The Capital IQ database covering the period from 2001 to 2012 is our main source of debt types and for creating the debt structure index. Capital IQ contains data regarding every debt component in a firm’s capital structure. The broader of its two groupings (namely capital structure type) categorizes debt types into seven groups. A finer grouping (named capital structure sub-type) elaborates on the terms of each contract, such as seniority, security, and interest charges. To minimize any subjectivity due to debt-type aggregation, we use the following seven main debt types used by Capital IQ: (1) Capital Leases, (2) Commercial Papers, (3) Lines of Credit, (4) Term Loans, (5) Bonds and Notes, (6) Other Debt, and (7) Trusts. In contrast, Colla, Ippolito, and Li
(2013) split the notes category into senior and subordinate and merge trusts with the other debt
category. This leads to a significant loss of observations in the data since senior and subordinate
sub-types constitute only a portion of the “note” debt type. Such losses include debt sub-types
such as profit participating certificates; promissory note loans; Class A, B, C and D bonds; market debt securities; interest-bearing bonds; (long-term) borrowings from certain institutions; bank
loans in other currencies; credit from banks, and so forth.12
We draw data for the firm-specific variables in the resulting sample from Compustat. The variables chosen are based on their relevance to capital structure theories (Frank and Goyal, 2009; Titman and Wessels, 1988; Rajan and Zingales, 1995; Parsons and Titman, 2009; Graham and Leary, 2011) and their relevance to debt structures (Rauh and Sufi, 2010; Colla, Ippolito, and Li, 2013). We briefly describe the rationale for the choice of each of the possible determinants of debt structure used herein, and provide further details on their computations in Appendix 2. We begin with the firm-specific variables which are all winsorized at the 1% level at both tails, and then
standardized by their respective standard deviations. Market leverage is included to account for
the possible effect of relative leverage on the choice of debt types and their weightings. Maturity
is included since longer debt maturities can lock a firm into a certain debt type over time and thus
influence debt-type stability. Market to book ratio is included to capture growth opportunities since
firms with more growth opportunities may use lower leverage (Myers, 1977). Firm size and firm
sales are included since leverage can increase with firm size and firm sales (Lewellen, 1971).
Cash-flow volatility increases default probabilities and thus decreases leverage capacity (Saretto
12 For robustness, we test our results with the classification of Colla, Ippolito and Li (2013) and document
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and Tookes, 2013) and increases debt-structure heterogeneity (Colla, Ippolito, and Li, 2014).13
Profitability is included because it can increase leverage according to the cash flow hypothesis (Jensen, 1986) or decrease leverage according to the pecking order theory (Myers, 1984), although
its effect on debt structure is found to be mixed (Colla, Ippolito, and Li, 2013). Tangibility is
included because it can influence leverage (Saretto and Tookes, 2013) and firms with more
tangible assets have lower debt heterogeneity (Colla, Ippolito, and Li, 2013). The rating dummy
variable is included since having a bond rating facilitates access to capital and can lead to higher
leverage (Faulkender and Petersen, 2006) and more feasible debt type choices. The dividend payer
dummy variable is included since it can affect capital structures (DeAngelo and Masulis, 1980).
Marginal Tax rates are included because they can increase the incentive to use debt (Graham, 2000) and can influence the relative value of long-term debt contracts (Brick and Ravid, 1985).
Idiosyncratic volatility is included since increased volatility may induce a firm to delever or choose different debt types or different mixes of debt types.
We also include various possible non-firm determinants. Industry heterogeneity is included
since certain industry affiliations can significantly determine corporate capital structure behaviour. For example, Rauh and Sufi (2012) show how similar “lines of business” can affect corporate
capital structures over time. Rollover risk is included since greater rollover risk can induce firms
to choose longer maturities and therefore induce greater stability in debt-type structures. GDP per capita, GDP growth and inflation are included because their higher values are associated with better economic conditions and a greater availability of capital for firms. Finally, term spread changes are included because of their effect on the choice of certain debt maturities and the resulting impact on the stability of the structure of debt types.
Table 3.1 reports summary statistics for the firms in our sample and their characteristics. There are only moderate differences between the summary statistics reported in the left- and right-most panels for the full sample and for the sample of firms with at least 10 years of observations (henceforth referred to as the longer-lived firms). The longer-lived firms tend to be moderately larger, with higher cash flow volatilities, profitabilities, and asset tangibilities. They also have a larger mean proportion of rated firms (33% versus 22%), a larger mean proportion of dividend
13 This variable is measured as in Kryzanowski and Mohsni (2013) and alternatively as the volatility of a
firm’s earnings per share (Compustat item # 19) over the past six years as a test of robustness which yields similar empirical results.
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payers (31% versus 26%), longer mean debt maturities (72% versus 68%) and similar mean idiosyncratic volatilities (14% versus 14%).
[Please insert Table 3.1 here]