Seton Hall University School of Law. The Types of Chapter 11 Cases Stephen J. Lubben. Working Draft

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1 Seton Hall University School of Law The Types of Chapter 11 Cases Stephen J. Lubben Daniel J. Moore Professor of Law Working Draft (please do not circulate or quote without my consent) 1 Electronic copy available at:

2 The Types of Chapter 11 Cases Stephen J. Lubben, Daniel J. Moore Professor of Law, Seton Hall University School of Law* Abstract Bankruptcy scholars like to divide chapter 11 cases into piles. Big cases compared with small cases. Public company debtors compared with private companies. Cases greater than or less than a specified asset size. But much of this dividing happens with little more than hunch to support it, and sometimes the division is never discussed but only implied. In this short paper I look at a new dataset of almost one thousand chapter 11 cases that were filed in I find three distinct kinds of chapter 11 cases, but also find that the very biggest cases are not as different in result as their extra cost might suggest. I ultimately argue that very large cases would benefit from codification of many of the administrative procedures that are now routinely implemented by way of pleading, as doing so would reduce the direct costs of these cases. * I appreciate the comments received at the Third Annual Conference on Empirical Legal Studies at Cornell Law School, and particularly the thoughtful comments of Rich Hynes. This paper is based on data gathered by the author as part of the ABI Chapter 11 Professional Fee Study, which was funded by a grant from the American Bankruptcy Institute, the ABI Endowment Fund, and the National Conference of Bankruptcy Judges. I am grateful for the ABI and NCBJ s assistance with this important project. All conclusions are my own and do not necessarily reflect the views of the ABI or the NCBJ or their members. 2 Electronic copy available at:

3 Ah, your favourites are among these? I continued, turning to an obscure cushion full of something like cats. A strange choice of favourites! she observed scornfully. Unluckily, it was a heap of dead rabbits. I hemmed once more, and drew closer to the hearth, repeating my comment on the wildness of the evening. EMILY BRONTË, WUTHERING HEIGHTS 11 (1847). I. Introduction Chapter 11 scholars like to divide cases, and thus debtors, into groups (or heaps) of cases. Often this is done by splitting small businesses from publicly traded companies. (Baird 2004). Another similar approach involves the implicit or explicit severing of publicly traded debtors from the rest. (Adler & Ayers 2001; Bebchuk 1988). Alternatively, one can declare that all debtors above a set measure of size constitute the relevant universe of cases, and again ignore the rest. (LoPucki 2005). Somewhat akin to this is the move of selecting some number of the largest cases from a broader random sample, and declaring that these are distinct from the others for reasons that remain vague. (Lubben 2000). Of course, none of this has much study behind it, and any particular group of cases may be something other than what is supposed. To be sure, a debtor with $200 million in assets and shares traded on the New York Stock Exchange is probably unlike the typical small business debtor, but is such a debtor really that different from a $200 million privately held firm? And is a $250 million publicly traded company really different from a $300 million company in ways that matter for chapter 11 scholarship? If the answer to either question is no, the foundations of a wealth of corporate bankruptcy scholarship begin to look rather suspect. 3 Electronic copy available at:

4 Line drawing is necessary, of course, because we intuitively know that there are differences within the universe of chapter 11 cases. But line drawing should proceed on a principled basis, especially now that an increasing wealth of empirical data illuminates chapter 11. (Baird & Morrison 2005; Lubben 2008; Warren & Westbrook 1999). This paper uses a new dataset of almost one thousand chapter 11 cases filed in 2004 and explores one recently discovered break point: the point at which the sum of the debtor s assets and liabilities exceeds $2.8 million. As explained in Lubben (2008), this is the point at which the relationship between the direct costs of a chapter 11 case and the debtor s size seems to change radically. As such, it offers a unique, non-arbitrary point at which to divide the big and small chapter 11 cases for further study. But in the course of examining this natural break point, I argue that the data actually reveals three broad types of cases: small cases, big cases, that exceed the $2.8 million threshold, and the truly big cases that exceed $60 million. 1 Considering chapter 11 in these three parts reveals a world where the very big cases routinely deploy a variety of techniques that are all but unknown in the cases of their smaller counterparts: first day motions, ordinary course professional procedures, critical vendor payment, and even DIP financing help to define two almost totally separate chapter 11 experiences. Standing in the middle, the merely big cases borrow from the cases on either side and thus represent the transition from one world to another. But given the escalating cost structure, one might expect even more differences than I find. The very big case debtors retain many more professionals and deal with many more filed claims in their cases. Creditors committees are also much more likely in these cases, and in general we see a much more elaborate process in the very big chapter 11 cases. But the chance of formulating a chapter 11 plan, while nominally higher in the big cases, is not significantly so. Instead the escalating cost structure of the bigger cases 1 In Lubben (2008) I used the sum of asset and debts as the measure of debtor size, in this paper I divide that number by two using the average of assets and debts to allow for greater interpretability of the results. 4

5 seems to primarily result from the additional administrative burden of these cases. Many more claims are filed, committees appointed, and professionals retained, resulting in greater cost. While it would be easy to ascribe the underachievement of the very big cases to the effects of a particular jurisdiction or uncontrolled direct costs, it is equally plausible that the extra costs associated with these cases suggest the need for changes in the Bankruptcy Code that would streamline the big cases. For example, if many very large case implements an ordinary course professional system, why not codify such a system to avoid the need to draft and file a motion in each and every large chapter 11 case? In short, highlighting the many different types of cases that hide under the bland rubric of chapter 11 may emphasize the increased problems of addressing all business reorganization in a single chapter. Given Congress recent efforts to call out small business cases for special treatment, the data presented here might suggest the need for analogous but, hopefully, more considered treatment for the very largest cases. II. The Dataset There are 945 chapter 11 cases in the dataset, and all cases were filed in The cases were drawn from 33 districts around the United States. Three districts from each of the eleven numbered judicial circuits were selected: one from each of the high, low and median population states in the circuit, as determined by the July 1, 2003 Population Estimate published by the U.S. Census. In states with multiple districts, the district that included the highest population city was chosen. Up to forty cases were selected from each district. Up to twenty cases were selected from the first six months of 2004, and up to twenty more cases were selected from the final half of the year. Within each six-month period, cases were selected in the order they were filed. Only business entities are included in the sample; cases involving individual 2 The dataset used for this paper is described at length in Lubben (2008). 5

6 debtors were passed over as part of the selection process. A series of related cases, filed together, was counted only as a single case, with the lead (or first-filed, if there is no lead) case included in the database. To account for the method of case selection, which may overemphasize cases from small population districts, cases in the dataset are weighted. In particular, cases in the dataset are weighted by the average number of business cases in a district between period, divided by the number of cases from that district that were included in the dataset. The data is then rescaled to return the sample to its original size. The use of weighted data creates some challenges in this paper. None of the leading statiscial software packages offers a simple soultion to the analysis presented. Stata SE 3 does, however, allow me to properly account for the structure of the data through the survey set of commands. In particular, although only a limited set of procedures is available under the survey commands, these procedures account for both the weighting of the data and the possible clustering by judicial district. Finally, because I define cases in this dataset by reference to the direct costs of those cases, I exclude cases without any apparent direct costs. As reported in Lubben (2008), approximately 35% of chapter 11 cases apparently result in no payments to professionals either before or during the chapter 11 process. Cases with no requested fees or expenses also face an 80% probability of being dismissed, making them poor examples of the workings of chapter 11 as a reorganization tool. (Lubben 2008). 3 Throughout I use Stata SE version 10 (64 bit) for OSX. 6

7 In particular, many of the differences that I examine in this paper would be simply irrelevant to the debtors without direct cost information whether small, medium or large, these debtors simply will never have creditors committees or other key chapter 11 features, as their chapter 11 cases will come to an end before any of these chapter 11 specific developments might occur. Basic descriptive statistics for the dataset, related to the data shown graphically in Figure 1, are shown on Table A in the appendix. III. Big and Small Cases Defined Unlike many other jurisdictions, in any given year the United States Bankruptcy Courts see thousands of chapter 11 cases, filed by a broad range of debtors. This depth is the result of not only the relatively large population of the United States, as compared with 7

8 other economically developed jurisdictions, but also a long history of corporation reorganization that extends back to the last half of the nineteen century. (Lubben 2004). One consequence of this wealth of corporate bankruptcy cases is that few articles are written about the average American chapter 11. Instead, most scholarship focuses the minority of cases that have outsized economic effects: the big cases, however defined. What distinguishes a big case from its smaller counterpart is often unclear, especially since the enactment of the current Bankruptcy Code in 1978 consolidated all business reorganization cases within chapter 11. In theoretical writing the precise point of division may be less important, save for the potential confusion among international readers who may not appreciate the implicit distinction that is often made. But in empirical studies the failure to fully explore the implications of one s favored demarcation point raises the question of whether the reported results are robust to alternative conceptions of what makes a case big. In this paper I move the dividing line substantially lower than most prior efforts. This new approach is rooted in the observation, first made in Lubben (2008), that the costs of chapter 11 accelerate markedly once the debtor s size, measured by the sum of assets and liabilities, pass log 15. 8

9 In this paper I measure debtor size somewhat differently the average of assets and debts, rather than the sum of the two and examine two divisions in the data, again based on the relationship between the direct costs of a case and the size of the debtor. In particular, I first divide the debtors at approximately the same inflection point first suggested in Lubben (2008), modified somewhat to reflect the new measure of size. I also split the cases at the upper end at another point in which the relationship between fees and size seems to flatten out namely at the point were debtor size exceeds $60 million. As noted in Lubben (2008), the very largest cases seem to exhibit economies of scale, and this new division recognizes that effect by considering the largest cases as a distinct group. I then consider this tri-part classification scheme in contrast with a simple division of the cases into quartiles by debtor size, to consider the robustness of my proffered approaches. Figure 2 shows the data with the break points for my classification scheme indicated. Sorting the cases by quartiles obviously results in equal sized groups of cases, in this 9

10 sample there are essentially 144 cases per quartile. Reference to Figure 2 makes clear that my suggested sorting method is unbalanced, inasmuch as there are many fewer large cases than there are in the other groups. The precise makeup of each method of partitioning the cases is set forth on Table 2A below. Table 2A: Sample distribution by size measures Q1 Q2 Q3 Q4 Total Small Medium Large Total An initial glimpse of the utility of considering debtors as grouped by cost structure can be seen on Figure 3, where we can see that the median cost of each of the two largest groups of cases exceeds the upper end of the range for the next group of cases. 10

11 Thus throughout this paper I describe chapter 11 as consisting of the aforementioned three groups of cases, contrasting this approach with a division of the debtors by quartiles. Using these divisions among debtors have several obvious advantages over prior attempts at defining big and small chapter 11 cases. Most importantly, these points are based on observed differences in the cases, rather than an intuited belief that a difference exists. This approach avoids arbitrarily censoring the data and suggests the need to consider a wider range of cases as among those that are big, and thus economically significant. Table A in the appendix sets forth descriptive statistics for the various groups of debtors. IV. Difference and Similarities Throughout this part of the example I explore differences in the cases based on both my proffered classification scheme and the cases divided into quartiles. Numerical tables that correspond to each of the graphs below appear in the Appendix. Differences that are described below are significant, and for purposes of this paper differences described as being significant refer to p-values of less than.05. A. Early Case Differences From a pure statutory point of view, all chapter 11 cases are the same at inception: all cases must file a petition and all cases must pay the same filing fee. But after that brief moment of unity, the various types of chapter 11 cases diverge substantially. 11

12 For example, small and medium sized cases rarely employ local counsel, while the big cases use local counsel more often than not. Big and medium cases regularly deploy first day motions in their chapter 11 cases to smooth over the transition into bankruptcy, while only 4% of small cases use such motions. Similarly, motions to pay critical trade vendors, a point of much contention (LoPucki 2005), are used by more than 30% of the large debtors and less than one percent of the small debtors. Importantly, none of these distinctions are evident when the cases are sorted by quartiles. Indeed, no significant differences appear from such a division, suggesting that quartiles do not capture meaningful characteristics of the debtors in the early phase of chapter 11 cases. 12

13 B. In-Case Distinctions As debtors move into the operative phase of their chapter 11 cases, the three groups of cases continue to forge distinct paths. And here we begin to see even greater separation among all three types of chapter 11 debtors. Figure 5 examines the appointment of committees and the use of either claims agents or ordinary course professional (OCP) systems. Claims agents, retained by more than a third of the very large debtors, supplant the bankruptcy court clerk s normal role in docketing claims and mailing notices of key events in the case. 4 Typically these are data management firms that are thought to provide a service to both the debtor and the clerk s office, whose resources are not overwhelmed by a single large case. 4 See 28 U.S.C. 156(c). 13

14 Ordinary course professional procedures reflect the simple reality that, despite the plain language of the Bankruptcy Code, the court could never review the retention and compensation of every professional retained by a large corporate enterprise, and that a large portion of these professionals are providing services that are largely unrelated to the bankruptcy process. (Lubben 2007). In such situations, the court authorizes the use of abbreviated retention and compensation for these non-bankruptcy professionals, but Figure 5 clearly indicates this to be an experience virtually exclusive to the very biggest cases. Indeed, all three attributes are common in large chapter 11 cases, and comparatively rare in medium and small cases. Creditors committees are appointed in only 10% of the small cases, and almost 40% of the medium cases. But committees are appointed in more than 97% of the big cases. This single point alone doubtlessly explains much of the accelerated cost structure highlighted in Figure 2, inasmuch as the mere appointment of a committee results in a two-part increase in direct costs. The committee retains professionals that are paid by the estate, and the very presence of the committee and its professionals results in an additional party that the debtor s own professionals must consult. (Lubben 2008). While Figure 5 shows some similarity between the medium and big cases with regard to creditors committees, the Figure also shows that the medium and small cases share a similarity in their relatively infrequent use of either claims agents or ordinary course professional systems. More than 92% of big cases have claims agents, and more than 65% of the big cases have OCP systems in place. On the other hand, these features are essentially non-existent in small cases, and occur in less than 10% of the medium cases. Considering these same factors by quartiles again provides little useful information. In large part this is rooted in the distribution information provided on Table 2A sorting cases by quartiles seems to result in too much lumping of unrelated cases into the largest case quartile. 14

15 Figure 6 further highlights the elite nature of the small group of cases that make up the big category under my method of sorting cases. In each instance on Figure 6, the big cases are significantly different from the other two groups of cases, and there are no significant differences among the small and medium sized cases. Monthly compensation systems allow for the partial payment of professionals on a regular basis, perhaps alleviating the court s need to act promptly on formal fee applications and reducing the risk of complete non-payment of the professionals. More than 85% of the large cases used such a system in their chapter 11 case. The big debtors are unique in their frequent use of financial advisors most often investment bankers and their more general use of additional professionals beyond bankruptcy counsel. More than 94% of the big debtors retained financial advisors, as contrasted with less than five percent of the other types of debtors. Likewise, more than 65% of the big cases saw the debtor retain three or more professionals in addition to 15

16 bankruptcy counsel, while less than 20% of either the medium and small cases retained that many additional debtor professionals. The big debtors also tend to retain larger law firms as their primary counsel. The average law firm retained by the large debtors has more than 790 attorneys, almost ten times larger than the average law firms retained by big or small debtors. Given these differences among all three types of chapter 11 cases, it is somewhat surprising to find that the cases are largely similar or at least not significantly dissimilar -- across a wide range of measures of their terminal period in bankruptcy. None of the big cases were dismissed, whereas about a third of the small and medium sized cases were dismissed. But there was no significant difference among the cases on the issue of conversion to chapter 7 or time spent in chapter 11. Table 7: Case outcomes/duration (by size) Linearized Mean Std. Err. [95% Conf. Interval] Case dismissed? (y/n) Small Medium Large Case converted? (y/n) Small Medium Large Plan filed? (y/n) Small Medium Large Log of time in chapter 11 (years) Small Medium Large N=552 16

17 Most surprisingly, as shown on Table 7, there is not even a significant difference among the three groups of cases in terms of formulating a plan. Given the elaborate administrative and professional assistance typical in the very big cases, should we expect that they would achieve an even higher success rate than the merely big cases? Alternatively, it may be that these very large cases would collapse as an administrative matter, and that the distinctions noted above are needed to simply provide the very large cases with a degree of success, as measured by plan confirmation, that is common in the middle range of cases. If so, this would support my initial proposition that very big cases might benefit from codifying many of the routine procedures now accomplished by way of motion practice. V. Conclusion Line drawing and categorizing is the necessary, yet often unspoken, reality of much bankruptcy scholarship. As this short paper has shown, where the lines are drawn matters. Moreover, there is at least some suggestion that previous efforts at line drawing have put the lines in the wrong places. In particular, if the three types of cases I have worked with in this paper represent the basic types of chapter 11 cases, previous efforts that have drawn distinctions at much higher case size levels warrant reexamination. Of course, the three categories presented in this paper may represent only the beginning of this effort it seems likely that a richer sample of the very largest cases might reveal further subdivisions among those cases. 17

18 References Barry E. Adler & Ian Ayres, A Dilution Mechanism for Valuing Corporations in Bankruptcy, 111 Yale L.J. 83 (2001). Lucian Arye Bebchuk, A New Approach to Corporate Reorganizations, 101 Harv. L. Rev. 775 (1988). Douglas G. Baird & Edward R. Morrison, Serial Entrepreneurs and Small Business Bankruptcies, 105 Colum. L. Rev (2005). Douglas G. Baird, Remembering Pine Gate, 38 J. Marshall L. Rev. 5 (2004). Lynn M. LoPucki, Courting Failure: How Competition for Big Cases Is Corrupting the Bankruptcy Courts (2005). Stephen J. Lubben, Corporate Reorganization & Professional Fees, 82 Am. Bankr. L.J. 77 (2008). Stephen J. Lubben, The Microeconomics of Chapter 11, Part 1, 4 Int l. Corp. Rescue 31 (2007). Stephen J. Lubben, Railroad Receiverships and Modern Bankruptcy Theory, 89 Cornell L. Rev (2004) Stephen J. Lubben, The Direct Costs of Corporate Reorganization: An Empirical Examination of Professional Fees in Large chapter 11 Cases, 74 Am. Bankr. L.J. 509 (2000). Elizabeth Warren & Jay Lawrence Westbrook, Financial Characteristics of Businesses in Bankruptcy, 73 Am. Bankr. L.J. 499 (1999). 18

19 Appendix Table A: Descriptive Statistics of Sample Mean Std. Err. [95% Conf. Interval] Lead Debtor Total Assets ($$$, from schedules) $35,400, $18,000, $1,187, $71,900, Lead Debtor Total Debts ($$$, from schedules) $11,000, $4,276, $2,289, $19,700, N=579 Table B: Descriptive Statistics of Sample by Size (Lubben/Lowess Categories) Debtor Size Mean Std. Err. [95% Conf. Interval] Lead Debtor Total Assets ($$$, from schedules) Small $739, $27, $684, $795, Medium $9,017, $1,918, $5,109, $12,900, Large $641,000, $239,000, $154,000, $1,130,000, $11,000, $4,276, $2,289, $19,700, Lead Debtor Total Debts ($$$, from schedules) Small $281, $20, $238, $323, Medium $5,108, $1,090, $2,886, $7,331, Large $177,000, $55,700, $63,100, $290,000, N=579 Table C: Descriptive Statistics of Sample by Size (Quartiles; 1=Smallest) Debtor Size Mean Std. Err. [95% Conf. Interval] Lead Debtor Total Assets ($$$, from schedules) 1 $336, $46, $241, $432, $1,044, $56, $929, $1,159, $2,753, $133, $2,480, $3,026, $127,000, $72,600, $20,800, $275,000, Lead Debtor Total Debts ($$$, from schedules) 1 $62, $17, $26, $98, $444, $34, $374, $515, $1,369, $106, $1,152, $1,586, $39,000, $17,500, $3,265, $74,700, N=579 19

20 Figure 4: Early case distinctions, by Lubben/Lowess categories Linearized Mean Std. Err. [95% Conf. Interval] Local Counsel Small Medium Large First Day Papers Small Medium Large Critical Trade Small Medium Large DIP Loan Small Medium Large N=574 Figure 4: Early case distinctions, by quartiles (1=smallest) Linearized Mean Std. Err. [95% Conf. Interval] Local Counsel First Day Papers Critical Trade

21 DIP Loan N= Figure 5: In case distinctions, by Lubben/Lowess categories Linearized Mean Std. Err. [95% Conf. Interval] Committee Small Medium Large Claims Agent Small Medium Large OCP System N=574 Small Medium Large Figure 5: In case distinctions, by quartiles (1=smallest) Linearized Mean Std. Err. [95% Conf. Interval] Committee Claims Agent

22 OCP System N= Figure 6: Direct cost distinctions, by Lubben/Lowess categories Linearized Mean Std. Err. [95% Conf. Interval] Monthly Comp. Sys? Financial Adviser? 3 or more profs.? N=574 Small Medium Large Small Medium Large Small Medium Large Figure 6: Direct cost distinctions, by quartiles (1=smallest) Linearized Mean Std. Err. [95% Conf. Interval] Monthly Comp. Sys? Financial Adviser? 3 or more profs.? N=