Layoffs, shareholders' wealth, and corporate performance

Journal of Empirical Finance 8 Ž2001. 171–199 www.elsevier.comrlocatereconbase

Layoffs, shareholders’ wealth, and corporate performance Peter Chen a , Vikas Mehrotra b, Ranjini Sivakumar c , Wayne W. Yu d,) a

c

d

Department of Accounting, School of Business and Management, Hong Kong UniÕersity of Science and Technology, Hong Kong, China b Department of Finance and Management Science, School of Business, UniÕersity of Alberta, Edmonton, Alberta, Canada Centre for AdÕanced Studies in Finance, School of Accountancy, UniÕersity of Waterloo, Waterloo, Ontario, Canada Department of Accountancy, Faculty of Business and Information Systems, Hong Kong Polytechnic UniÕersity, Hong Kong, China Accepted 15 February 2001

Abstract We examine the relation between layoffs and stockholders’ wealth, and corporate performance subsequent to layoffs. We find that layoffs are preceded by a period of poor stock market and earnings performance, and are followed by significant improvements in both. On average, layoff announcements are associated with a significantly negative stock market response, with the lowest returns associated with layoffs attributed to declining demand. We do not find any evidence that layoff announcements are followed by reduced total employment in the subsequent 3 years; however, we find evidence of improving profit margins and improved labor productivity following layoffs. We find no evidence that the eventual turnaround in firm performance following layoff decisions is due to mean reversion in accounting earnings. Finally, we find that layoff firms tend to increase corporate focus. Our findings support the view that a layoff decision is a rational response to ensure corporate survival. q 2001 Elsevier Science B.V. All rights reserved. JEL classification: G34; J60 Keywords: Layoffs; Corporate restructuring; Work force reduction

)

Corresponding author. Tel.: q852-2766-7970; fax: q852-2330-9845. E-mail address: [email protected] ŽW.W. Yu..

0927-5398r01r$ - see front matter q 2001 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 7 - 5 3 9 8 Ž 0 1 . 0 0 0 2 4 - X

172

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

1. Introduction Although personnel reductions or layoffs have received considerable attention in popular media, there has not been much academic financial research on their cause or effects. In contrast, there have been numerous studies on other major resource-allocation decisions such as the undertaking of major capital expenditure ŽMcConnell and Muscarella, 1985., asset dispositions ŽJohn and Ofek, 1995; Weisbach, 1995., internal restructuring ŽBrickley and Van Drunen, 1990. and plant closings ŽBlackwell et al., 1990.. This study is among the first to examine the relationship between layoffs, corporate performance and shareholders’ wealth. We define a layoff as a permanent termination of a significant number of employees from the payroll of an organization. There are several reasons why firms may announce layoffs. First, layoffs can occur when firms experience declining product demand that also reduces labor demand. Second, firms may announce layoffs when new capital or technology changes the production process in such a way that reduces the demand for labor. Third, layoffs can also be a means to cut costs and to undertake strategic asset redeployment: witness the unprecedented corporate restructuring activity in the 1990s in the US. Notwithstanding these economic reasons, the popular media view layoffs with considerable distrust. The extant literature appears to be divided on the effects of layoffs on corporate performance. Proponents of the firm as a nexus of contracts argue that layoffs represent changes of sub-optimal contracts in response to changes in the external and internal environments, and thus should have a positive impact on corporate performance and firm survival Žsee, e.g., Jensen and Meckling, 1976; Jensen, 1993.. On the other hand, some economists and organizational management specialists argue that layoffs represent a breach of implicit contracts, and will eventually increase future contracting costs and undermine employee morale as well as long-term performance ŽShleifer and Summers, 1988; Cascio, 1993; Brockner et al., 1986.. We believe that our sample of layoffs allows us to empirically separate these two competing views on layoffs. We use the Wall Street Journal Index to identify US companies that make layoff announcements during the period 1990 to 1995. Our final sample includes 349 layoff announcements and covers both the early recession and the later expansion phase of the US business cycle. We have the following major findings. Layoff decisions are made after a period of marked underperformance based on the firm’s accounting earnings and stock returns. In the year prior to layoff decisions, the average layoff firm’s equity produces a return of y8%, compared to 9% on the value-weighted CRSP index. Accounting measures of operating income also decline each year in the 3 years prior to layoff announcements. Brickley and Van Drunen Ž1990. provide similar evidence on a sub-sample of corporate restructuring motivated by cost-cutting. However, while the operating performance

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

173

for the median layoff firms declines prior to the layoff, it remains above the operating performance for the median industry firm. We find that layoff decisions seem to arrest the decline in both the firm’s stock market and operating performance. The average buy-and-hold equity return in the year following layoff decisions is not different from the return on the valueweighted CRSP index. Operating performance Žas measured by earnings before interest and taxes normalized by assets. also improves following layoffs. Our evidence points to both employee productivity gains and margin improvements as the source of the performance improvement. These results do not support the belief that firms that layoff employees ultimately hurt themselves. Somewhat contrary to anecdotes reported in popular media, we find that layoff announcements are associated with a significantly negative stock market response.1 The average 2-day abnormal stock return associated with layoff announcements is y1.2%. The announcement response is more negative for layoffs motivated by declining demand. We find no evidence that layoff announcements are followed by reduced total employment at the median firm. While median employment figures do decline in the year of the layoff, they recover to pre-layoff levels 3 years later. Firms temporarily cut capital expenditure along with the layoffs; however, both capital expenditure and the ratio of capital expenditure per employee are significantly higher by the third year following layoff decisions than in any previous year. We also find evidence of an increase in corporate focus as measured by the number of business segments the firm reports for accounting purposes, and as measured by a sales-based Herfindahl index. Given recent evidence that corporate focus is value increasing for shareholders, such action is consistent with the view that layoffs form an important component of restructuring by poorly performing firms. We also document the reasons cited by management for layoffs. A large number of layoffs, not surprisingly, are motivated by a desire to cut costs. Other reasons cited by management include a slackening of product demand, poor prior period earnings, and an unspecified desire to restructure. We do not find evidence of unusual control activity, labor strikes, or chapter 11 filings prior to layoffs. Our work is most closely related to Brickley and Van Drunen Ž1990. and John et al. Ž1992.. Brickley and Van Drunen Ž1990. examine a broad variety of internal corporate restructuring, and provide evidence on the stock market response to and the accounting performance following these events. They conclude that firms restructure in response to market pressure, and experience a small positive stock price effect at the announcement of restructuring plans. However, they do not find any evidence of earnings improvement in the following 3-year period. John et al.

1

See, e.g., Heard on the Street: Stocks of Companies Announcing Layoffs Fire up Investors . . . , The Wall Street Journal, Dec. 10, 1991.

174

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

Ž1992. take a different approach and, instead of asking how firms respond to a specific event, examine the commonality of events for a sample of large firms that experience negative earnings in any year during 1980–1987 followed by at least 3 years of positive earnings. They find that firms in their sample tended to increase focus and reduce total employment, leverage, and R & D spending in the period following negative earnings. However, both these studies do not examine changes in stockholders’ wealth and firm performance following layoff decisions in particular. A number of prior studies have also examined the stock price reactions to layoff announcements. For example, Worrell et al. Ž1991. report significant negative stock price reactions for a sample of 194 layoffs over the period from 1979 to 1987. They attribute such evidence as supporting the view that layoffs reduce an organization’s effectiveness by undermining employee morale. Lin and Rozeff Ž1993. also find negative announcement day effect of layoffs and attribute this finding to firms experiencing declining demand. Lee Ž1997. compares the market reactions to layoff announcements in the US and Japan, and finds that stock price effects are negative in both countries. She attributes the negative market reaction to layoff announcements as an indication of financial distress and long-term performance decline, but does not provide empirical support for this conjecture. Palmon et al. Ž1997. examine 140 layoff announcements during 1982 to 1990 and report a negative market reaction to layoffs attributed to demand decline, but a positive market reaction to layoffs motivated by efficiency improvement. Palmon et al. find that investors treat the reasons cited for layoffs as credible signals of future performance; specifically, investors consider layoffs associated with cost-reduction to enhance firm value, and layoffs associated with adverse market conditions to signal poor firm performance. Hallock Ž1998. examines whether CEOs heading firms that announce layoffs have an increase in compensation following layoff decisions. For a sample of 550 of the largest US corporations during the period 1989 to 1995, he finds that market reaction to layoff announcements is significantly negative and that layoffs have little impact on CEO pay. Our study is distinctive in many respects. First, we examine a more recent time period, including a period of mild recession at the start of the 1990s. Second, we relate the announcement effect of layoffs to both management-cited reasons for the layoffs, and actual changes in the firm’s operating and asset characteristics. This allows us to explain why the average market reaction to layoffs is negative, despite overall performance improvements following layoffs. Third, we provide evidence on whether the performance gains from layoffs come from productivity increases or profit margins or both. In addition, our examination of the firm’s assets includes variables that measure focus and variables that capture capital expenditures. Consistent with the findings in Palmon et al. Ž1997., we find that market reactions to layoff announcements are on average negative, and more pronounced for layoffs that are attributed to demand decline. Hallock’s Ž1998. evidence that layoffs are not engineered to enrich CEOs is consistent with our findings that

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

175

layoff decisions emerge out of necessary adjustments to changes in the product markets and the firm’s competitive environment. The rest of the paper is organized as follows: Section 2 describes our sample selection procedure and provides some descriptive statistics. In Section 3, we report the stated reasons for the layoffs. Results are reported in Section 4, and Section 5 concludes the paper.

2. Sample characteristics We obtained layoff announcements from the Wall Street Journal Index from January1990 through December 1995.2 We examined the Wall Street Journal Index to verify if the sample firm was involved in an earlier layoff within a one-year period prior to the announcement.3 If we found evidence of prior layoff announcements, the earlier announcement was retained instead. We eliminated those announcements where there was another contaminating news report Žsuch as an earnings report. either on the day of or the day prior to the Wall Street Journal article. Finally, we eliminated those firms that were not available on either the CRSP or Compustat databases. Our resultant sample contains 349 layoff announcements during 1990–1995. There are 302 different firms represented in our final sample; 258 of these have only one layoff during the sample period, 41 firms have two layoffs, and three firms have three layoffs. There are a total of 290 independent layoffs after removing overlapping layoffs within 3 years. Table 1 reports the distribution of our sample of layoff announcements. In Panel A, we provide the industry classification of the firms in our sample. As shown in the table, our sample firms represent 45 industries at the two-digit SIC code level, with Industrial Machinery and Computers ŽSIC 3500. being the only group with more than 10% of the sample Ž47 firms..4 Panel B presents the distribution of layoff announcements over the sampling period. The relatively large numbers of layoff announcements in 1991 and 1992 reflect the economic downturn in these years. They are consistent with Hallock Ž1998. who also reports a higher frequency of layoff announcements in these 2 years for large US corporations. Table 2 displays firm characteristics for the sample median firm around the layoff year. The median firm in our sample has assets Žin book value terms. and sales worth US$2.81 billion and US$2.51 billion, in the fiscal year containing the 2 The choice of the sample period was partly influenced by the increasing anxiety over jobs that has characterized the first half of this decade. 3 We study only layoffs of a permanent nature, therefore, we eliminate cases of temporary layoffs for the three auto manufacturers. 4 Excluding these 47 firms makes no material difference to our results.

176

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

Table 1 Distribution of layoff announcements during 1990–1995 by two-digit SIC codes and by year. The sample excludes cases where there was another contaminating news report Žsuch as an earnings report. on the day of or the day prior to the Wall Street Journal article, and where data were not available on the CRSP or Compustat databases Panel A: Distribution of layoff announcements over two-digit SIC industries Industry

Two-digit SIC code

Frequency

Metal Mining Coal Mining Oil and Gas Exploration Heavy Construction Food Products Paper Products Furniture and Fixture Paper and Allied Products Printing and Publishing Chemicals Petroleum Refining Rubber and Plastics Leather and Leather Products Stone, Clay, Glass Products Primary Metals Fabricated Metals Industrial Machinery and Computers Electricals Transportation Instrumentation Miscellaneous Manufacturing Rail Roads Transit and Trains Water Transportation Air Transport Communications Utilities Durable Goods Non-durable Goods—Wholesale General Merchandise Food Stores Apparel Home Furniture Restaurants Miscellaneous Retail Depository Institutions Non-depository Credit Institutions Brokerage Insurance Holding Companies Business Services Motion Pictures

10 12 13 15 20 21 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 44 45 48 49 50 51 53 54 56 57 58 59 60 61 62 63 67 73 78

4 1 8 1 10 2 1 9 10 27 11 2 1 2 15 6 47 20 23 13 3 3 1 2 9 19 13 4 4 8 1 2 2 3 2 21 2 4 6 1 22 1

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

177

Table 1 Ž continued . Panel A: Distribution of layoff announcements over two-digit SIC industries Industry Amusements, Recreation Health Services Education Services TOTAL

Two-digit SIC code 79 80 82

Frequency 1 1 1 349

Panel B: Distribution of layoff announcements over the sample period Year of layoff announcements

Frequency

1990 1991 1992 1993 1994 1995 Total for the period 1990–1995

45 74 74 56 55 45 349

layoff announcement. The median book value of assets and sales increases every year from year y3 to year q3.5 The median work force declines from 13.3 thousand in the year before to 13 thousand in the year of the layoff announcement. However, we find that by the end of the second year after the layoff announcement, total work force at the median firm rebounds to 13.4 thousand, a level that is higher than in any of 3 years prior to the layoff announcement. While we cannot prove it, we suspect that firms use layoffs to selectively reduce employment in less productive areas, allowing them to increase employment in more productive areas within the firm.6 Furthermore, we do not find any evidence of a permanent reduction in capital expenditure. There appears to be a temporary decrease in total capital expenditure and capital expenditure per employee in year 0 and year q1, however by year q3, both total capital expenditure and capital expenditure per employee are higher than in previous years. The temporary reduction of capital expenditure along with layoffs is consistent with a management motive to cut costs. We do find a reduction in research and development to sales ratio, which is consistent with both depressed sales during the pre-layoff period, and actual reductions in R & D dollars. However, we find that the dollar R & D expense at the median firm is unchanged. We also find that the ratio of the market-to-book value of our sample firms is higher following the layoff announcement. The market-to-book ratio is 1.23, 1.20, 5

The announcement year is defined to be year 0, and other years are defined relative to year 0. See, e.g., the Wall Street Journal May 17, 1996. AT&T is described as selectively increasing employment while in the midst of a work force reduction implementation plan. 6

178

Relative year

y3

y2

y1

0

q1

q2

q3

Total assets ŽUS$000,000. Annual sales ŽUS$000,000. Net income ŽUS$000,000. No. of employees, 000 Capital expenditure ŽUS$000,000. R&DrSales Ž%. Market-to-book ratio COGSrSalesŽ%. Selling and administrative expenserSales Ž%. Operating incomerAssets Ž%. Net incomerAssets Ž%. Sales per employee ŽUS$000. Assets per employee ŽUS$000. Capital expenditure per employee ŽUS$000. Sample size

11,583 Ž2471. 6406 Ž2146. 314.6 Ž75.5. 37.3 Ž13.3. 27.50 Ž16.80.

12,483 Ž2508. 6799 Ž2355. 355.8 Ž94.7. 37.0 Ž12.6. 29.43 Ž19.56.

13,237 Ž2687. 7007 Ž2336. 322.9 Ž74.1. 36.8 Ž13.3. 31.20 Ž21.42.

14,695 Ž2813. 7251 Ž2510. 369.2 Ž74.7. 37.0 Ž13.0. 29.12 Ž20.60.

16,069 Ž3123. 7684 Ž2750. 426.5 Ž90.7. 36.8 Ž13.0. 29.23 Ž19.70.

17,751 Ž3475. 8128 Ž2877. 490.3 Ž117.1. 36.6 Ž13.4. 32.81 Ž23.96.

20,038 Ž3784. 8910 Ž3204. 622.9 Ž158.9. 37.7 Ž13.4. 32.72 Ž20.41.

7.30 Ž3.30. 1.53 Ž1.23. 63 Ž66. 25.53 Ž22.14.

6.10 Ž3.30. 1.53 Ž1.20. 63 Ž66. 26.96 Ž23.28.

7.00 Ž3.20. 1.49 Ž1.21. 65 Ž68. 29.99 Ž23.61.

7.80 Ž3.20. 1.46 Ž1.26. 65 Ž67. 30.95 Ž23.14.

6.60 Ž3.20. 1.55 Ž1.32. 64 Ž66. 26.20 Ž23.02.

6.80 Ž2.90. 1.62 Ž1.39. 64 Ž67. 26.66 Ž22.84.

7.30 Ž2.90. 1.61 Ž1.40. 64 Ž66. 26.67 Ž22.77.

13.54 Ž13.80. 3.84 Ž3.91. 195.83 Ž147.79. 442.42 Ž155.85. 14.78 Ž9.04.

14.01 Ž14.00. 3.82 Ž3.82. 209.33 Ž156.54. 476.99 Ž168.80. 15.99 Ž9.64.

11.61 Ž12.85. 0.55 Ž2.80. 212.68 Ž161.36. 486.27 Ž173.98. 16.12 Ž9.69.

9.25 Ž11.05. y2.66 Ž1.21. 232.87 Ž169.07. 534.97 Ž189.91. 15.54 Ž9.13.

11.20 Ž12.60. 0.56 Ž3.08. 251.71 Ž181.64. 606.22 Ž199.38. 15.56 Ž8.81.

11.83 Ž12.73. 2.81 Ž3.31. 261.64 Ž192.79. 615.2 Ž214.78. 15.39 Ž8.58.

12.70 Ž13.30. 2.98 Ž3.88. 288.69 Ž206.64. 682.57 Ž242.96. 18.71 Ž9.66.

346

349

349

349

349

334

317

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

Table 2 Mean and median values of selected operating characteristics for firms that announced layoffs during 1990–1995. Relative years are measured with the year of the layoff announcement defined as year zero. Median numbers are in parentheses

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

179

and 1.21 in year y3, year y2, and year y1, and increases to 1.32, 1.39, and 1.40 in year q1, year q2, and year q3. The median change in the market-to-book ratio from year y1 to year q1 is 0.11 Žsignificant at the 1% level. and from year 0 to year q1 is 0.13 Žsignificant at the 1% level.. The market-to-book results are consistent with cumulative buy-and-hold equity returns for our sample firms from year y1 to year q1 documented in Section 4.2. To examine productivity improvements, we look at sales per employee. In the year before the layoff, an employee at the median firm generates US$161,360 in sales. Sales per employee increases secularly to US$206,640 at the median firm 3 years after the layoff, an increase that exceeds the inflation amount during this time period. Our evidence on profitability is similar. We also provide industry-adjusted productivity and profitability statistics in Section 4.3, Tables 6 and 7. Table 3 presents summary statistics on the size of layoffs, control activity, and bankruptcy filings for our sample firms. The median Žmean. size of layoffs announced is 500 Ž1701., representing 4.55% Ž8.74%. of the existing number of employees. Approximately 23% of our sample firms Ž71 out of 302. experience a change in top management Ždefined as either the President, the CEO, or the Chairperson of the Board. in the 12 months preceding the layoff announcement. In the 12 months following layoff announcements, the top management turnover rate

Table 3 Number and percentage of employees involved in layoffs, and the number of management turnovers, labor strikes, chapter 11 filings, and firms engaged in acquisitions and divestitures around layoff announcements. Total sample size is 349 Average Žmedian. number of employees involved in layoff Average Žmedian. percentage of workforce involved in layoff Number of firms with top management turnover in 12 months preceding layoff announcement Number of firms with top management turnover in 12 months following layoff announcement Number of firms involved in acquisitions in 12 months preceding layoff announcement Number of firms involved in acquisitions in 36 months following layoff announcement Number of firms involved in asset divestitures in 36 months following layoff announcement Number of firms that were subject of takeover interest in 36 months following layoff announcement Number of firms with labor strikes in 12 months preceding layoff announcement Number of chapter 11 filings in 12 months preceding layoff announcement Number of chapter 11 filings in 36 months following layoff announcement

1701 Ž500. 8.74% Ž4.55%. 71 84 51 148 116 22 2 5 5

180

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

increases to 28% Ž84 out of 302 firms.. By comparison, the top management turnover rate in Warner et al. Ž1988. ranges from 11.5% to 18.3%, depending on the sample. The high management turnover rate surrounding layoffs is consistent with findings of Warner et al. Ž1988. and Weisbach Ž1988. that poor performance increases the probability of management turnover. We note only two incidents of labor strikes and five incidents of Chapter 11 filings in the calendar year prior to the layoff announcement. There are an additional five Chapter 11 filings in the 36-month period following layoff announcements. It does not appear that layoffs are chiefly a response to post-merger rationalization of resources: only 51 Žor 16.9%. firms were involved in acquisitions in the year preceding the layoff announcement. By contrast, we note 148 incidents of acquisitions in the 36 months following layoff announcements. More than one-third of our sample firms are engaged in asset divestitures following layoff announcement Ž116 out of 302 firms.. It does not appear that our sample firms make popular takeover targets—only 22 firms Ž7% of our sample. were the subjects of takeover interest in the 36 months following layoff announcements. Therefore, layoffs appear to be voluntary decisions effected by internal control mechanisms.

3. Reasons for layoffs In Table 4, we report the reasons for layoffs as stated in the full text of the layoff announcement article appearing in the Wall Street Journal ŽWSJ.. A desire

Table 4 Stated reasons and announcement date returns for 349 layoffs announced during 1990–1995. Excess returns are computed by comparing the 2-day announcement date return Žday y1 and 0. for the sample firm to the 2-day return for the value weighted CRSP index. Days are measured relative to the Wall Street Journal announcement date, which is defined as day zero. Numbers in brackets are median values. Means and medians are tested against zero by the appropriate t-statistic and the Wilcoxon sign rank test statistic Reason for layoffs

Sample size

Mean announcement date excess return Žmedian.

All layoffs Cost cutting Demand decline Low prior earnings Restructuring

349 128 89 91 107

y0.012 ) ) ) Žy0.005 ) ) ) . y0.011) ) Žy0.007 ) . y0.024 ) ) ) Žy0.014 ) ) ) . y0.012 ) ) Žy0.005 . y0.005 Žy0.005 .

)

Denotes significance at the 10%level. Denotes significance at the 5% level. ))) Denotes significance at the 1% level. ))

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

181

to cut costs emerges as the most common reason for layoffs Ž128 out of 349 announcements., followed by a general desire to restructure Ž107.. Poor prior period performance is cited by 91 firms as a reason for layoffs and 89 firms explicitly cite demand decline as the reason for layoffs. The total across all categories exceeds our sample size because some firms mention multiple reasons for layoffs. Typical announcement blurbs are reproduced in Appendix A. While steering clear of the debate on the welfare impact of layoffs, we do wish to point out that financial economists appear to be divided on the issue: many view layoffs as a breach of implicit contracts, with implications for future contracting and direct labor costs Žsee, e.g., Shleifer and Summers, 1988.. Others counter that layoffs are an integral part of the competitive response by corporations striving to achieve efficiency gains. Thus, Jensen Ž1993. argues that organizations must change contracts that are no longer optimal and points to bankruptcy protection as an accepted example of sub-optimal contract revision. Layoffs represent an extreme form of contract revision, and in a free market system, firms engaging in layoffs presumably do so with the full understanding of the resultant contracting cost changes. We believe that the impact of layoffs on firm value and performance is ultimately an empirical matter.

4. Results 4.1. Announcement day returns Stock markets may infer both positive and negative news from layoff announcements. If layoffs are seen as cost-cutting devices in the shareholders’ interests, the market response to their announcement should be positive. On the other hand, if layoffs are interpreted as signaling unexpected lower future earnings, the market response to their announcements would be negative. The negative market response may also arise, even if layoffs are announced as cost-cutting measures in the interest of shareholders, if the scale of layoffs is less than expected by capital market participants. In all cases, the impact of layoff announcements is expected to be weaker if market participants believe that they were forthcoming. We define the day of the Wall Street Journal announcement as day 0 and compute excess returns, ER j , for each firm in the interval Žy1,0.. First, the announcement period return for firm j, R j , is computed as follows: 0

Rjs

Ł Ž 1 q R j,t . y 1

tsy1

where R j,t s the cum-dividend return on day t for firm j.

Ž 1.

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

182

The announcement period market portfolio return, R m , for the period Žy1,0. is calculated using the value weighted CRSP index.7 For each firm, we compute an excess announcement period return ŽER j . as follows: ER j s R j y R m

Ž 2.

We take the mean excess return across our sample as follows: ERs

1

N

Ý ER j N

Ž 3.

js1

We perform statistical tests on the mean announcement period excess return, ER, using the average across our sample firms. The following statistic is assumed distributed Student’s t: ts

ER sr'N y 1

Ž 4.

where s is the sample standard deviation of the ER j ’s and N is the sample size. Table 4 reports the mean and median excess returns associated with layoff announcements. For the whole sample, the mean and median excess returns for the 2-day interval are y1.2% and y0.5% Žboth significant at the 1% level., respectively.8 Fifty-seven percent of our sample firms have a negative 2-day return. The negative mean excess return around layoff announcements contrasts with the small positive excess return associated with restructuring announcements reported in Brickley and Van Drunen Ž1990.. It is possible that firms that cite declining product market demand are chiefly responsible for generating negative announcement period excess returns. In Table 4 we also provide estimates of announcement period excess returns classified by the stated reason for layoffs. Firms claiming declining demand indeed experience the lowest mean excess returns equal to y2.4%.9 However, we find that firms citing cost cutting and low prior period earnings as the reason for layoffs also experience a significant mean excess return of y1.1% and y1.2%, respectively. Only firms that mention a general desire to restructure Žwithout specifying details. 7 We also computed holding period excess returns using the equally weighted market index. Excess returns using the equally weighted index are larger in absolute value, although the pattern of significance is unaffected by the choice of the index, and we only report the value weighted results. 8 The magnitude of the two-day announcement return is comparable with findings in a number of prior studies. Hallock Ž1998. reports a mean two-day announcement return of y0.3% Ž t-statistic s 3.04. for a sample of 1287 layoff announcements during 1987 to 1995. Palmon et al. Ž1997. found a mean 2-day return of y1.82% for a sample of large layoffs during the period from 1982 to 1990. Lee Ž1997. reports a mean 2-day return of y1.78% for a sample of 300 layoff announcements by US firms and of y0.56% for a sample of 58 layoff announcements made by Japanese firms. 9 For comparison, Palmon et al. Ž1997. report a two-day announcement return of y1.82% for 57 layoffs that are attributed to demand decline out of a total 140 layoff announcements.

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

183

do not experience statistically significant negative announcement-period excess returns, although the point estimate is y0.5%.10 4.2. Prior and subsequent stock returns Layoffs are important events in the life of a firm. Agency theoretic models of managerial behavior predict that layoffs, to the extent they reduce resources under management control, do not provide direct benefits to incumbent management. In agency models of the firm, savings from excess work force reductions accrue primarily to stockholders. In fact, both Shleifer and Summers Ž1988. and Weisbach Ž1995. seem to suggest that incumbent management incur private costs in severing implicit contracts with co-workers who are fired. Hence, it should come as no surprise that layoffs follow a period of abnormally poor stock market and earnings performance by the firm that imposes external pressure on the firm’s management. Table 5 reports average cumulative raw stock returns and average cumulative excess stock returns for our sample firms. The mean cumulative raw and excess return in the 3 years prior to the layoff announcement Ždays y750 to y2. is 10% and y22% Žboth significant at the 1% level. while the corresponding measure in the year prior to the layoff announcement Ždays y250 to y2. is y8% and y17% Žsignificant at the 5% and 1% levels.. These results are consistent with the findings of Brickley and Van Drunen Ž1990., who provide similar evidence on a sub-sample of corporate restructuring motivated by cost cutting. In Fig. 1, we plot the mean cumulative excess returns from day y250 to 250. The decline in stock returns for our sample firms is in evidence up to a year prior to the layoff announcement, and accentuates in the prior 6 months. The layoff announcement appears to arrest the decline in stock returns. The mean cumulative excess returns of 0.2% from day 1 to day 250 and 2.7% from day 1 to day 500 are not significant at conventional levels. However, the cumulative excess return of 9% from day 1 to day 750 Žthe 3-year buy-and-hold return. is positive and significant at the 5% level. We interpret our results as consistent with the hypothesis that layoffs are a necessary element of retrenchment in the face of poor market performance, and are followed by an improvement in stock prices. A concern with the above results is that long-run returns may be measured with bias if size and book-to-market effects are ignored. To address this concern, we compute size and book-to-market adjusted returns using an approach suggested by Spiess and Affleck-Graves Ž1999.. We select a matched portfolio of firms by minimizing the sum of the absolute percentage deviations in sizes and book-tomarket ratios between layoff firms and control firms in year y1. Long-period 10 We also calculated the risk-adjusted announcement period excess returns for the overall sample and for various sub-samples in Table 4. The results are qualitatively similar and are available upon request.

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

184

Table 5 Mean cumulative raw returns for 349 layoffs announced during 1990–1995, and mean cumulative excess returns Žover the value weighted CRSP index return.. Days are measured relative to the Wall Street Journal announcement date, which is defined as day zero ŽFrom day, to day.

Cumulative buy-and-hold return

Fraction negative

Cumulative excess return

Fraction negative

Žy750,y2. Žy500,y2. Žy250,y2. Žy1, 0. Žq1,q250. Žq1,q500. Žq1,q750.

0.103 ) ) ) 0.017 y0.080 ) ) y0.012 ) ) ) 0.170 ) ) ) 0.473 ) ) ) 0.926 ) ) )

0.45 0.49 0.55 0.57 0.34 0.27 0.25

y0.22 ) ) ) y0.18 ) ) ) y0.17 ) ) ) y0.012 ) ) ) 0.002 0.027 0.090 ) )

0.75 0.75 0.74 0.75 0.52 0.51 0.61

))

Denotes significance at the 5% level. Denotes significance at the 1% level.

)))

returns for the layoff firms are then adjusted for the corresponding returns for the size and book-to-market matched portfolio. These results are discussed below. In the year prior to the layoff announcement Žday y250 to day y2. the size and book-to-market adjusted return is y23% Žsignificant at the 1% level., compared to y17% for the market adjusted return in the same period reported earlier. The 2-day announcement return Žday y1 and day 0. is y1.3% Žsignificant

Fig. 1. Culmulative excess returns ŽCAR. for 349 firms announcing layoffs during 1990–1995.

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

185

at the 1% level. and is similar to the market-adjusted return of y1.2% reported in Section 4.1. However, for the post-layoff periods, the size and book-to-market adjusted returns are somewhat different. The cumulative size and book-to-market adjusted returns are y1.8% for year q1 Žday 1 to day 250., y0.8% from day 1 to day 500 Žthe 2-year buy-and-hold return., and y3.3% from day 1 to day 750 Žthe 3-year buy and hold return.. However, none of these post-layoff period returns is significant at the 10 % level. Recall that the market adjusted one and 2-year buy and hold returns following the layoffs are also statistically insignificant but the 3-year buy and hold return is positive insignificant at the 5% level. Overall, results from the size and book-to-market adjusted long-run returns continue to support the hypothesis that layoff firms display significant underperformance prior to the layoffs, while their post-layoff stock market performance does not significantly differ from their peers. 4.3. Operating performance surrounding layoff announcements In this subsection we examine corporate performance surrounding the layoff announcement. Table 6 reports operating performance characteristics of our sample firms in the 3 years prior to and following layoff announcements.11 Our primary operating performance measure is the ratio of earnings before interest, taxes, and special charges to the book value of total assets,12 which we label ROA ŽPanel A.. We also provide an alternative specification in Panel B where we normalize operating earnings by sales13 to avoid measuring spurious changes in performance caused by asset write-offs. We use operating earnings because net-income based measures are affected by restructuring related special charges and often difficult to identify on a common basis across all firms in the sample. We also report the median year-to-year change in operating performance and other variables following layoff announcements. Barber and Lyon Ž1995. show that tests involving changes provide more power to detect abnormal performance than those based on levels because changes incorporate a firm’s past performance in its earnings expectations model. Industry-adjusted statistics are based on a control sample of firms from the same two-digit SIC that do not lay off employees in the prior 3 years. Industry adjusted medians are computed by subtracting the median value for all firms in the same two-digit SIC code from the corresponding value of the layoff firm variable. 11

It is possible that the presence of firms with multiple layoffs may induce dependence among the observations and bias the t-statistics we report. We repeat all the tests in Table 6 using only the mean observation for each firm, thereby eliminating such dependence. These results are materially very similar, and available upon request. 12 Compustat data item a13 divided by data item a6. 13 Compustat data item a13 divided by data item a12.

186

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

Table 6 Selected operating characteristics for firms announcing layoffs during 1990–1995. Industry adjusted medians are computed by subtracting the median value for all firms in the same two-digit SIC code from the corresponding layoff firm variable. Time is measured with the year of the layoff announcement defined as year zero. Annual median changes and industry-adjusted medians are tested against zero using the Wilcoxon sign rank test statistic. Panel A reports operating earnings-to-asset ŽROA. ratios ŽCompustat data item a13 divided by data item a6.. Panel B reports operating earnings-to-sales ratios ŽCompustat data item a13 divided by data item a12.. Panel C reports cost-of-goods-sold to sales ratios ŽCompustat data item a41 divided by data item a12.. Panel D reports sales-and-administration expenses to sales ratios ŽCompustat data item a189 divided by data item a12.. Panel E reports sales to employee ratios ŽCompustat data item a12 divided by data item a29.. Panel F reports capital expenditure to employee ratios ŽCompustat data item a128 divided by data item a29. Relative year Sample size

y3 346

y2 349

y1 349

Panel A: Operating earningsrassets ŽROA. Time period

Layoff firms median

Industry adjusted median

Year y3 Year y2 Year y1 Year 0 Year q1 Year q2 Year q3 From year y3 to y2 From year y2 to y1 From year y1 to 0 From year 0 to q1 From year q1 to q2 From year q2 to q3 From year 0 to q3

0.138 0.140 0.129 0.111 0.126 0.127 0.133 y0.000 y0.005 ) ) ) y0.009 ) ) ) 0.006 ) ) ) 0.004 ) ) ) 0.005 ) ) ) 0.017 ) ) )

0.024 ) ) ) 0.025 ) ) ) 0.017 ) ) ) 0.008 ) ) 0.015 ) ) ) 0.011) ) ) 0.017 ) ) ) y0.0004 y0.005 ) ) ) y0.007 ) ) ) 0.002 ) ) 0.003 ) ) 0.005 ) ) ) 0.010 ) ) )

Panel B: Operating earningsrsales Time period

Layoff firms median

Industry adjusted median

Year y3 Year y2 Year y1 Year 0 Year q1 Year q2 Year q3 From year y3 to y2 From year y2 to y1 From year y1 to 0 From year 0 to q1 From year q1 to q2 From year q2 to q3 From year 0 to q3

0.142 0.145 0.129 0.121 0.132 0.136 0.147 0.0002 y0.004 ) ) ) y0.006 ) ) ) 0.008 ) ) ) 0.005 ) ) ) 0.005 ) ) ) 0.021) ) )

0.030 ) ) ) 0.028 ) ) ) 0.020 ) ) ) 0.012 ) ) ) 0.019 ) ) ) 0.026 ) ) ) 0.030 ) ) ) 0.0002 y0.005 ) ) ) y0.006 ) ) ) 0.003 ) ) 0.003 ) ) 0.004 ) ) ) 0.010 ) ) )

0 349

q1 349

q2 334

q3 317

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

187

Table 6 Ž continued . Relative year Sample size

y3 346

y2 349

y1 349

0 349

q1 349

q2 334

q3 317

Panel C: Cost-of-goods soldrsales Time period

Layoff firms median

Industry adjusted median

Year y3 Year y2 Year y1 Year 0 Year q1 Year q2 Year q3 From year y3 to y2 From year y2 to y1 From year y1 to 0 From year 0 to q1 From year q1 to q2 From year q2 to q3 From year 0 to q3

0.662 0.657 0.676 0.673 0.664 0.667 0.663 y0.0004 0.002 ) ) 0.004 ) ) ) y0.004 ) ) ) y0.002 y0.002 y0.012 ) ) )

y0.017 ) ) ) y0.021) ) ) y0.012 ) ) y0.008 ) y0.011) ) y0.013 ) ) y0.010 ) ) 0.0003 0.003 ) ) 0.005 ) ) ) y0.002 y0.0001 y0.004 y0.008 ) )

Panel D: Sales and administrative expensesrsales Time period

Layoff firms median

Industry adjusted median

Year y3 Year y2 Year y1 Year 0 Year q1 Year q2 Year q3 From year y3 to y2 From year y2 to –1 From year y1 to 0 From year 0 to q1 From year q1 to q2 From year q2 to q3 From year 0 to q3

0.221 0.233 0.236 0.231 0.230 0.228 0.228 0.002 0.002 ) ) ) 0.002 ) y0.002 y0.004 ) ) ) y0.002 ) ) y0.007 ) ) )

y0.005 y0.002 y0.002 y0.003 y0.006 y0.008 ) ) y0.010 ) ) 0.002 ) ) 0.002 ) 0.0002 y0.001 y0.006 ) ) ) y0.003 ) ) ) y0.007 ) ) )

Panel E: Salesremployee, US$000 Time period

Layoff firms median

Year y3 Year y2 Year y1

147.79 156.54 161.36

Industry adjusted median 16.521) ) ) 18.859 ) ) ) 18.265 ) ) ) (continued on next page)

188

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

Table 6 Ž continued . Relative year Sample size

y3 346

y2 349

y1 349

0 349

q1 349

q2 334

q3 317

Panel E: Salesremployee, US$000 Time period

Layoff firms median

Industry adjusted median

Year 0 Year q1 Year q2 Year q3 From year y3 to y2 From year y2 to y1 From year y1 to 0 From year 0 to q1 From year q1 to q2 From year q2 to q3 From year 0 to q3

169.07 181.64 192.79 206.64 6.843 ) ) ) 6.068 ) ) ) 10.092 ) ) ) 11.586 ) ) ) 9.882 ) ) ) 9.323 ) ) ) 33.474 ) ) )

25.914 ) ) ) 34.43 ) ) ) 34.50 ) ) ) 39.226 ) ) ) 1.079 ) ) y0.479 3.889 ) ) ) 4.753 ) ) ) 3.106 ) ) ) 3.508 ) ) ) 10.414 ) ) )

Panel F: Capital expenditureremployee, US$000 Time period

Layoff firms median

Industry adjusted median

Year y3 Year y2 Year y1 Year 0 Year q1 Year q2 Year q3 From year y3 to y2 From year y2 to y1 From year y1 to 0 From year 0 to q1 From year q1 to q2 From year q2 to q3 From year 0 to q3

9.043 9.643 9.694 9.130 8.810 8.581 9.658 0.518 ) ) ) 0.048 y0.005 y0.082 0.426 ) ) 0.927 ) ) ) 1.078 ) ) )

2.602 ) ) ) 2.641) ) ) 2.305 ) ) ) 2.278 ) ) ) 1.812 ) ) ) 0.946 ) ) ) 1.628 ) ) ) 0.141 y0.337 y0.460 ) ) ) y0.313 ) ) y0.277 0.053 y0.564

)

Denotes significance at the 10% level. Denotes significance at the 5% level. ))) Denotes significance at the 1% level. ))

Not surprisingly, layoff announcements are made after a period of declining earnings. As can be seen from Panel A in Table 6, the median ROA measure declines from 0.138 in year y3 to 0.111 in the year of the layoff announcement. The largest decline in ROA occurs between year y1 and year 0 and equals y0.009 Žsignificant at the 1% level.. Industry adjusted ROA demonstrates a similar declining pattern form 0.024 in year y3 to 0.008 in year 0. The changes in

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

189

industry-adjusted ROA from year y2 to year y1 and from year y1 to year 0 are statistically significant at the 1% level. In contrast, in the years following the layoff, the sample firms show significant improvements in industry adjusted ROA in each of the 3 years following the layoff. We would like to emphasize that the firms in the sample have declining performance but their performance is not poor relative to their industry medians. Specifically, when we compare ROA levels to that of the industry we find that they do better than the industry in the seven-year window around the layoff. However, when we look at annual changes in the ROA, our sample firms do worse prior to the layoff. These results suggest that layoffs are associated with declining firm performance rather than financial distress and are part of the firms’ overall strategy to restructure and return to profitability. In Panel B, a similar pattern emerges when we scale operating income by sales. To better understand management motives for layoffs, we next examine profitability and efficiency ratios following layoffs. We report the cost of goodssold-to-sales ratio, and the selling and administrative expenses to sales ratio surrounding layoffs to address profitability. Sales per employee, and capital expenditure per employee are examined to gauge the change in labor productivity and capital investment surrounding layoff announcements. In Panel C, we report the median cost of goods sold to sales ratio. The median ratio of cost of good sold to sales increases from 0.662 in year y3 to 0.673 in year 0, and then declines to 0.663 in year q3. Prior to the layoff, the median firm’s ratio of cost of goods sold to sales increases from year y2 to year y1 and from year y1 to 0, significant at the 5% and 1% level, respectively, in both raw and industry-adjusted terms. Following the layoff, the decrease in the median ratio of cost of goods sold to sales ratio Žindustry-adjusted. from year 0 to year q3 is y0.012 Žy0.008., significant at the 1% Ž5%. level. The decline in the costs of goods sold to sales ratio is consistent with the improved earnings performance reported above. In Panel D, we report the ratio of sales and administrative expenses to sales surrounding layoff announcements. As shown in the table, median sales and administrative expense as a percentage of sales increases from 22.1% in year y3 to 23.6% in year y1, and then decreases monotonically in the 3 years following layoffs Žit equals 22.8% in year q3.. These results suggest that layoffs appear to be important decisions in turning around firms with poor performance. Furthermore, the source of performance improvement is not limited to employee productivity gains Žsee Panel E. alone. All measures of profitability examined here show improvements following layoffs. Labor productivity is examined in Panel E. The median industry adjusted sales per employee in year q3 is nearly double the ratio in year y1 Žnote that the industry adjustment offsets changes induced by inflation.. This finding suggests that layoffs lead to significant improvement in labor productivity. It does not support the view that layoffs will eventually hurt employee morale and reduce productivity Žsee e.g., Brockner et al., 1986..

190

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

In Panel F, we report the capital expenditure per employee surrounding layoffs. The median ratio of capital expenditure per employee for the layoff sample is significantly higher than the industry’s. There is some evidence to suggest that firms that announce layoffs also reduce capital expenditure. The change in industry adjusted capital expenditure per employee from year y1 to year 0 and from year 0 to year q1 is y0.460 and y0.313, significant at the 1% and 5% level, respectively. From year 0 to year q3, the industry adjusted change in capital expenditure per employee is y0.564, although it is statistically insignificant. Overall, the results here do not indicate a permanent reduction in capital expenditure. The temporary reduction in capital expenditure per employee concurrent with layoffs is consistent with the findings in John et al. Ž1992. that firms voluntarily cut employment, R & D expenditures in response to performance decline. Finally, to confirm that the performance improvements are attributable to layoffs and not due to mean reversion, we conduct a sensitivity check using a control sample of firms that do not lay off employees. More specifically, a firm is selected as a control firm if it is from the same industry Žbased on the two-digit SIC., has a similar firm size Žbased on book value of assets., and is the closest to the layoff firm in ROA ranking in the year prior to the layoff.14 The results are presented in Table 7. The first column displays performance improvements over various time periods for the layoff firms, the second column for the non-layoff firms. The last column shows the p-values for testing significant differences in the various performance improvements between the layoff firms and non-layoff firms. The findings are similar to the improvements in industry-adjusted performance measures reported in Table 6. Results in Panel A and Panel B indicate that, relative to the non-layoff firms, the layoff firms’ improvements in both ROA and operating margin are stronger, especially for the period of year 0 to year q3. Results from Panel C show a similar pattern—the layoff firms’ labor productivity Žsales per employee. increases faster than the non-layoff firms. Overall, the results consistently indicate improvements in firm performance for the layoff firms subsequent to layoffs. 4.4. Changes in corporate focus following layoffs Evidence presented so far indicates that layoff decisions seem to reverse prior poor stock and poor operating performance. In this subsection, we examine if layoffs also lead to any change in corporate focus. Since an increase in corporate

14 After applying this procedure we find that in the year when the control sample is selected, the control firms have a higher median ROA of 0.124 compared to 0.103 for the control sample based on industry alone. The corresponding values for median return on sales are 0.152 and 0.094, respectively.

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

191

Table 7 Selected operating characteristics for the layoff firms during 1990–1995 and control firms. The control firms are selected from firms with the same two-digit SIC code as the layoff firm and based on similar size and return on assets as the layoff firms in the year prior to the layoff. Time is measured with the year of the layoff announcement defined as year zero. Panel A reports operating earnings-to-asset ŽROA. ratios ŽCompustat data item a13 divided by data item a6.. Panel B reports operating earnings-to-sales ratios ŽCompustat data item a13 divided by data item a12.. Panel C reports sales-to-employee ratios ŽCompustat data item a12 divided by data item a29.. Annual median changes and industry medians changes are tested against zero using the Wilcoxon sign rank test statistic. The tests for significant differences between the sample firms and the control firms are Wilcoxon sign rank tests Ž Z . Time period

Layoff firms median

Non-layoff firms median

p-value for difference

Panel A: Operating Earningsr Assets (ROA) From year y1 to 0 y0.010 ) ) ) From year 0 to q1 0.007 ) ) ) From year q1 to q2 0.004 ) ) ) From year q2 to q3 0.006 ) ) ) From year 0 to q3 0.019 ) ) )

0.0002 0.0038 ) ) ) 0.004 ) ) 0.0015 0.0064 ) ) )

0.0001) ) ) 0.0515 ) 0.6353 0.0796 ) 0.0133 ) )

Panel B: Operating earningsr sales From year y1 to 0 y0.007 ) ) ) From year 0 to q1 0.008 ) ) ) From year q1 to q2 0.005 ) ) ) From year q2 to q3 0.005 ) ) ) From year 0 to q3 0.022 ) ) )

0.0037 ) 0.0019 ) 0.0055 ) ) ) 0.0042 0.0087 ) ) )

0.0001) ) ) 0.0067 ) ) ) 0.2522 0.0976 ) 0.0045 ) ) )

Panel C: Salesr employee, US$000 From year y1 to 0 9.6 ) ) ) From year 0 to q1 11.7 ) ) ) From year q1 to q2 8.3 ) ) ) From year q2 to q3 9.5 ) ) ) From year 0 to q3 32.4 ) ) )

7.0 ) ) ) 7.0 ) ) ) 4.0 ) ) ) 5.0 ) ) ) 24.0 ) ) )

0.3107 0.0053 ) ) ) 0.0648 ) 0.0286 ) ) 0.0004 ) ) )

)

Denotes significance at the 10% level. Denotes significance at the 5% level. ))) Denotes significance at the 1% level. ))

focus is value increasing for shareholders, it is consistent with the view that layoffs form an important component of restructuring by poorly performing firms. Our first measure of focus is the number of business segments for which the firm reports selected accounting information. The mean number of segments reported declines from 2.52 to 2.17 from year y3 to q1 Žsee Table 8.. The decline is statistically significant at 1% level. While the change in reported segments appears to be small, it is similar in magnitude to the reduction in the number of business segments reported in John and Ofek Ž1995. for a sample of asset sales. Furthermore, 11% of layoff firms report fewer business segments

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

192

Table 8 Change in corporate focus for a sample of firms announcing layoffs during 1990–1995. Mean and median tests are based on t-statistics and Wilcoxon signed-rank statistics Number of observations Number of business segments reported (layoff year s yr 0) Pre-layoff year Žyr y3. 236 Pre-layoff year Žyr y1. 260 Post-layoff year Žyr q1. 263 Post-layoff year Žyr q2. 255 Post-layoff year Žyr q3. 210

Mean

2.52 2.25 2.17 2.11 2.17

Median

2.00 1.5 1.00 1.00 1.00

Change in the number of segments Year y3 to year 1 Year y1 to year 1 Year y1 to year 2 Year y1 to year 3

236 260 255 210

Sales based Herfindahl index Pre-layoff year Žyr y3. Pre-layoff year Žyr y1. Post-layoff year Žyr q1. Post-layoff year Žyr q2. Post-layoff year Žyr q3.

236 260 263 255 210

0.73 0.75 0.76 0.77 0.76

0.86 0.99 1.00 1.00 1.00

Change in the Herfindahl index Year y3 to year 1 Year y1 to year 1 Year y1 to year 2 Year y1 to year 3

236 260 255 210

0.02 ) ) 0.01) 0.02 ) ) 0.02 ) )

0) ) 0 0) 0) )

y0.25 ) ) ) y0.07 ) ) y0.17 ) ) y0.20 ) )

0) ) ) 0) ) 0) ) 0) )

)

Denotes significance at the 10% level. Denotes significance at the 5% level. ))) Denotes significance at the 1% level. ))

following layoffs Žfrom year y1 to year q1., while only 4.4% report more Žfrom year y1 to year q1.. The median change in number of segments reported is zero. Our second measure of focus, a sales-based Herfindahl index, also increases following layoffs. The index Ž H . is calculated as the sum of the squared ratio of segment i sales Ž Si . to total sales across n segments within the firm: 2

n

Hs Ý is1

Si

 0 n

Ž 5.

Ý Si

is1

The Herfindahl index is bound between 0 and q1, with higher values indicating more focus. The mean value of H increases from 0.73 to 0.76 from the

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

193

year y3 to the year q1. The increase is significant at the 0.05 level. H in years 2 and 3 are 0.77 and 0.76, indicating no reversal of the increase in focus, and is again significantly greater than in the year prior to the layoff. The median layoff firm reported only one business segment in the years surrounding layoffs, and the median change in H from year y1 to q1 is zero. To the extent an increase in corporate focus is value increasing for shareholders, it is consistent with the view that layoffs form an important component of restructuring by poorly performing firms. However, we do not examine whether focus increase is an industry-wide phenomenon. 4.5. Results from multiple regression analysis To isolate the informational content of layoff announcements, we report results from multiple regression analysis. As indicated earlier, market reactions to layoff announcement are likely to depend on the information conveyed to outsiders through layoff announcements. If layoff announcements convey unexpected demand decline in the product markets, we expect a negative market reaction. On the other hand, layoffs can also be signals to outsiders that management is taking actions to restore corporate performance, then we might expect a positive market reaction. Given the multiple reasons often stated by management for layoffs, we employ multiple regression analysis to see whether market reactions can be explained by these stated reasons for layoffs as follows: AR s a 0 q a 1 LAYOFF – SIZE q a 2 DEMANDq a 3 COST q a 4 RESTRUCTURINGq a 5 LOW – EARNINGSq a 6 HQ q a 7 NEWMGMTq a 8 ACQ q a 9 FOCUS PRE q a 10 C – AROAy1 q a 11C – AROA 0 q a 12 C – ACPXEMPy1 q a 13 C – ACPXEMP0

Ž 6.

where: AR s the 2-day excess announcement date return; LAYOFF – SIZE s Number of laid-off employeesrTotal Employees; DEMANDs 1 if the stated reason was demand decline and 0 otherwise; COSTs 1 if the stated reason was cost reduction and 0 otherwise; RESTRUCTURINGs 1 if the stated reason was restructuring and 0 otherwise; LOW – EARNINGSs 1 if the stated reason was low earnings and 0 otherwise; HQ s 1 if the layoffs related to headquarters staff and 0 otherwise; NEWMGMTs 1 if there was a change in top management 12 months prior to the layoff announcement and 0 otherwise; ACQ s 1 if the firm made acquisitions in the 12 months preceding the layoff announcement and 0 otherwise; FOCUS PRE s 1 if the firm increased its focus in the period y2 to the year of the layoff and 0 otherwise; C – AROAy1 s the change from year y2 to year y1 in the industry-adjusted ROA; C – AROA 0 s the change from year y1 to year 0 in the industry-adjusted ROA; C – ACPXEMPy1 s the change from year y2 to year

194

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

y1 in the industry-adjusted capital expenditure per employee; C – ACPXEMP0 s the change from year y1 to year 0 in the industry-adjusted capital expenditure per employee. Panel A in Table 9 reports the results of multiple regression analysis. Although our sampling criteria remove layoffs if they occur within one year of the previous layoff, several firms have multiple layoff announcements during our sample period and these announcements may not be independent. There are a total of 290 independent layoffs after removing overlapping layoffs within 3 years. We first report regression results in the first two columns in Panel A using the full sample of 349 layoffs. We repeat the same regressions using the sub-sample of independent observations obtained by removing layoffs that occur within 3 years of the previous layoffs. These results are reported in the third and fourth columns in Panel A. With the full sample, the coefficient on demand decline is negative and significant in both specifications of the regression. These results are consistent with the findings in Palmon et al. Ž1997.. However, other reasons for layoffs are not significantly related to the layoff announcement period return. We do find that layoff announcements are more positive for firms that have undergone a change in top management in the last twelve months. This supports the view that financial markets were aware of the firm’s problems when the prior manager was removed, Notes to Table 9: LAYOFF – SIZE is the ratio of the number of laid-off employees to total employees. DEMAND is an indicator variable that takes on a value of 1 if the stated reason was demand decline. COST is an indicator variable that takes on a value of 1 if the stated reason was cost reduction. RESTRUCTURING is an indicator variable that takes on a value of 1 if the stated reason was restructuring. LOW – EARNINGS is an indicator variable that takes on a value of 1 if the stated reason was low earnings. HQ is an indicator variable that takes on a value of 1 if the layoffs related to headquarters staff. NEWMGMT is an indicator variable that takes on a value of 1 if there was a change in top management 12 months prior to the layoff announcement. ACQ is an indicator variable that takes on a value of 1 if the firm made acquisitions in the 12 months preceding layoff announcement. FOCUS pre is an indicator variable that takes on a value of 1 if the firm increased its focus in the period-2 to the year of the layoff. FOCUS post is an indicator variable that takes on a value of 1 if the firm increased its focus in the period 0 to 2 years after the layoff. AROA t is the industry adjusted operating earnings scaled by assets in the year t, where t s 0 for the layoff announcement year. C – AROA t is the change from year t y1 to year t in the industry adjusted operating earnings scaled by assets. C – AROA 0q 3 is the change from year 0 to year 3 in the industry adjusted operating earnings scaled by assets. ACPXEMPt is the industry adjusted capital expenditure per employee in year t, where t s 0 for the layoff announcement year. C – ACPXEMPt is change from year t y1 to year t in the industry-adjusted capital expenditure per employee. C – ACPXEMP0q 3 is change from year 0 to year 3 in the industry-adjusted capital expenditure per employee. ASGXOS t is the industry adjusted sales and general administrative expenses scaled by sales in year t, where t s 0 for the layoff announcement year. C – ASGXS 0q 3 is the change from year 0 to year 3 in the industry-adjusted sales and general administrative expenses scaled by sales. p-values are denoted in parentheses. ) Denotes significance at the 10% level. )) Denotes significance at the 5% level. ))) Denotes significance at the 1% level.

Table 9 Linear regression model estimates for firms announcing layoffs in the period 1990–1995. The dependent variables are the 2-day excess announcement date return and 3-year excess holding period return. Excess return is computed by comparing return for the sample firm to return for the value weighted CRSP index. Days are measured relative to the Wall Street Journal announcement date, which is defined as day 0. The year of the layoff announcement is defined as year 0 Dependent Variable

Full sample

INTERCEPT LAYOFF – SIZE DEMAND COST RESTRUCTURING LOW EARNINGS HQ NEWMGMT ACQ FOCUS pre C – AROA y1 C – AROA 0 C – ACPXEMP y1 C – ACPXEMP0 AROA 2 ACPXEMP2 ASGXOS 2 C – AROA 0q3 C – ASGXS 0q3 C – ACPXEMP0q3 FOCUS post ADJ. R 2

y0.0101 Ž0.1884 . 0.0314 Ž0.3869 . y0.0213 ) ) Ž0.0158 . 0.0052 Ž0.5023 . y0.0006 Ž0.9444 . 0.0005 Ž0.9542 . 0.0120 Ž0.4205 . 0.0195 ) ) Ž0.0382 . y0.0060 Ž0.5684 . 0.0050 Ž0.5712 .

0.0228

y0.0038 Ž0.6420 . y0.0918 Ž0.1182 . y0.0203 ) ) Ž0.0233 . 0.0014 Ž0.8634 . 0.0011 Ž0.9032 . y0.0041 Ž0.6488 . 0.0080 Ž0.6091 . 0.0188 ) Ž0.0553 . y0.0033 Ž0.7591 . 0.0084 Ž0.3544 . y0.2321) ) ) Ž0.0053 . 0.1179 Ž0.1338 . y0.0004 Ž0.4819 . 0.0004 Ž0.5737 .

0.0604

Panel B: 3-year excess holding period return With overlapping layoffs within 3 years deleted

Full sample

y0.0062 Ž0.4629 . 0.0403 Ž0.2868 . y0.0278 ) ) ) Ž0.0069 . 0.0046 Ž0.6055 . y0.0112 Ž0.2352 . y0.0024 Ž0.8085 . 0.0129 Ž0.4400 . 0.0167 Ž0.1303 . y0.0039 Ž0.7417 . y0.0028 Ž0.7856 .

y0.0771 y0.3915 0.1375 0.0899 0.0311 0.2069 0.0157 y0.1276

0.0264

y0.0003 Ž0.9737 . y0.1098 ) Ž0.0832 . y0.0260 ) ) Ž0.0102 . y0.0021 Ž0.8182 . y0.0121 Ž0.2126 . y0.0091 Ž0.3614 . 0.0152 Ž0.3639 . 0.0118 Ž0.3021 . 0.0029 Ž0.8028 . 0.0081 Ž0.4426 . y0.3216 ) ) ) Ž0.0012 . y0.0139 Ž0.8721 . y0.0004 Ž0.4726 . 0.0003 Ž0.6782 .

0.0738

Ž0.5600 . Ž0.6668 . Ž0.3274 . Ž0.4882 . Ž0.8177 . Ž0.1446 . Ž0.9457 . Ž0.3821 .

1.4311) ) Ž0.0472 . y0.0009 Ž0.8205 . y0.7451 Ž0.1699 . 1.1516 Ž0.2056 . y2.2870 ) Ž0.0897 . 0.0051 Ž0.3160 . y0.1448 Ž0.1687 . 0.0390

With overlapping layoffs within 3 years deleted y0.0971 y0.6613 0.2131 0.1267 0.0550 0.2705 y0.0295 y0.0903

Ž0.5609 . Ž0.5460 . Ž0.2524 . Ž0.4574 . Ž0.7482 . Ž0.1354 . Ž0.9220 . Ž0.6307 .

1.5887 ) Ž0.0823 . y0.0018 Ž0.7108 . y0.8005 Ž0.2427 . 1.2828 Ž0.2417 . y2.8061) Ž0.0912 . 0.0048 Ž0.4377 . y0.2036 Ž0.1305 . 0.0264

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

Panel A: 2-day excess announcement date return

195

196

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

hence the announcement day returns are less negative. In contrast to Palmon et al. Ž1997. we find that layoff size is not significant. The coefficient on FOCUS PRE is also insignificant. In the second column of Panel A, we estimate an alternative specification where we add variables that capture changes in industry-adjusted ROA and capital expenditure per employee from year y2 to year y1 and from year y1 to year 0. The results are similar, with the additional evidence that the change in ROA performance from year y2 to year y1 is seen to be negatively related to the announcement date return. This implies that the layoff announcement is perceived as worse news for firms that have done relatively well in the previous year. In other words, to the extent that layoffs are correlated with deteriorating performance, the market reacts more negatively to the layoff announcement if it is less expected. Our results appear to be generally robust when we restrict our sample to independent layoffs. The key differences are with regards to the size of the layoff and the presence of new management. Announcement date returns are negatively related Ž p-values 0.08. to the size of the layoff for the restricted sample. However, the presence of new management is not significant for the restricted sample. Finally, we estimate an alternative regression specification where the dependent variable is the cumulative excess return over the 750 days following the layoff announcement. The results are reported in Panel B in Table 9. Our interest in examining this alternative specification is to explain the post-layoff stock returns. With both the full sample and the independent sample, we find that the post-layoff excess returns are positively related to the industry-adjusted ROA in year q2 and negatively related to the change in industry-adjusted selling, general and administrative expenses from year 0 to year q3.

5. Conclusion We examine the relation between layoffs and corporate performance and stockholders’ wealth. We find that layoffs follow a period of poor stock and operating performance, and are followed by improvements in both. Relative to their industry peers, operating performance of layoff firms declines each year from 3 years prior to until the year of the layoffs. However, for up to 3 years subsequent to layoffs, operating performance improves relative to the industry. For example, layoff firms’ profit margins and labor productivity Žsales per employee. are significantly higher than industry peers. Therefore, our empirical evidence does not lend credence to the belief that firms that layoff employees ultimately hurt themselves. We find no evidence that the eventual turnaround in firm performance following layoff decisions is due to mean reversion in accounting earnings.

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

197

Second, we find that layoff announcements are associated with a significantly negative stock market response—the average 2-day abnormal stock return associated with layoff announcements is y1.2%. Layoffs that are attributed to demand decline by management have significant larger negative market reaction. Third, we do not find any evidence that layoff announcements are followed by reduced total employment at the median firm. While median employment figures do decline in the year of the layoff, they recover to pre-layoff levels 3 years later. We find evidence of a temporary decline in capital expenditure in the layoff announcement year and the year following layoff decisions, but the decline disappears by the third year following layoff announcements. The temporary decline in capital expenditure along with layoff decisions is consistent with management desire to cut costs and improve earnings performance. The finding of no permanent decline in capital expenditure contradicts the view that layoffs are socially destructive. Finally, we find evidence of an increase in corporate focus as measured by the number of business segments the firm reports for accounting purposes, and as measured by a sales-based Herfindahl index. It appears that layoffs are part of an overall design to restructure and return to profitability. Taken together, our findings suggest that layoffs emerge out of a genuine desire to restructure in response to demand shocks in the product market and declining performance. The eventual turnaround in firm performance following layoff decisions supports that view that revisions of labor contracts in the case of layoffs are necessary and constructive steps to ensure corporate survival.

Acknowledgements We would like to thank the editor and two anonymous referees for their helpful comments and suggestions that have significantly improved the paper. We also thank seminar participants at the University of Alberta, the University of Bologna, the University of Sydney, Tsinghua University, China, Xavier Labour Relations Institute, India, the 1997 Northern Finance Association Annual Conference, Winnipeg, and workshop on governance at the Malaysian Institute of Economic Research.

Appendix A. Typical layoff announcements and their reasons An example of a layoff announcement motivated by a desire to cut costs is the following quote attributed to the chairman of Aetna Life and Casualty. ŽWSJ, June 30, 1992.: Aetna managers are taking these actions in response to my challenge to them to run their operations more cost effectively.

198

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

Demand decline was cited as a major reason for the layoff announcement at Rohr, a maker of aircraft parts ŽWSJ, May 12, 1992.: . . . Rohr Inc. plans to reduce its salaried work force by 15%, or about 750 people, by August 1. The aircraft-parts maker is reacting to cutbacks in orders for small jetliners. Poor prior period performance was cited as a reason for layoffs at Key Tronic ŽThe Wall Street Journal, Aug. 30, 1991.: Key Tronic Corp., a computer keyboard manufacturer, said it would lay off 200 employees, or 11% of its work force, because of operating losses . . . Martin Marietta’s, layoff announcement was of a more general nature in that it did not specify any particular reason for the company’s layoff decision. The WSJ article ŽJan 8, 1991. cites Martin’s chairman: . . . the reshuffling will allow the company’s resources Ato match the marketplace of the ’90sB. In reading through the full texts of the WSJ articles, we got the impression that companies make layoff decisions reluctantly, almost as a last resort, and not without concern for their employees. Amoco’s announcement of cutting 8500 jobs from its payrolls Ž16% of its work force. was made in the face of major restructurings at several oil companies in the past year in response to excess industry capacity. Amoco’s chairman acknowledged that ŽWSJ, July 9, 1992.: . . . the human impact of our decisions is very real and very painful . . . Žhowever. we can’t go forward with the idea that margins will save us.

References Barber, B.M., Lyon, J.D., 1995. Detecting abnormal operating performance: the empirical power and specification of test-statistics. Journal of Financial Economics 41, 359–399. Blackwell, D.W., Marr, M.W., Spivey, M.F., 1990. Plant-closing decisions and the market value of the firm. Journal of Financial Economics 26, 277–288. Brickley, J.A., Van Drunen, L.D., 1990. Internal corporate restructuring: an empirical analysis. Journal of Accounting and Economics 12, 251–280. Brockner, I., Greenberg, J., Brockner, A., Bortz, J., Davy, J., Carter, C., 1986. Layoffs, equity theory, and work performance: further evidence of the impact of survival guilt. Academy of Management Journal 10, 233–250. Cascio, W.F., 1993. Downsizing: what do we know? what have we learned? Academy of Management Executive 7, 95–104. Jensen, M., 1993. The modern industrial revolution, exit, and the failure of internal control systems. Journal of Finance 48, 831–880. Jensen, M., Meckling, W., 1976. Theory of the firm: managerial behavior, agency costs and ownership structure. Journal of Financial Economics 3, 305–360.

P. Chen et al.r Journal of Empirical Finance 8 (2001) 171–199

199

John, K., Ofek, E., 1995. Asset sales and increases in focus. Journal of Financial Economics 37, 105–126. John, K., Lang, L., Netter, J., 1992. The voluntary restructuring of large firms in response to performance decline. Journal of Finance 57, 891–917. Hallock, K., 1998. Layoffs, top executive pay, and firm performance. American Economic Review 88, 711–723. Lee, P., 1997. A comparative analysis of layoff announcements and stock price reactions in the United States and Japan. Strategic Management Journal 18, 879–894. Lin, K.C., Rozeff, M.S., 1993. Capital market behavior and operational announcements of layoffs, operational closings, and pay cuts. Review of Quantitative Finance and Accounting 3, 29–45. McConnell, J.J., Muscarella, C.J., 1985. Corporate capital expenditure decisions and the market value of the firm. Journal of Financial Economics 14, 399–422. Palmon, O., Sun, H.L., Tang, A.P., 1997. Layoff announcements: stock market impact and financial performance. Financial Management 26, 54–68. Shleifer, A., Summers, L., 1988. Breach of trust in hostile takeovers. In: Auerbach, A. ŽEd.., Corporate Takeovers: Causes and Consequences. University of Chicago Press, Chicago, pp. 33–56. Spiess, D.K., Affleck-Graves, J., 1999. The long-run performance of stock returns following debt offerings. Journal of Financial Economics 54, 45–73. Warner, J.B., Watts, R.J., Wruck, K., 1988. Stock prices and top management changes. Journal of Financial Economics 20, 461–492. Weisbach, M.S., 1988. Outside directors and CEO turnover. Journal of Financial Economics 20, 431–460. Weisbach, M.S., 1995. CEO turnover and the firm’s investment decisions. Journal of Financial Economics 27, 159–188. Worrell, D.L., Sharma, V.M., 1991. Layoff announcements and stockholder wealth. Academy of Management Journal 34, 662–678.