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This article was downloaded by: [University of Western Ontario]On: 10 November 2014, At: 23:06Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number:1072954 Registered office: Mortimer House, 37-41 Mortimer Street,London W1T 3JH, UK
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The Effect of BankruptcyFilings on Rivals' OperatingPerformance: Evidence from51 Large BankruptciesRobert E. KennedyPublished online: 21 Jul 2010.
To cite this article: Robert E. Kennedy (2000) The Effect of Bankruptcy Filingson Rivals' Operating Performance: Evidence from 51 Large Bankruptcies,International Journal of the Economics of Business, 7:1, 5-25, DOI:10.1080/13571510084032
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ISSN 1357-1516 Print/ISSN 1466-1829 Online/00/010005-21 � 2000 Taylor & Francis Ltd
International Journal of theEconomics of Business, Vol. 7, No. 1, 2000, pp. 5 ± 25
I would like to thank George Baker, Adam Brandenburger, Judith Chevalier, Ken Corts, PankajGhemawat, Stuart Gilson, Cynthia Montgomery, Karen Wruck, and two anonymous referees for usefulcomments on this paper and earlier versions of this research. The Division of Research at the HarvardBusiness School provided financial support.Robert E Kennedy, Harvard Business School, Soldiers Field, Boston, MA 02163, USA; e-mail
The Effect of Bankruptcy Filings on Rivals’
Operating Performance: Evidence from 51 Large
Bankruptcies
ROBERT E. KENNEDY
ABSTRACT I examine the operating performance of financially distressed firms and theirrivals in the periods surrounding 51 bankruptcy filings. The analysis indicates that filingsare associated with declines in rivals’ revenues and profit margins. The declines occur priorto and coincident with bankruptcy filings, but dissipate quickly after a filing occurs. Theadverse effect on rivals’ profit margins appears to be caused by changes in firms’ productmarket conduct, as it is robust to several methods used to screen out filings where a commonshock has occurred. I then examine whether market structure affects the link between filingsand rivals’ profit margins. The market structure effects appear to be small.
Key words: Financial distress; Distress and competition; bankruptcy.
JEL classifications: L0, L1, M2.
[B]ankrupt carriers severely damage the economic health of the entireairline industry. They transmit their financial condition to other, solventcarriers much like a virus is transmitted from the sick to the healthy.
Aviation Week & Space Technology, 3, May 1993, p. 66.
A reeling, shell shocked Federated went into Chapter 11 . . . Trying togenerate sales that would keep them alive, the Campeau stores took thecleaver to prices and drove their competitors to do likewise.
Fortune, 18, June 1990, p. 48.
Chapter 11 gives managers a last, potentially destructive shot at puttingthe firm right.
The Economist, 1, August 1992, p. 63.
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6 R. E. Kennedy
1. Introduction
While anecdotal evidence suggests that a firm’s financial condition affects itsbehavior in the product market, the economics literature has traditionally assertedthat it does not (e.g. Modigliani and Miller, 1958). I examine the operatingperformance of 51 financially distressed firms and their rivals, drawn from 40industries, in the quarters surrounding bankruptcy filings. I find that filings areassociated with declines in rivals’ profitability prior to and coincident withbankruptcy filings, but that this effect dissipates quickly after a filing occurs.
Several analyses have examined the links between financial distress and productmarket conduct but none have examined the effect on rivals’ operating performancein a cross-industry study. The closest antecedent is Borenstein and Rose (1995),which analyzes pricing behavior near seven Chapter 11 filings1 in the airlineindustry. Their focus is on Chapter 11 status and they find little evidence thatbankruptcy per se affects airlines’ pricing behavior. They note in passing, however,that the financial distress which precedes a filing appears to be associated withsomewhat lower prices. This analysis differs from Borenstein and Rose (BR) in twoways. First, it covers 40 industries, while BR examine only a single industry.Second, whereas BR use direct measures of price and quantity, I use accountingmeasures of performance. The cross-industry scope of my analysis allows me toidentify a general, not industry-specific, link between bankruptcy filings and rivals’operating performance. This comes at the cost of using accounting measures, whichprovide less insight into the specific mechanisms by which rivals’ performance isaffected than would data on prices and quantities. The findings presented below areconsistent with BR in that they reveal that rivals’ margins decrease prior to a filingand recover following a filing.
A second line of research examines debt levels and product market behavior. Thedynamics associated with high debt may be similar to those associated with financialdistress because both threaten managers’ and shareholders’ control of the firm. Inboth situations, managers and shareholders retain control of the firm in favorablestates of the world but lose control in unfavorable ones. Brander and Lewis (1986)and Maksimovic (1988) develop models which conclude that high debt levelsprompt firms to act more aggressively in the product market, thereby harmingrivals. Esty (1993) presents evidence from the Savings and Loan industry thatsupports the more debt makes firms more aggressive’ hypothesis. However, Phillips(1990) predicts that high debt softens product market behavior and benefits rivals.Phillips (1990) and Chevalier (1995) present evidence that supports the view thathigh debt levels cause firms to act less aggressively in the product market.
Financial distress might affect rivals either before or after a filing. If a firm’smanager and/or owner foresees that bankruptcy is likely, and if bankruptcy status iscostly to the decision-maker, he might take actions in the product market to increaseshort-term cash flow in an effort to postpone bankruptcy or reduce its likelihood. Abankruptcy filing generally limits the decision-maker’s control of the firm, and thatcould lead to a post-filing change in conduct. This analysis therefore examines theoperating results of bankrupt firms and their rivals both before and after filings. Ofcourse, a change in rivals’ operating performance might be caused by a change inthe filing firm’s conduct, by a change in rivals’ conduct (such as predation), or bya common shock. Several methods are used to eliminate industries where acommon shock may have occurred, thus isolating effects due to product marketconduct. Because the analysis uses accounting measures of operating performance,
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Bankruptcy Filings and Rivals’ Performance 7
not price and quantity data, I am not able to isolate the specific mechanisms bywhich rivals are affected.
The analysis finds that bankruptcy filings are associated with declines in rivals’profitability. Profit margins for both financially-distressed firms and their rivalsdecline prior to and coincident with bankruptcy filings. Following a filing, bothfiling firms’ and rivals’ profit margins recover, perhaps indicating that Chapter 11status reduces distress. The effect remains after controlling for common shocks. Thissuggests that the decline in margins is due to changes in product marketconduct.
The effect on rivals identified in the analysis is quite large. For example, in thefive quarters surrounding Continental Airlines’ 1990 bankruptcy filing, the negativeeffect on rivals is estimated to have reduced their gross profits by more than $590million. Contrary to the pattern often inferred in the business press (that Chapter11 status harms rivals), the analysis suggests that the effect on rivals occurs prior toa filing, and that Chapter 11 status may relieve financial distress and reduce theintensity of product market rivalry.
Additional analysis indicates that market structure has some influence on themagnitude of the effect on rivals. The negative effect increases slightly when abankrupt firm’s market share is high, when rivals are focused in a bankrupt firm’sindustry, and when industry sales growth is high. The negative effect on rivals issomewhat reduced in concentrated industries.
The remainder of this paper is organized as follows. Section two discusses theempirical strategy and the data set. Section three presents econometric results forthe base case analysis. Section four presents additional empirical analyses designedto separate common shocks from firm-specific shocks. Section five examineswhether market structure alters the effect on rivals. Section six concludes.
2. Empirical Strategy and the Data Set
This section discusses the empirical strategy, the variables used in the analysis, theeconometric specification, and the construction of the data set.
Empirical Strategy
I start with the assumption that managers do not bankrupt entirely healthy firms.This means that at some point prior to a bankruptcy filing a firm will experiencedistress. Bankruptcy may be a helpful or even unavoidable response by the firm, butit is assumed to carry costs for the firm’s owners and/or managers. The firm mightfile for bankruptcy quickly, or it could adjust its conduct in the product market inan effort to postpone bankruptcy or reduce its likelihood. Such conduct likelyinvolves actions aimed at increasing short-term cash flow, for example cheating in aco-operative pricing game or working to steal customers from rivals in ways thatwould be unattractive without the advantage of forestalling bankruptcy.
After a bankruptcy filing occurs, the decision-makers in charge of the firm oftenchange. This might lead to post-filing changes in the firm’s product market conduct.The analysis therefore examines the operating results of the bankrupt firm and itsrivals from four years before to four years after a bankruptcy filing. Ideally, theanalysis would focus on changes in prices and quantities sold for both the bankruptfirm and its rivals. Because those data are difficult to assemble and compare in across-industry analysis, I focus on changes in sales revenue and in profit margins.
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8 R. E. Kennedy
Variable Definitions, Control Issues, and the Econometric Specification
The analysis uses a panel data set of quarterly observations on filing and rival firmperformance. Three operating measures are employed as dependent variables tomeasure performance in separate specifications. The measures are: the percentagechange in sales revenue, the change in gross margin, and the change in operatingmargin. The variables used in the analysis are defined in Table 1.
The key independent variables in the analysis are a series of dummy variablesthat indicate when (relative to the filing quarter) each observation occurs. There are32 relative quarter dummy variables, which range from RQM15 (for relative quarterminus 15) to RQ0, and on to RQ16.
Table 1. Base case variable definitions
Dependent variables
% change in salesrevenue = percentage change in sales from the previous quarter
D gross margin = change in gross profit as a percentage of sales from quarter to quarter.= {(gross profit)/sales}t ± {(gross profit)/sales}t ± 1
D operating margin = change in operating profit as a % of sales from quarter to quarter.= {(operating profit)/sales}t ± {(operating profit)/sales}t ± 1
Independent variables
NDELTGDPc = percentage change in real GDP from quarter to quarter= (GDPc ± GDPc ± 1/GDPc ± 1 , where c ranges from Q2.1982 to Q4.1992
Relative quarterdummy variables = dummy variable to indicate when observation occurs relative to the filing
quarter. 32 dummy variables range from RQM15 (± 15) to RQ16 ( + 16).
Table 2. Market structure variable definitions
Dependent variablesD gross margin = change in gross profit as a percentage of sales from quarter to quarter.
= {(gross profit)/sales}t ± {(gross profit)/sales}t ± 1
Independent variables
Concentration = industry C4, measured on last full calendar year preceding the bankruptcy filing.Calculated from Compustat’s line of business database.
Industry growth = three-year compound industry growth rate. Calculated for years ± 4 to ± 1 usingdata from Compustat’s line of business database.
Rivals’ Focus = principal industry revenues as a percentage of total revenues. Calculated for thefull calendar year prior to bankruptcy filing using Compustat’s line of businessdata.
Filing firm = filing firm’s revenues as a percentage of industry revenues. Calculated for marketshare the full calendar year prior to bankruptcy filing using Compustat’s line ofbusiness data.
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Bankruptcy Filings and Rivals’ Performance 9
To control for business cycle effects, I include a business cycle variable(NDELTGDP) which represents the percentage change in seasonally-adjusted realGDP from the previous calendar quarter. The variable is calculated as the ratio:
GDPc ± GDPc ± 1
GDPc ± 1
,
and is calculated for each quarterly observation (that is, c ranges from Q2.1982 toQ4.1992).
As in any inter-industry analysis of panel data, it is important to control for fixedfirm effects. Fixed firm effects are addressed by first differencing all key variables(within the observations for each firm). Variance due to fixed firm effects would beeliminated by this procedure. Therefore, the variables used in the regressions reflectchanges in the level of key variables, not the absolute level of those variables. Thegross margin and operating margin dependent variables were defined as apercentage of sales before they were first differenced, and the change in levels is usedin the regression. For example, if gross margin declined from 25% to 20% of sales,this is calculated as ± 0.05 (± 5%), not as a decline of 20%, {(0.20 ± 0.25)/0.25}.2
Finally, multi-industry firms must be considered carefully. Thirty-seven of 51filing firms and 165 of 238 rivals in the data set operate in more than one four-digitindustry. The analysis uses data for each firm’s principal industry only. The datawere collected from Compustat’s line of business data, which presents operatingperformance for up to ten business segments. In most cases, one of these segmentscorresponds to the principal industry, which is the basis on which the firms wereselected. If reported segments do not precisely match the principal industry,additional variance would be introduced into the analysis. This incremental variancewould most likely involve overly broad segment reporting, which introduces a biasthat would tend to conceal the effect I am looking for, not enhance it.
Several other factors that might affect the dependent variables in our analysis arediscussed in Sections 4 and 5. These include common shocks, multiple filings in anindustry, and elements of market structure such as the concentration ratio, theindustry’s growth rate, a firm’s degree of diversification, and the market share of thedistressed firm. Controls for these factors are not included in the base case analysis,but will be discussed in Sections 4 and 5.
The base case analysis thus uses the following econometric specification:
D Yij t = b 0NDELTGDPc + b 1.32 ^ + 16t = ± 15 RQt
where i is the industry number (i = 1 . . . 51), j is the competitor (j = 1 . . . 5), tis event time (t = ± 15 . . . + 16), c is calendar quarter (c = Q2.1982 . . . Q4. 1992)and the asterisk D Y represents quarterly observations for each firm on percentagechange in sales, and the first difference of gross margin and operating margin.
To summarize, the analysis uses an OLS specification in which firm perform-ance measures are regressed onto a business cycle variable and 32 relative-quarterdummy variables. Variance due to fixed firm effects is eliminated by firstdifferencing the data series for each firm.
Constructing the Data Set
Data were gathered from the Compustat PC Plus database for all Chapter 11bankruptcy filings that occurred between January 1982 and December 1992 and in
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10 R. E. Kennedy
which the filing firm had revenues of $200 million or more in the four quarters priorto the filing. The $200 million revenue cutoff was chosen to yield a large, butmanageable number of filings. The sample period was that for which quarterly line-of-business data were available from Compustat. The data set contains detailedquarterly financial information on 51 filing firms, as well as on each of these firm’sfive largest competitors Ð if five such competitors were listed on Compustat. Thegoal was to gather information on each filing firm and its rivals for four yearspreceding and four years succeeding the bankruptcy filing. The list of filing firms iscontained in Appendix 1.
The data set was constructed as follows. First, all Chapter 11 filings since 1982in which the filing firm had revenues greater than $200 million were identified. Ofthe 59 bankruptcies that met this criterion, eight financial firms were eliminated,leaving 51 events.3
Second, the five largest rivals within each filing firm’s principal industry wereidentified using the filing firm’s primary four-digit SIC classification. In cases whereCompustat listed fewer than five rivals, the analysis uses only those firms listed.Compustat did not list fewer than three rivals for any industry. Data were availablefor 238 (of a potential 255) rivals. The missing observations for rivals is likely relatedto firm size because Compustat does not contain information on very small firms.This may introduce a size bias into the sample.
Next, Compustat PC Plus was used to collect quarterly income statement andbalance sheet information for each of the 289 firms (51 filing firms and 238 rivals).Data were collected on each firm for 33 quarters Ð 16 quarters prior to the filing,the filing quarter, and 16 quarters following the filing. If the data had beencomplete, this would have yielded 9537 quarterly observations. Many of the dataseries for individual firms were truncated because quarterly data were available onlyfrom 1982 forward. If the Chapter 11 filing occurred before January 1986 or afterJanuary 1988, the data series for that industry would be truncated at the beginningor end of the series respectively. There is no reason to believe that the truncationerrors are systematic in a way that would affect this analysis. More industryobservations are truncated at the end of the series Ð e.g. the bankruptcy occurredafter January 1988 Ð than are truncated at the beginning.
This procedure produced 5911 quarterly observations. There were 1004quarterly observations on 51 firms that filed for bankruptcy and 4907 observationson 238 rivals. The observations come from 40 industries.
3. Base Case Results
This section presents the base case empirical results for filing firms and then forrival firms in the periods surrounding a filing. While the primary focus of thisanalysis is the effect of a bankruptcy filing on rival firms’ profitability, I start byexamining filing firms’ performance in the periods surrounding their filings. I thenconsider the effect of a filing on rivals’ financial performance.
Columns 1± 3 of Appendix 2 present empirical results which indicate thatbankruptcy filings are associated with large cumulative declines in sales revenue andprofitability.4 This deterioration ends shortly after the filing, with gross andoperating margins recovering sharply and sales revenue staying depressed. Whilefew of the individual coefficients are significant at traditional levels, the coefficientson the relative-quarter variables within three quarters of a filing display a distinctpattern and are jointly significant.5 A joint significance test on the relative-quarter
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Bankruptcy Filings and Rivals’ Performance 11
variables ranging from relative quarter ± 3 through relative quarter + 3 indicates thatthe group is significant at the 2.5% level for the sales revenue and operating marginspecifications.
Figures 1a and 1b illustrate the economic performance of distressed firms in thequarters surrounding a filing. The figures chart the cumulative change in profitmargins and sales revenue from relative quarter minus four. Gross and operatingmargins decline by a cumulative 7.1% and 6.4% of sales respectively, with the lowpoint in the filing quarter. Sales revenue declines by a cumulative 23%, with the lowpoint one quarter subsequent to the filing. Taken together, columns 1± 3 indicate
Figure 1a. Cumulative change in filling firm’s profit margins.
Figure 1b. Cumulative change in filing firm’s sales revenue.
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12 R. E. Kennedy
that most of the deterioration in distressed firms’ profitability occurs prior to andcoincident with a bankruptcy filing. The act of filing for Chapter 11 protectionappears to have a positive impact on filing firms’ margins, but not on their sales.
I now turn to the effect on rivals’ operating performance. Columns 4± 6 ofAppendix 2 present results for the sales revenue, gross margin, and operatingmargin specifications run on data from distressed firms’ rivals. The analysis revealsa significant effect on rivals’ economic performance. The coefficient on the businesscycle variable is positive in all three specifications and significant at the one percentlevel in two of three specifications. Rivals’ profit margins decline in the periodsbefore and immediately following a filing. Rivals’ sales revenues grow at abnormallyhigh rates prior to a filing, but fall in the filing and immediately subsequentquarters. As before, the coefficients on the relative quarter variables between ± 3 and+ 3 display a distinct pattern. In each of the profit margin specifications (columns5 and 6), four of the seven variables are significant at 5% or better. The sevenrelative quarter dummy variables (from ± 3 to + 3) show joint significance at the 1%level in each of the three specifications. As before, the results can be more easilyinterpreted when charted (see Figures 2a and 2b).
The gross margin level of a typical rival falls by a cumulative total 5.7% of salesbetween four quarters prior to and one quarter following a filing. Operating marginsfall by a cumulative 4.7% of sales over the same period. Both margins recover in thesecond and third quarters following a filing.
Taken together, these results support the view that financial distress in one firmhas a large negative effect on rivals’ profit margins.
The data do not allow me to examine changes in prices and quantities directly, butthe pattern of results is suggestive. I start with the assumption that gross margins are afirst order approximation of variable margins and that incremental unit costs do notchange during the observation period.6 If these assumptions hold, the decline in bothfiling firms’ and rivals’ gross margins in the periods prior to a filing suggests that unitprices decline, perhaps indicating that price competition intensifies. Further, filingfirms’ sales revenues decline by much more than gross margins, but rivals’ salesrevenues grow at abnormally high rates in the periods prior to a filing. This suggeststhat rivals may be stealing unit sales from distressed firms in the periods prior to afiling. The data do not allow me to determine whether the apparent price declines andaccompanying shift in unit sales is initiated by filing or rival firms.
4. Alternate Explanations: Multiple Filings and Industry Shocks
One explanation for the pattern of results sketched in columns 4± 6 of Appendix 2 isthat firms’ conduct in the product market has changed. But this pattern might alsoappear if an industry experienced a common shock that sent one firm intobankruptcy, while also adversely affecting rivals’ operating performance. Alterna-tively, if several bankruptcies occurred independently, even in the absence of acommon shock, the econometric specification might report a statistical relationshipbetween the (independently-) distressed firms’ performance. This section examineseach of these explanations and concludes that neither explains the base case results.
Industry Shocks vs. Firm Specific Events
If a common shock caused one firm to declare bankruptcy while adversely affectingother firms’ performance, we might observe the pattern reported in Appendix 2. If
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Bankruptcy Filings and Rivals’ Performance 13
such a scenario occurred, the effect on rivals would be merely coincident with thebankruptcy, due to the shock, and not related to the filing firm’s situation. Toexamine this possibility, I use two methods to segment the bankruptcy filings intoevents associated with common shocks and those associated with firm-specificdistress. Industries where the bankruptcy filing appears to be associated with firm-specific distress are then analyzed separately. If the effect on rivals’ performance is
Figure 2a. Cumulative change in rival firms’ profit margins.
Figure 2b. Cumulative change in rival firms’ sales revenue.
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14 R. E. Kennedy
present in industries where no common shock occurred, the effect is likely due to achange in product market conduct.
The first methodology used to eliminate bankruptcies associated with commonshocks involves analyzing the correlation, between firms in an industry, of changesin gross margin ratios. If a common shock had occurred, all firms in the industrywould suffer a discrete change in profitability and firms’ gross margins would showa high correlation through time. On the other hand, if the filing was due to firm-specific factors, the profit margins of firms in the industry would likely show a lowercorrelation.
The following procedure was employed. First, the unweighted average grossprofit margin for each period was calculated for the non-filing firms in eachindustry. A correlation coefficient was calculated between changes in the filingfirm’s gross margin and changes in the industry average gross margin. Theindustries were then ranked according to this correlation and divided into thirds.7
Columns 1± 3 of Appendix 3 present the empirical results for the 14 industrieswith the lowest gross margin correlations Ð those least likely to have experienced acommon shock. These regressions are run on a much smaller data set than the basecase regressions. There were 14 events and approximately 1000 data points includedin each specification, leading to much lower t-statistics. Nevertheless, the resultsagain closely mirror the base case. Rivals’ gross margins decline by a cumulative6.2% of sales and operating margins decline by 6.5%Ð with the low point occurringone quarter subsequent to the filing. The joint significance levels for relative quartervariables ± 3 to + 3 have also declined somewhat, with significance of 5% in the salesspecification, 2.5% in the gross margin specification, and 1% in the operatingmargin specification. The results are charted in Figures 3a, 3b and 3c. As the figuresindicate, the negative effect on rivals’ flow profitability remains.
Figure 3a. Rivals’ financial performance: cumulative change in sales revenue (foursamples).
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Bankruptcy Filings and Rivals’ Performance 15
Figure 3b. Rivals’ financial performance: cumulative change in gross margin (foursamples).
Figure 3c. Rivals’ financial performance: cumulative change in operating margin (foursamples).
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16 R. E. Kennedy
The second method used to control for common shocks employs a Grangercausality test to identify and eliminate industries experiencing a sustained decline.8
Lagged changes in industry average gross margin were tested to determine if theyadd explanatory power to changes in the filing firm’s gross margin. If this were thecase, it would indicate that the financial performance of all firms in the industrymoved together, possibly because they had experienced a common shock orextended decline.
There were 14 events with insufficient data to construct a causality test.9 Of theremaining 37 events, only four showed `Granger causality’ at the 5% significancelevel.10 I eliminated the 14 insufficient data’ events and the four Granger causality’events, leaving a sample where the cause of the bankruptcy does not appear to beassociated with industry factors. The three specifications were then run on thissubset of the data. The results reported in columns 4 ± 6 of Appendix 3 indicate thatthe negative effect on rivals’ profit margins is still present. Rivals’ gross marginsdecline by a cumulative 6.3% and operating margins decline by 5.4%. As before,both measures recover starting two quarters subsequent to the bankruptcy filing. Ajoint significance test indicates that the seven relative quarter variables from ± 3 to+ 3 are significant at 5% for the sales revenue specification, 2.5% for the grossmargin specification, and 1% for the operating margin specification. See Figures 3a,3b and 3c.
Multiple Filings in an Industry
Even if an industry did not experience a common shock, several independentbankruptcy filings might lead to a spurious statistical relationship between distress-related performance declines. I verify that this scenario is not responsible for thebase case results by eliminating all industries with multiple filings.
There were five industries with multiple filings, accounting for a total of 16filings. There were only three instances when any two of these filings were separatedby less than four calendar quarters.11 The base case regressions were rerun withobservations from the 16 filings omitted. Appendix 4 presents the empiricalresults.
The signs and magnitude of the coefficients in Appendix 4 on key variables areconsistent with the base case results. Rivals’ gross margins decline by a cumulative6.6%, while operating margins decline by 8.0%. Both declines are slightly largerthan in the base case. As before, the low point for rivals occurred one quartersubsequent to the bankruptcy filing. Despite fewer degrees of freedom, thesignificance levels on individual coefficients are somewhat increased. The sevendummy variables are jointly significant at the 1% level in both the gross margin andoperating margin specifications, and at 5% in the sales revenue specification. Theseresults are not consistent with a hypothesis that multiple event industries areresponsible for the base case results.
The results presented in this section are not consistent with a hypothesis that thebase case results are the result of common shocks or multiple filings in an industry.I use two methods (the correlation of gross margins and a Granger causality test) toidentify and eliminate Chapter 11 filings that appear to be associated with commonshocks. When the empirical specifications were run on bankruptcy filings thatappear to be associated with firm-specific distress, the negative effect on rivals’ flowprofitability remains and, in every case, closely mirrors the base case. Although themagnitude of the negative effect on rivals’ profit margins varies somewhat, it is
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Bankruptcy Filings and Rivals’ Performance 17
5. Market Structure Effects
Section IV supports the hypothesis that financial distress leads to a change inproduct market conduct. The base case results are robust to two alternateexplanations, common shocks and multiple filings. I now examine whether marketstructure has an effect on the link between distress and conduct. This sectionexamines four measures of market structure: industry concentration, the rate ofindustry sales revenue growth, the filing firm’s market share, and rivals’ focus intheir primary industry.
I use the gross margin specification, as this measure most closely approximatesthe marginal contribution of incremental sales. Each market structure measure isincorporated into the analysis by interacting the market structure measure with 11new relative quarter variables. The industry concentration, industry growth rate, andfiling firm’s market share variables are common for all industry observations. Thecoefficients are estimated using the following specification:
D Yij t = b 0NDELTGDPc + ^ + 5t = ± 5 (MktSti) ´ RQt + ^ + 16
t = ± 15 RQt
where D Y represents quarterly observations on the first difference of gross marginfor each firm. MktSt represents the market structure variable (C4 level, industrygrowth, or filing firm’s market share). See Tables 1 and 2 for variabledefinitions.
The specification for the rivals’ focus variable differs slightly, as this variable ismeasured for each rival:
D Yij t = b 0NDELTGDPc + ^ + 5t = ± 5 (rivalsfocusi j) ´ RQt + ^ + 16
t = ± 15 RQt
Concentration level represents the industry C4 ratio (calculated using salesrevenues) for the calendar year prior to the bankruptcy filing. Industry growth iscalculated as the compound growth rate of industry revenues for the three yearspreceding the bankruptcy filing. Filing firm’s market share is calculated as the filingfirm’s principal industry sales revenue (in the full calendar year prior to filing)divided by industry sales revenue. Finally, rivals’ focus represents the percentage ofa rival’s total sales revenue that come from its principal industry. This final variableis calculated for each rival firm.
The results for specifications with the market structure variables reveal smallinteractions with the adverse effect on rivals. The results are reported in Appendix5. Only 10 of 44 coefficients on the market structure variables are significant at 10%or better, and four of these occur in the distressed firm’s market share specification. Asa group, the 11 interacted variables are jointly significant in three of the fourspecifications (concentration, filing firm’s market share, and rival’s focus).
present in each subset of the data. Prior to a filing, product market rivalry appearsto become more intense. Rivals’ gross and operating margins decline while salesrevenue grows at an above average rate. In the filing quarter, rivals’ sales revenueslumps, while margins continue to decline. These results do not support the viewthat the negative effect on rivals is due to industry shocks or to multiple filings. Thedecline in rivals’ performance appears to be associated with changes in filing and/orrival firm behavior.
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18 R. E. Kennedy
The industry concentration specification indicates that the negative effect on rivalsis slightly smaller in concentrated industries. The results are consistent with ahypothesis that firms in concentrated industries are better able to manage thecompetitive disruption that typically accompanies financial distress. Only two of the11 interactive terms are significant, but nine of 11 have coefficients greater thanzero. The 11 interactive terms are jointly significant at 5%.
The industry growth specification provides no strong results. Seven of 11interactive terms are negative. The coefficients are not jointly significant.
The results from the filing firm’s market share specification indicate that thenegative effect on rivals increases with the filing firm’s market share. As we mightexpect, distress in a large firm appears to have a larger effect on rivals’ operatingperformance than distress in a small firm. A 20% increase in filing firm market sharereduces rivals’ gross margin by a cumulative 1.2% in the quarter following a filing.Three of 11 interactive terms are significant at the 5% level, and a fourth issignificant at the 10% level.
Finally, the rivals’ focus specification indicate that greater focus increases thenegative effect on rivals. However, this effect is very small. A 50% increase in arival’s focus (e.g. from 20% of sales revenue in the principal industry to 70%),would increase the negative effect on gross margins by only 0.16%. Although the 11interactive terms are jointly significant at 5%, the magnitude of the coefficientssuggest the effect is minuscule.
Figure 4 illustrates the incremental effect that several market structure factorshave on the base case results. The figure illustrates the effect of a 40% increase inindustry concentration, a 10% increase on annual industry revenue growth, a 50%increase in a rival’s primary-industry focus, and a 20% increase in the filing firm’smarket share.
Figure 4. Market structure effects on rivals’ gross margin: cumulative change (fourmeasures).
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Bankruptcy Filings and Rivals’ Performance 19
6. Concluding Remarks
I examine the operating performance of 51 financially distressed firms and theirrivals before and after Chapter 11 bankruptcy filings. The empirical results indicatethat distressed firms’ operating performance deteriorates rapidly in the quartersprior to a filing, while rivals’ sales revenue, gross margins, and operating margins allfollow a distinct pattern in the quarters surrounding a filing. Rival’s profit marginsdecline in the quarters preceding and coincident with a filing, and then recover insucceeding quarters. Their sales revenues grow at an above-average rate prior to afiling, but slump when the filing occurs. The pattern observed is highly significant,even after controlling for industry and business cycle effects.
This analysis is the first I know of that documents the magnitude and timing ofa relationship between financial distress in one firm and the operating performanceof the distressed firm’s rivals. I do not propose a specific mechanism by which thiseffect occurs because my data do not allow me to distinguish between potentialmechanisms ± for example, an increase in the rate at which the distressed firmdiscounts future cash flows, or predation by rivals. The effect on rivals differssignificantly from the conventional wisdom, which suggests that Chapter 11 statusharms rivals.
Notes
1. Chapter 11 is a section of the U.S. Bankruptcy code that is designed to allow a financially distressedfirm to protect the value of its assets while a plan of reorganization is worked out. A firm’smanagement (or court-appointed trustee) is given 120 days to develop a reorganization plan (thisperiod can be extended with the court’s permission). During this period, all proceedings by creditorsagainst the firm are halted. The reorganization plan goes into effect if it is accepted by each class ofcreditors, as well as shareholders, and confirmed by the court. Reorganization plans typically involvepartial debt forgiveness and/or conversion of debt into equity.
2. An alternative was to use the percentage change in margin percentage (e.g. a gross margin declinefrom 25% to 20% is recorded as a 20% decline). All the regressions have been run with this alternatemeasure with no qualitative change in results. All regressions in this paper are presented using Whitecorrected t-statistics.The sales revenue variable is defined as the percentage change from the previous quarter less thequarterly sales growth trend line of 0.04128 (4.128%). Because this dependent variable alreadyrepresents the change in sales, it is not first differenced.
3. Financial firms were eliminated because of the particular relationship between a financial firm’sconduct and its operating performance. Current financial results for these firms primarily reflectdecisions taken in previous periods ± e.g. prior lending decisions and loan loss accruals. Decisionstaken by management in the current quarter have a relatively small effect on current results. Whilethis is true to some extent for all firms, the effect appears to be much stronger in financial firms.
4. In order to conserve space, the coefficients are reported for eight quarters preceding and succeedinga filing. All specifications were run with 15 quarters preceding and 16 quarters succeeding afiling.
5. Coefficients on other relative quarter variables do not exhibit a distinct pattern and few arestatistically significant. Of the 75 relative quarter coefficients four or more quarters from the filing,only three are significant at the 5% level.
6. Both assumptions are reasonable first order approximations, although neither is precisely true. Theissues with accounting measures of operating performance are well understood. Some elements ofcost of goods sold are essentially fixed (for example, depreciation), so gross margins may be slightlypro-cyclical. It is also possible that unit costs rise as a firm encounters financial distress. If the firm’sstate is well known, suppliers may raise prices to compensate for the risk of non-payment. Despitethese caveats, the assumptions appear reasonable as a first approximation.
7. Using the correlation of operating margins produced exactly the same groupings. The correlationbetween the gross margin and the operating margin was 0.57.
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20 R. E. Kennedy
8. The filing firm’s profit margins were regressed first onto its own history, and then onto its ownhistory as well as average industry profit margins. The lagged values from 8, 7, 6, and 5 quartersprior to each profit margin observation were employed as independent variables
9. The `insufficient data’ events were numbers 9, 10, 11, 15, 18, 20, 23, 27, 30, 36, 40, 45, 46, and50. Please see Appendix 1 for the key to firms and industries.
10. These were Texaco (industry 1), National Gypsum (19), American Healthcare (42), and WesternCompany of North America (51). Only industry 42 showed causality that was significant at the 1%level.
11. The five industries were steel (SIC 3312, 3 firms), airlines (SIC 4512, 4 firms, 5 events), homeimprovement retailing (SIC 5211, 2 firms), discount retailing (SIC 5311, 3 firms), and retailing(SIC 5331, 3 firms). The three cases where filings occurred within 4 quarters of each other are: first,the second Continental Airlines filing (Q4, 1991) and America West (Q2, 1991); second, EasternAirlines (Q1, 1989) and Braniff Inc. (Q3, 1989); and third, Michigan General Corp (Q2, 1987) andHandyman Corp (Q4, 1986). A complete listing of all filing firms and their industries and filingdates is contained in Appendix I.
References
ª Viewpoint: Airline Bankruptcy Virus Must be Stopped,º Aviation Weeks & Space Technology, 3 May 1993,p. 66.
Borenstein, S. and Rose, N.L. ª Bankruptcy and Pricing in U.S. Airline Markets,º American Economic
Review, 1995, 85(2), pp. 397± 402.Brander, J.A. and Lewis, T.R. ª Oligopoly and Financial Structure: The Limited Liability Effect,º
American Economic Review, 1986, 75(5).Chevalier, J. ª Capital Structure and Product Market Competition: Empirical Evidence from the
Supermarket Industry,º American Economic Review, 1995, 85(3), pp. 415± 35.ª Bankruptcy: When Firms Go Bust,º The Economist, 1 August 1992, p. 63.Esty, B., ª Organizational Form, Leverage, and Incentives: A Study in the S&L Industry,º Harvard
Business School Working Paper, 1993.ª The Biggest, Looniest Deal Ever,º Fortune, 18 June 1990, p. 48.Maksimovic, V., ª Capital Structure in Repeated Oligopolies,º RAND Journal of Economics, 1988, 19, pp.
389± 407.Modigliani, F. and Miller, M.H. ª The Cost of Capital, Corporate Finance, and the Theory of
Investment,º American Economic Review, 1958, 48, p. 261.Phillips, G., ª Increased Debt and Industry Product Markets: An Empirical Analysis, Journal of Financial
Economics, 1990, 37(1), pp. 189± 238.
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Bankruptcy Filings and Rivals’ Performance 21
Appendix 1. List of Bankruptcy Events
Industry No. Company YY.Q Four digit SIC
1 Texaco 87.2 2911
2 Continental Air (2) 90.4 4512
3 Federated Stores 90.1 5311
4 Ames Dept Stores 90.1 5331
5 LTV Steel 86.3 3312
6 Circle K 90.2 5412
7 Allied Stores 89.1 5311
8 Revco Drug 88.3 5912
9 Wickes 82.2 2221
10 Carter Hawley Hale 91.1 5311
11 Wilson Foods 83.2 2011
12 Hills Dept. Stores 91.1 5331
13 Best Products 91.1 5399
14 Manville 82.3 2621
15 Eastern Air Lines 89.1 4512
16 Interco 91.1 2510
17 America West Airlines 91.2 4512
18 McCrory Corp. 92.1 5331
19 National Gypsum 90.4 1540
20 Walter Industries 89.4 3320
21 Continental Air (1) 83.3 4512
22 Sunbeam 88.1 3634
23 Commonwealth Oil 79.3 4220
24 Storage Technologies 84.4 3572
25 Finevest Foods 91.1 2020
26 Greyhound Bus Lines 90.2 4100
27 A.H. Robbins 85.3 2834
28 Smith Int’ l 86.1 3533
29 Wheeling Pittsburgh 85.2 3312
30 United Mrchts & Mftrs 77.3 2200
31 Eagle-Pitcher Inds 91.1 3714
32 Maxicare Health 89.1 6324
33 US Home Corp 91.2 1531
34 Resorts Int’ l 89.4 7990
35 Copeland Enterprises 91.2 5812
36 Allis-Chalmers Corp. 87.2 2390
37 Braniff Inc. 89.3 4512
38 Todd Shipyards 87.3 3730
39 Bicoastal Corp. 89.4 3690
40 UNR Industries 82.3 3317
41 MMR Holdings 90.1 1731
42 American Healthcare 87.3 8062
43 Lionel Corp. 82.1 5945
44 CF&I Steel 90.4 3312
45 CSS Industries 79.3 2771
46 Michigan General Corp. 87.2 5211
47 Lone Star Industries 90.4 3241
48 Care Enterprises 88.1 8051
49 WTD Industries 91.1 2421
50 Handyman Corp. 86.4 5211
51 Western Co.of North Am. 88.1 1389
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22 R. E. Kennedy
Appendix 2. Base case econometric results (t statistics)
Dependentvariable
Filing firms
C1Change in
salesrevenue
(%)
C2Change in
grossmargin
C3Change inoperatingmargin
Rival firms
C4Change in
salesrevenue
(%)
C5Change in
grossmargin
C6Change inoperatingmargin
NDELTGDP 1.679 0.956a 1.013b 2.924a 0.220 0.450c
(0.85) (1.91) (2.26) (4.57) (1.14) (2.81)
RQM8 0.195 ± 2.200 ± 2.004a ± 7.286c ± 0.972a ± 1.280c
(0.03) (1.57) (1.65) (3.89) (1.74) (2.73)
RQM7 ± 7.364 0.540 0.829 ± 0.166 ± 0.385 ± 0.263(1.32) (0.38) (0.69) (0.09) (0.69) (0.57)
RQM6 0.505 ± 0.540 0.094 4.070b 0.513 0.205(0.09) (0.38) (0.08) (2.19) (0.93) (0.44)
RQM5 1.328 1.898 1.072 2.869 ± 0.399 0.078(0.24) (1.34) (0.88) (1.56) (0.72) (0.17)
RQM4 ± 1.501 ± 0.507 ± 1.836 ± 7.197c 1.073b ± 0.451(0.27) (0.36) (1.54) (3.97) (1.97) (0.99)
RQM3 ± 5.160 ± 2.289 ± 2.084a ± 2.016 ± 1.937c ± 1.125b
(0.91) (1.61) (1.75) (1.12) (3.59) (2.50)
RQM2 ± 1.619 ± 2.284 ± 1.037 2.488 0.247 0.270(0.28) (1.58) (0.87) (1.38) (0.46) (0.61)
RQM1 1.404 ± 0.993 ± 0.420 2.990a ± 1.567c ± 0.830a
(0.25) (0.70) (0.35) (1.68) (2.95) (1.91)
RQ0 ± 17.631c ± 1.518 ± 2.867b ± 7.250c ± 1.304b ± 1.910c
(3.12) (1.08) (2.40) (4.10) (2.45) (4.40)
RQ1 ± 12.883b 2.406a 0.980 ± 2.133 ± 1.087b ± 1.135c
(2.30) (1.76) (0.81) (1.22) (2.07) (2.67)
RQ2 3.751 ± 1.075 2.758b 3.076 0.854 1.467c
(0.67) (0.76) (2.19) (1.75) (1.63) (3.45)
RQ3 1.022 0.633 1.069 3.889b 0.424 0.608(0.18) (0.45) (0.85) (2.18) (0.80) (1.40)
RQ4 ± 9.702 1.388 ± 0.079 ± 2.795 ± 0.577 ± 0.322(1.71)a (0.96) (0.06) (1.55) (1.07) (0.73)
RQ5 ± 6.023 0.079 0.530 ± 1.417 ± 0.314 ± 0.510(1.05) (0.05) (0.41) (0.79) (0.59) (1.17)
RQ6 2.784 1.094 2.211 2.204 0.801 0.760a
(0.46) (0.71) (1.64) (1.16) (1.41) (1.67)
RQ7 7.513 ± 1.855 1.434 5.450c 1.563b ± 0.742(1.12) (1.10) (0.98) (2.64) (2.53) (1.51)
RQ8 ± 1.245 ± 1.268 ± 3.371b ± 5.170b 0.340 0.094(0.17) (0.69) (2.08) (2.34) (0.51) (0.18)
R2 7.96% 4.18% 6.45% 4.06% 1.48% 2.32%n 946 933 820 4713 4638 4066Joint significance level
of RQ(± 3 ® + 3) 2.5% ± 2.5% 1% 1% 1%
a10% significance; b5% significance; c1% significance.
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Bankruptcy Filings and Rivals’ Performance 23
Appendix 3. Firm-specific Distress as Determined by Gross Margin
Correlation and Granger Causality Test (t statistics)
Dependentvariable
Gross margin correlationRival firms
C1Change in
salesrevenue
(%)
C2Change in
grossmargin
C3Change inoperatingmargin
Granger causality testRival firms
C4Change in
salesrevenue
(%)
C5Change in
grossmargin
C6Change inoperatingmargin
NDELTGDP 2.81b 0.06 0.32 3.25 0.25 0.55c
(2.10) (0.16) (0.93) (3.96) (1.02) (2.79)
RQM8 ± 6.88a 0.38 ± 0.75 ± 8.87c ± 0.78 ± 1.63c
(1.80) (0.35) (0.78) (3.92) (1.18) (2.98)
RQM7 3.46 ± 0.34 0.12 0.45 ± 0.63 ± 0.18(0.91) (0.30) (0.13) (0.20) (0.95) (0.34)
RQM6 2.68 0.69 0.87 4.47b 0.57 0.27(0.71) (0.63) (0.92) (2.00) (0.87) (0.50)
RQM5 ± 0.96 ± 2.66b ± 1.85a 5.31b ± 0.40 0.44(0.71) (2.45) (1.94) (2.41) (0.62) (0.82)
RQM4 ± 5.85 ± 0.44 ± 1.32 ± 8.97c 1.06a ± 0.77(1.58) (0.40) (1.37) (4.07) (1.64) (1.45)
RQM3 ± 1.10 ± 0.65 ± 0.45 ± 0.52 ± 2.37c ± 1.29b
(0.29) (0.60) (0.46) (0.23) (3.68) (2.43)
RQM2 7.33b 0.00 ± 0.47 2.53 0.23 ± 0.12(2.02) (0.00) (0.51) (1.15) (0.35) (0.21)
RQM1 ± 1.16 ± 3.87c ± 2.51c 5.54b ± 1.46b ± 0.43(0.32) (3.71) (2.85)c (2.55) (2.30) (0.83)
RQ0 ± 7.66b ± 0.89 ± 1.76a ± 9.72c ± 1.25a ± 1.97c
(2.14) (0.84) (1.99) (4.47) (1.95) (3.80)
RQ1 ± 3.62 ± 0.80 ± 1.32 ± 1.97 ± 1.48b ± 1.57c
(1.05) (0.80) (1.58) (0.93) (2.37) (3.10)
RQ2 6.16a 1.05 2.18c 3.72a 1.08a 1.64c
(1.76) (1.05) (2.65) (1.73) (1.73) (3.83)
RQ3 4.06 0.69 0.96 7.03c 0.51 0.93a
(1.18) (0.69) (1.16) (3.23) (0.80) (1.79)
RQ4 ± 4.67 ± 0.49 ± 0.70 ± 6.05c ± 0.47 0.39(1.35) (0.49) (0.83) (2.76) (0.73) (0.73)
RQ5 2.09 ± 0.20 ± 0.13 ± 1.14 ± 0.54 ± 0.69(0.60) (0.19) (0.15) (0.52) (0.84) (1.33)
RQ6 3.53 1.22 2.38c 2.62 ± 1.50b 0.46(0.90) (1.08) (2.58) (1.11) (2.17) (0.83)
RQ7 7.24a ± 0.57 ± 0.19 9.28c ± 1.52b ± 0.33(1.82) (0.49) (0.20) (3.51) (1.96) (0.53)
RQ8 ± 10.53b ± 0.05 ± 1.78a ± 8.41c 0.13 0.35(2.47) (0.04) (1.80) (2.95) (0.16) (0.29)
R2 6.13% 3.72% 6.10% 6.34% 1.59% 2.98%n 1070 1053 905 3478 3424 3023Joint significance level
of RQ (± 3 ® + 3) 5% 2.5% 1% 5% 2.5% 1%
a10% significance; b5% significance; c1% significance.
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24 R. E. Kennedy
Appendix 4. Excluding 16 Multiple Filing Industries (t statistics)
Dependentvariable
Rival firms
C1Change in
sales revenue(%)
C2Change in
gross margin
C3Change in
operating margin
NDELTGDP 4.340a ± 0.089a 0.041(4.51) (0.29) (0.16)
RQM8 ± 3.442 ± 1.339 ± 1.318a
(1.23) (1.54) (1.84)
RQM7 ± 0.952 0.089 0.164(0.34) (0.10) (0.23)
RQM6 5.740b 0.687 0.084(2.08) (0.81) (0.12)
RQM5 ± 3.870 ± 1.170 ± 0.900(1.40) (1.37) (1.28)
RQM4 ± 3.910 1.270 ± 1.023(1.44) (1.51) (1.47)
RQM3 ± 3.314 ± 2.019b ± 0.656(1.22) (2.41) (0.94)
RQM2 5.061a 1.211 ± 1.401b
(1.88) (1.46) (2.06)
RQM1 ± 1.569 ± 2.515c ± 1.869c
(0.59) (3.09) (2.87)
RQ0 ± 3.775c ± 2.109c ± 2.644c
(1.44) (2.59) (4.06)
RQ1 ± 2.628b ± 1.163 ± 1.397b
(1.02) (1.45) (2.22)
RQ2 5.810b 1.77b 2.558c
(2.24) (2.23) (4.04)
RQ3 ± 0.197 0.316 0.296(0.07) (0.39) (0.46)
RQ4 1.904 ± 0.950 ± 0.482(0.72)a (1.17) (0.74)
RQ5 ± 2.612 ± 0.168 ± 0.736(1.00) (0.20) (1.16)
RQ6 3.290 ± 1.179 0.890(1.19) (1.40) (1.35)
RQ7 3.145 ± 2.516c ± 1.340a
(1.03) (2.68) (1.86)
RQ8 ± 3.519 0.204 ± 0.258(1.06) (0.19) (0.33)
R2 c 4.18% 2.19% 3.23%n 2795 2730 2335Joint significance level
of RQ (± 3 ® + 3) 5% 1% 1%
a10% significance; b5% significance; c1% significance
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Bankruptcy Filings and Rivals’ Performance 25
Appendix 5. Industry Characteristics (t Statistics)
Market structurevariable
IndustryC3 level
Industrygrowth
Distressed firm’smarket share
Rivals’focus
NDELTGDP 0.230c 0.045c 0.049 0.072b
(3.73) (2.69) (0.20) (2.41)
M5 0.060 0.753 0.127 0.000(0.54) (0.46) (0.81) (0.02)
M4 0.136 ± 0.085 ± 0.201 0.040(1.25) (0.12) (1.28) (0.03)
M3 ± 0.102 ± 1.886 ± 0.284a ± 0.230(0.93) (1.18) (1.89) (1.56)
M2 0.200a 0.359 ± 0.319b ± 0.030(1.83) (0.28) (2.08) (0.21)
M1 0.049 ± 1.833 ± 0.311 ± 0.040(0.46) (1.10) (2.01)b (0.31)
P0 0.032 1.690 0.380 0.280a
(0.31) (1.35) (2.45)b (1.92)
P1 0.256b ± 3.656b ± 0.205 ± 0.297b
(2.44) (2.41) (1.31) (2.05)
P2 0.081 3.148a 0.155 0.063(0.77) (1.86) (1.01) (0.43)
P3 0.064 ± 2.106 0.010 0.000(0.60) (1.32) (0.06) (0.02)
P4 ± 0.084 ± 0.129 0.141 0.040(0.43) (0.08) (0.82) (0.30)
P5 0.010 ± 1.221 0.032 0.138(0.10) (0.74) (0.19) (0.91)
R2 4.77% 5.18% 6.34% 4.93%n 4638 4967 3697 4420Joint significance level of
market structure vars 5% 1% 5%
a10% significance b5% significance c1% significance
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