (5) (6) Mean Median Mean Median t-test MW-test (p-value) (p-value) Share of Total Debt Debt Types Commercial Paper 0.001 0.000 0.022 0.000 -18.882 -33.999 (0.000) (0.000) Revolving Credit 0.257 0.000 0.093 0.000 35.494 12.896 (0.000) (0.000) Term Loans 0.209 0.000 0.144 0.000 12.362 -0.559 (0.000) (0.288) Senior Bonds 0.280 0.005 0.523 0.624 -35.444 -33.763 (0.000) (0.000) Sub. Bonds 0.073 0.000 0.160 0.000 -17.800 -26.813 (0.000) (0.000) Capital Leases 0.128 0.000 0.019 0.000 33.104 12.448 (0.000) (0.000) Other Debt 0.051 0.000 0.035 0.000 5.768 -32.061 (0.000) (0.000) Market Leverage 0.170 0.106 0.306 0.264 -37.630 -42.619 (0.000) (0.000) # Obs. 9510 4732 34
  • 37. Panel B: Credit Ratings and Debt Structure Debt Types CP RC TL SBN SUB CL Other # Obs. AAA 0.143 0.000 0.067 0.532 0.067 0.017 0.165 49 0.104 0.000 0.003 0.557 0.000 0.000 0.099 AA +/– 0.144 0.020 0.032 0.674 0.000 0.016 0.087 103 0.109 0.000 0.000 0.719 0.000 0.000 0.037 A +/– 0.087 0.034 0.029 0.745 0.020 0.008 0.065 636 0.000 0.000 0.000 0.820 0.000 0.000 0.006 BBB +/– 0.023 0.115 0.061 0.704 0.042 0.017 0.036 1225 0.000 0.003 0.000 0.809 0.000 0.000 0.002 BB +/– 0.000 0.123 0.201 0.374 0.251 0.022 0.029 1604 0.000 0.010 0.007 0.254 0.000 0.000 0.000 B +/– 0.000 0.073 0.234 0.391 0.261 0.024 0.018 1044 0.000 0.000 0.018 0.265 0.000 0.000 0.000 CCC+ or below 0.000 0.051 0.245 0.494 0.187 0.028 0.003 71 0.000 0.000 0.133 0.627 0.000 0.000 0.000 Unrated 0.001 0.257 0.209 0.280 0.073 0.128 0.051 9510 0.000 0.000 0.000 0.006 0.000 0.000 0.000 35
  • 38. Table 4. Cluster Analysis The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Tables A1 and A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. For the cluster analysis we employ the Stata command cluster kmeans. We use the Euclidean Distance measure and run kmeans with up to 15 clusters. Using the stopping rule based on the Calinski/Harabasz index, we obtain seven clusters. We present the sample mean and median of debt types and key firm characteristics for the seven clusters sorted by ascending median firm size, using firm-year observations. Debt Types Mkt Profitab Tangibi Firm Dividen Cluster CP RC TL SBN SUB CL Other Lev. ility lity M/B Size CF Vol. d Payer Rated Rating # Obs. 1 0.000 0.012 0.014 0.019 0.002 0.945 0.008 0.034 -0.036 0.165 2.499 261.71 0.042 0.091 0.033 22.666 1074 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.003 0.058 0.109 1.852 104.79 0.023 0.000 0.000 23.000 2 0.001 0.068 0.819 0.042 0.034 0.026 0.009 0.220 0.066 0.268 1.660 807.27 0.022 0.218 0.221 20.823 2549 0.000 0.000 0.875 0.000 0.000 0.000 0.000 0.150 0.107 0.201 1.245 179.70 0.013 0.000 0.000 23.000 3 0.001 0.021 0.011 0.027 0.005 0.022 0.913 0.130 0.051 0.230 1.869 2393.62 0.025 0.243 0.143 21.061 490 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.044 0.101 0.151 1.400 189.28 0.014 0.000 0.000 23.000 4 0.001 0.829 0.056 0.054 0.023 0.026 0.011 0.206 0.094 0.269 1.482 514.65 0.022 0.268 0.091 21.972 2623 0.000 0.884 0.000 0.000 0.000 0.000 0.000 0.144 0.114 0.186 1.125 204.45 0.013 0.000 0.000 23.000 5 0.001 0.058 0.073 0.050 0.799 0.016 0.008 0.291 0.076 0.229 1.524 1195.01 0.023 0.134 0.530 17.674 1465 0.000 0.000 0.000 0.000 0.858 0.000 0.000 0.241 0.101 0.143 1.257 651.69 0.012 0.000 1.000 16.000 6 0.009 0.018 0.016 0.925 0.005 0.013 0.013 0.209 0.070 0.297 1.764 4419.45 0.026 0.453 0.495 16.496 3943 0.000 0.000 0.000 0.965 0.000 0.000 0.000 0.160 0.118 0.242 1.297 880.67 0.011 0.000 0.000 23.000 7 0.036 0.173 0.116 0.542 0.053 0.036 0.037 0.295 0.109 0.332 1.424 4480.68 0.017 0.434 0.523 16.243 2098 0.000 0.118 0.002 0.565 0.000 0.000 0.000 0.246 0.121 0.280 1.111 1172.52 0.008 0.000 1.000 16.000 Total 0.008 0.203 0.187 0.361 0.102 0.092 0.046 0.215 0.071 0.272 1.678 2347.89 0.024 0.307 0.332 18.985 14242 0.000 0.000 0.000 0.130 0.000 0.000 0.000 0.159 0.111 0.200 1.250 392.238 0.012 0.000 0.000 23.000 36
  • 39. Table 5. Conditional Debt Structure The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Table A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Panel A presents the shares of observations with significant amounts of various debt types conditional on a particular debt type (given in the first column of the table) exceeding 30% of total debt. Panel B presents the mean and median of each debt type as a fraction of total debt conditional on a particular debt type (given in the first column of the table) exceeding 30% of total debt. We report both t-tests on means and sign tests on medians being strictly greater than 30% (one-sided), as well as the number of observations for which a particular debt type is strictly greater than 30%. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Panel A: Shares of Observations Conditional on Significant Amounts of Debt Types Debt Types CP RC TL SBN SUB CL Other # Share of Obs. Sample Debt Types CP>30% 1.000 0.016 0.039 0.643 0.000 0.008 0.016 129 0.009 RC>30% 0.001 1.000 0.128 0.190 0.056 0.017 0.011 3562 0.250 TL>30% 0.002 0.138 1.000 0.131 0.090 0.027 0.008 3302 0.232 SBN>30% 0.013 0.107 0.069 1.000 0.042 0.019 0.016 6289 0.442 SUB>30% 0.000 0.105 0.157 0.140 1.000 0.009 0.006 1895 0.133 CL>30% 0.001 0.047 0.067 0.089 0.014 1.000 0.023 1310 0.092 Other>30% 0.003 0.061 0.042 0.152 0.019 0.047 1.000 643 0.045 37
  • 40. Panel B: Debt Structure Conditional on Significant Amounts of Debt Types Debt Types CP RC TL SBN SUB CL Other # Obs. Debt Types CP>30% 0.413*** 0.033 0.031 0.352*** 0.001 0.011 0.028 129 0.453*** 0.000 0.000 0.398*** 0.000 0.000 0.000 129 2 5 83 0 1 2 RC>30% 0.001 0.718*** 0.085 0.122 0.036 0.025 0.013 3562 0.000 0.741*** 0.000 0.000 0.000 0.000 0.000 2 3562 455 676 199 61 39 TL>30% 0.001 0.095 0.722*** 0.091 0.055 0.026 0.010 3302 0.000 0.000 0.744*** 0.000 0.000 0.000 0.000 5 455 3302 431 298 88 27 SBN>30% 0.015 0.080 0.055 0.779*** 0.028 0.022 0.022 6289 0.000 0.000 0.000 0.840*** 0.000 0.000 0.000 83 676 431 6289 266 117 98 SUB>30% 0.001 0.080 0.104 0.089 0.705*** 0.016 0.010 1895 0.000 0.000 0.000 0.000 0.704*** 0.000 0.000 0 199 298 266 1895 18 12 CL>30% 0.000 0.032 0.044 0.056 0.008 0.845*** 0.014 1310 0.000 0.000 0.000 0.000 0.000 1.000*** 0.000 1 61 88 117 18 1310 30 Other>30% 0.004 0.042 0.031 0.092 0.011 0.032 0.788*** 643 0.000 0.000 0.000 0.000 0.000 0.000 0.975*** 2 39 27 98 12 30 643 38
  • 41. Table 6. Conditional Debt Structure on Lagged Debt Types The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Table A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Panel A presents the shares of observations with significant amounts of various debt types conditional on a particular lagged debt type (given in the first column of the table) exceeding 30% of total debt. Panel B presents the mean and median of each debt type as a fraction of total debt conditional on a particular lagged debt type (given in the first column of the table) exceeding 30% of total debt. We report both t-tests on means and sign tests on medians being strictly greater than 30% (one-sided), as well as the number of observations for which a particular debt type is strictly greater than 30%. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Panel A: Shares of Observations Conditional on Significant Amounts of Lagged Debt Types Debt Types CP RC TL SBN SUB CL Other # Share of Obs. Sample Lagged Debt Types CP>30% 0.538 0.019 0.038 0.779 0.000 0.000 0.010 104 0.007 RC>30% 0.004 0.733 0.176 0.241 0.077 0.043 0.022 2563 0.180 TL>30% 0.001 0.198 0.762 0.180 0.103 0.040 0.026 2321 0.163 SBN>30% 0.013 0.131 0.092 0.883 0.054 0.026 0.022 4654 0.327 SUB>30% 0.000 0.124 0.173 0.205 0.816 0.016 0.009 1487 0.104 CL>30% 0.000 0.101 0.116 0.136 0.028 0.754 0.041 852 0.060 Other>30% 0.013 0.180 0.151 0.234 0.040 0.066 0.550 471 0.033 39
  • 42. Panel B: Debt Structure Conditional on Significant Amounts of Lagged Debt Types Debt Types CP RC TL SBN SUB CL Other # Obs. Lagged Debt Types CP>30% 0.281 0.037 0.037 0.526*** 0.001 0.007 0.027 104 0.326 0.000 0.000 0.556*** 0.000 0.000 0.000 56 2 4 81 0 0 1 RC>30% 0.002 0.569*** 0.128 0.177 0.054 0.047 0.023 2563 0.000 0.613*** 0.000 0.007 0.000 0.000 0.000 10 1878 452 618 197 110 57 TL>30% 0.001 0.146 0.585*** 0.136 0.065 0.040 0.026 2321 0.000 0.000 0.623*** 0.000 0.000 0.000 0.000 3 460 1769 418 239 92 60 SBN>30% 0.015 0.103 0.075 0.710*** 0.037 0.031 0.027 4654 0.000 0.000 0.000 0.799*** 0.000 0.000 0.000 62 611 426 4108 250 120 101 SUB>30% 0.001 0.095 0.127 0.143 0.602*** 0.021 0.014 1487 0.000 0.000 0.000 0.000 0.612*** 0.000 0.000 0 184 257 305 1214 24 14 CL>30% 0.000 0.076 0.091 0.105 0.020 0.676*** 0.032 852 0.000 0.000 0.000 0.000 0.000 0.976*** 0.000 0 86 99 116 24 642 35 Other>30% 0.008 0.139 0.121 0.177 0.032 0.063 0.459*** 471 0.000 0.000 0.000 0.000 0.000 0.000 0.366** 6 85 71 110 19 31 259 40
  • 43. Table 7. Explaining Persistence in Debt Structure The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Tables A1 and A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Each regression includes the lagged values of the dependent variable, profitability, asset tangibility, market-to-book ratio (M/B), size, cash flow volatility, and dividend payer. Industry fixed effects are based on two- digit SIC codes. Rating fixed effects are based on 22 rating dummies and the unrated dummy. Panel A reports the OLS regression results where the dependent variable is measured as a fraction of total debt. Panel B reports the probit regression results where the dependent variable takes a value of one if the particular debt type exceeds 30% of total debt, and zero otherwise. Standard errors clustered at the firm level are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Panel A: OLS Regressions Debt Types Lagged Regressors Mkt Lev CP RC TL SBN SUB CL Other Mkt. Leverage 0.787*** (0.009) Debt Type 0.584*** 0.681*** 0.729*** 0.759*** 0.814*** 0.776*** 0.547*** (0.042) (0.011) (0.011) (0.009) (0.011) (0.015) (0.025) Profitability -0.020*** 0.002 0.059*** 0.034** -0.058*** -0.008 -0.010 -0.001 (0.006) (0.002) (0.013) (0.014) (0.016) (0.007) (0.013) (0.007) Tangibility 0.012 -0.001 -0.025* 0.033** 0.035** -0.011 -0.020** -0.008 (0.008) (0.002) (0.014) (0.014) (0.014) (0.007) (0.008) (0.009) M/B 0.000 0.000 -0.004** -0.002 0.004* -0.002*** 0.004** -0.001 (0.001) (0.000) (0.002) (0.002) (0.002) (0.001) (0.002) (0.001) Size 0.005*** -0.000 -0.005** -0.011*** 0.012*** 0.003** -0.002* 0.002 (0.001) (0.000) (0.002) (0.002) (0.002) (0.001) (0.001) (0.001) CF Volatility -0.025** 0.001 -0.019 -0.026 0.002 -0.005 0.035 0.002 (0.012) (0.001) (0.021) (0.016) (0.037) (0.014) (0.037) (0.009) Dividend Payer 0.003 0.000 0.008 -0.004 0.012** -0.010*** -0.002 0.004 (0.003) (0.001) (0.006) (0.005) (0.006) (0.003) (0.003) (0.003) Constant 0.018 0.056** 0.101 0.109*** -0.003 -0.018 0.011 0.084 (0.018) (0.027) (0.064) (0.036) (0.039) (0.016) (0.016) (0.057) Ind. FE Yes Yes Yes Yes Yes Yes Yes Yes Rating FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs. 10265 10265 10265 10265 10265 10265 10265 10265 Adj. R2 0.760 0.539 0.546 0.561 0.672 0.720 0.624 0.349 41
  • 44. Panel B: Probit Regressions (Debt Type>30%) Debt Types Lagged Regressors Mkt Lev CP RC TL SBN SUB CL Other Mkt. Leverage 1.813*** (0.046) CIQ Type 1.861*** 1.823*** 2.050*** 2.254*** 2.680*** 2.440*** 2.171*** (0.202) (0.040) (0.041) (0.040) (0.056) (0.065) (0.076) Profitability -0.574*** 2.359*** 0.449*** 0.342*** -0.473*** -0.105 -0.055 -0.112 (0.127) (0.896) (0.135) (0.118) (0.096) (0.147) (0.123) (0.148) Tangibility 0.418*** -0.215 -0.142 0.081 0.290*** -0.307** -0.581*** -0.269 (0.113) (0.428) (0.110) (0.103) (0.106) (0.154) (0.181) (0.199) M/B -0.249*** 0.078 -0.045*** -0.027* 0.009 -0.074*** 0.042*** -0.014 (0.028) (0.053) (0.016) (0.014) (0.013) (0.025) (0.015) (0.020) Size 0.051*** 0.028 -0.054*** -0.094*** 0.089*** 0.067*** -0.056*** 0.001 (0.016) (0.051) (0.014) (0.014) (0.014) (0.020) (0.020) (0.024) CF Volatility -1.380 -9.556 -1.008* -1.215** 0.041 -0.116 0.201 -0.339 (0.878) (7.577) (0.541) (0.582) (0.224) (0.513) (0.153) (0.387) Dividend Payer -0.081* 0.268 0.035 -0.053 0.076* -0.272*** -0.034 0.067 (0.045) (0.205) (0.044) (0.044) (0.043) (0.065) (0.073) (0.071) Constant -2.090*** -5.265 -6.165*** -0.792 -1.731*** -6.973*** -10.019*** -2.213*** (0.454) (0.000) (0.419) (0.487) (0.395) (0.330) (0.899) (0.470) Ind. FE Yes Yes Yes Yes Yes Yes Yes Yes Rating FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs. 10644 5916 10124 10050 10219 10007 9108 9519 2 Adj. R 0.476 0.525 0.391 0.417 0.538 0.612 0.519 0.369 42
  • 45. Table 8. Rating Changes and Debt Structure The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Table A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Profitability, asset tangibility, market to book ratio (M/B), size, cash flow volatility, and dividend payer are lagged one year. Industry fixed effects are based on two-digit SIC codes. Rating fixed effects are based on 22 rating dummies and the unrated dummy. Data on ratings ranging from AAA to D are from Compustat (280). Downgrade(–1) and Upgrade(–1) indicate respectively a change in debt one year before the change in rating occurs. Downgrade and Upgrade indicate respectively a decrease and an increase in rating during the year in which the change in debt occurs. Downgrade(+1) and Upgrade(+1) indicate respectively a change in debt one year after the change in rating occurs. Panel A reports the effect of a change in rating on the change in each debt type both for mean (first row) and median (second row) values. T-tests (for means) and sign tests (for medians) are based on the null hypothesis that the change in debt type is zero. Panel B reports regression results to explain changes in debt structure due to downgrades. Panel C reports regression results to explain changes in debt structure due to upgrades. Standard errors clustered at the firm level are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Panel A: Changes in Debt Structure Due to Rating Downgrades and Upgrades Debt Types ∆Mkt Lev ∆CP ∆RC ∆TL ∆SBN ∆SUB ∆CL ∆Other # Obs Downgrade(–1) 0.021*** -0.007** 0.002 -0.007 0.015 0.000 -0.001 -0.002 334 0.013** 0.000** 0.000 0.000 0.001*** 0.000 0.000*** 0.000*** Downgrade -0.026*** -0.009*** -0.006 0.015* 0.019* -0.010* 0.002 -0.006 334 -0.024*** 0.000*** 0.000 0.000 0.001*** 0.000 0.000** 0.000 Downgrade(+1) -0.015** -0.005* 0.001 0.007 0.005 -0.008 0.003 0.000 334 -0.013*** 0.000 0.000 0.000 0.000 0.000 0.000*** 0.000 Upgrade(–1) -0.055*** 0.000 -0.008 -0.002 0.033*** -0.025*** 0.004 -0.004 277 -0.040*** 0.000 0.000 0.000** 0.000** 0.000 0.000 0.000* Upgrade -0.041*** 0.009** 0.006 0.007 0.005 -0.027** 0.001 -0.004 277 -0.028*** 0.000** 0.000 0.000 0.000 0.000* 0.000** 0.000** Upgrade(+1) 0.001 0.006 0.024* -0.005 -0.002 -0.022* -0.004 0.001 277 -0.002 0.000 0.000 0.000 0.000 0.000** 0.000 0.000** 43
  • 46. Panel B: Explaining Changes in Debt Structure Due to Downgrades Debt Types Mkt Lev CP RC TL SBN SUB CL Other Year Before 0.050*** 0.012** 0.019 0.001 -0.003 -0.010 -0.014 -0.008 (0.018) (0.006) (0.015) (0.017) (0.033) (0.027) (0.009) (0.014) Current Year 0.010 0.008 -0.013 0.008 0.018 -0.029** 0.001 0.002 (0.010) (0.005) (0.010) (0.013) (0.020) (0.014) (0.004) (0.007) Year After -0.001 0.003 -0.030** -0.035* 0.074** -0.014 -0.002 0.004 (0.017) (0.006) (0.014) (0.019) (0.031) (0.024) (0.005) (0.015) Constant 0.224*** 0.074*** -0.056* 0.309*** 0.721*** -0.178*** 0.017 0.110*** (0.039) (0.016) (0.031) (0.049) (0.083) (0.057) (0.017) (0.040) Ind. FE Yes Yes Yes Yes Yes Yes Yes Yes Rating FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs. 774 774 774 774 774 774 774 774 Adj. R2 0.552 0.391 0.136 0.181 0.312 0.321 0.196 0.125 Panel C: Explaining Changes in Debt Structure Due to Upgrades Debt Types Mkt Lev CP RC TL SBN SUB CL Other Year Before -0.060*** -0.008 0.018 -0.053* 0.006 0.003 0.002 0.037** (0.015) (0.007) (0.025) (0.031) (0.046) (0.031) (0.007) (0.017) Current Year -0.012 -0.002 -0.024 0.024 0.005 -0.015 0.009 0.000 (0.008) (0.003) (0.017) (0.021) (0.023) (0.018) (0.006) (0.008) Year After -0.018 -0.000 0.052** -0.024 -0.054 0.012 -0.000 0.013 (0.013) (0.005) (0.025) (0.027) (0.044) (0.028) (0.008) (0.016) Constant 0.228*** -0.004 0.448*** -0.100 0.158 0.749*** -0.118*** -0.150** (0.038) (0.042) (0.074) (0.141) (0.211) (0.067) (0.043) (0.058) Ind. FE Yes Yes Yes Yes Yes Yes Yes Yes Rating FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs. 606 606 606 606 606 606 606 606 Adj. R2 0.440 0.197 0.140 0.258 0.281 0.194 0.074 0.098 44
  • 47. Table 9. Leverage and Debt Structure Regressions The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Tables A1 and A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of (Total Debt + Market Value of Equity) (the same denominator used for computing market leverage, see Table A1). Profitability, asset tangibility, market to book ratio (M/B), size, cash flow volatility, and dividend payer, are lagged one year. Industry fixed effects are based on two-digit SIC codes. Rating fixed effects are based on 22 rating dummies and the unrated dummy. The estimation is via the Seemingly Unrelated Regression (SUR) specification across the system of equations. Standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Mkt Debt Types Scaled by Total Debt + Market Value of Equity Lagged Regressors Leverage CP RC TL SBN SUB CL Other -0.069*** -0.000 0.008* 0.012** -0.073*** -0.006 -0.005*** -0.005*** Profitability (0.008) (0.000) (0.005) (0.005) (0.006) (0.004) (0.001) (0.001) Tangibility 0.135*** -0.000 0.017*** 0.034*** 0.099*** -0.022*** 0.010*** -0.002 (0.009) (0.000) (0.005) (0.005) (0.007) (0.005) (0.001) (0.002) M/B -0.030*** -0.000 -0.009*** -0.007*** -0.009*** -0.003*** -0.001*** -0.001*** (0.001) (0.000) (0.001) (0.001) (0.001) (0.001) (0.000) (0.000) Size 0.012*** 0.000*** -0.003*** -0.004*** 0.014*** 0.006*** -0.001*** 0.001*** (0.001) (0.000) (0.001) (0.001) (0.001) (0.001) (0.000) (0.000) CF Volatility -0.044*** 0.000 -0.026*** -0.018** -0.001 0.002 -0.002 -0.000 (0.016) (0.001) (0.009) (0.009) (0.012) (0.008) (0.002) (0.003) Dividend Payer -0.023*** 0.000** -0.004* -0.004* 0.003 -0.019*** -0.002*** 0.002** (0.004) (0.000) (0.002) (0.002) (0.003) (0.002) (0.000) (0.001) Constant 0.000 0.000 0.080*** 0.070*** 0.000 -0.050** 0.000 0.000 (0.000) (0.000) (0.026) (0.027) (0.000) (0.024) (0.000) (0.000) Ind. FE Yes Yes Yes Yes Yes Yes Yes Yes Rating FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs. 10737 10737 10737 10737 10737 10737 10737 10737 Adj. R2 0.385 0.244 0.152 0.151 0.293 0.184 0.101 0.048 45
  • 48. Table 10: Financing Gap and Debt Structure The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Tables A1 and A2 for variables definitions. Financing gap is defined as the sum of dividends, investment, change in working capital, minus the cash flow after interest and taxes, and scaled by lagged total capital. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of (Total Debt + Market Value of Equity) (the same denominator used for computing market leverage, see Table A1). Data on ratings ranging from AAA to D are from Compustat (280). Industry fixed effects are based on two-digit SIC codes. Rating fixed effects are based on rating dummies that take values ranging from 1 (rating class AAA) to 23 (not rated). The estimation is via the Seemingly Unrelated Regression (SUR) specification across the system of equations. Standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Debt Types ∆Mkt Lev. ∆CP ∆RC ∆TL ∆SBN ∆SUB ∆CL ∆Other ∆Profitability -0.204*** -0.001 -0.050*** -0.065*** -0.063*** -0.022*** -0.004*** 0.001 (0.009) (0.001) (0.006) (0.007) (0.007) (0.005) (0.001) (0.002) ∆Tangibility 0.172*** -0.001 0.062*** 0.071*** 0.009 0.012 0.013*** 0.006 (0.019) (0.001) (0.013) (0.014) (0.014) (0.010) (0.002) (0.005) ∆M/B 0.006*** 0.000 0.001 0.004*** -0.000 0.000 0.000* 0.001 (0.001) (0.000) (0.001) (0.001) (0.001) (0.001) (0.000) (0.000) ∆Size 0.264*** 0.001** 0.059*** 0.088*** 0.079*** 0.027*** 0.004*** 0.006*** (0.005) (0.000) (0.003) (0.004) (0.004) (0.003) (0.001) (0.001) Financing Gap 0.008*** 0.000 0.001 -0.001 0.006*** 0.003** -0.000 -0.001* (0.003) (0.000) (0.002) (0.002) (0.002) (0.001) (0.000) (0.001) Constant 0.028 0.001 0.016 0.017 -0.005 0.004 -0.000 -0.002 (0.032) (0.002) (0.020) (0.022) (0.023) (0.015) (0.003) (0.008) Ind. FE Yes Yes Yes Yes Yes Yes Yes Yes Rating FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Obs. 10265 10265 10265 10265 10265 10265 10265 10265 Adj. R2 0.360 0.015 0.073 0.103 0.111 0.036 0.023 0.010 46
  • 49. Table 11. Robustness Checks The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Table A2 for variable definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Panel A presents the sample mean and median of debt types and key firm characteristics for the seven clusters sorted by ascending median firm size, using firm-level observations obtained by time series average. Panel B presents the shares of observations with significant amounts of various debt types conditional on a particular debt type (given in the first column of the table) exceeding 10% of total debt. Panel C presents the mean and median of each debt type as a fraction of total debt conditional on a particular debt type (given in the first column of the table) exceeding 10% of total debt. We report both t-tests on means and sign tests on medians being strictly greater than 10% (one-sided), as well as the number of observations for which a particular debt type is strictly greater than 10%. Panel D presents the shares of observations with significant amounts of various debt types conditional on a particular three-year lagged debt type (given in the first column of the table) exceeding 30% of total debt. Panel E presents the mean and median of each debt type as a fraction of total debt conditional on a particular three-year lagged debt type (given in the first column of the table) exceeding 30% of total debt. We report both t-tests on means and sign tests on medians being strictly greater than 30% (one-sided), as well as the number of observations for which a particular debt type is strictly greater than 30%. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Panel A: Cluster Analysis Using Firm-Level Observations Obtained by Time Series Average Debt Types Mkt Profitab Tangibi Firm Dividen Cluster CP RC TL SBN SUB CL Other Lev. ility lity M/B Size CF Vol. d Payer Rated Rating # Obs. 1 0.000 0.043 0.046 0.054 0.004 0.837 0.015 0.034 -0.058 0.143 2.582 232.30 0.043 0.073 0.026 22.752 319 0.000 0.000 0.000 0.000 0.000 0.977 0.000 0.007 0.034 0.087 2.009 91.30 0.028 0.000 0.000 23.000 2 0.003 0.102 0.087 0.081 0.021 0.084 0.621 0.113 0.032 0.212 1.934 2046.31 0.026 0.211 0.132 21.313 158 0.000 0.000 0.000 0.000 0.000 0.000 0.552 0.055 0.098 0.141 1.508 154.76 0.015 0.000 0.000 23.000 3 0.001 0.099 0.763 0.055 0.034 0.033 0.016 0.199 0.047 0.252 1.869 570.50 0.025 0.213 0.182 21.254 579 0.000 0.008 0.750 0.000 0.000 0.000 0.000 0.132 0.103 0.171 1.396 164.04 0.015 0.000 0.000 23.000 4 0.001 0.757 0.087 0.071 0.032 0.037 0.015 0.199 0.086 0.273 1.546 438.70 0.024 0.251 0.071 22.226 537 0.000 0.746 0.000 0.003 0.000 0.000 0.000 0.159 0.112 0.189 1.184 193.84 0.015 0.000 0.000 23.000 5 0.012 0.181 0.164 0.490 0.058 0.062 0.033 0.252 0.063 0.305 1.585 2743.16 0.027 0.276 0.350 18.933 618 0.000 0.157 0.138 0.510 0.000 0.002 0.000 0.208 0.102 0.240 1.264 443.58 0.013 0.000 0.000 23.000 6 0.001 0.076 0.107 0.101 0.674 0.033 0.013 0.284 0.073 0.219 1.616 1086.64 0.026 0.119 0.481 18.233 331 0.000 0.014 0.018 0.011 0.655 0.000 0.000 0.242 0.105 0.129 1.339 617.74 0.015 0.000 0.500 18.667 7 0.015 0.038 0.025 0.875 0.010 0.015 0.020 0.214 0.044 0.289 1.891 4734.52 0.030 0.447 0.500 16.336 790 0.000 0.000 0.000 0.888 0.000 0.000 0.000 0.171 0.115 0.232 1.344 1007.69 0.012 0.143 0.500 18.310 Total 0.006 0.198 0.202 0.338 0.092 0.114 0.048 0.201 0.047 0.258 1.816 2028.38 0.028 0.264 0.283 19.660 3332 0.000 0.044 0.031 0.202 0.000 0.000 0.000 0.148 0.104 0.182 1.358 308.25 0.015 0.000 0.000 23.000 47
  • 50. Panel B: Shares of Observations Conditional on Significant Amounts of Debt Types Debt Types CP RC TL SBN SUB CL Other # Obs. Share of Sample Debt Types CP>10% 1.000 0.103 0.091 0.888 0.019 0.022 0.160 418 0.029 RC>10% 0.009 1.000 0.278 0.415 0.134 0.080 0.042 4958 0.348 TL>10% 0.008 0.306 1.000 0.351 0.181 0.085 0.040 4495 0.316 SBN>10% 0.051 0.281 0.216 1.000 0.102 0.073 0.069 7321 0.514 SUB>10% 0.003 0.276 0.338 0.312 1.000 0.046 0.032 2401 0.169 CL>10% 0.004 0.197 0.191 0.267 0.055 1.000 0.044 2010 0.141 Other>10% 0.059 0.182 0.158 0.443 0.067 0.077 1.000 1136 0.080 Panel C: Debt Structure Conditional on Significant Amounts of Debt Types Debt Types CP RC TL SBN SUB CL Other # Obs. Debt Types CP>10% 0.252*** 0.032 0.032 0.576*** 0.009 0.010 0.047 418 0.210*** 0.000 0.000 0.658*** 0.000 0.000 0.007 418 43 38 371 8 9 67 RC>10% 0.003 0.570*** 0.121*** 0.204*** 0.058 0.028 0.016 4958 0.000 0.541*** 0.000 0.024 0.000 0.000 0.000 43 4958 1376 2057 662 395 207 TL>10% 0.002 0.128*** 0.582*** 0.165*** 0.078 0.031 0.014 4495 0.000 0.000 0.559*** 0.001 0.000 0.000 0.000 38 1376 4495 1579 812 383 179 SBN>10% 0.014 0.115*** 0.084 0.696*** 0.041 0.027 0.024 7321 0.000 0.000 0.000 0.770*** 0.000 0.000 0.000 371 2057 1579 7321 750 536 503 SUB>10% 0.001 0.110*** 0.132*** 0.135*** 0.598*** 0.017 0.012 2401 0.000 0.000 0.000 0.002 0.561*** 0.000 0.000 8 662 812 750 2401 111 76 CL>10% 0.001 0.100 0.093 0.148*** 0.027 0.613*** 0.019 2010 0.000 0.000 0.000 0.000 0.000 0.644*** 0.000 9 395 383 536 111 2010 88 Other>10% 0.016 0.084 0.068 0.249*** 0.028 0.034 0.520*** 1136 0.000 0.000 0.000 0.004 0.000 0.000 0.374*** 67 207 179 503 76 88 1136 48
  • 51. Panel D: Shares of Observations Conditional on Significant Amounts of Three-Year Lagged Debt Types Debt Types CP RC TL SBN SUB CL Other # Share of Obs. Sample Lagged Debt Types CP>30% 0.538 0.019 0.038 0.779 0.000 0.000 0.010 76 0.005 RC>30% 0.004 0.733 0.176 0.241 0.077 0.043 0.022 1468 0.103 TL>30% 0.001 0.198 0.762 0.180 0.103 0.040 0.026 1180 0.083 SBN>30% 0.013 0.131 0.092 0.883 0.054 0.026 0.022 2625 0.184 SUB>30% 0.000 0.124 0.173 0.205 0.816 0.016 0.009 864 0.061 CL>30% 0.000 0.101 0.116 0.136 0.028 0.754 0.041 418 0.029 Other>30% 0.013 0.180 0.151 0.234 0.040 0.066 0.550 261 0.018 Panel E: Debt Structure Conditional on Significant Amounts of Three-Year Lagged Debt Types Debt Types CP RC TL SBN SUB CL Other # Obs. Lagged Debt Types CP>30% 0.196 0.054 0.052 0.610*** 0.001 0.007 0.035 76 0.154 0.000 0.000 0.641*** 0.000 0.000 0.001 25 4 6 65 0 0 2 RC>30% 0.002 0.443*** 0.161 0.238 0.073 0.061 0.021 1468 0.000 0.396*** 0.000 0.017 0.000 0.000 0.000 5 805 301 454 146 83 26 TL>30% 0.002 0.201 0.445*** 0.183 0.073 0.065 0.029 1180 0.000 0.000 0.409*** 0.000 0.000 0.000 0.000 2 318 670 274 122 72 32 SBN>30% 0.017 0.123 0.104 0.638*** 0.047 0.038 0.031 2625 0.000 0.000 0.000 0.738*** 0.000 0.000 0.000 38 399 332 2047 162 92 72 SUB>30% 0.000 0.120 0.171 0.237 0.411*** 0.041 0.020 864 0.000 0.000 0.000 0.008 0.361*** 0.000 0.000 0 129 176 268 470 31 12 CL>30% 0.000 0.124 0.185 0.185 0.048 0.420*** 0.037 418 0.000 0.000 0.000 0.000 0.000 0.236 0.000 0 64 93 91 25 186 19 Other>30% 0.014 0.122 0.186 0.256 0.075 0.079 0.264 261 0.000 0.000 0.000 0.000 0.000 0.000 0.000 4 38 61 90 26 22 79 49
  • 52. Table 12. Fallen Angels and Rising Stars The sample consists of non-utilities (excluding SIC codes 4900-4949) and non-financials (excluding SIC codes 6000-6999) U.S. firms covered by both Capital IQ and Compustat from 2001 to 2007. After some filtering, there are 14,242 firm-year observations involving 3,332 unique firms in the sample. See Table A2 for variables definitions. All variables are winsorized at the 0.5% in both tails of the distribution. Within the debt types, total debt is decomposed into commercial paper, revolving credit, term loans, senior bonds, subordinated bonds, capital leases, and other debt. All debt types are calculated as a fraction of total debt. Fallen angel refers to a downgrade from investment grade to speculative grade. Rising star refers to an upgrade from speculative grade to investment grade. We report the effect of a change in rating on the change in each debt type, both for mean (first row) and median (second row) values. T-tests (for means) and sign tests (for medians) are based on the null hypothesis that the change in debt type is zero. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Debt Types ∆Mkt Lev ∆CP ∆RC ∆TL ∆SBN ∆SUB ∆CL ∆Other # Obs Fallen angels(-1) 0.046** -0.014 -0.006 -0.020 0.027 0.014 -0.001 0.000 32 0.029 0.000* 0.000 0.000* 0.014 0.000 0.000** 0.000 Fallen angels -0.040 -0.006 -0.034 0.000 -0.009 0.036 0.020 -0.007 32 -0.044 0.000 0.000 0.000 0.007 0.000 0.000 0.000 Fallen angels(1) -0.025 -0.002 0.005 0.037** -0.023 -0.024 0.003* 0.002 32 -0.037** 0.000 0.000 0.000** 0.000 0.000 0.000 0.000 Rising stars(-1) -0.078*** 0.000 -0.028* -0.035 0.036 -0.020 0.042 0.003 21 -0.067*** 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Rising stars -0.015 0.000 0.132* -0.008 0.057 -0.132** -0.045 -0.004 21 -0.005 0.000 0.000 0.000 0.043* 0.000 0.000 0.000 Rising stars(1) 0.025* 0.000 0.011 0.073 -0.034 -0.004 -0.001 -0.044 21 0.027 0.000 0.000 0.000 0.000 0.000 0.000 0.000 50