Role of Initial Leverage Across Rated and Not-Rated Firms

In this section, we study how different capital structure determinants influence leverage choices for rated and not-rated firms. We begin by showing that the associations between leverage and previously identified determinants differ for rated and not-rated firms by estimating the following

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𝐿𝑒𝑣_𝑖,𝑡𝑙 = 𝛽0+ 𝛽1𝐿𝑒𝑣𝑖,𝑡−10𝑙 + 𝛽2𝐿𝑜𝑔(𝑆𝑖𝑧𝑒𝑖,𝑡−1) + 𝛽3 𝐿𝑜𝑔(𝑆𝑎𝑙𝑒𝑠)𝑖,𝑡−1+ 𝛽4𝑀𝑇𝐵𝑖,𝑡−1+ 𝛽5 𝑃𝑟𝑜𝑓𝑖𝑡𝑖,𝑡−1+ 𝛽6𝑇𝑎𝑛𝑔𝑖,𝑡−1+ 𝛽7𝐼𝑛𝑑𝐿𝑒𝑣𝑖,𝑡−1+ 𝛽8𝐶𝐹𝑉𝑜𝑙𝑖,𝑡−1+ 𝛽9𝐷𝑖𝑣𝑃𝑎𝑦𝑒𝑟𝑖 +

𝜀_𝑖,𝑡 (2.6)

The independent variables in (2.6) include the corresponding book or market leverage lagged ten years (referred to as initial leverage herein), firm size, sales, market/book ratio, profitability, tangibility, industry leverage, volatility of cash flows, and dividend payer dummy (equal to 1 if a firm pays a dividend and zero otherwise). Appendix 1 provides definitions and the expected coefficient signs for each of these variables in Table A1 based on the generally consistent findings previously reported in the literature (particularly, Frank and Goyal, 2009; Titman and Wessels, 1988; Rajan and Zingales, 1995; Mackay and Phillips, 2005; Parsons and Titman, 2008; and Graham and Leary, 2011). Some of these studies deal with firm, year and industry fixed effects and net debt and equity issuance (e.g., Frank and Goyal, 2009; Lemmon, Roberts and Zender, 2008). As Parsons and Titman (2008) note, the use of lagged regressors is the default method for

regression-based empirical research on capital structure. Gulen and Ion (2016) argue that using

lagged regressors significantly reduces simultaneous effects and omitted variable bias. By standardizing all variables, each estimated coefficient is interpreted as the change in the dependent variable from a one standard deviation change in the independent variable. The reported t-values are robust to year clustering effects.

For comparability with prior studies, we begin with a classic pooled regression. The R2 _values

reported by Lemmon, Roberts and Zender (2008) range between 18% and 29% and that reported by Hanousek and Shamshur (2011) is around 8%. Our pooled regression results for the full (All) sample are reported in Panel A of Table 2.10. Results for market and book leverages (Mkt and Book Lev) are reported in columns 1 to 7 and 8 to 14, respectively. The explanatory power of initial leverage as the only explanatory variable is reported in four columns (1, 2 and 7 and 8) and the absence of this variable is reflected in columns 4 and 10. Columns 2 and 8 show that adding year fixed effects and reflecting year clustering adds minimally to the explanatory power of the regressions. While all of the estimated coefficients for the initial leverages are highly significant, the explanatory power is higher using market (6% to 8%) versus book (0% to 1%) leverage. The traditional variables together explain as much as 18% of market leverage (column 4) and adding the initial market leverage increases the R-square to 20% (column 6). The R-squares for the book

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leverage regressions including the traditional variables remain at 11% after adding the initial book leverages (columns 10 and 12).

[Please insert Table 2.10 about here]

From the second columns in Panels B and C of table 2.10, we observe that the explanatory power of the initial leverage by itself is higher at 10% for the rated firms compared to 3% for the not-rated firms. Furthermore, a one standard deviation increase in the initial market leverage corresponds to a 29% and 15% increase in the current leverage ratios for rated and not-rated firms, respectively. The explanatory powers of the traditional variables separately and together with the initial market leverage in columns (4) and (5) are substantially lower for not-rated than for rated firms (16-17% and 43-44%, respectively). The large differences in the explanatory power of the initial leverage between the not-rated and rated firms illustrate how the effects of their leverage histories differ in arriving at their current capital structures.

To further study stability, we examine the firm fixed-effects influence on the regression results using regression model (1) for rated and not-rated firms. In these estimations the intercept is allowed to vary on a firm-to-firm basis while the error variances and the slopes are held constant. We expect to see an increase in the explanatory power for a fixed-effects regression for firms with

more stable capital structures.

The fixed-effect dummies in (1) are estimated using the least squares dummy variable (LSVD) technique. The significance of the model and each of the coefficient estimates is tested using the incremental F-test. These regression results including all but the initial leverage regressor for the “all”, “rated” and “not-rated” samples are reported in Panels A, B and C, respectively, of Table 2.11. The first (last) two columns of each panel report results for market (book) leverage. The even (odd) numbered columns report results for regressions with (no) fixed effects or clustered standard errors and t-values which are robust to clustering on both firm and time using the method suggested by Petersen (2009).

[Please insert Table 2.11 about here]

As expected, the explanatory power of the regressions increases substantially after the inclusion of firm fixed-effects for all three samples. For the full sample (see Panel A of Table 2.11), the explanatory power increases from 18% to 71% for market leverage and from 11% to 70% for book

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leverage. This result is consistent with the notion of leverage persistence, as noted by Hennessy, Livdan, and Miranda (2010) and Malmendier, Tate, and Yan (2011). The explanatory power is consistently higher with fixed effects for the sample of rated than that for not-rated firms (see Panels B and C of Table 2.11) for both market and book leverages. This further supports our conjecture that the leverages of rated and not-rated firms are influenced differently by time- invariant unobserved effects.