Ofek Capital

Journal of Financial Economics 34 (1993) 3330. North-Holland

Capital structure and firm response to poor performance

An empirical analysis*

Received September 1991. final version received September 1992

This paper tests the relation between capital structure and a firm’s response to short-term financial distress. In a sample of 358 firms that perform poorly for a year, higher predistress leverage increases the probability of operational actions, particularly asset restructuring and employee layoffs. Higher predistress leverage also increases the probability of financial actions such as dividend cuts. These results are consistent with Jensen’s (1989) argument that higher predistress leverage increases the speed with which a firm reacts to poor performance. Interestingly, higher managerial holdings reduce the probability of operational actions. especially those that do not generate cash.

1. Introduction

Firms experiencing poor performance respond either operationally, by making changes in top management [Gilson (1989)] or in organizational strategy and structure [Wruck (1990)], or financially, through debt restructuring and bankruptcy filings [Gilson, John, and Lang (1990)]. Typical responses to a short period of poor performance include asset restructuring, employee layoffs, and management replacement [John, Lang, and Netter (1992)]. Why firms choose certain responses over others, however, is largely unexplored. Analyzing the responses to short-term distress may shed light on how to hasten a firm’s reaction to distress and thus preserve value by triggering earlier changes.

Corresportd~nce ro: Eli Ofek. Stern School of Business. New York University, 44 West 4th Street, Suite 9-190. New York. NY 10012-1126, USA.

*This paper is based on the second chapter of my dissertation. I appreciate helpful comments from Douglas Diamond (my chairman), Robert Gertner, Steven Kaplan, Larry Lang, Richard Ruback (the editor). Robert Vishny, Lawrence Weiss (the referee), and seminar participants at New York University and the University of Chicago.

0304-405X/93,‘$06.00 c 1993-~Elsevier Science Publishers B.V. All rights reserved

4 E. Ofek, Predi.wess leoerage and firm response to poor performance

Jensen (1989) argues that highly-leveraged firms will respond faster to a decline in firm value than their less-leveraged counterparts because a small decline in value can lead to default. Jensen’s argument implies that a highly- leveraged firm is more likely to restructure its operations and its financial claims quickly, preserving its going-concern value. When leverage is initially low, default occurs only after continuing losses drive firm value substantially below the predistress level. With low leverage, a firm is less likely to respond to short- term operational distress, and will therefore lose more of its going-concern value. This paper examines the relation between a firm’s capital structure and its operational and financial response to distress, particularly with regard to Jensen’s argument that high leverage increases the probability of response to short-term distress.

The sample consists of 358 firms that, over the period 1983-1987, experience a year of average or above-average performance (base year) followed by a year of very poor performance (distress year), defined as an annual stock return in the bottom decile of the market. Empirical results show that a firm’s leverage in the base year has a positive and highly significant effect on the probability that some operational action will be taken in the distress year. Consistent with Jensen’s argument, higher leverage also significantly increases the probability that certain specific operational actions, such as asset restructuring and employee layoffs, will be taken when performance deteriorates. Highly-leveraged firms are more likely to liquidate assets and raise cash, which they use to repay debt, although these firms are also more likely to take actions such as discontinuing operations and consolidating production facilities that reduce costs but do not increase current cash flow. However, leverage does not appear to influence the probability of management replacement.

High leverage also significantly increases the probability of debt restructuring following a short period of distress, in contrast to Gilson, John, and Lang (1990), who find no relation between leverage and debt restructuring following a long period of distress. In addition, given default, a firm is less likely to file for bankruptcy protection after a short period of distress; of the 42 firms in the sample that were severely financially distressed (i.e., which had either defaulted or filled for bankruptcy protection or had successfully restructured their debt to avoid default), only four (10%) filed for bankruptcy in their first year of poor performance. By comparison, Gilson, John, and Lang report that 53% of severely-distressed firms that had performed poorly for at least three years filed for bankruptcy protection. The difference in the proportion of bankruptcy filings between the two samples may be attributable to the duration of the poor performance. Finally, leverage significantly increases the probability of dividend cuts in poorly-performing firms.

This paper also distinguishes between firms performing poorly in relation to the market and those performing poorly in relation to their industry. The positive relation between leverage and operational actions is missing for firms

E. Ofek, Predistress leverage andfirm response lo poor performance 5

that perform poorly in relation to thier industry only, highlighting the impor-

tance of an absolute decline in firm value in generating the effect of leverage. Leverage is highly related to a firm’s response to trouble, but managerial

holdings also appear to play a role. The larger the share of the firm’s equity held by management, the lower the probability of operational actions, especially those that do not generate cash inflow (such as replacing management, laying off employees, and discontinuing operations). The existence of a large outside stockholder with 5% or more of the firm’s shares, on the other hand, does not significantly increase the probability of operational action. This result does not vary across levels of outside stock ownership, or with the identity of the stockholder.

The paper is organized in seven sections. Section 2 states the hypothesis. Section 3 describes the sample formation and data sources, and provides information about the firms in the sample and the actions they take. Section 4 provides the overall results for the relation between the firm’s capital structure and its operational actions; it also describes the results for the groups performing poorly in relation to the market and their industry. Section 5 delves further into the relations between leverage and the firm’s actions. Section 6 describes the relation between management holdings and company actions as well as the effect of stock ownership by outside investors. Section 7 concludes the paper.

2. Theoretical prediction on the relation between leverage and response to distress

Various theoretical models have explored the relation between a firm’s capital structure ~ characterized by its debt-to-equity ratio and managerial holdings _ and its actions. This section summarizes some of the theoretical predictions and the empirical findings as applied to poorly performing firms.

Poor performance and a decline in value often require the firm to respond operationally. Operational responses include: (1) changing the asset structure by selling assets, divesting divisions, and discontinuing unprofitable operations, (2) changing the size and scope of operations by consolidating production facilities and laying off employees, and (3) changing top management. Jensen’s (1989) argument implies a positive relation between leverage and operational actions by poorly-performing firms.

A firm’s operational actions can be further classified according to whether or not they generate cash. There are subtle differences between models in their predictions about the type of actions that high leverage induces. Some models use the free cash flow argument to predict a positive relation between leverage and actions that generate short-term cash flow [Jensen (1986) and Stulz (1990)]. These models imply that debt-service obligations will induce poorly-performing firms to sell assets and divest operations. Other models predict that default will cause a positive relation between leverage and any operational action that

increases firm value [Harris and Raviv (1990) and Ofek (1991)]. These models imply that debtholders monitor the firm after default and induce it to take value-maximizing actions regardless of the effect on short-term cash flow. In general, high leverage increases the probability of discontinuing unprofitable operations, laying off employees, and replacing inefficient management.

The effect of leverage on the probability of debt restructuring and bankruptcy is positive almost by definition. Only firms with liabilities that they cannot (or will not) pay must restructure their debt or file for bankruptcy protection.’ However, capital structure may affect the choice between debt restructuring and bankruptcy.

Jensen (1989) argues that highly-leveraged firms are more likely to restructure their debt as firm value falls. For these firms, financial restructuring is preferable to a costly bankruptcy, especially when the going-concern value of the firm is vastly greater than its liquidation value. Bankruptcy is, therefore, more likely when firms become leveraged as a result of continuing losses and a steep decline in firm value that brings going-concern value closer to liquidation value. Jensen’s argument would predict a smaller fraction of bankruptcy filings and a higher fraction of debt restructuring in a sample of financially-distressed firms that experience a shorter period of poor performance.

Firms also respond to poor performance with dividend cuts. A financially- distressed firm is more likely than a nondistressed firm to cut its dividend to preserve internal funds for regular operations. In addition, debt covenants may restrict dividend payments, as noted by Smith and Warner (1979). DeAngelo and DeAngelo (1990) report that 67% of firms that suffer a decline in operating performance and profitability over a period of at least three years cut dividends in the first year of distress. This paper extends their work by testing whether a firm’s level of debt before the performance decline is related to its dividend decision.

3. The data

3.1. Sumple colkection

In order to test the relation between leverage and the response to a rapid decline in value, the sample must contain firms with a year of average or superior performance (base year) immediately preceding a year of extreme poor performance (distress year). The sampling procedure first identifies all firms whose stock is publicly traded on either the New York Stock Exchange, the

‘Leverage, defined as the ratio of total debt to total capital, is not always the best measure of a firm’s liabilities. A.H. Robins is an example of a firm with low leverage that filed for bankruptcy protection as a result of pending lawsuits that created contingent liabilities.

E. Ofek, Prrdistresr leoerage and.firm response to poor petfhnance 7

American Stock Exchange, or NASDAQ during the base and distress years. The sample covers the period 198331987, with 1983-1986 as the base year and 198441987 as the distress year. The choice of sample period results from data availability constraints; Lexis provides on-line financial statements starting in 1984, and 1987 was the last year for which financial statements were available when the sample was collected. (Financial statements are required to analyze a firm’s response to distress.) Returns are available on the Center for Research in Security Prices (CRSP) tape. To insure data availability, the market value of each firm’s equity on the last trading day of the base year must be at least $30 million. Finally, financial firms and public utilities are excluded from the sample.

Only firms that experience poor performance and a rapid decline in value are selected for the sample. Such a decline is defined as an annual stock return in the bottom 10% of all returns in the market after having been in the top 67% the year before. Each firm in the sample thus has a minimum drop of 23% in the ranking of all stock returns and a maximum drop of 100%. There are 378 firms that meet the data requirements. However, public data is insufficient for 20 of the firms, leaving a final sample of 358 firms. The total number of firms selected for each year ranges between 73 and 100.

Selecting firms with one year of poor performance ensures identification of responses to short-term distress, making it possible to evaluate the speed with which a firm reacts to a decline in value. Using a short period of poor performance also makes possible a comparison with responses to long-term distress, as documented by, among others, Gilson (1989) and Gilson, John, and Lange (1990). Moreover, using a shorter period of poor performance avoids bringing into the sample firms that became highly leveraged and financially distressed as a result of continuing poor performance to which they did not react.

Panel A of table 1 provides information about the market return and the stock returns of the sample firms. The total market return is positive for each of the five years sampled. On three occasions the market return is higher in the base year than in the distress year, and in one instance the distress-year return is higher than the base-year return. But since the sample is based on a firm’s rank in the market, the differences in market returns between the base and distress years are not likely to introduce bias. The median and average base-year returns for the sample firms are always positive. With the exception of the median return in 1984, the base year returns are higher than the average market return. The median return of the full sample in the base year is 38.2%. The sample’s base-year return indicates that the firms selected do not suffer from poor performance during that year.

The sample firms’ stock returns in the distress year indicate very poor performance. The median and average return in any of the four years is significantly negative and substantially lower than the average market return. In each year, both the median and the average return are at least 60% below the

8 E. Qfek, Predistress leverage and firm response to poor performance

Table 1

Description of the sample of 358 firms that are in the top 67% of the market in the base year and the bottom 10% in the distress year. The sample period is 198331987.

(A) Samplefirms’ stock returns in the base and distress years”

Total sample Average market return”

Base year 1983, distress year 1984 Market return




Returns in the base year Returns in the distress year

Low High Ave Med Low High Ave Med

- 15.3 1,940.o 71.2 38.2 19.0

15.9 1,940.o 104.7 55.4 23.1

- 15.3 295.7 19.9 3.3 5.1

15.9 628.0 89.8 53.4 30.9

0.8 736.4 59.6 30.9 16.9

- 97.9 - 21.0 - 54.8 - 54.4 13.9

- 85.3 - 49.3 - 60.0 - 58.3 5.1

- 94.4 - 21.0 - 38.5 - 36.1 30.9

- 96.3 - 39.2 - 53.1 - 50.8 16.9

- 91.9 - 49.7 - 62.4 - 62.3 2.7

“Characteristics of the sample stock returns (in percent) in the year of normal performance (base year) and the immediately following year of poor performance (distress year).

bAverage market return is the average of the value-weighted market return in the base year or the distress year.

(B) Distribution of actions taken in the period of poor performance

Number of Action taken in the distress year occurrences

Rate of occurrencesa

Total in sample

Operational actions Operational actionb Employee layoffs’ Asset restructuringd Management replacement’ Asset restructuring with cash inflow’ Asset restructuring with no cash inflowg

Financial actions Dividend cuth Debt restructuring’ Bankruptcy filings

358 100%

189 53% 14 28% 82 23% 76 21% 53 15% 44 12%

37 38

4

47% 11%

1%

“The rate of occurrence is the ratio between the number of occurrences of a certain action and the number of observations in that sample. The number of observations in each sample is 358 except for employee layoffs (264) and dividend cut (78).

“Operational actions comprise: asset restructuring, employee layoffs, and management replacement. ‘Employee layoffs occur if the firm lays off at least 10% of its employees, or if the WSJI reports layoffs. dAsset restructuring occurs if the firm undertakes divestitures, sells assets, or discontinues operations. ‘Management replacement occurs if the firm replaces one of its top three managers. ‘Asset restructuring with cash inflow occurs if the firm divests or sells assets in the distress year. *Asset restructuring with no cash inflow occurs if the firm discontinues operations or consolidates operations

in the distress year. “Dividend cuts occur if the firm reduces its annual regular dividend in the distress year compared with the

base year. ‘Debt restructuring occurs if the firm restructures its debt following default or to avoid default.

E. Ofek. Predisrress leverage andJirm response to poor performance 9

Table 1 (continued)

(C) Sample’s capital structure characteristics and profitability

Mean Med Low High

Capital structure characteristic Market value of equity in base year (millions) Leverage in base year” Leverage at the end of distress year Current ratio in base yea? Current ratio in distress year Ratio of long-term to total debt’ Ratio of public debt to total debtd Equity holdings - Management Equity holdings - Outsider

Changes in profitability measurese Change in EBITD’ Change in operating marginsg Change in EBITD/total assets”

$211 $85 0.20 0.12 0.35 0.31 3.80 2.31 3.35 2.07 0.71 0.80 0.12 0.00 0.26 0.22 NA 0.06

- 0.99’ - 0.96’ - 1.00’

- 0.33’ - 0.39’ - 0.41’

$30 $6,428 0.00 0.87 0.00 0.98 0.2 1 130.22 0.05 104.34 0.00 1.00 0.00 1.00 0.00 0.88 NA 0.93

- 109.97 14.75 - 59.11 7.92 - 94.25 28.53

“Leverage is defined as the ratio of the book value of debt to the sum of the book value of debt and the market value of equity.

bCurrent ratio is defined as the ratio of current assets to current liabilities. ‘Ratio of book value of debt with maturity of more than one year to total book value of debt. dRatio of book value of public debt to total book value of debt. ‘The change in profitability variable X is calculated as (X, - X,)/Absolute value (X0). ‘EBITD is earnings before interest, taxes, and depreciation. gOperating margins equal the ratio of EBITD in year t to sales in that year. “EBITD/Assets = ratio of EBITD to total book value of assets at year end. ‘Significant at the 5% level. ‘Significant at the 1% level.

(D) Frequency of large nonmanagerial stockholder types in sample firmsa

Number of firms where such stockholder

Identity of nonmanagerial stockholder Exists Is largest investor

Individual 39 29 Corporation 60 56 Corporation friendly to management 14 16 Bank or insurance company 40 29 Mutual fund investment manager 72 56 Employee trust 11 11 Group of investors 14 12

“The distribution of nonmanagerial stockholders with at least 5% of the firm’s common stock by type. The frequency is provided for firms in which a certain type of nonmanagerial stockholder exists and is the largest nonmanagerial investor.

market return. The median return for the full sample in the distress year is - 54%. It appears that the sample successfully captures firms that performed

well in the base year and poorly in the next year. Another indication that sample firms’ performance declined substantially is

provided in table 1, panel C, which reports a large drop in the sample’s cash flow

10 E. Qfek. Predistress leverage andfirm response to poor performunce

performance during the distress year. There is a - 39% median change in earnings before interest, taxes, and depreciation (EBITD), standardized by sales, from the base year to the distress year. A decline is also observed for the median change in EBITD ( - 33%) and the median change in EBITD standardized with the book value of assets ( - 41%). All of these changes are significant at the 1% level.

3.2. Firm actions

The behavior of the sample firms during the year of poor performance is documented with information on a wide array of actions, including:

(1) Asset restructuring; defined as the divestiture or spin off of a subsidiary or a division, the sale of a substantial part of the firm’s operating or nonoperat- ing assets, the discontinuance of operations in a division, line of business, or geographic region, or the restructuring of operations through closing or consolidating of plants or regional headquarters.

(2) Employee layoffs; defined to occur if the Wall Street Journal Index (WSJI) reports employee layoffs for that year or if the firm lays off at least 10% of its work force during the distress year (without engaging in a divestiture, a substantial sale of assets, or an acquisition). For firms whose fiscal year ends in a month other than December, the 10% reduction in the work force is measured over the two years between the financial statement for the fiscal year ending in the base year and the financial statement for the fiscal year ending in the year following the distress year.

(3) Top management replacement; defined to occur if the firm changes its top management in the distress year, either by appointing a new manager to one of the top three positions (chairman of the board, president, or chief execu- tive officer) or by releasing an incumbent manager.

(4) Debt restructuring; defined to occur if a firm reaches an agreement with its creditors to restructure its debt. A new debt agreement is classified as a debt restructuring only if it comes after either a violation of debt covenants or a default by the firm, or if the firm describes it as a debt restructuring or debt reorganization in its financial statements. This definition underestimates the frequency of debt restructuring agreements because it does not include new debt agreements that are restructuring, in effect, but are not indentified as such by the firm. For example, a new agreement that is signed to prevent a firm from defaulting will often not be included as a debt restructuring because of identification problems.

(5) Bankruptcy filing; defined to occur for firms that file for bankruptcy under Chapter 11 or Chapter 7 of the bankruptcy code.

(6) Dioidend changes; defined as changes in the total annual regular dividend from the base year to the distress year.

The first three actions are operational actions. They affect the investment decisions of the firm as well as its operational strategy. The last three actions are financial actions that affect both the value of various claims and the cash flow distribution to the firm’s owners. There is, of course, an interaction: financial actions such as bankruptcy filings affect the firm’s operations.

Information is also collected about actions such as acquisitions and attempts to change control. Data on asset restructuring, debt restructuring, and bankruptcy filings are collected from the firm’s financial statements. In 13 cases, the firm’s financial statements for the distress year are not available and information is collected from the WSJI. The information about management changes comes primarily from the WSJI, and is verified in the listings of Standard & Poor’s Directory of Corporations. The number of employees in each firm and the changes in dividend payout are retrieved from the Industrial Compustat and CRSP data bases, respectively. Information about covenant restrictions on the sample firms for the base year are retrieved from Moody’s Industrial, Transpor- tation, and Utility manuals.

Operational response comes quickly. An operational action (asset restructuring, management replacement, or employee layoffs) is taken by 53% of the firms in the sample during the distress year. Panel B of table 1 summarizes the sample firms’ actions during the distress year. The most common operational action is asset restructuring, reported by 82 firms (23% of the sample). Within this group, 23 firms announce a divestiture, 31 announce a major sale of assets, 24 discontinue operations, and 23 restructure their operations. Changes in top management are made by 76 firms (21% of the sample), a significantly higher rate than the 11.5% annual management turnover in a random sample [Warner, Watts, and Wruck (1988)], but lower than the 30% annual turnover rate in a sample of firms performing poorly for three years [Gilson (1989)]. Seventy-four firms lay off a substantial part of their work force (28% of the sample, excluding firms making acquisitions, divestitures, or asset sales, for which layoffs are not measured).

During the distress year, there are attempts to change control in 11 firms. Ten firms receive either a leveraged buyout bid or a takeover bid, but only three of the bids are successful. The sampling method, which requires low returns in the distress year, tends to underestimate the frequency of these events for poorly- performing firms and to overestimate the ratio between unsuccessful and successful offers. [See Clark and Ofek (1992) for further evidence on acquisitions of distressed firms.]

Only four firms (1% of the sample) filed for bankruptcy protection during the distress year. This number is small, considering that the sample firms suffer a large decline in stock prices and that 42 firms are financially distressed (a firm

12 E. Ofek. Predisrress leaerage and firm response to poor performance

is financially distressed if it defaults or files for bankruptcy protection, or restructures its debt). In contrast, Gilson, John, and Lang (1990), who use a similar definition of financial distress, report that 53% of financially-distressed firms file for bankruptcy protection. The difference may be attributable to differences in the length of the distress period. Firms that have been distressed for only a short time are more successful in negotiating agreements with debtholders which enable them to continue operating outside bankruptcy. Many of these agreements, however, provide only short-term relief from creditors, either by refinancing defaulted debt with short-term debt or by waiving covenant restrictions temporarily. If poor performance persists, financial distress continues and the likelihood of bankruptcy increases, as in Gilson, John, and Lang.

Of the sample firms that pay dividends in the base year, 47% cut their annual dividend during the distress year (the average reduction is 48%) and 28% increase their dividend. The proportion of cuts is significantly higher than in a random sample, reported by Aharoni and Swary (1980) to be 2%.* DeAngelo and DeAngelo (1990) report that 67% of firms that perform poorly for at least three years cut their dividends during the first year of poor performance, while 20% increase their dividends. Their higher rate of dividend reduction may be a result of managers in their sample anticipating longer-term distress and reacting with a dividend cut in the first year.

3.3. The capital structure characteristics qf the sample

For each firm in the sample, information on debt, liquidity, and other financial characteristics is retrieved from the Industrial Compustat tape. The information for the base year is taken from financial statements for the fiscal year ending on or before December 31 of the base year. The financial information for the distress year is taken from financial statements for the fiscal year ending on or after December 31 of the distress year.

Panel C of table 1 summarizes the financial data for the sample firms. The median equity value on the last trading day of the base year (predistress value) is $85 million, and the average value is $211 million. The smallest firm in the sample has an equity value of $30 million, and the largest equity value of the firm is $6,427 million.

For this study, leverage is defined as the total book value of the firm’s long- and short-term debt, divided by the sum of the market value of the equity and the book value of the debt. The average leverage in the base year is 0.20,

‘Aharoni and Swary use a sample of 6,338 quarters of dividend announcements, whereas I use annual changes in dividend. A 2% rate of quarterly dividend cuts implies that the upper bound on the annualized dividend-cut rate in their sample is 8%, which is still significantly lower than the rate in my sample.

E. Ofek, Predistress leverage and,firm response to poor performance 13

increasing to 0.35 in the distress year (the median increases from 0.12 to 0.31).3 When leverage is defined as the ratio of the book value of debt to the book values of debt plus equity, the average leverage is 0.33 in the base year and 0.38 in the distress year. The distress-year leverage is biased downward, however, because 13 observations are missing. Most of these observations are financially- distressed firms for which the relevant data are not available. Leverage varies widely among firms in the sample, ranging between zero and 0.87 in the base year and between zero and 0.98 in the distress year.

The current ratio (current assets divided by current liabilities) is used as a measure of liquidity. The median level of the current ratio in the sample drops from 3.8 in the base year to 3.4 in the distress year. This measure ranges between 0.2 and 130 in the base year, which implies a large variation in liquidity across the sample firms.

Information about the distribution of each firm’s stockholding is collected from proxy statements, usually from the base year. The data taken from the proxy statements include management holdings as well as the holdings and identities of nonmanagement investors who own at least 5% of the firm’s equity (large outside stockholders). The median level of management holdings in the sample is 22% (the average is 26%) and the median ownership by the largest outside stockholder is 6%. Panel D of table 1 shows the frequency distribution of the different types of large outside stockholders. In 72 firms (20% of the sample), a mutual fund or other investment management firm holds more than 5% of the equity, and in 56 of these firms the fund is also the largest outside investor. Large blocks of shares (more than So/,) are held by another corporation in 60 firms, by a bank or an insurance company in 40 firms, and by an individual in 39 firms.

The capital-structure characteristics of the sample in the base year are generally similar to the characteristics reported in other samples, but management holdings are larger than reported in most studies [Jensen and Warner (1988)]. However, this sample includes a larger fraction of smaller firms, which tend to have larger managerial holdings.

4. Empirical results

The primary focus is to test whether a firm’s predistress capital structure affects the actions likely to be taken in periods of poor performance. Logit regressions are used to estimate the relation between capital structure and the probability that various actions will occur. (The results in this paper continue to

3The results do not change if the book value of equity is used instead of the market value in calculating leverage, or if firm value (the denominator in the leverage ratio) is defined as the market value of common and preferred stock and the book value of debt.

14 E. Qfek, Predisrress leverage and firm response to poor pet@mance

hold if probit analysis is used instead.) The dependent variables are binary variables that take the value of one if a certain action occurs and zero otherwise. The hypotheses tested in this section predict a positive relation between predistress leverage and the following: any operational action, asset restructuring, employee layoffs, management replacement, debt restructuring, bankruptcy filing, and dividend cuts.

4.1. Results for the fill sample

Table 2 reports the relation between the probabilities of the various firm responses and the following capital structure variables: leverage at the end of the base year, the firm’s current ratio, managerial holdings in the firm, and outside holdings. The log of the firm’s equity at the end of the base year and the firm’s stock return during the distress year are used as control variables.

The first regression measures the effect of the capital structure variables on the probability that any operational action will be taken. The most striking result is the positive and highly signijcant relation between thefirm’s leverage in the base

year and the probability that an operational action will be taken in the distress

year. Apparently, firms with more debt in their capital structure when times are good are more likely to respond operationally when bad times arrive. This result is consistent with Jensen’s (1989) prediction that firms with high leverage are more likely to respond quickly to a decline in value.

The remaining regressions in table 2 report the relation between individual operational or financial actions and capital structure. Among specific operational actions, asset restructuring and employee 1ayofSs are both positively related

to higher leverage in the base year (t = 5.01 and t = 2.55, respectively). Highly- leveraged firms that experience short-term distress are more likely to react with asset sales, discontinued operations, and employee layoffs. The only operational action not significantly affected by leverage is management replacement in contrast to Gilson (1989) who finds that higher leverage increases the probability of management replacement for firms in financial distress. The difference in results may be attributable to differences in the duration of distress: Gilson’s firms have been distressed for at least three years whereas in this paper, firms are in their first year of distress.

The lack of a short-term relation between predistress leverage and management replacement may lie in a slow transfer of control of the firm to the debtholders. Apparently, debtholders can more easily exert pressure on management to change the firm’s asset structure or to reduce costs through employee layoffs than to replace management. A longer period of distress is required before debtholders gain enough control to force’ a management change. The results in this paper, combined with Gilson’s findings, emphasize the role of long-term distress in debtholder-initiated management turnover.

E. Qfek. Predistress leueruge andjirm resportse to poor pet-formance 15

Of the financial actions taken by the firm, debt restructuring and bankruptcy filing are more likely in highly-leveraged firms than in those with less debt. Higher leverage creates a tradeoff between a quicker response to distress and the possibility of a costly bankruptcy. As previously discussed, however, the bankruptcy rate in the sample remains low (1%).

Leverage is also positively and signljicantly related to the probability of dividend cuts. Firms with more debt are more likely to cut their dividends as firm performance deteriorates. The cut in dividends may be caused by financial covenants that restrict dividend payments, or by the cash-flow shortage and financial distress that debt service obligations create.

The use of a binary variable that takes the value of one if the firm cuts its dividend and zero otherwise results in a loss of some of the information in the firm’s dividend decision; dividend increases are ignored, and the size of the dividend change is not considered. To capture this information, a variable is introduced that equals the dollar change in the annual regular dividend from the base year to the distress year, standardized by the stock price at the end of the base year. An ordinary-least-squares regression measures the relation between the predistress capital structure and the dividend decision in the distress year. The outcome of this regression shows that higher predistress leverage is associated with larger dividend reductions in poorly-performing firms (significant at the 5% level).

Higher managerial holdings reduce the probability of management turnover and have a negative but insignificant effect on other operational actions. Weisbach (1988) finds that the CEO’s share of the equity has a negative but insignificant effect on the probability of a CEO change. The presence of a large outside stockholder has a negative but insignificant relation to the probability of an operational action, and a mixed effect on the various individual actions a firm might take.

Except in the case of management replacement, greater liquidity in the base year decreases the probability that an action will be taken. Only the relation between liquidity and asset restructuring, however, is statistically significant. This result suggests that the actions are not taken as a result of liquidity problems. Alternatively, the current ratio at the end of the base year may fail to capture liquidity problems during the distress year. When logit regressions are run using the current ratio at the end of the distress year as an explanatory variable, liquidity has more effect on some of the actions: liquidity is negatively and significantly related (at the 5% level) to the probability of asset and debt restructuring, and negatively but not significantly related to the probability of employee layoffs and management turnover. However, since liquidity at the end of the distress year is also related to the firm’s cash flow, it is hard to determine whether the relation between the current ratio and the various actions stems from real liquidity problems or is the result of a spurious correlation generated by the negative relation between the firm’s performance and the occurrence of

Tab

le

2

Cap

ital

stru

ctur

e an

d re

spon

ses

to

dist

ress

fo

r a

sam

ple

of 3

58 f

irm

s th

at

are

in t

he

top

67%

of

the

m

arke

t in

the

ba

se

year

an

d th

e bo

ttom

10

%

in t

he

dist

ress

ye

ar.

The

sa

mpl

e pe

riod

is

19

83-1

987.

” _

R-s

q L

ever

age

Liq

uidi

ty

Man

ager

ial

Out

side

Fi

rm

Res

pons

e L

Rb

Con

stan

t ba

se

year

’ ba

se

year

d ho

ldin

gs’

hold

ings

’ si

z@

Ret

urn”

Ope

ratio

nal

actio

ns’

0.07

5 -

0.94

3 2.

514q

-

0.01

3 -

0.94

3 -

1.20

4 0.

285’

-

0.71

1 36

.44

(1.3

5)

(4.3

5)

(0.8

3)

(1.4

9)

(1.6

3)

(2.4

2)

(0.9

2)

Ass

et

rest

ruct

urin

g’

0.16

5 -

3.50

54

3.29

54

- 0.

180”

-

0.47

0 -

1.58

0 0.

305’

1.

757”

62

.4q

(3.8

5)

(5.0

1)

(1.7

3)

(0.5

9)

(1.6

1)

(2.2

0)

(1.7

9)

Em

ploy

ee

layo

ff?

0.06

I

- 1.

791’

1.

938’

-

0.04

2 -

1.14

6 -

1.37

0 0.

153

0.82

8 18

.5’

(1.9

8)

(2.5

5)

(0.9

0)

(1.3

5)

(1.3

1)

(1.0

4)

(0.8

3)

Man

agem

ent

turn

over

’ 0.

037

- I.

105

- 0.

346

0.00

3 -

1.73

3’

0.91

7 0.

164

- 1.

392

13.4

(1

.35)

(0

.55)

(0

.21)

(2

.06)

(1

.17)

(1

.26)

(1

.48)

Deb

t re

stru

ctur

ing”

0.

183

- 1.

303

4.03

3q

- 0.

191

- 0.

310

- 0.

024

- 0.

138

- 1.

859

43.2

q (1

.05)

(5

.02)

(1

.21)

(0

.29)

(0

.02)

(0

.68)

(1

.36)

Ban

krup

tcy

filin

g”

0.23

9 -

5.69

9”

4.86

5’

- 0.

225

2.03

4 2.

827

0.23

8 ~

5.27

7 10

.4

(1.7

3)

(2.2

7)

(0.4

8)

(0.7

3)

(0.9

4)

(0.4

7)

(1.4

0)

Div

iden

d cu

t0

0.10

5 -

4.73

09

1.48

5’

- 0.

184

- 0.

691

0.79

3 0.

340’

2.

303”

24

.8“

(3.9

9)

(1.9

1)

(1.2

8)

(0.6

3)

(0.7

9)

(2.0

4)

(1.8

8)

Div

iden

d yi

eld

chan

geP

0.03

7 0.

004

- 0.

003’

-

0.00

0 -

0.00

1 -

0.00

2 -

0.00

1’

- 0.

002

(2.2

5)

(2.3

4)

(0.0

1)

(0.8

1)

(0.8

5)

(1.9

8)

(1.2

7)

“Coe

ffic

ient

s of

log

istic

an

d or

dina

ry-l

east

-squ

ares

re

gres

sion

s es

timat

ing

the

rela

tion

betw

een

the

prob

abili

ty

of v

ario

us

actio

ns

in a

per

iod

of d

istr

ess

and

capi

tal

stru

ctur

e ch

arac

teri

stic

s in

the

pr

evio

us

year

(r

-sta

tistic

s in

par

enth

eses

).

All

the

regr

essi

ons

are

logi

t re

gres

sion

s,

exce

pt

for

the

divi

dend

yi

eld

chan

ges

regr

essi

on

whi

ch

is O

LS.

T

he

num

ber

of o

bser

vatio

ns

in e

ach

regr

essi

on

is b

etw

een

348

and

352,

with

th

e ex

cept

ion

of e

mpl

oyee

la

yoff

s w

ith

255

obse

rvat

ions

. ‘R

-sq

is t

he

likel

ihoo

d ra

tio

inde

x fo

r th

e lo

git

regr

essi

ons.

L

R

is t

he

likel

ihoo

d ra

tio

(dis

trib

uted

x2

), w

hich

is

the

ra

tio

of t

he

log

likel

ihoo

ds

of t

he

regr

essi

on

and

a re

gres

sion

w

ith

only

a

cons

tant

, ‘L

ever

age

is t

he

ratio

of

boo

k va

lue

of d

ebt

to

the

sum

of

boo

k va

lue

of d

ebt

and

mar

ket

valu

e of

equ

ity.

dLiq

uidi

ty

base

ye

ar

is t

he

curr

ent

ratio

of

the

fi

rm

in t

he

base

ye

ar.

‘Man

ager

ial

hold

ings

is

the

pe

rcen

tage

of

equ

ity

owne

d by

to

p m

anag

ers

and

dire

ctor

s,

from

th

e pr

oxy

stat

emen

t. ‘O

utsi

de

hold

ings

is

the

pe

rcen

tage

of

equ

ity

owne

d by

th

e la

rges

t no

nman

ager

ial

stoc

khol

der.

gFir

m

sire

is

the

lo

g of

the

fi

rm’s

eq

uity

va

lue

in t

he

mar

ket

on

the

last

tr

adin

g da

y of

the

ba

se

year

. “R

etur

n is

the

an

nual

st

ock

retu

rn

duri

ng

the

dist

ress

ye

ar.

‘Ope

ratio

nal

actio

ns

equa

ls

I if

ass

et

rest

ruct

urin

g,

empl

oyee

la

yoff

s,

or

man

agem

ent

repl

acem

ent

occu

r. ‘A

sset

re

stru

ctur

ing

equa

ls

I if

the

fi

rm

unde

rtak

es

dive

stitu

res,

se

lls

asse

ts,

or

disc

ontin

ues

oper

atio

ns.

‘Em

ploy

ee

layo

ffs

equa

ls

I if

the

fi

rm

lays

of

f at

le

ast

10%

of

its

em

ploy

ees

or

if t

he

WSJ

I re

port

s la

yoff

s.

‘Man

agem

ent

repl

acem

ent

equa

ls

1 if

the

fi

rm

repl

aces

on

e of

its

to

p th

ree

man

ager

s.

“Deb

t re

stru

ctur

ing

equa

ls

I if

the

fi

rm

rest

ruct

ures

its

deb

t. “B

ankr

uptc

y fi

ling

equa

ls

1 if

the

fi

rm

file

s fo

r ba

nkru

ptcy

pr

otec

tion

in t

he

dist

ress

ye

ar.

“Div

iden

d cu

t eq

uals

1

if t

he

firm

cu

ts

its a

nnua

l re

gula

r di

vide

nd

in

the

dist

ress

ye

ar.

PDiv

iden

d yi

eld

chan

ge

is e

qual

to

the

dol

lar

chan

ge

in t

he a

nnua

l re

gula

r di

vide

nd

betw

een

the

base

an

d di

stre

ss

year

s st

anda

rdiz

ed

by t

he s

tock

pr

ice

at

the

end

of t

he

base

ye

ar.

4Sig

nitic

ant

at

the

1%

leve

l. Si

gnif

ican

t at

th

e 5%

le

vel.

‘Sig

nifi

cant

at

th

e 10

%

leve

l.

18 E. tlfek, Predistress lewrage andJirm response to poor performance

these actions, Using flow variables such as the interest coverage ratio in the distress year or the ratio of funds from operations to sales to measure liquidity does not change the results; none of the actions is significantly related to these flow measures of liquidity. (Results with the distress-year liquidity variables are not reported in the tables.)

Firm size, measured as the log of equity value at the end of the base year, is positively and significantly related to the probability of operational actions and to the specific step of asset restructuring. This relation may reflect a bias against small firms in the availability of public information. It may also be influenced by the fact that larger firms often operate in several lines of business and different geographic regions, so that they may be better able to restructure at the onset of distress, whereas the possibility of asset restructuring in smaller firms is more limited.

The logit regressions are repeated with additional industry dummy variables that equal one if a firm has a certain three-digit SIC code and zero otherwise; to control for the possibility that industry effects cause a spurious correlation between the actions tested and the capital-structure characteristics of the firm. All the relations described in table 2 continue to hold after controlling for industry effects.

4.2. The t$ect of industry distress

A sample of firms with poor performance relative to their industry (industry distress) is also collected, using a procedure analogous to that used to collect the sample firms with poor performance relative to the market (market distress). This new sample is used to test the relation between capital structure and a firm’s response to industry distress, and to contrast that relation with the response to market distress. Only firms with returns available on CRSP and with equity values of at least $30 million at the end of the base year are considered and, as before, financial firms and public utilities are excluded. A firm is defined as being in industry distress if its stock return falls to the bottom 10% in its industry after having been in the top 67%. Firms are considered to be in the same industry if they have the same three-digit SIC code, and there are at least 20 firms in the initial sample with the same code.

The industry-distress sample is collected over the same period as the market- distress sample. Of the 144 firms in industry distress, 88 are also in the market- distress sample. To sharpen the contrast, these firms are excluded from the industry-distress analysis. The final sample consists of the 56 firms that suffer from industry distress but not market distress. Information about base-year capital structure and distress-year actions is collected for these firms.

The stock return in the distress year of firms in industry distress only is significantly higher than that of firms in market distress; for example, the difference in the median return for these samples is 17% (return characteristics

E. Qfek. Predistress leceruge and firm response to poor performance 19

of firms with industry distress are not reported in the tables). This pattern arises because firms performing poorly in relation to an industry that has unexpectedly good performance enter the sample even though their market value does not decline substantially.

Panel A of table 3 reports the results from the logit regressions for the sample of firms with market distress only. Here, the effect of leverage on the probability of an operational action is positive and significant (at the 1% level). Leverage also has a positive and significant effect on the probabilities of asset restructuring and employee layoffs. The effects of management holdings and holdings by outside investors are similar to those for the full sample, although in several cases they lose their significance.

The results for firms with only industry distress are different. For these firms, panel B shows that leverage is not related to the probability of an operational action, nor is it related to the probabilities of the individual actions. The effects of other variables, such as liquidity, management holdings, and outside holdings, are generally similar for the two groups.

The industry-distress and market-distress samples also differ in the relation between the distress-year stock returns and the probability of operational actions. For firms in industry distress only, higher returns are significantly associated with lower probabilities of any operational action as well as asset restructuring, employee layoffs, and management turnover specifically. For firms in market distress, the relation is negative in most cases but has smaller coefficients and is not statistically significant. This result implies that the level of market distress influences whether firms in industry distress are disciplined. Yet for firms already in market distress, the gradations in distress are not related to any of the actions they take while in distress.

The main difference in the effect of capital structure on industry and market distress is the absence of a significant relation between leverage and operational actions among firms in industry distress only. A possible explanation is that the value of the debt declines if firm value declines, but not if the firm’s performance relative to its industry is poor, so that debtholders exert significant pressure only on firms in market distress. The covenants held by the sample firms support this explanation. Violations of these covenants are contingent on absolute performance measures but not on relative measures.

The importance of some form of market distress in triggering the effect of leverage is evident when the industry-distress sample is expanded to include the 88 firms in both industry and market distress. The effect of leverage on the probability of operational actions becomes positive and statistically significant. A similar result is found in a sample of firms in industry distress and with negative EBITD in the year of distress (these results are not reported).

The sample selection criteria could also explain the lack of a relation between leverage and operational actions in response to industry distress. Industry distress is judged on the basis of the three-digit SIC code in the CRSP files,

Tab

le

3

Com

pari

son

of r

espo

nses

to

poo

r pe

rfor

man

ce

rela

tive

to t

he

mar

ket

(mar

ket

dist

ress

) an

d re

lativ

e to

the

in

dust

ry

(ind

ustr

y di

stre

ss)

for

a sa

mpl

e of

358

fi

rms

that

ar

c in

the

to

p 67

%

of t

he

mar

ket

(ind

ustr

y)

in

the

base

ye

ar

and

the

botto

m

10%

in

the

di

stre

ss

year

. T

he

sam

ple

peri

od

is

1983

-198

7.”

Res

pons

es

Ope

ratio

nal

actio

ns

Ass

et

rest

ruct

urin

g

Em

ploy

ee

layo

ffs

Man

agem

ent

turn

over

R-s

q L

R

Con

stan

t L

ever

age

base

ye

ar

Liq

uidi

ty

base

ye

ar

Man

ager

ial

hold

ings

O

utsi

de

hold

ings

0.08

-

1.82

9’

2.81

1b

~ 0.

004

- 0.

233

29.6

b (2

.19)

(4

.24)

(0

.23)

(0

.32)

0.20

-

4.22

gb

3.80

9”

- 0.

213d

0.

204

58.g

b (3

.93)

(4

.82)

(1

.70)

(0

.23)

0.08

-

1.89

4d

2.28

5’

- 0.

033

- 1.

633

17.2

b (1

.70)

(2

.53)

(0

.75)

(1

.51)

0.05

-

2.44

1’

- 0.

249

0.01

7 -

0.66

3 13

.0’

(2.4

6)

(0.3

3)

(0.9

4)

(0.6

9)

(A)

Cap

ital

stru

ctur

e an

d re

spon

ses

to m

arke

t di

stre

ss

- 0.

884

(1.0

0)

- 2.

422’

(2

.05)

- 1.

864

(1.4

4)

2.32

3’

(2.4

8)

Ope

ratio

nal

actio

ns

Ass

et

rest

ruct

urin

g

Em

ploy

ee

layo

ffs

Man

agem

ent

turn

over

0.24

0.

08 1

1.

648

18.8

b (0

.03)

(0

.89)

0.16

-

1.24

9 1.

688

9.8

(0.5

0)

(0.9

7)

0.30

8.

093d

-

2.98

5 12

.0*

(1.7

0)

(1.1

1)

0.20

-

2.33

9 0.

424

12.2

d (0

.88)

(0

.22)

(B)

Cap

ital

stru

ctur

e an

d re

spon

ses

to i

ndus

try

dist

ress

- 0.

242

(0.9

1)

- 0.

301

(1.0

3)

- 0.

155

(0.4

7)

~ 0.

179

(0.6

4)

- 0.

159

(0.0

9)

0.52

8 (0

.28)

- 6.

088

(1.3

8)

~ 1.

691

(0.8

0)

- 2.

498

(0.9

9)

- 1.

955

(0.6

3)

- 4.

450

(1.2

3)

- 3.

683

(0.9

8)

Firm

si

ze

Ret

urn

dist

ress

ye

ar

0.45

9b

(3.2

9)

0.50

gb

(3.1

2)

0.27

2 (1

.52)

0.27

2*

(1.7

4)

- 1.

391

(1.5

1)

1.00

0 (0

.87)

- 0.

230

(0.1

9)

- 0.

927

(0.8

2)

0.61

0d

(1.8

4)

0.44

5 (1

.32)

- 0.

403

(0.8

4)

0.73

3’

(2.0

6)

- 4.

690b

(2

.88)

- 3.

170d

(1

.93)

- 9.

050’

(2

.41)

- 2.

68gd

(1

.67)

“Coe

ffic

ient

s of

log

istic

re

gres

sion

s es

timat

ing

the

rela

tion

betw

een

the

prob

abili

ty

of a

ctio

n in

the

per

iod

of p

oor

perf

orm

ance

an

d th

e ca

pita

l st

ruct

ure

char

acte

rist

ics

of t

he

firm

for

the

gr

oup

of f

irm

s w

ith

only

ab

solu

te

or o

nly

rela

tive

poor

pe

rfor

man

ce

(t-s

tatis

tics

in p

aren

thes

es).

D

efin

ition

s of

all

term

s ar

e gi

ven

in f

ootn

otes

b-

p of

tab

le

2. T

he

num

ber

of o

bser

vatio

ns

in e

ach

regr

essi

on

in p

anel

A

is

betw

een

261

and

264,

exc

ept

for

empl

oyee

la

yoff

s,

with

18

7; t

he

num

ber

of o

bser

vatio

ns

in e

ach

regr

essi

on

in p

anel

B

is

55 o

r 56

, ex

cept

fo

r em

ploy

ee

layo

ffs,

w

ith

40.

“Sig

nifi

cant

at

th

e 1%

le

vel.

‘Sig

nifi

cant

at

th

e 5%

le

vel.

‘?3i

gnif

ican

t at

th

e 10

%

leve

l.

E. Qfek, Predistress leverage andJim response to poor peyformance 21

without considering that some firms may operate in more than one industry. Some firms, if results in their other businesses were bad enough, might end up in the industry-distress sample even if they operated well in their primary industry. These firms are not likely to take actions in the year of distress; their inclusion adds noise to the sample and could cloud the evidence.

Merck, Shleifer, and Vishny (1989) report that top management turnover, which they associate with successful monitoring by the board, is more likely to occur in firms that underperform their industry. They find no more management turnover in troubled industries than in others, however, and interpret the results to mean that the board of directors is an ineffective monitor in troubled industries (firms with market distress only would fall in this category). The findings in this paper indicate that debtholder monitoring may supplement board monitoring: debt disciplines firms in market distress but not firms that underperform their industry.

5. Leverage characteristics and short-term cash flow

This section further investigates the relation between a firm’s actions and the characteristics of its debt, such as maturity and ownership. It also investigates whether the relation between the firm’s operational actions and leverage stems from a need to raise cash to service the debt in the year of distress.

5.1. Debt maturity, debt ownership, and the3rm’s actions

The relation between a firm’s leverage and its operational actions is significantly positive. Total debt, however, is an aggregation of long- and short-term debt as well as public and private debt, each of which may have a different effect on the firm’s actions. The importance of debt ownership is documented by Gilson, John, and Lang (1990), who report that firms with a high ratio of bank debt are more likely to successfully restructure their debt, and by Hoshi, Kashyap, and Scharfstein (1990) who report that, in Japan, financially-distressed firms with a bank affiliation perform better than those with no such affiliation at the onset of distress. Baldwin, Mason, and Hughes (1983) show that high levels of short-term debt forced Massey Ferguson to quickly sell assets, discontinue operations, lay off employees, and negotiate debt restructuring agreements with creditors.

The results of selected regressions that measure the effect on various actions of debt maturity and ownership are reported in table 4. Debt with a maturity of more than one year is considered long-term debt, and debt with a maturity of less than one year is short-term debt. The first regression tests the relation between long- and short-term leverage and the probability of operational actions, and finds a positive and significant result in both cases (although the

Tab

le

4

Deb

t ch

arac

teri

stic

s an

d th

e fi

rm’s

act

ions

fo

r a

sam

ule

of 3

58 f

irm

s th

at

are

in t

he t

oo

67%

of

the

mar

ket

in t

he b

ase

vear

an

d th

e bo

ttom

10

%

in t

he d

istr

ess

year

. T

he

sam

ple

peri

od

is

1983

-198

7.”

Res

pons

es

R-s

q L

iqui

dity

Fi

rm

LR

C

onst

ant

base

ye

ar

size

Ope

ratio

nal

actio

ns

Ope

ratio

nal

actio

ns

Ope

ratio

nal

actio

ns

Deb

t re

stru

ctur

ing

Ope

ratio

nal

actio

ns

Deb

t re

stru

ctur

ing

Man

agem

ent

turn

over

Lev

erag

e M

atur

ity

Ow

ners

hip

Lon

g-te

rm

Shor

t-te

rm

Priv

ate

Publ

ic

base

ye

ar

ratio

b ra

tio’

leve

rage

d le

vera

ged

leve

rage

’ le

vera

ge’

2.29

4’

3.32

8”

(3.6

1)

(1.9

3)

0.06

7 -

1.78

8’

~ 0.

013

0.31

4’

33.1

’ (3

.18)

(0

.79)

(2

.77)

0.08

1

- 1.

976’

2.

365’

0.

454

- 0.

034

0.29

8g

36.4

’ (3

.10)

(3

.65)

(1

.02)

(0

.53)

(2

.50)

0.07

5 ~

2.06

7’

3.25

8’

1.04

1 0.

001

0.34

8’

33.5

’ (3

.21)

(4

.06)

(0

.88)

(0

.01)

(2

.90)

0.18

2 -

2.64

5g

5.11

9’

- 0.

009

- 0.

093

- 0.

091

41.4

’ (2

.50)

(5

.15)

(0

.01)

(0

.57)

(0

.46)

0.07

3 -

1.99

3’

2.86

0’

- 0.

554

- 0.

007

0.34

7’

32.5

’ (3

.13)

(4

.18)

(1

.06)

(0

.12)

(2

.89)

0.19

3 -

2.77

0’

4.97

0’

- 3.

260g

-

0.07

6 -

0.05

0 43

.8’

(2.6

2)

(5.2

5)

(2.5

7)

(0.4

8)

(0.2

5)

0.02

8 -

2.76

3’

1.06

5 -

2.23

6g

0.08

1

0.24

4h

9.1h

(3

.81)

(1

.48)

(2

.51)

(1

.21)

(1

.82)

__

__~

“Coe

ffic

ient

s of

lo

gist

ic

regr

essi

ons

estim

atin

g th

e re

latio

n be

twee

n ca

pita

l-st

ruct

ure

char

acte

rist

ics

and

the

prob

abili

ty

of

actio

ns

in

the

peri

od

of

poor

pe

rfor

man

ce

for

a sa

mpl

e of

fir

ms

expe

rien

cing

on

e ye

ar

of d

istr

ess.

T

hese

re

gres

sion

s es

timat

e th

e ef

fect

of

var

ious

de

bt

char

acte

rist

ics

on

the

actio

ns

(t-s

tatis

tics

in p

aren

thes

es).

D

efin

ition

s of

all

term

s ar

e gi

ven

in f

ootn

otes

bb

p of

tab

le

2. T

he

num

ber

of o

bser

vatio

ns

in e

ach

regr

essi

on

is b

etw

een

318

and

355.

“M

atur

ity

ratio

is

the

ra

tio

of t

he

book

va

lue

of l

ong-

term

de

bt

to

tota

l de

bt,

for

firm

s w

ith

posi

tive

debt

in

th

e ba

se

year

. ‘O

wne

rshi

p ra

tio

is t

he

ratio

of

the

bo

ok

valu

e of

pub

lic

debt

to

to

tal

debt

, fo

r fi

rms

with

po

sitiv

e de

bt

in

the

base

ye

ar.

“Lon

g(sh

ort)

-ter

m

leve

rage

is

the

ra

tio

of t

he

book

va

lue

of l

ong(

shor

t)-t

erm

de

bt

to

the

book

va

lue

of t

otal

de

bt

plus

th

e m

arke

t va

lue

of e

quity

, in

the

ba

se

year

. ‘Pri

vate

(p

ublic

) le

vera

ge

is t

he r

atio

of

the

bo

ok

valu

e of

non

publ

ic

(pub

lic)

debt

to

the

bo

ok

valu

e of

tot

al

debt

pl

us

mar

ket

valu

e of

equ

ity,

in t

he

base

ye

ar.

‘Sig

nifi

cant

at

th

e 1%

le

vel.

‘Sig

nifi

cant

at

th

e 5%

le

vel.

$ign

ific

ant

at

the

10%

le

vel.

E. Ofek. Predistress Ieoeruge and firnl response to poor performance 23

regression does not measure whether one maturity plays a greater role than the other). The second regression uses the ratio of long-term debt to total debt to test whether the maturity structure is related to operational actions. The results are positive but not significant, indicating that the relation between operational actions and leverage arises from all maturities. Although not reported in this table, the same pattern holds for the results of the individual actions.

Private debt is associated with intermediaries that monitor the firm, especially as it gets into trouble. Public debt is often held by many small investors who do not monitor the firm’s actions. Ninety-six firms in the sample have some public debt outstanding at the end of the base year, three have only public debt, and 322 have some private debt. Panel C in table 1 shows that, on average, 12% of the sample’s outstanding debt is public. The ratio of public debt to total debt ranges from 0% to 100%. The third regression in table 4 estimates the effect of public and private leverage on operational actions. The effect of private leverage is positive and significant at the 1% level whereas public leverage has a positive but insignificant relation to operational actions. This finding may indicate that private debt has a stronger effect on the firm’s actions than public debt, but may also reflect the low proportion of public debt in the sample firms’ obligations.

To further estimate the effect of debt ownership on the probability of operational actions, the fourth regression in table 4 uses the ratio of public debt to total debt (ownership ratio) as an explanatory variable. A negative coefficient for this ratio implies that the probability of an operational action is reduced when a firm relies more heavily on public debt in its debt structure. Although the coefficient is indeed negative, it is not significantly different from zero, making the evidence inconclusive. This result may be caused by the heterogeneity of the private debt, which includes bank debt, privately-placed debt, capital leases, mortgages, industrial bonds, and other forms of debt.

The effect of debt ownership on most of the individual actions is similar to its effect on operational actions, although in some cases there appears to be a significant ownership effect. The probability of debt restructuring is positively and significantly related to private leverage, but is not related to public leverage; in addition, firms with higher ratio of public debt to total debt are less likely to restructure their debt. This result supports the argument that public debt is harder to negotiate, consistent with the results reported by Gilson, John, and Lang (1990).

Management replacement is also significantly related to the debt ownership ratio. The lower the proportion of public debt, the higher is the probability of management replacement. This finding is consistent with the argument that a management change results from active monitoring by debtholders, since private debtholders are likely to be more vigilant than holders of public debt.

24 E. Qfek. Predislress leverage and,firm response to poor performance

5.2. Leverage and cash generation

The probability of asset restructuring in a poorly-performing firm increases with the firm’s leverage. One explanation is that firms are forced to sell assets or divest businesses to raise cash to meet debt payments. Such actions increase cash flow in the short term but may not be consistent with a long-term value- maximizing strategy because of possible liquidity costs associated with distressed-assets sales. There are several types of asset restructuring, however, that cannot be used to immediately retire debt. Such actions include discontinuing or consolidating operations and closing plants or headquarters. These actions are often associated with large charges to earnings and may require a large cash outflow in the year in which they are taken. In the long run, however, such actions reduce costs and the outflow of funds, and thus increase the cash available to repay debt. A positive relation between leverage and actions that increase only long-term cash flow indicates a long-run value-maximizing strategy induced by the existence of debt.

To test whether capital structure is related differently to the different types of

asset restructuring, two binary variables are introduced. The first equals one if

the restructuring generates immediate cash inflow; the second equals one if the

restructuring does not generate immediate cash flow. Logit regressions incor- porating the new dependent variables (the first two regressions in table 5) show

that leverage has a positive efSect on both types of restructuring, significant at the

1% level.

The positive relation between predistress leverage and cash-generating actions is consistent with free cash flow models that use debt-service obligations to reduce overinvestment. But the positive relation between predistress leverage and operational actions that do not generate immediate cash implies that short-term debt service is not the only motive behind the firm’s actions. Both types of actions, those that generate cash and those that do not, are consistent with models that highlight debtholder monitoring triggered by default.

The third regression in table 5 delves further into the relation between

capital structure and operational actions that do not generate immediate cash. It uses a binary variable that equals one if the firm takes any operational

action that does not generate short-term cash inflow. Such actions, which include laying off employees and making management changes as well as discontinuing or restructuring operations, are not subject to the liquidity cost associated with a distressed-asset sale. The regression results reinforce the claim that the relation between leverage and operational actions is not solely the result of pressure to sell assets and immediately repay debt, and that debt obligations encourage firms to take actions that reduce cost and increase long-term cash flow. Leverage is positively and significantly related to the probability of operational actions that do not generate current cash inflow.

Tab

le

5

Shor

t-te

rm

cash

in

flow

an

d ca

pita

l st

ruct

ure

for

a sa

mpl

e of

358

fir

ms

that

ar

e in

the

to

p 67

%

of t

he

mar

ket

in t

he

base

ye

ar

and

the

botto

m

10%

in

the

h

dist

ress

ye

ar.

The

sa

mpl

e pe

riod

is

19

83-1

987.

” .o

2 R

-sq

Lev

erag

e L

iqui

dity

M

anag

eria

l O

utsi

de

Firm

R

espo

nsiv

e ac

tions

R

etur

n -%

L

Rb

Con

stan

t ba

se

year

ba

se

year

ho

ldin

gs

hold

ings

si

ze

dist

ress

ye

ar

Z

B

Ass

et

rest

ruct

urin

g 0.

168

- 4.

785’

3.

425’

-

0.18

2 0.

956

- 0.

220

0.23

6 :

2.64

1g

with

ca

sh

infl

owb

49.4

’ (4

.36)

(4

.74)

(1

.41)

(1

.07)

(0

.21)

(1

.49)

(2

.30)

2

Ass

et

rest

ruct

urin

g 0.

134

- 2.

893’

1.

952’

-

0.25

9”

- 2.

1548

-

2.43

3”

0.30

0h

0.81

4 6

with

no

ca

sh

infl

ow’

35.4

’ (2

.65)

(2

.61)

(1

.74)

(1

.97)

(1

.81)

(1

.88)

(0

.68)

2

Ope

ratio

nal

actio

ns

0.04

7 -

0.72

9 0.

988”

-

0.01

3 -

1.62

0g

- 1.

045

0.26

48

- 0.

800

; w

ith

no

cash

in

flow

d 22

.8’

(1.0

6)

(1.9

4)

(0.7

9)

(2.5

2)

(1.4

3)

(2.3

2)

(1.0

4)

2

Ope

ratio

nal

actio

ns

0.04

9 ~

0.47

5 1.

511’

-

0.01

2 -

1.46

9g

- 1.

275”

0.

204”

-

0.59

9 w

ith

no

debt

pa

ymen

t’

23.8

’ (0

.69)

(2

.89)

(0

.78)

(2

.33)

(1

.75)

(1

.79)

$

(0.7

9)

2

“Coe

ffic

ient

s of

log

istic

re

gres

sion

s es

timat

ing

the

rela

tion

betw

een

a fi

rm’s

cap

ital

stru

ctur

e an

d th

e pr

obab

ility

of

ope

ratio

nal

actio

n in

a p

erio

d of

poo

r 8 B

perf

orm

ance

. T

he

actio

ns

are

grou

ped

in a

ccor

danc

e w

ith

the

cash

fl

ow

they

ge

nera

te

at t

he t

ime

they

ar

e ta

ken

(r-s

tatis

tics

in p

aren

thes

es).

D

efin

ition

s of

P

all

term

s ar

e gi

ven

in f

ootn

otes

bb

p of

tab

le

2. T

he

num

ber

of o

bser

vatio

ns

in e

ach

regr

essi

on

is b

etw

een

348

and

352.

‘A

sset

re

stru

ctur

ing

with

ca

sh

infl

ow

equa

ls

1 if

the

fi

rm

dive

sts

or

sells

ass

ets

in t

he

dist

ress

ye

ar.

&.

B

‘Ass

et

rest

ruct

urin

g w

ith

no

cash

in

flow

eq

uals

1

if t

he

firm

di

scon

tinue

s op

erat

ions

or

co

nsol

idat

es

oper

atio

ns

in t

he

dist

ress

ye

ar.

9 dO

pera

tiona

l ac

tions

w

ith

no c

ash

infl

ow

equa

ls

1 if

the

firm

la

ys

off

empl

oyee

s or

rep

lace

s its

man

agem

ent

or r

estr

uctu

res

its a

sset

s w

ith

no c

ash

infl

ow.

$

‘Ope

ratio

nal

actio

ns

with

no

deb

t pa

ymen

t eq

uals

1

if t

he f

irm

ta

kes

an o

pera

tiona

l ac

tion

with

no

cas

h in

flow

or

if

the

firm

div

ests

or

sel

ls a

sset

s bu

t th

e 3

proc

eeds

ar

e no

t us

ed

to

repa

y de

bt.

‘Sig

nifi

cant

at

th

e 1%

le

vel.

2 $ gS

igni

fica

nt

at

the

5%

leve

l. “S

igni

fica

nt

at

the

10%

le

vel.

a

26 E. Ofek, Predisrress leverage andji’rm response IO poor performance

6. Management holdings, equity holdings, and firm actions

6.1. The eflect of management holdings

Fama and Jensen (1983) argue that the market for corporate control, which disciplines and replaces inferior managers, has less effect on firms with large management holdings. It is harder to dislodge poorly-performing managers in these firms and to discipline them when they grant themselves high salaries and other personal benefits that reduce the value of the firm. This entrenchment hypothesis predicts that managers with large holdings in the firm are less likely to be replaced. Managerial entrenchment may come in other forms, however. Jensen (1986) argues that free cash flow may lead managers to negative NPV investments, and Stulz (1990) assumes that managers value investments because their perquisites increase with investments, even in negative-NPV projects. These arguments imply that entrenched management is likely to avoid actions that reduce the firm’s investment, i.e., changes that reduce the assets, employees, or market share under managerial control.

The negative relation (signi$cant at the 5% level) between managerial holdings and operational actions with no immediate cash pow, presented in table 5, supports the entrenchment hypothesis. It implies that managers avoid taking actions such as closing plants, discontinuing operations, laying off employees, and replacing executives. It is possible, however, that this relation results mostly from the negative relation between managerial holdings and managerial turnover. The second regression in table 5 shows that the significantly negative relation between managerial holdings and restructuring that does not generate immediate cash holds even when managerial turnover is excluded. Managerial holdings have no effect on cash-generating restructuring, perhaps because such restructuring does not reduce free cash flow, and the cash received could be available for future investments.

6.2. Equity holdings and the.firm’s actions

The size of outside stockholding does not significantly increase the probability of any of the actions shown in table 2. In fact, the presence of a large nonmanagerial investor reduces the probability that an operational action will be taken. This result is surprising because large equityholders are expected to play an important role in monitoring the firm’s actions. Conceivably, different types of outside stockholders differ in their willingness to monitor the firm when bad news about the firm’s performance emerges. To test this possibility, variables are created representing seven types of investors: an individual, a corporation that is not controlled by the firm’s management, a corporation friendly to

E. Qfek. Predistresr Iecerqe ond,firm response to poor pe(formunce 21

management,4 a mutual fund, pension fund, or other investment management firm, a bank or an insurance company, an employee trust, and a group of related investors. A variable takes the value of zero if no outside stockholder of that type holds 5% or more of the firm’s equity. If such an investor exists, the variable equals the holdings (in percent) of the largest investor of that type.

Table 6 reports the results of the logit regressions for the different types of stockholders. No type except an investment management firm has a significant effect on the probability of an operational action in general or any of the individual actions, indicating that the firm’s actions while in distress are unre- lated to the type of outside stockholder. The only significant relations are negative relations between ownership by investment management firms and the probability of operational actions and of management turnover, consistent with Pound’s (1988) finding that institutional investors tend to support management in proxy contests.

Another possible explanation for the lack of evidence of stockholder monitoring is that it is not monotonic in the percentage of the stock held by investors; rather, the effect of stockholding varies across different ranges. However, a piecewise logit regression that tests for this nonmonotonic effect finds no significant relation between the size of the largest nonmanagerial stockholder and any of the responsive actions at all ranges of outside stock ownership. (These results are not reported in this paper.)

One reason for the apparent lack of monitoring may be that even large nonmanagerial stockholders cannot control the firm’s actions. In the sample, the median share of firm equity held by an outside investor is 6%, whereas the median share held by management is 22%. Outside stockholders may simply be too small to dictate to the incumbent management.

7. Conclusion

This paper examines the relation between a firm’s capital structure and its response to short-term financial distress. A sample of 358 firms with one year of normal performance followed by a year of extreme poor performance is used to conduct the tests. The results show that highly-leveraged firms are more likely than their less-leveraged counterparts to respond operationally to short-term distress. Such firms are also more likely to take individual actions such as restructuring assets and laying off employees when performance deteriorates. In

4A friendly corporation is one in which the management of the sample firm holds a substantial part of the equity, or a corporation in which one of the top managers is a top manager in the sample firm. Since sample firms do not report the stockholding of such corporations as part of the management holdings in their proxy statements. they are not so identified in this paper. However, holdings by corporations in which the firm’s management holds more than 50% of the equity are always considered management holdings.

Tab

le

6

Typ

e of

non

man

ager

ial

inve

stor

an

d op

erat

iona

l ac

tions

fo

r a

sam

ple

of 3

58 f

irm

s th

at

are

in t

he t

op

67%

of

the

mar

ket

in t

he b

ase

year

an

d th

e bo

ttom

10

% i

n th

e di

stre

ss

year

. T

he

sam

ple

peri

od

is

1983

- 19

87.“

. b

LE

!VeK

tge

Liq

uidi

ty

Man

ager

ial

Res

pons

es

cons

tant

ba

se

year

ba

se

year

0

hold

ings

Ope

ratio

nal

actio

ns

- 0.

853

2.51

4 -

0.01

2 -

1.08

6’

( 1.2

0)

(4.2

9)

(0.7

7)

(1.6

7)

Ass

et

rest

ruct

urin

g -

3.42

1’

3.26

5’

- 0.

171’

-

0.46

1 (3

.69)

(4

.89)

(1

.66)

(0

.56)

Em

ploy

ee

layo

ffs

- 1.

665’

1.

833d

-

0.04

2 ~

1.21

0 (1

.78)

(2

.37)

(0

.91)

(1

.41)

Man

agem

ent

turn

over

-

0.88

3 -

0.39

1 O

X04

2.

135*

(1

.07)

(0

.60)

(0

.27)

(2

.44)

IND

0.38

1 (0

.18)

- 3.

585

(1.1

4)

- 4.

254

(0.8

2)

4.62

7’

(1.8

0)

CO

RP

- 0.

891

(0.9

8)

- 1.

331

(1.1

5)

- 0.

201’

(0

.17)

0.72

4 (0

.76)

FCO

R

- 2.

267

(1.5

9)

- 1.

888

(0.7

9)

- 5.

713

(1.3

5)

- 0.

312

(0.2

2)

BA

IN

1.09

0 (0

.3 I)

4.61

3 (1

.25)

- 0.

264

(0.0

6)

~ 0.

874

(0.2

1)

IVM

G

- 5.

730’

(1

.84)

- 3.

132

(0.7

7)

- 2.

060

(0.5

0)

- 8.

302’

(1

.84)

EM

PT

- 0.

014

(0.0

0)

- 4.

939

(0.7

9)

~ 2.

304

(0.5

2)

1.23

2 (0

.30)

CROCI

- 0.

877

(0.3

8)

- 0.

276

(0.1

I)

- 0.

769

(0.2

6)

0.71

6 (0

.29)

Ret

urn

~ 0.

795

(1.0

0)

1.78

8’

(1.7

9)

0.63

0 (0

.63)

- 1.

664’

(1

.70)

Firm

si

ze

0.28

9d

(2.4

1)

0.27

7d

(1.9

6)

0.15

8

(1.0

4)

0.19

01

(1.4

3)

“Coe

ffic

ient

s of

log

istic

re

gres

sion

s es

timat

ing

the

rela

tion

betw

een

capi

tal-

stru

ctur

e ch

arac

teri

stic

s an

d th

e pr

obab

ility

of

act

ion

in t

he p

erio

d of

poo

r pe

rfor

man

ce

for

a sa

mpl

e of

fi

rms

expe

rien

cing

on

e ye

ar o

f di

stre

ss.

The

se

regr

essi

ons

estim

ate

the

effe

ct o

f th

e ty

pe o

f th

e la

rges

t no

nman

ager

ial

stoc

khol

der

(t-s

tatis

tics

in p

aren

thes

es).

D

efin

ition

s of

all

term

s ar

e gi

ven

in

foot

note

s b

p of

tab

le

2.

bThe

fol

low

ing

vari

able

s eq

ual

the

actu

al

equi

ty

hold

ings

if

the

type

of

sto

ckho

lder

ow

ns

0.05

or

mor

e of

the

equ

ity

and

0 ot

herw

ise.

T

he

diff

eren

t ty

pes

of o

utsi

de

stoc

khol

ders

ar

e:

IND

=

ind

ivid

ual,

CO

RP

=

a c

orpo

rati

on,

FCO

R

= a

cor

pora

tion

that

is

man

aged

or

con

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E. Qfek. Predisrress leverage andfirm response to poor performance 29

addition to responding quickly operationally, highly-leveraged firms are more likely to respond financially, through dividend cuts, debt restructuring, and bankruptcy.

There are several differences in a firm’s response to short-term and long-term distress. Higher leverage significantly increases the probability of debt restructuring following a short period of distress, whereas Gilson, John, and Lang (1990) find no relation between leverage and debt restructuring following a long period of distress. Of the sample firms in financial distress, only 10% file for bankruptcy in their first year of poor performance. By comparison, other studies report that 53% of firms that perform poorly for at least three years file for bankruptcy protection. Another difference in a firm’s response to short- and long-term distress is the effect of leverage on management turnover; in the first year of distress, leverage has no effect on management turnover, whereas Gilson (1989) reports a significant increase in the probability of management turnover in financially distressed firms following a long period of distress.

The larger the share of firm equity held by management, the lower is the probability that an operational action will be taken (especially when the action does not generate cash inflow). This result is consistent with the management entrenchment arguments of Jensen (1986) and Stulz (1990), and highlights situations in which larger management holdings can actually discourage value- maximizing behavior. Overall, the paper’s finding that highly-leveraged firms react faster to a decline in performance than do less-leveraged companies is consistent with Jensen (1989), and suggests that a choice of high leverage during normal operations subjects the firm to the discipline that debt provides. High leverage appears to induce a firm to respond operationally and financially to adversity after a short period of poor performance, helping to avoid lengthy periods of losses with no response. The existence of debt in the capital structure may thus help to preserve the firm’s going-concern value.

References

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Baldwin, Carliss, Scott Mason, and JH Hughes, 1983, The case of Massey Ferguson - 1980, Case study (Harvard Business School, Cambridge, MA).

Clark, Kent and Eli Ofek, 1992, Mergers as a means of restructuring troubled firms: An empirical investigation, Working paper (New York University, New York, NY).

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Gilson, Stuart, 1989, Management turnover and financial distress, Journal of Financial Economics 25, 241-262.

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J.F.E.-B

30 E. Ofek, Predistress leverage and,firm response to poor performance

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Weisbach, Michael, 1988, Outside directors and CEO turnover. Journal of Financial Economics 20, 43 l-460.

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Ofek Capital

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