# The impact of financing surpluses and large financing deficits on tests of the pecking order theory.

This paper extends the basic pecking order model of Shyam-Sunder and Myers by separating the effects of financing surpluses, normal deficits, and large deficits. Using a panel of US firms over the period 1971-2005, we find that the estimated pecking order coefficient is highest for surpluses (0.90), lower for normal deficits (0. 74), and lowest when firms have large financing deficits (0.09). These findings shed light on two empirical puzzles: 1) small firms, although having the highest potential for asymmetric information, do not behave according to the pecking order theory, and 2) the pecking order theory, has lost explanatory power over time. We provide a solution to these puzzles by demonstrating that the frequency of large deficits is higher in smaller firms and increasing over time. We argue that our results are consistent with the debt capacity in the pecking order model.**********

In explaining firms' financing behavior, the pecking order theory has become a widely debated model of capital structure choice. According to the pecking order theory, firms have no well-defined optimal debt ratio (Myers, 1984). Instead, due to asymmetric information, firms adopt a hierarchical order of financing preferences; internal financing is preferred to external financing. If external financing is needed, firms first seek debt funding. Equity is only issued as a last resort.

The seminal paper by Shyam-Sunder and Myers (1999) introduces an empirical test for the pecking order theory. According to this test, the pecking order implies that firms issue or retire an amount of debt equal to the funds flow deficit, which is the inadequacy of internal cash flows for real investments and dividend commitments. In a simple regression of a firm's net debt issued on the financing deficit, the slope coefficient provides information on the proportion financed by debt of a one dollar increase in deficits and the pecking order implies that this coefficient is close to unity. Using a small sample of firms that survive the entire 1971-1989 period, Shyam-Sunder and Myers (1999) conclude that the pecking order model is an excellent first-order descriptor of financing behavior because they find an estimated pecking order coefficient of 0.75. Frank and Goyal (2003) test the pecking order model using a more comprehensive data set. They find substantially lower coefficients and demonstrate that larger firms exhibit greater pecking order behavior than smaller firms. This size effect is corroborated by Fama and French (2002). From a pecking order perspective, this correlation is counterintuitive as small firms have the highest potential for asymmetric information, which is the actual driver of the pecking order in the Myers and Majluf (1984) model. We refer to the size anomaly as the first pecking order puzzle. Another finding of Frank and Goyal (2003) is that the pecking order model has lost its explanatory power over the years. For the period 1971-1989, their estimated coefficient is 0.28, whereas for 1990-1998 it is as low as 0.15. Frank and Goyal's (2003) analysis does not explain this trend. We consider the decreasing pecking order coefficient over time to be the second pecking order puzzle.

The main goal of this paper is to examine whether two modifications in Shyam-Sunder and Myers' (1999) model can solve these puzzles and whether these modifications can shed light on conflicting findings of prior studies. Our first modification relates to the fact that the model does not discriminate between the effects of financing deficits and financing surpluses. Instead, the model is typically estimated over both surpluses and deficits and imposes a common homogeneous pecking order coefficient. However, the implications of deficits and surpluses are different; in the case of a deficit, a firm has to issue securities, while it repurchases securities when it has a surplus. As Myers and Majluf's (1984) theory for the pecking order for issuance decisions differs from a theory on repurchase decisions in Shyam-Sunder and Myers (1999), we allow for an asymmetry between the effects of surpluses and deficits.

Our second modification results from Chirinko and Singha (2000), who argue that Shyam-Sunder and Myers' (1999) empirical model is flawed for firms with large deficits. When firms follow the pecking order, their large financing needs may exceed the unused debt capacity and they will finance the remainder of their financing needs with equity. Therefore, our model distinguishes three situations: 1) firms with surpluses, 2) firms with "normal" deficits, and 3) firms with large deficits. We hypothesize that the pecking order test yields coefficients reasonably close to unity for firms with "normal" deficits, but expect lower coefficients for firms with larger deficits as these firms are more likely to reach their debt capacity.

We test our capital structure models by using a large panel of US firms taken from Compustat over the period 1971-2005. We corroborate the results of Frank and Goyal (2003) as the estimated pecking order coefficient is 0.26 over the full period, lower in small firms, and decreasing over time. Next, we extend the analysis by estimating the pecking order model for subgroups with deficits and surpluses. We find a strong asymmetry in pecking order behavior. For surpluses, the estimated pecking order coefficient is 0.90, whereas for deficits, it is only 0.15. This finding indicates that the average estimate of 0.26 hides a substantial degree of asymmetry across financing surpluses and deficits.

Next, we test the pecking order model for different deficit sizes. In firms with normal deficits, the pecking order coefficient is around 0.74. In contrast, large deficit firms exhibit a coefficient of 0.09. This low coefficient is potentially explained by firms' debt capacities; firms with large deficits are more restricted in issuing debt. To test the impact of the debt capacity, we build on the previous literature and construct subgroups with different levels of financial constraints. We find that the pecking order model is a good description of the financing behavior of nonconstrained firms and that the lower pecking order coefficients in certain subsamples are indeed consistent with firms' restrictions in issuing debt. For all subgroups, a large deficit decreases the pecking order coefficient.

The distinction between surpluses, normal deficits, and large deficits appears to explain both pecking order puzzles. The size anomaly results from the fact that large financing deficits are much more common for relatively small firms, whereas financing surpluses are scarcer for small firms. The second puzzle, that the pecking order model loses explanatory power over time, is also explained by large deficits as these have become more common in recent years.

Our paper is largely related to Agca and Mozumdar (2007) and Lemmon and Zender (2010). The paper by Agca and Mozumdar (2007), in particular, also uses a piecewise linear specification to account for debt capacity. We corroborate their findings with regard to the effect of the debt capacity; the pecking order model performs worse for firms with smaller debt capacities. We demonstrate that the distribution of deficit sizes has an additional decreasing effect on the pecking order coefficient for firms with small debt capacities, since the constrained firms have the largest financing needs. We further add to these papers the notion that firms act very differently in response to financing surpluses and deficits, and provide insight into the changes of the financing deficit over time.

The remainder of this paper is organized as follows. In Section I, we present the pecking order model and its empirical implications. In Section II, the data are described and Section III describes the empirical results of this study. Finally, we present our conclusions in Section IV.

I. Theory

This section describes some of the empirical and theoretical studies on the pecking order theory and explains how our paper relates to these studies.

A. Pecking Order Theory

Donaldson (1961) is the first to describe firms' preferences for internal funds over external funds, and firms' preferences for issuing debt over issuing equity. Myers and Majluf (1984) explain these preferences in a theoretical model that deals with capital structure decisions of firms with external financing needs. Myers and Majluf (1984) demonstrate that firms' managers, when acting on behalf of the current shareholders, pass up good investments in case the new shareholders will capture the benefits of the investment. Consequently, investors will reason that an investment decision without a security issue signals good news, while issuing shares signals bad news. The latter signal reduces the price investors are willing to pay for the equity issue resulting in a pecking order of corporate financing; managers will prefer using internal financing, then using debt, and then issuing equity.

A pecking order model for repurchase decisions is presented in Shyam-Sunder and Myers (1999). They assume that firms' managers differ in their degree of optimism. Managers, who are less optimistic than investors, do not want to repurchase shares as they perceive the price as being too high. Optimistic managers, however, want to repurchase shares forcing up stock prices if they try to do so. With the new stock price, there will be fewer managers who are more optimistic than the investors, and the stock price impact of an attempted repurchase increases. In the end, the repurchase price reaches such a high level that no manager wants to repurchase equity. Accordingly, all managers end up paying down debt.

When one compares the pecking order theory for issue decisions with the pecking order theory for repurchase decisions, it becomes clear that both theories provide differing rationales. For example, the level of optimism of a firm's manager is not required to explain issuance decisions, while it is an essential part of the pecking order theory for repurchase decisions. An empirical test of the pecking order model should, therefore, distinguish between issuance and repurchase decisions.

B. Testing the Pecking Order Theory

Shyam-Sunder and Myers (1999) aim to capture the pecking order theory in an empirical model that relates financing deficits to net debt issues: (1)

[DELTA][D.sub.it] = [alpha] + [[beta].sub.po] x [DEF.sub.it] + [[epsilon].sub.it], (1)

where [DEF.sub.it] is the financing deficit of firm i in year t, and [DELTA][D.sub.it] is the net debt issued for firm i in year t. Both variables are scaled by assets. In the case where firms have unconstrained access to debt, the pecking order theory predicts that the amount of debt issued will equal the deficit. Hence, the pecking order coefficient ([[beta].sub.po]) equals one and the intercept term [alpha] is zero. Note that the size of the financing deficit is endogenous. Firms can, to certain limits, decide how much money to attract and invest. In reality, a firm's debt capacity is limited due to financial distress costs. Therefore, Shyam-Sunder and Myers (1999) hypothesize that [[beta].sub.po] is close to one, but not precisely one. For a sample of 157 firms with continuous data over the period 1971-1989, Shyam-Sunder and Myers (1999) find an estimated coefficient of 0.75 and conclude that the pecking order model is "an excellent first order descriptor of corporate financing behavior."

Frank and Goyal (2003) substantially extend the sample of firms used to test the pecking order model. Estimating Shyam-Sunder and Myers' (1999) regression specification using a comprehensive data set with over 140,000 observations over the period 1971-1998, Frank and Goyal (2003) find substantially lower coefficients. Furthermore, they test whether small firms issue less equity than large firms, as investors of smaller firms face more information asymmetry (Collins, Kothari, and Rayburn, 1987; Brennan and Hughes, 1991), and information asymmetry increases firms' reluctance to issue equity (Korajczyk, Lucas, and McDonald, 1991; Choe, Masulis, and Nanda, 1993). Contrary to this hypothesis, Frank and Goyal (2003) find that large firms exhibit more pecking order behavior than smaller firms. This size anomaly is also found by Fama and French (2002, p. 30), who consider it a "deep wound" on the pecking order theory.

Frank and Goyal (2003) also find that the pecking order model loses its explanatory power over the years. Because the average publicly traded firm becomes smaller over time (Fama and French, 2005), the size and time effects can be related. However, Frank and Goyal (2003) conclude that the time period effect is not entirely due to the higher amount of small firms in the 1990s. For each of the size quartiles, the pecking order model coefficients are lower after 1989 than prior to 1989.

C. Large Deficits and Firms' Debt Capacities

In a critical comment on Shyam-Sunder and Myers' (1999) pecking order model, Chirinko and Singha (2000) confirm that the pecking order coefficient can be significantly smaller than one even when firms follow the financing hierarchy prescribed by the pecking order model. The rationale is that if deficits are sufficiently large, firms might be constrained in their ability to issue debt and have to finance the remainder of the deficit with equity. According to Chirinko and Singha (2000), these constraints are specifically high when firms have high leverage ratios. We elaborate on the critique of Chirinko and Singha (2000) by empirically demonstrating the influence of large deficits.

Agca and Mozumdar (2007) and Lemmon and Zender (2010) estimate Shyam-Sunder and Myers' (1999) regression specification for subsamples based on firms' debt capacities. They find that the pecking order model works best for firms that are not constrained in their debt issuing. Although the size of the deficit is not the main focus of either of the papers, both papers control for the effects of larger deficits: Lemmon and Zender (2010) include a quadratic term of the deficit whereas Agca and Mozumdar (2007) use a piecewise linear specification. Following our discussion of the different effects of financing surpluses (i.e., negative deficits), a quadratic term of the financing deficit seems inappropriate as a negative deficit becomes positive when squared. Therefore, we differ from both papers in taking the effect of surpluses into account when testing the relationship between financing deficit, debt capacity, and firms' financing decisions. We examine how our findings relate to these papers in Section III.D.

II. Data

In our empirical analysis, we employ a broad cross section of US firms from the Compustat database covering the period 1971-2005. The starting point is 1971 as we require flow of funds data to compute the financing deficit, and these data are not available prior to 1971. We compute the financing deficit as the sum of the change in working capital, the investments, and the cash dividends minus the internal cash flows. By definition, the financing deficit is equal to the sum of net debt issues and net equity issues. Financing deficits (surpluses) and issues (repurchases) are scaled by the book value of total assets. Regulated utilities (SICs 4900-4999), financial firms (SICs 6000-6999), and individual firm-years with missing values for financing deficit/surplus, net debt issues, and net equity issues are excluded. We further exclude firm-years for which the financing deficit, change in working capital, investments, cash dividends, internal cash flows, net debt issues, or net equity issues exceed 400% of the firm's total book assets. Although these requirements make our sample comparable to Frank and Goyal (2003), we deviate from the criteria in Shyam-Sunder and Myers (1999) in which firms are required to provide data in each year of their sample period. We will revisit this issue in Section III. Our final sample contains 22,197 firms and covers 233,909 firm-year observations. (2)

Table I presents summary statistics of the fund flow and financing variables in our analysis and how they are computed from Compustat items. Although we are not able to perfectly replicate Frank and Goyal's (2003) sample, Table I closely corresponds to their Table II (p. 229). Table I also presents the composition of the financing deficit and the magnitude of these components in different years. The average internal cash flows and the average working capital decline over the years, whereas the average cash dividends remain relatively stable over the time period.

Table II provides detailed information regarding the financing deficits and surpluses over the sample period.

Although the yearly percentage of firms with financing deficits varies between 44% (1975 and 1976) and 65% (1997), it does not portray a strong time trend. The average size of the deficits varies substantially over time. It fluctuates around 0.10 over the first 10 years of our sample period, but it is around 0.26 over the last 10 years. This trend is caused by the growing magnitude of the deficits at the 75th percentile. In the 1970s, the deficit at the 75th percentile is about 0.12, whereas in the late 1990s, it has increased to 0.40.

The average size of the surpluses hardly fluctuates over time. The median is 0.02 or 0.03. Furthermore, the average surplus size is lower than the average deficit size. The overall mean surplus is 0.06 and the overall mean deficit is 0.21. This difference is again caused by the levels of the largest deficits as large surpluses are virtually absent. (3)

III. Empirical Tests

In this section, we test the pecking order theory for groups with financing surpluses, nonlarge deficits, and large deficits. We also indicate the impact of these segregations on the pecking order puzzles.

A. The Pecking Order Puzzles

We first replicate Frank and Goyal's (2003) key findings that illustrate the pecking order puzzles. Table III illustrates a replication of Frank and Goyal's (2003) Table VI using our sample of Compustat firms and provides updated results for the second subperiod starting in 1990. In this table, we present pooled OLS estimates of the pecking order coefficients for different time periods and size quartiles.

For the entire sample period, 1971-2005, the estimated pecking order coefficient is 0.255, which is comparable to the estimates reported in Frank and Goyal (2003). The interpretation of this coefficient is that an increase in the deficit of one dollar will, on average, be financed with 25.5 cents of debt. Although this pecking order coefficient is significantly different from zero, it is actually evidence contrary to the pecking order model. Apparently, on average, 74.5 cents of a one dollar increase in deficits is met by an equity issue.

The additional results in Table III highlight the two pecking order puzzles. Prior to 1989, the estimated pecking order coefficient for the quartile containing the smallest firms is 0.223, while for the largest firms, it is considerably higher at 0.763. After 1989, the pecking order coefficient is 0.667 as compared to 0.207 for the smallest firms. (4)

B. Deficits and Surpluses

To investigate the differences in pecking order behavior for firm-years with financing deficits and firm-years with financing surpluses, we estimate a regression specification that allows for an asymmetry between positive and negative deficits. The following model allows for such an asymmetry:

[DELTA][D.sub.it] = [alpha] + [[beta].sub.1] x [d.sub.it] + [[beta].sub.po] x [DEF.sub.it] + [[beta].sub.sur] x [d.sub.it] x [DEF.sub.it] + [[epsilon].sub.it], (2)

where [d.sub.it] is a dummy variable that equals one if [DEF.sub.it] < 0, and zero otherwise. The term [[beta].sub.1] x [d.sub.it] allows for different intercepts for the samples of deficits and surpluses.

Table IV reports our estimation results for Equation (2). The pecking order coefficient ([[beta].sub.po]) of 0.155 implies that firms with deficits issue, on average, 15.5 cents of debt for each additional dollar of the financing deficit. Accordingly, most of the deficits are covered with equity issues. The coefficient estimates are similar in the pre-1989 and post-1989 periods. The coefficient [[beta].sub.sur] represents the difference in the pecking order coefficients for deficits and surpluses, and is significantly different from zero at the 1% level. For firms with financing surpluses, the estimated pecking order coefficient is 0.746 higher than for firms with deficits, and again, the effect is similar in the two subperiods. These results imply that the pecking order coefficient is 0.901 ([[beta].sub.po]+ [[beta].sub.sur]) for surpluses. That is, on average, 90 cents of a dollar increase of the financing surplus are used to repurchase debt. Overall, firms seem to have a strong preference for buying back debt when there is a surplus, but do not seem to follow the pecking order when they have a deficit. Hence, we conclude that a correct empirical pecking order specification requires a differentiation between financing deficits and financing surpluses.

The financing deficit is calculated by subtracting the internal cash flows of a firm in a particular year from the sum of the cash dividends, net investments, and changes in working capital in that year. We decompose the deficits and surpluses in Table V to investigate how the components differ for firm-years with deficits and firm-years with surpluses. Table V also presents means and medians of several other firm characteristics for firm years with deficits and those with surpluses.

The results in Table V illustrate that even though the average firm with deficits has lower cash flows, the key determinant for firms to incur a deficit is that they invest a large share of their capital. Cash dividends do not strongly depend on whether a firm has a positive or a negative deficit. Apparently, firms do not use dividend cuts to finance capital expenditures. Table V also confirms that median asset sizes do not differ much for firms years with financing deficits (median of $68 million) and firm-years with financing surpluses (median of $71 million). The average issue size of 0.21, however, is significantly different from the average repurchase size of 0.06. This issue size is likely to have an effect on firms' financing behavior, particularly in case of a deficit, due to firms' debt capacities (Chirinko and Singha, 2000). We investigate the effect of having a large deficit on the pecking order coefficient in Table VI.

Table VI presents the pooled OLS estimates of the pecking order model over the full sample period 1971-2005, across different subsamples by deficit and surplus size (excluding firm-years with DEF = 0). We separate firm-years with deficits and surpluses and within these two sets, we distinguish between quartiles. The effects of the repurchase sizes indicates that the estimated pecking order coefficient is 0.789 for the smallest repurchases, 0.881 and 0.815 for repurchases that are around the median size, and 0.923 for the largest repurchase sizes. Apparently, for each quartile of the surplus distribution, the pecking order model is a good and similar descriptor of firms' financing behavior. For deficits, we observe a very different pattern. Although the pecking order model appears to provide a reasonable description for smaller issues (pecking order coefficients of 0.601 and 0.741), it is only a weak explanation for somewhat larger issues (coefficient of 0.429). The most striking result, however, is found for the largest deficits. For these largest deficits, the estimated pecking order coefficient is only 0.089. This result implies that when firms face large deficits, they issue, on average, far more equity than debt.

C. Large Financing Deficits

Table VI makes a somewhat ad hoc distinction between smaller and larger deficits on the basis of the quartiles of the distribution. As a result, a "large deficit" in these tables is empirically defined as being larger than 0.237. To investigate the impact of this cut off point on the resulting estimates for the pecking order coefficient, we extend the pecking order model in Equation (1) by allowing a different intercept and slope coefficient for larger deficits, where the threshold between "large" and "nonlarge" is varied over all possible values. That is, we estimate

[DELTA][D.sub.it] = [alpha] + [[beta].sub.1] x [b.sub.it] + [[beta].sub.po] x [DEF.sub.it] + [[beta].sub.largedef] x [b.sub.it] x [DEF.sub.it] + [[epsilon].sub.it], (3)

with [b.sub.it] = I([DEF.sub.it] > x), where I(.) is an indicator function (equal to one if the condition in parentheses is satisfied and zero otherwise), and x is a threshold value for the financing deficit that is chosen a priori. Because x is unknown, we vary x between 0.0001 and 2, and investigate the impact on the resulting pecking order coefficients. This procedure is similar to allowing a structural break in the coefficients of a linear model where the breakpoint is unknown (Stock and Watson, 2003, chap. 12). For each value of x, the specification in Equation (3) provides two pecking order coefficients: 1) one coefficient for observations below a certain deficit level ([[beta].sub.po]), and 2) one coefficient for observations above a certain deficit level ([[beta].sub.po] + [[beta].sub.largedef]). In our estimation, we exclude firm-years with financing surpluses. The results of this exercise are summarized in Panel A of Figure 1.

[FIGURE 1 OMITTED]

Any specific financing deficit level in Figure 1 represents a threshold level. The solid line illustrates the coefficient for deficits below this threshold and the dotted line is the coefficient for deficits above the threshold. In the below-threshold sample, we find that as more observations with large deficits are included in the estimation of [[beta].sub.po], this pecking order coefficient decreases. (5) The maximum estimate for [[beta].sub.po] is 0.734 corresponding to a financing deficit of 0.059. Hence, if we define a deficit as large if it exceeds 5.9% of total assets, the estimated pecking order coefficient of the observations with deficits below 0.059 is maximized. (6) Adding firm-years with higher deficits would decrease this pecking order coefficient.

Panel B of Figure 1 reports the results of a similar regression specification for financing surpluses:

[DELTA][D.sub.it] = [alpha] + [[beta].sub.1] x [b.sub.it] [[beta].sub.po] x [SUR.sub.it] + [[beta].sub.largesur] x [b.sub.it] x [SUR.sub.it] + [[epsilon].sub.it], (4)

where [SUR.sub.it] is the financing surplus of firm i in year t, and [b.sub.it] is one if [SUR.sub.it] is larger than a certain surplus level and zero otherwise. The results confirm the findings of Table VI as we do not find evidence that the magnitude of the financing surplus has a strong effect on the pecking order coefficients.

D. Firms' Debt Capacities

Although the size of the deficit is important in establishing whether a firm is able to issue debt, not all firms are similarly constrained in their debt issuing even with equal financing needs. For example, Lemmon and Zender (2010) argue that firms with rated debt outstanding are less restricted in issuing debt than firms with no rated debt outstanding. Agca and Mozumdar (2007) argue that a firm's total sales, tangibility, profitability, and market-to-book ratio are strong predictors of its debt capacity. Total sales, tangibility, and profitability have a positive correlation with firms' ability to borrow, whereas market-to-book ratios are negatively related with firms' debt capacities.

We will test how our findings on large deficits relate to proxies of firms' debt capacities. For example, do firms with small debt capacities already issue equity for relatively small deficits? We use firms' size, tangibility, profitability, market-to-book ratios, and their rated debt outstanding to determine firms' ability to borrow. We test an extended regression model of the pecking order theory allowing for differential coefficients for firm-years with surpluses, small deficits (deficits below 0.059), medium deficits (in which firms' debt capacities do limit the firm to some extent), and deficits that have a high probability of posing constraints on firms' use of debt (deficits above 0.237):

[DELTA][D.sub.it] = [alpha] + [[beta].sub.1] x [d.sub.it] + [[beta].sub.2] x [b.sub.it] + [[beta].sub.3] x [c.sub.it] + [[beta].sub.4] x [DEF.sub.it] + [[beta].sub.5] x [d.sub.it] x [DEF.sub.it] + [[beta].sub.6] x [b.sub.it] x [DEF.sub.it] + [[beta].sub.7] x [c.sub.it] x [DEF.sub.it] + [[epsilon].sub.it], (5)

where [d.sub.it] is a dummy variable that equals one if [DEF.sub.it] < 0 and zero otherwise, [b.sub.it] is a dummy variable that equals one if [DEF.sub.it] [greater than or equal to] 0.059 and zero otherwise, and [c.sub.it] is a dummy variable that equals one if [DEF.sub.it] [greater than or equal to] 0.237 and zero otherwise. This regression specification allows the distinction of four effects: 1) an effect of surpluses ([[beta].sub.4] + [[beta].sub.5]), 2) an effect of deficits for which firms are not restricted by their debt capacities ([[beta].sub.4),] 3) an effect of deficits in which firms' debt capacities do limit the firm to some extent ([[beta].sub.4] + [[beta].sub.6]), and 4) an effect of deficits that have a high probability of posing constraints on firms' use of debt ([[beta].sub.4] + [[beta].sub.6] + [[beta].sub.7]). Model 1 of Table VII presents the estimation result for our total sample. As expected, the pecking order coefficient increases for firms with surpluses ([[beta].sub.5]) and decreases for firms with high levels of deficits ([[beta].sub.6] and [[beta].sub.7]).

To examine how our findings on the effects of surpluses and large deficits relate to predictors of firms' debt capacities, we construct various subsamples. We first divide our sample in firm-years in which a firm has rated debt outstanding, and firm-years in which a firm has no rated debt outstanding in line with arguments by Lemmon and Zender (2010). Models 2 and 3 of Table VII demonstrate that firms with rated debt outstanding are more likely to cover financing deficits with debt. The pecking order coefficient for small deficits is 0.802 for firms with rated debt outstanding and 0.649 for firms with no rated debt outstanding. The coefficient of 0.649 indicates that even firms that are restricted in their debt issuing display a tendency to issue debt for small deficits. These findings support the pecking order theory. For both subsamples, the pecking order coefficients decrease for larger deficits. For deficits above 23.7% of total assets, the pecking order coefficient is 0.297 for firms with rated debt outstanding and 0.093 for firms with no rated debt outstanding. These results confirm the arguments of Lemmon and Zender (2010) that nonrated firms have lower pecking order coefficients for deficits. Our results for the size of the deficit are present in both rating based subsamples. In other words, the pecking order coefficient decreases when debt is not rated and deficits are larger.

Models 4 and 5 of Table VII report the results of our estimation when we construct subsamples based on firms' total sales, tangibility, profitability, and market-to-book ratios. The least constrained sample has above median sales (>$59.1 million), above median tangibility (> 25.4% of total assets), above median profitability (> 7.5% of total assets), and below median market-to-book ratios (< 1.265). The sample with constrained firm characteristics has below-median size, below-median tangibility, below-median profitability, and above-median market-to-book ratios. In line with Agca and Mozumdar (2007), we find that for the least constrained sample, the pecking order model predicts firms' financing choices well. The pecking order coefficients are 0.814 for small deficits, 0.806 for median deficits, and 0.672 for large deficits. We find that most firms in our most constrained sample (i.e., the high end of the constraints spectrum) are not able to cover the deficits with debt. For small deficits,

the pecking order coefficient is only 0.149. The pecking order coefficient for large deficits is even significantly lower for these constrained firms.

In Models 6 and 7, we combine our subsamples. That is, the least constrained observations have above-median sales, above-median tangibility, above-median profitability, below-median market-to-book ratios, and rated debt outstanding, whereas the most constrained sample has below median size, below median tangibility, below median profitability, above median market-to-book ratios, and no rated debt outstanding. Interestingly, the pecking order coefficient for the nonconstrained sample is 0.703, which is somewhat smaller than the pecking order coefficients found in Models 2 and 4 and smaller than the pecking order coefficient for medium deficits (0.752) in the same subsample. (7) Still, the patterns that we find are similar to the patterns in our estimation of Models 4 and 5.

For surpluses, the coefficients are relatively high in all subsamples. Firms that are constrained are more likely to repurchase debt when having a surplus (coefficients of 0.906, 0.918, and 0.918) than firms that are not constrained (coefficients of 0.754, 0.844, and 0.754), which is in line with the constrained firms reducing debt to relax the constraints.

Table VII also reports the proportional occurrences of surpluses, small deficits, medium deficits, and large deficits for our subsamples. Note that constrained firms are more often confronted with large deficits. Model 7 indicates that 45% of the cases in our mostly constrained sample face financing needs above 23.7% of total assets. For nonconstrained firms (Model 6), this percentage is only 1%. Hence, we conclude that the relatively low pecking order coefficient for constrained firms is caused by their limited ability to borrow and the relatively large financing requirements of these firms.

E. Equity Issuers

Another test on the debt capacity is to specifically look at firms that issue equity, which is comparable to Leary and Roberts (2010). Leafy and Roberts (2010) compare firms violating the pecking order theory with nonrestricted borrowers in the private debt market to examine whether the debt capacity causes the violation. They find a substantial portion of the violators to be facing debt capacity constraints. However, the majority of firms issuing equity do not seem to be significantly different in terms of firm characteristics from firms tapping the private debt market.

[FIGURE 2 OMITTED]

Therefore, we specifically look at firms with large deficits to see whether the debt capacity can explain why some firms issue debt, while others opt for equity. Our sample consists of those observations in which the deficit exceeds 0.237. We count observations in which more than 75% of the large deficit is covered by debt as a debt issue, and observations in which less than 25% of the large deficit is covered by debt as an equity issue.

With our subsamples that are based on firms' total sales, tangibility, profitability, market-to-book ratios, and rated debt outstanding, we find that of the nonconstrained firms facing a large deficit, 79.8% choose to issue debt. For the constrained firms, this percentage is 15.1%. Hence, the debt capacity seems to have a strong impact on firms' financing decisions.

F. Explaining the Time and Size Effect

In this section, we will examine whether the deviant results for large deficits on firms' financing behavior potentially explain the size and time puzzles. We investigate the distributions of deficit sizes for subsamples of firm sizes in Figure 2. We calculate the cumulative percentages of observations for financing deficits between zero and two. The lines in Figure 2 present the percentages of observations that are below the deficit levels on the x-axis. The closer the line is to the x-axis, the higher the proportion of relatively high deficits in a subsample.

Figure 2 indicates that given that a large firm has a deficit, this deficit will be above 0.2 in about 10% of the cases, whereas small firms face deficits above 0.2 in about 50% of the cases. These results indicate that large firms (the solid line) face the lowest number of large deficits followed by medium large firms and medium small firms. The smallest firms face most of the large deficits.

To examine what causes the relationship between firm size and deficit size, we describe, in Table VIII, the means and standard deviations of the components of the financing deficits for each size quartile.

The group of small firms has more volatile internal cash flows. The standard deviation of small firms' internal cash flows is 0.598 compared to 0.084 for large firms. The volatilities of the other components of the deficit are also higher for small firms. Hence, small firms have more volatile deficits. This means that small firms are more often confronted with considerably large deficits. As these large deficits are almost exclusively covered with equity issues, small firms do not appear to act according to the pecking order model despite the findings of Collins, Kothari, and Rayburn (1987) and Brennan and Hughes (1991) that small firms have a larger likelihood of high information asymmetry.

Our analysis regarding financing surpluses and large financing deficits also allows us to examine why net debt issues decreasingly cover the financing deficits over time. Figure 3 illustrates the pecking order coefficients based on estimating Equation (1) by OLS for each year separately, together with the percentages of firms with financing surpluses and large financing deficits (above 0.237).

In general, a rise in the percentage of financing surpluses increases the pecking order coefficient (i.e., see, e.g., the years 1975 and 2005). The percentage of firms with large deficits is highly correlated with changes in the pecking order coefficient. For instance, the downfall of the pecking order coefficient in 1983 and the swell in 1982 and 2001 relate to an increase of the percentage of firms with large deficits in 1983 and a decrease in 1982 and 2001. Overall, the time effect can largely be explained by our analysis of financing surpluses and large financing deficits.

[FIGURE 3 OMITTED]

The asymmetry between surpluses, normal deficits, and large deficits potentially explains the findings of prior studies. For example, Shyam-Sunder and Myers (1999) find that the pecking order coefficient is 0.75. Apart from the fact that their sample period ends in 1989, Shyam-Sunder and Myers' (1999) sample differs from other papers on the pecking order specification as it only includes firms that have continuous data on flow of funds for the whole sample period. This requirement decreases their sample to 157 firms. In following their data selection procedure, we obtain a sample of 690 firms, for which we find a pecking order coefficient of 0.77. Although Frank and Goyal (2003) already highlight the severe sample selection bias in the sample of Shyam-Sunder and Myers (1999), an inspection of the resulting sample reveals that large financing deficits occur much more often when gaps in the data are permitted. This is due to the fact that firms with large financing deficits are less likely to survive the entire sample period. For example, when examining the frequency of financing deficits above 0.237, we find that these deficits only occur in 3% of Shyam-Sunder and Myers' (1999) firm-years compared to 14% in our original sample. Also, the percentage of firms with large deficits is low as their sample is biased toward relatively large firms because these firms have more data available. The lack of large financing deficits substantially increases Shyam-Sunder and Myers' (1999) pecking order coefficient. In addition, the pecking order coefficient is enhanced by the relatively large percentage of firm-years with financing surpluses (47%) in their sample.

IV. Conclusion and Implications

Frank and Goyal (2003) test the pecking order theory of corporate leverage with a model developed by Shyam-Sunder and Myers (1999), and conclude that net equity issues track the financing deficit more closely than net debt issues do. They find two puzzling results: 1) the net debt issues decreasingly explain the deficits over time and 2) especially small firms do not behave according to the pecking order theory. The latter result is particularly counterintuitive, as the pecking order relies on the existence of information asymmetry. This asymmetry is higher for investors of small companies.

The main goal of this paper is to explain the correlations between size, time, and pecking order behavior by separating financing deficits from financing surpluses and by taking issue sizes into account. We demonstrate that the debt issues provide an excellent fit for financing surpluses, a reasonable fit for small and medium financing deficits, and an extremely poor fit for large financing deficits. As small firms have more large deficits and fewer surpluses, they are found to issue relatively more equity than large firms do. The pecking order coefficient decreases over time due to an increasing number of firms with large deficits in the Compustat data set.

Our findings are consistent with the predictions of a pecking order model that considers firms' debt capacities. Because large financing needs have the potential of exceeding the unused debt capacity of firms, these firms are restricted in the issuing of debt. In the case of a surplus, firms' debt capacities do not pose any restrictions on the repurchase of debt. For firms that are expected to be least constrained in issuing debt, we find the pecking order coefficients to be substantially higher than the coefficients of the overall sample as reported in Frank and Goyal (2003).

The differences in pecking order coefficients between financing surpluses, normal deficits, and large deficits have implications for other empirical tests in the capital structure literature that apply the Shyam-Sunder and Myers (1999) technique. Examples are Litov (2006), who examines the debt-equity choice for firms in different quintiles of managerial entrenchment, and Bharath, Pasquariello, and Wu (2009), who examine the debt-equity choice for firms into deciles that are based on the market's assessment of their adverse selection risk. As managerial entrenchment and the risk of adverse selection relate to firms' sizes and risk taking, the distributions of financing surpluses and large deficits are likely to differ among these papers' quintiles and deciles. For instance, firms with more managerial entrenchment are more likely to be large, which results in a lower frequency of large deficits. Hence, including the effects of surpluses and large deficits in their tests will help in interpreting their results or might provide an alternative explanation for their results altogether.

References

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Graham, J.R. and C.R. Harvey, 2001, "The Theory and Practice of Corporate Finance: Evidence from the Field," Journal of Financial Economics 60, 187-243.

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The authors thank Marie Dutordoir, Kose John, Marieke van der Poel, Abraham Ravid, Miguel Rosellon, seminar participants at Rotterdam School of Management, Erasmus University, and an anonymous referee for helpful comments.

(1) Prior to this model, the pecking order was usually tested with event study methodology. Most studies find an insignificant market reaction to debt issues, a significantly negative market reaction to equity issues, and a significantly positive market reaction to equity repurchases. For overviews of this literature, see Eckbo and Masulis (1995) and Ritter (2003). Another way of testing the pecking order theory is by conducting a survey. Graham and Harvey (2001) survey 392 CFOs in the US and interpret the reported importance of financial flexibility in managers' financing decisions as support for the pecking order theory. Bancel and Mittoo (2004) survey 87 CFOs in 16 European countries and find that financial flexibility is also an important consideration for managers in these countries. Brounen, de Jong, and Koedijk (2004) survey 313 CFOs in four European countries and conclude that financial flexibility is important but not driven by information asymmetry.

(2) Several cash flow statement items are recoded as zero if they were reported missing or combined with other data items in Compustat. The data are often coded as missing when a firm does not report a particular item or when it combines items. See Frank and Goyal's Table VIII (2003, p. 242) for the specific cash flow statement items that are recoded.

(3) We have tested whether winsorizing the size of the deficit at the 95% level will have a strong effect on our results. We find that both the asymmetry between the effects of deficits and surpluses and the asymmetry between the effects of different deficit sizes (measured in quartiles) remain present when we winsorize the financing deficit.

(4) The differences in the average pecking order coefficients before and after 1989 are possibly caused by only a few years. The solid line in Figure 3 demonstrates the evolution over the years of the estimated pecking order coefficients using the entire sample of firms. It can be seen that the pecking order model describes most of firms' financing behavior in the 70s, but is a poor descriptor of firms' financing behavior in the 80s and 90s. Although the pecking order coefficient does not decline linearly, Figure 3 indicates a trend toward a decreasing impact of the deficit on firms' debt issues over time.

(5) For very small deficits, the pecking order coefficient is highly volatile. This volatility is caused by the observations for which the deficit is practically zero, but where a firm still repurchases an amount of debt.

(6) This pattern is stable over time. We compute the correlation coefficients for seven time intervals of five years for deficit values between 0.01 and 1, and find an average correlation coefficient of 0.96.

(7) Apparently, the threshold value for large deficits exceeds 5.9% of total assets for our sample of nonconstrained firms with rated debt outstanding. When estimating the threshold level for this subsample with a similar procedure as in Section III.C, we find that the threshold level is 33.4% of total assets. The pecking order coefficient that corresponds to deficits below 0.334 is 0.784 for nonconstrained firms with rated debt outstanding.

Abe de Jong, Marno Verbeek, and Patrick Verwijmeren *

* Abe de Jong is a Professor of Corporate Finance at Roterdam School o]Management, Erasmus University in Rotterdam, The Netherlands and Professor of Financial Accounting at the University of Gronigen, The Netherlands. Marno Verbeek is a Professor of Finance at Rotterdam School of Management, Erasmus University in Rotterdam, The Netherlands. Patrick Verwijmeren is a Lecturer at the University of Melbourne, Australia.

Table I. Corporate Cash Flows This table reports the corporate cash flows and the issuance of securities for the sample period 1971-2005. Financial firms and utilities are excluded. The sample additionally excludes firm-years with gaps in the reporting of relevant flow of funds data. The net debt issues are computed as Item 111--Item 114, and the net equity issues are computed as Item 108--Item 115. All variables are scaled by total assets. The table is a replication of Table II of Frank and Goyal (2003). Average Funds Flow and Financing as a Percentage of Total Assets 1971 1975 1980 1985 1990 Cash dividends (a) 0.015 0.012 0.014 0.015 0.016 Investments (b) 0.101 0.077 0.110 0.132 0.067 Working capital (c) 0.033 0.015 0.033 -0.028 -0.016 Internal cash flow (d) 0.101 0.096 0.095 0.026 0.006 Financing deficit (a+b+c+d) 0.048 0.009 0.062 0.093 0.061 Net debt issues 0.017 0.003 0.012 0.018 0.006 Net equity issues 0.030 0.006 0.050 0.075 0.055 Net external financing 0.048 0.009 0.062 0.093 0.061 N 2,992 5,802 5,709 6,488 6,668 Average Funds Flow and Financing as a Percentage of Total Assets 1995 2000 2005 Cash dividends (a) 0.014 0.010 0.014 Investments (b) 0.094 0.084 0.069 Working capital (c) 0.023 -0.015 -0.014 Internal cash flow (d) -0.006 -0.084 -0.051 Financing deficit (a+b+c+d) 0.137 0.162 0.120 Net debt issues 0.026 0.018 0.029 Net equity issues 0.111 0.144 0.091 Net external financing 0.137 0.162 0.120 N 9,009 8,562 5,900 (a) Item 127. (b) For firms reporting Format Codes 1-3, investments equal Item 128 + Item 113 + Item 129 + Item 219--Item 107 - Item 109. For firms reporting Format Code 7, investments equal Item 128 + Item 113 + Item 129--Item 107--Item 109--Item 309--Item 310. (c) For firms reporting Format Code 1, change in net working capital equals Item 236 + Item 274 + Item 301. For firms reporting Format Codes 2 and 3, change in net working capital equals--Item 236 + Item 274--Item 301. For firms reporting Format Code 7, change in net working capital equals--Item 302--Item 303--Item 304--Item 305--Item 307 + Item 274--Item 312--Item 301. (d) For firms reporting Format Codes 1-3, internal cash flow equals Item 123 + Item 124 + Item 125 + Item 126 + Item 106 + Item 213 + Item 217 + Item 218. For firms reporting Format Code 7, internal cash flow equals Item 123 + Item 124 + Item 125 + Item 126 + Item 106 + Item 213 + Item 217 + Item 314. Table II. Deficits and Surpluses, 1971-2005 This table demonstrates the distribution of financing deficits and financing surpluses over time. A firm has a deficit if the sum of the firm's investments, cash dividends, and increase in working capital exceed the firm's internal cash flows. A firm has a surplus if its internal cash flows in a year exceed the sum of the firm's investments, cash dividends, and increase in working capital. When a firm has a surplus, it repurchases securities, whereas the firm has to issue securities when it faces a deficit. Next to means and medians, we also provide information on the 25th and 75th percentiles to portray the distribution of the deficits and surpluses more accurately. Deficits and surpluses are scaled by assets. Under "%", we report the percentage of firms having a deficit or a surplus in that particular year. These percentages do not add up to 100% as some firm-years have a financing deficit of exactly zero. Deficits 25th 75th Year N % Mean percentile Median percentile 1971 2,992 0.55 0.11 0.02 0.07 0.14 1972 3,200 0.57 0.11 0.02 0.06 0.14 1973 3,970 0.56 0.09 0.02 0.05 0.11 1974 5,638 0.48 0.09 0.02 0.06 0.11 1975 5,802 0.44 0.08 0.02 0.05 0.10 1976 5,871 0.44 0.09 0.02 0.05 0.11 1977 5,912 0.49 0.09 0.02 0.05 0.11 1978 5,779 0.51 0.10 0.02 0.05 0.12 1979 5,618 0.52 0.11 0.02 0.06 0.12 1980 5,709 0.55 0.15 0.02 0.07 0.17 1981 5,709 0.56 0.18 0.02 0.07 0.21 1982 5,948 0.53 0.16 0.02 0.07 0.17 1983 6,136 0.58 0.23 0.03 0.10 0.31 1984 6,168 0.56 0.18 0.02 0.08 0.21 1985 6,488 0.57 0.21 0.02 0.08 0.26 1986 6,657 0.58 0.25 0.03 0.11 0.33 1987 6,782 0.56 0.25 0.03 0.11 0.33 1988 6,747 0.50 0.18 0.02 0.08 0.21 1989 6,612 0.51 0.20 0.02 0.08 0.23 1990 6,668 0.48 0.18 0.02 0.07 0.20 1991 6,867 0.50 0.20 0.02 0.07 0.22 1992 7,102 0.53 0.22 0.02 0.08 0.27 1993 7,630 0.57 0.23 0.02 0.10 0.30 1994 8,184 0.59 0.22 0.02 0.09 0.27 1995 9,009 0.61 0.25 0.03 0.10 0.32 1996 9,179 0.63 0.28 0.03 0.13 0.39 1997 8,930 0.65 0.25 0.03 0.11 0.33 1998 9,164 0.64 0.28 0.03 0.12 0.35 1999 9,078 0.62 0.31 0.03 0.12 0.43 2000 8,562 0.61 0.30 0.03 0.10 0.41 2001 7,945 0.56 0.21 0.02 0.07 0.21 2002 7,541 0.51 0.21 0.01 0.06 0.20 2003 7,369 0.55 0.23 0.02 0.07 0.26 2004 7,043 0.60 0.26 0.02 0.07 0.32 2005 5,900 0.60 0.24 0.02 0.07 0.29 Avg 6,683 0.56 0.21 0.02 0.08 0.23 SD 1,536 0.05 0.07 0.00 0.02 0.09 Surpluses 25th 75th Year % Mean percentile Median percentile 1971 0.40 0.03 0.01 0.02 0.03 1972 0.39 0.03 0.01 0.02 0.03 1973 0.40 0.04 0.01 0.02 0.04 1974 0.45 0.05 0.01 0.02 0.05 1975 0.49 0.06 0.01 0.02 0.06 1976 0.49 0.05 0.01 0.02 0.05 1977 0.44 0.05 0.01 0.02 0.05 1978 0.42 0.05 0.01 0.02 0.05 1979 0.42 0.05 0.01 0.02 0.05 1980 0.40 0.06 0.01 0.02 0.05 1981 0.38 0.05 0.01 0.02 0.05 1982 0.40 0.06 0.01 0.02 0.06 1983 0.37 0.06 0.01 0.02 0.06 1984 0.39 0.06 0.01 0.02 0.06 1985 0.38 0.07 0.01 0.03 0.06 1986 0.37 0.08 0.01 0.03 0.07 1987 0.39 0.07 0.01 0.03 0.07 1988 0.43 0.07 0.01 0.03 0.07 1989 0.42 0.06 0.01 0.03 0.06 1990 0.44 0.06 0.01 0.03 0.06 1991 0.43 0.06 0.01 0.03 0.06 1992 0.41 0.06 0.01 0.03 0.06 1993 0.37 0.06 0.01 0.03 0.06 1994 0.35 0.05 0.01 0.02 0.06 1995 0.33 0.05 0.01 0.02 0.06 1996 0.31 0.05 0.01 0.03 0.06 1997 0.31 0.06 0.01 0.03 0.06 1998 0.31 0.06 0.01 0.03 0.07 1999 0.33 0.07 0.01 0.03 0.07 2000 0.34 0.07 0.01 0.03 0.07 2001 0.37 0.07 0.01 0.03 0.07 2002 0.42 0.07 0.01 0.03 0.07 2003 0.38 0.06 0.01 0.03 0.07 2004 0.34 0.06 0.01 0.03 0.07 2005 0.36 0.06 0.01 0.03 0.07 Avg 0.38 0.06 0.01 0.03 0.06 SD 0.05 0.01 0.00 0.01 0.01 Table III. Pecking Order Tests for Small and Large Firms Before 1989 and After 1989 The sample period is 1971-2005. Financial firms and utilities are excluded. The sample additionally excludes firm-years with gaps in the reporting of relevant flow of funds data. Firms are yearly sorted into quartiles based on total assets. The estimated regression specification is [DELTA][D.sub.it] = [alpha] + [[beta].sub.po] x [DEF.sub.it] + [[epsilon].sub.it] where [DELTA][D.sub.it] is the amount of net debt issued and [DEF.sub.it], is the financing deficit. All variables are scaled by total assets. White standard errors appear in parentheses. 1971-1989 Overall Overall Smallest [alpha] -0.007 *** -0.007 *** -0.025 *** (0.000) (0.001) (0.002) [[beta].sub.po] 0.255 *** 0.332 *** 0.223 *** (0.005) (0.009) (0.011) N 233,909 107,738 26,656 [R.sup.2] 0.234 0.298 0.191 1971-1989 Medium Medium Largest Small Large [alpha] -0.015 *** -0.007 *** -0.001 *** (0.001) (0.001) (0.001) [[beta].sub.po] 0.517 *** 0.672 *** 0.763 *** (0.015) (0.015) (0.020) N 27,000 27,009 27,029 [R.sup.2] 0.498 0.648 0.756 1990-2005 Overall Smallest Medium Small [alpha] -0.008 *** -0.027 *** -0.019 *** (0.001) (0.002) (0.001) [[beta].sub.po] 0.226 *** 0.207 *** 0.205 *** (0.005) (0.008) (0.010) N 126,171 30,763 31,783 [R.sup.2] 0.209 0.200 0.169 1990-2005 Medium Largest Large [alpha] -0.007 *** 0.001 (0.001) (0.000) [[beta].sub.po] 0.410 *** 0.667 *** (0.009) (0.009) N 31,791 31,802 [R.sup.2] 0.340 0.650 *** Significant at the 0.01 level. Table IV. Pecking Order Tests for Financing Deficits and Surpluses The sample period is 1971-2005. We exclude financial firms, utilities, and firm-years with gaps in the reporting of relevant flow of funds data. This table determines the significance of a dummy for financing surpluses and tests the model [DELTA][D.sub.it] = [alpha] + [[beta.sub.1] x [d.sub.it] + [[beta].sub.po] x [DEF.sub.it], + [[epsilon].sub.it] x [d.sub.it] x [DEF.sub.it], + [[epsilon].sub.it] where [DELTA][D.sub.it] is the amount of net debt issued, DEFT, is the financing deficit, and d;, is a dummy variable that equals one if [DEF.sub.it], < 0 and zero otherwise. All variables are scaled by total assets. White standard errors appear in parentheses. Overall 1971-1989 1990-2005 [alpha] 0.029 *** 0.035 *** 0.023 (0.001) (0.001) (0.001) [[beta].sub.1] -0.027 *** -0.034 *** -0.021 *** (0.001) (0.001) (0.001) [[beta].sub.po] 0.155 *** 0.169 *** 0.153 *** (0.005) (0.008) (0.006) [[beta].sub.sur] 0.746 *** 0.765 *** 0.714 (0.013) (0.018) (0.018) N 233,909 107,738 126,171 [R.sup.2] 0.390 0.495 0.324 *** Significant at the 0.01 level. Table V. Characteristics for Financing Deficits and Surpluses The sample period is 1971-2005. We exclude financial firms, utilities, and firm-years with gaps in the reporting of relevant flow of funds data. This table determines the differences between firm-years with financing deficits and firm-years with financing surpluses. Assets are determined by Compustat Item 6 and are reported in millions of dollars. The debt ratio is computed by dividing Item 9 by Item 6. The market-to-book ratio is (Item 24 x Item 25 - Item 60 + Item 6)/Item 6 and EBIT is Item 18 + Item 15 + Item 16. The issue size is equal to the net amount of equity issued/repurchased plus the net amount of debt issued/repurchased. Rated debt is a dummy variable that equals one if a firm has rated debt outstanding as reported with Compustat Item 280 and zero otherwise. The variables change in working capital, investments, cash dividends, internal cash flows, EBIT, and issue size are scaled by total assets. We estimate Mests with equal variances not assumed to test for equality of means. Deficit Surplus Mean Median Mean Median Change in working 0.05 0.04 -0.05 -0.01 capital Investments 0.14 0.10 0.04 0.04 Cash dividends 0.01 0.00 0.01 0.00 Internal cash -0.03 0.07 0.07 0.09 flows Assets 2013 68 1653 71 Debt ratio 0.21 0.17 0.19 0.14 Market-to-book 2.30 1.40 1.50 1.14 ratio EBIT -0.06 0.07 0.05 0.09 Issue/repurchase 0.21 0.08 0.06 0.03 size Rated debt 0.17 0.00 0.18 0.00 N 130,314 89,460 Differences of Means (t-statistics) Change in working 0.10 *** capital (73.25) Investments 0.10 *** (128.21) Cash dividends 0.00 (-9.43) Internal cash -0.10 *** flows (-79.76) Assets 360 *** (5.30) Debt ratio 0.02 (24.35) Market-to-book 0.80 *** ratio (83.37) EBIT -0.11 *** (-76.21) Issue/repurchase 0.15 *** size (256.38) Rated debt -0.01 ** (-2.14) N *** Significant at the 0.01 level. ** Significant at the 0.05 level. Table VI. Pecking Order Tests for Different Issue Sizes The sample period is 1971/2005. We exclude financial firms, utilities, and firm/years with gaps in the reporting of relevant flow of funds data. Firm/years are sorted into firm/years with financing deficits and financing surpluses, and within this segregation quartiles (over all years) are based on the total issue/repurchase size. The estimated regression specification is [DELTA][D.sub.it] = [alpha] + [[beta].sub.po] x [DEF.sub.it] + [[epsilon].sub.it] where [DELTA][D.sub.it] is the amount of net debt issued and [DEF.sub.it], is the financing deficit. All variables are scaled by total assets. White standard errors appear in parentheses. Deficit Overall Smallest Medium Medium Issue Small Large Size Issue Issue Size Size [alpha] 0.034 *** -0.003 *** -0.005 *** 0.024 *** (0.001) (0.000) (0.001) (0.002) [[beta].sub.po] 0.149 *** 0.601 *** 0.741 *** 0.429 *** (0.005) (0.030) (0.017) (0.012) N 130,314 32,578 32,579 32,579 [R.sup.2] 0.100 0.015 0.061 0.043 Deficit Surplus Largest Overall Smallest Medium Issue Repurchase Small Size Size Repurchase Size [alpha] 0.085 *** 0.002 *** -0.002 *** -0.001 *** (0.005) (0.001) (0.001) (0.001) [[beta].sub.po] 0.089 *** 0.901 *** 0.789 *** 0.881 *** (0.009) (0.012) (0.076) (0.064) N 32,578 89,460 22,365 22,365 [R.sup.2] 0.020 0.747 0.008 0.012 Surplus Medium Largest Large Repurchase Repurchase Size Size [alpha] -0.002 0.013 *** (0.001) (0.002) [[beta].sub.po] 0.815 *** 0.923 (0.026) (0.015) N 22,365 22,365 [R.sup.2] 0.042 0.735 *** Significant at the 0.01 level. Table VII. The Pecking Order Coefficients and Firms' Debt Capacities The sample period is 1971-2005. We exclude financial firms, utilities, and firm-years with gaps in the reporting of relevant flow of funds data. The estimated regression specification is [DELTA][D.sub.it] = [alpha] + [[beta].sub.1] [d.sub.it] + [[beta].sub.2] x [b.sub.it] + [[beta].sub.3] x [c.sub.it] + [[beta].sub.4] [DEF.sub.it] + [[beta].sub.5] x [d.sub.it] x [DEF.sub.it] + [[beta].sub.6] x [b.sub.it] x [DEF.sub.it] x [[beta].sub.7] x [c.sub.it] x [DEF.sub.it] + [[epsilon].sub.it] where [DELTA][D.sub.it];, is the amount of net debt issued, [DEF.sub.it] is the financing deficit, [d.sub.it] is a dummy variable that equals one if [DEFT.sub.it], [greater than or equal to] 0.237 and zero otherwise, [b.sub.it] is a dummy variable that equals one if [DEFT.sub.it] [greater than or equal to] 0.059 and zero otherwise, and c;, is a dummy variable that equals one if [DEFT.sub.it], [greater than or equal to] 0.237 and zero otherwise. Surpluses are deficits below zero, small deficits are deficits from 0% to 5.9% of total assets, medium deficits are deficits from 5.9% to 23.7% of total assets, and large deficits are deficits above 23.7% of total assets. The subsample "rated debt outstanding" solely includes observations in which rated debt is outstanding. This subsample only includes observations after 1985 since Compustat does not report credit ratings before 1985. The subsample "no rated debt outstanding" includes only observations without rated debt outstanding. The subsample "nonconstrained firm characteristics" solely includes observations with above-median sales, above-median tangibility, above-median profitability, and below-median market-to-book ratios. The subsample "constrained firm characteristics" includes only observations with below-median sales, below-median tangibility, below-median profitability, and above-median market-to-book ratios. All variables are scaled by total assets. White standard errors appear in parentheses. Overall Rated Debt No Rated Debt Outstanding Outstanding (1) (2) (3) [alpha] -0.002 *** -0.001 *** -0.003 *** (0.000) (0.000) (0.000) [[beta].sub.1] 0.004 *** 0.002 0.004 *** (0.001) (0.001) (0.001) [[beta].sub.2] 0.019 *** 0.014 *** 0.019 *** (0.001) (0.002) (0.001) [[beta].sub.3] 0.068 *** 0.088 *** 0.060 *** (0.005) (0.021) (0.005) [[beta].sub.4] 0.690 *** 0.802 *** 0.649 *** (0.008) (0.016) (0.009) [[beta].sub.5] 0.211 *** -0.048 0.257 *** (0.014) (0.041) (0.015) [[beta].sub.6] -0.218 *** -0.182 *** -0.194 *** (0.013) (0.026) (0.014) [[beta].sub.7] -0.383 *** -0.323 *** -0.362 *** (0.013) (0.057) (0.014) N 233,909 39,420 194,489 [R.sup.2] 0.411 0.544 0.406 [[beta].sub.sur] 0.901 0.754 0.906 [[beta].sub.po small] 0.690 0.802 0.649 [[beta].sub.po medium] 0.472 0.620 0.455 [[beta].sub.po large] 0.089 0.297 0.093 % Surplus 38% 40% 38% % Small def. 31% 35% 29% % Medium def. 17% 20% 17% % Large def. 14% 5% 16% Nonconstrained Constrained Firm Firm Characteristics Characteristics (4) (5) [alpha] -0.001 -0.002 *** (0.000) (0.000) [[beta].sub.1] 0.000 -0.006 (0.001) (0.005) [[beta].sub.2] -0.001 0.002 (0.002) (0.004) [[beta].sub.3] 0.039 0.017 (0.024) (0.010) [[beta].sub.4] 0.814 *** 0.149 *** (0.017) (0.039) [[beta].sub.5] 0.030 0.769 *** (0.028) (0.076) [[beta].sub.6] -0.008 0.096 (0.029) (0.052) [[beta].sub.7] -0.134 -0.164 *** (0.077) (0.037) N 18,890 19,940 [R.sup.2] 0.776 0.243 [[beta].sub.sur] 0.844 0.918 [[beta].sub.po small] 0.814 0.149 [[beta].sub.po medium] 0.806 0.245 [[beta].sub.po large] 0.672 0.081 % Surplus 42% 16% % Small def. 35% 25% % Medium def. 21% 14% % Large def. 2% 45% Nonconstrained Firm Constrained Firm Characteristics Characteristics and Rated Debt and No Rated Debt Outstanding Outstanding (6) (7) [alpha] 0.000 -0.002 *** (0.001) (0.000) [[beta].sub.1] -0.002 -0.006 (0.002) (0.005) [[beta].sub.2] -0.002 0.002 (0.004) (0.004) [[beta].sub.3] 0.072 0.018 (0.033) (0.010) [[beta].sub.4] 0.703 *** 0.154 *** (0.025) (0.039) [[beta].sub.5] 0.051 0.764 *** (0.062) (0.076) [[beta].sub.6] 0.049 0.091 (0.045) (0.052) [[beta].sub.7] -0.160 -0.165 *** (0.107) (0.037) N 6,453 19,795 [R.sup.2] 0.775 0.244 [[beta].sub.sur] 0.754 0.918 [[beta].sub.po small] 0.703 0.154 [[beta].sub.po medium] 0.752 0.245 [[beta].sub.po large] 0.592 0.080 % Surplus 36% 16% % Small def. 39% 25% % Medium def. 24% 14% % Large def. 1% 45% *** Significant at the 0.01 level. Table VIII. Firm Size Effects on the Mean and Volatility of the Financing Deficits The sample period is 1971-2005. We delete financial firms, utilities, and firm years with gaps in the reporting of relevant flow of funds data. This table determines the mean and standard deviation (in parentheses) of firms' deficits, cash dividends, investments, change in working capital, and internal cash flows. Firms are yearly sorted into quartiles based on total assets. The variable deficits include negative deficits (i.e., financing surpluses). All variables are scaled by total assets. Overall Smallest Medium Medium Small Large Averages (SD) Deficits 0.094 0.207 0.087 0.054 (0.298) (0.485) (0.251) (0.174) Cash dividends 0.013 0.011 0.011 0.013 (0.071) (0.115) (0.064) (0.044) Investments 0.093 0.073 0.092 0.106 (0.195) (0.280) (0.189) (0.155) Working capital -0.004 -0.064 0.035 0.032 (0.350) (0.593) (0.293) (0.194) Internal cash flow 0.009 -0.190 0.041 0.088 (0.343) (0.598) (0.211) (0.120) Percentages Surpluses 38% 32% 40% 41% Financing deficits 31% 39% 33% 30% above 0.059 Financing deficits 14% 26% 16% 10% above 0.237 Largest Averages (SD) Deficits 0.031 (0.113) Cash dividends 0.019 (0.033) Investments 0.100 (0.119) Working capital 0.009 (0.116) Internal cash flow 0.093 (0.084) Percentages Surpluses 41% Financing deficits 24% above 0.059 Financing deficits 4% above 0.237

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Author: | de Jong, Abe; Verbeek, Marno; Verwijmeren, Patrick |
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Publication: | Financial Management |

Article Type: | Report |

Geographic Code: | 1USA |

Date: | Jun 22, 2010 |

Words: | 11948 |

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