The University of Chicago
The Booth School of Business of the University of Chicago
The University of Chicago Law School
The Economic Consequences of the OSHA Cotton Dust Standards: An Analysis of Stock Price
Behavior
Author(s): John S. Hughes, Wesley A. Magat, William E. Ricks
Reviewed work(s):
Source: Journal of Law and Economics, Vol. 29, No. 1 (Apr., 1986), pp. 29-59
Published by: The University of Chicago Press for The Booth School of Business of the University of Chicago
and The University of Chicago Law School
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THE ECONOMIC CONSEQUENCES OF THE
OSHA COTTON DUST STANDARDS: AN
ANALYSIS OF STOCK PRICE BEHAVIOR*
JOHN S. HUGHES, WESLEY A. MAGAT, and WILLIAM E. RICKS
Duke University
I.
INTRODUCTION
IN a celebrated 1981 decision, the Supreme Court upheld the Occupational Safety and Health Administration's (OSHA) standards limiting the
exposure of textile workers to respirable cotton dust. This decision represented the culmination of a rule-making process, the beginnings of which
are traceable to seminal scientific studies first released almost ten years
earlier on the incidence of so-called brown lung disease. Reputed to be
quite costly,1 the standards attracted more media attention as well as
more Congressional, White House, and judicial involvement than almost
any regulation issued in recent years. The purpose of this study is to
assess the effect of major events in the development of the standards on
the expected profitability of firms in the textile industry by examining the
behavior of security prices at the time of those events.
While little formal research exists on the ecomomic effects of the cotton
dust standards beyond an analysis prepared for OSHA before the standards were issued,2 a study by Maloney and McCormick3 (M&M) offers
* The authors would like to thank the
participants in faculty seminars at the University of
British Columbia and the Fuqua School of Business at Duke University for several useful
suggestions. We are grateful to several other individuals and organizations who have helped
us better understand the development of the OSHA cotton dust standards, especially James
A. Merchant, John C. Lumsden, the American Textile Manufacturers Institute, the Amalgamated Clothing and Textile Workers Union, Cotton, Inc., the American Conference of
Governmental Industrial Hygienists, the Federal Trade Commission, and the U.S. International Trade Commission.
1 See the cost estimates in
note 52 infra.
2 Cotton
Dust-Technological
Feasibility and Inflationary Impact Statement (prepared
for the Occupational Safety and Health Administration by Research Triangle Institute,
Research Triangle Park, North Carolina 1976).
3 Michael T.
Maloney & Robert E. McCormick, A Positive Theory of Environmental
Quality Regulation, 25 J. Law & Econ. 99 (1982).
[Journal of Law & Economics, vol. XXIX (April 1986)]
? 1986 by The University of Chicago. All rights reserved. 0022-2186/86/2901-0008$01.50
29
30
THE JOURNAL OF LAW AND ECONOMICS
both theory and evidence regarding security market reactions over a
twelve-month period ending approximately when the formal regulatory
process began. Their study is of interest not only because they address
the same issues (albeit focusing on just a single stage in the standardsetting process) but also because, by relying on a different research design, they reach conclusions at variance with those that follow from our
analysis.
As social regulation of the health, environmental, and safety consequences of business activity grew dramatically in the 1970s, the theories
about this type of regulation developed quickly. The traditional view of
the effect of social regulation on business was that it reduces profits
through raising the costs of building and operating plants. However, more
sophisticated analyses recognized that social regulation is not necessarily
unprofitable for business. For example, M&M rigorously demonstrate the
sufficiency of a set of market conditions that guarantee that a costly
standard will raise market prices enough to raise the profits of most or all
firms in an industry. The more general analysis recognizes the heterogeneity among firms in characteristics such as compliance costs and argues
that the primary effects of regulation are distributional, with some firms
gaining profits and other firms losing.4
This recognition that regulation could benefit some firms at the expense
of others, as well as either raise or lower the average profitability of the
entire industry, naturally led to a search for methods to measure both
cross-firm and industry-wide effects empirically. "Event studies" based
on security price data soon surfaced as one of the most promising measurement approaches.5 Stock prices measure the market's expectation of
the present values of stockholders' interest in the future cash flows deriving from the operation of firms. To the extent that regulations affect either
the expected costs or the expected revenues of a firm, in an efficient
market the firm's stock price should reflect the corresponding changes in
present values. Thus under certain conditions it may be possible to infer
industry- and firm-specific effects of a regulation from changes in such
prices. Ideally, if the market did not anticipate a regulation, if the period
of time within which a regulation becomes publicly known is short and
well defined, and if other factors affecting profitability during the period
can be filtered out, then the net effect on stock prices should reflect the
expected effect of the regulation.
4
See, for example, Sam Peltzman, Toward a More General Theory of Regulation, 19 J.
Law & Econ. 211 (1976); and Robert A. Leone & C. James Koch, The Clean Water Act:
Unexpected Impacts on Industry, 3 Harv. Envtl. L. Rev. 84 (1979).
5 See, for example, G. William Schwert, Using Financial Data to Measure Effects of
Regulation, 24 J. Law & Econ. 121 (1982).
COTTON DUST STANDARDS
31
Unfortunately, regulations are often developed slowly, with public announcements at many stages in their evolution. In this case, it may still be
possible to use stock prices to measure changes in market expectations
regarding the net effects on future cash flows of the more important
events in the development of a regulation, as long as each specific event
can be clearly dated and adjustments can be made for other important
systematic factors affecting those prices. At best these measurements of
regulatory effect capture only the change in market expectations. The
measurements may resolve whether a particular event (for example, the
publication of a regulation in the Federal Register or a Supreme Court
decision) benefited or hurt stockholders relative to their earlier expectations about the effects of the regulation, but they will not measure the
total effects of the regulation.
The remainder of this paper is organized as follows. Section II reviews
the important events in the development of the OSHA cotton standards,
while Section III describes alternative hypotheses about effects of the
standards on the industry as a whole and on specific firms engaged in the
production of cotton textiles. Section IV describes the research design of
our study, and Section V presents our evidence on security market reactions to the events identified in Section II. Section VI returns to the earlier
M&M analysis and provides competing explanations for effects they detected. Finally, Section VII offers some concluding remarks about our
findings.
II.
REVIEW
HISTORICAL
The Occupational Safety and Health Administration's regulations on
exposure to cotton dust are claimed to reduce the incidence of byssinosis,
the acute and chronic respiratory disease contracted by workers who
process cotton and other similar fibers.6
Table 1 lists the seventeen events in the development of the standards
that we judge to be potentially important to investors' expectations.7
6
Depending on the intensity and duration of exposure, byssinosis victims experience
chest tightness, breathlessness, and irritation of the respiratory tract. Over 300,000 workers
in the United States are directly exposed to cotton dust, with most of the exposure in textile
plants. See James A. Merchant, Cotton Dust: A Scientist's View, in The Scientific Basis of
Health and Safety Regulation (Robert W. Crandall & Lester B. Lave eds. 1981). Some
groups estimate this number to be as high as 800,000 workers. According to OSHA estimates, almost 5,000 cases of byssinosis per year would be avoided by its standard in yarn
preparation areas, with additional reductions in incidence of byssinosis in other areas of the
cotton plant operations. See 43 Fed. Reg. 27350, June 23, 1978.
7 We identified the seventeen events
by thoroughly searching the Wall Street Journal
index of general news, reading the literature on the OSHA cotton dust standard, searching
the Federal Register notices and the Occupational Safety and Health Reporter, and interviewing several people with detailed knowledge of the development of the standard.
TABLE 1
IDENTIFIABLE EVENTS IN THE DEVELOPMENT OF THE OSHA
Description of Event
w
^
1. Skytop Conference on Respiratory
Disease in Industry (Merchant
papers)
2. ATMI forwards recommendation to
Department of Labor
3. American Industrial Health
Conference (Imbus address)
4. ACGIH adopts TLV of 200 ,ug/m3
5. NIOSH submits Criteria Document
to Department of Labor without
specific standards
6. OSHA publishes Advance Notice of
Proposed Rulemaking (referring to
200 Lg/m3 maximum standard)
7. ACTWU files petition with
Department of Labor for 100 i.g/m3
standard
8. ACTWU and NC-PIRG file suit
Date of
Event Itself
COTTON DUST STA
Source of Earliest
Public Disclosure
May 17-19, 1972
Journal of Occupational Med
(earlier sources unknown)
December 18, 1973
Occupational Safety and Hea
Reporter
Journal of Occupational Med
(earlier sources unknown)
Occupational Safety and Hea
Reporter
Occupational Safety and Hea
Reporter
April 29, 1974May 2, 1974
May 13, 1974
September 26, 1974
December 23, 1974
Federal Register
January 13, 1975
Occupational Safety and Hea
Reporter
December 24, 1975
Wall Street Journal
W
9. OSHA announces proposed
regulations (200 ig/m3)
10. Schultze requests Regulatory
Analysis Review Group review,
Marshall appeals, and Carter
brought into dispute*
11. Carter backs Department of Labor
(Marshall), Marshall and Bingham
issue statement, Department of
Labor promises to file standards
with Federal Register, and final
standard issued*
12. Senate votes to withhold
enforcement funds
13. Conferees refuse to delay standard
14. Federal court of appeals postpones
implementation
15. Federal court of appeals upholds
standard
16. Supreme Court agrees to hear case
17. Supreme Court upholds standard
December 20, 1976
Wall Street Journal
May 2, 1978 (first)
May 24, 1978 (last)
Wall Street Journal
June 7, 1978t (first)
June 19, 1978 (last)
Wall Street Journal
September 25, 1978
Wall Street Journal
October 8, 1978
October 20, 1978
Wall Street Journal
Wall Street Journal
October 24, 1979
Wall Street Journal
October 6, 1980
June 7, 1981
Wall Street Journal
Wall Street Journal
* These partiallydistinctevents are groupedprincipallybecause the dates of publicannouncementsdo not allow se
t Carter'sdecisionwas announcedin a news conferenceheld by Schultzeon this day; however,the first mediain
followingday.
34
THE JOURNAL OF LAW AND ECONOMICS
Most of them are well defined in terms of their period of influence on the
security markets. All but two were reported in the Wall Street Journal,
the Federal Register, or the Occupational Safety and Health Reporter.8
For two early events we were unable to find references other than conference proceedings published in the Journal of Occupational Medicine, a
monthly publication. Other events to be described below were not reported in the business, trade, or professional press, making it impossible
to define their dates of influence on the market in a manner sufficiently
precise to be useful in an event study. Besides the date and a description
of each event, Table 1 also lists the public source of earliest disclosure
and its date of publication.
Byssinosis has a long history of scientific study, with the first epidemiological studies published in the early 1800s.9 The first mandatory standard
in the United States was issued on September 20, 1968, when, under the
authority of the Walsh-Healey Act, the Secretary of Labor proposed the
1968 version of the American Conference of Governmental Industrial
Hygienists (ACGIH) list of threshold limit values (TLV) for workers employed under government contracts. This list included a 1,000 iLg/m3total
cotton dust recommendation.10 By the end of 1970 the Occupational
Safety and Health Act was signed into law, making the 1,000 pJg/m3total
cotton dust exposure standard mandatory for all textile workers. 1 During
this same period of the early 1970s, the seminal medical studies of byssinosis in the United States were being carried out. In March 1973 Mer-
8
The first two sources are published every business day, and the last is published every
Thursday.
9 In 1942 it was made a compensable occupational disease in England. In this country the
American Conference of Governmental Industrial Hygienists placed cotton dust on its tentative list of threshold limit values on April 26, 1964; and on May 16, 1966, that body adopted
1,000 ,Lg/m3 (micrograms/cubic meter) of total cotton dust as its recommended limit for
exposure. See the references in Merchant, supra note 6.
10
Ralph Nader then took up the crusade against byssinosis by writing a stinging letter to
the Secretary of Health, Education, and Welfare condemning the cotton textile industry as
being "powerful and callous enough to deny the undeniable [the existence of byssinosis]."
Cong. Rec., August 11, 1969. On May 2, 1970, the National Conference on Cotton Dust and
Health was held in Charlotte, North Carolina, to assess the health problems of cotton dust
exposure. Two hundred participants from industry, textile unions, governmental agencies,
and private medical practices gathered to assess the state of knowledge about byssinosis and
to recommend further action. Although the summary proceedings and recommendations
were not published until September 1971, industry attention to the problem was heightened
significantly at this meeting. See Organizing Committee of the National Conference on
Cotton Dust and Health, The Status of Byssinosis in the United States, 23 Archives of
Envtl. Health 230 (1971).
l In 1972, the British Occupational Hygiene Society recommended a new standard of 500
,Lg/m3less fly, where "fly" meant dust particles removed by a two-millimeter wire screen.
COTTON DUST STANDARDS
35
chant12 published his two studies that became the primary scientific basis
of the eventual OSHA standards. He began the research in 1970 and first
presented the papers at a conference held May 17-19, 1972 (event 1).
While the scientific evidence about the relation between cotton dust
exposure and byssinosis accumulated and ACGIH revised downward its
recommended cotton dust exposure limits, the National Institute of Occupational Safety and Health (NIOSH) started its own study. In anticipation
of eventual OSHA action, the American Textile Manufacturers' Institute
(ATMI) forwarded its recommendations for a work practice standard for
raw cotton dust to the Department of Labor on December 18, 1973 (event
2). At the American Industrial Health Conference, April 29-May 2, 1974,
Dr. Harold Imbus, Medical Director of Burlington Industries, presented
his current thinking about the forthcoming NIOSH cotton dust Criteria
Document and eventual OSHA cotton dust standards (event 3). On May
13, 1974, the ACGIH adopted a TLV of 200 pLg/m3of respirable cotton
dust, that is, without fly (event 4).
On September 26, 1974, NIOSH submitted its cotton dust Criteria Document to the Secretary of Labor (event 5). The Criteria Document stated
that "an environmental standard should be fixed ... at the lowest level
feasible," but it did not define the "lowest feasible" concentration. In
December 1974 NIOSH sent an internal memorandum advising the thenAssistant Secretary of Labor for Occupational Safety and Health that the
exposure standard should not be as high as 200 pLg/m3of lint-free cotton
dust. On December 23, 1974, OSHA announced an Advance Notice of
Proposed Rulemaking (event 6), seeking comments from interested parties on both the Criteria Document and the NIOSH memorandum. On
January 13, 1975, the Amalgamated Clothing and Textile Workers Union
(ACTWU) filed a petition with the Department of Labor requesting several modifications of the suggested standards, including a reduction of the
exposure limit to 100 pLg/m3(event 7). On December 24, 1975, the North
Carolina Public Interest Research Group (NC-PIRG) and ACTWU sued
the Labor Department for failure to promulgate protective standards
(event 8). About a year later (on December 20, 1976) OSHA responded by
proposing cotton dust regulations, including a 200 JLg/m3exposure standard for all phases of cotton manufacturing (event 9).
The final standard was promulgated on June 19, 1978, but several
events had occurred in the previous month and a half. Charles Schultze,
12
James A. Merchant et al., An Industrial Study of the Biological Effects of Cotton Dust
and Cigarette Smoke Exposure, 15 J. Occupational Med. 212 (1973); and James A. Merchant
et al., Dose Response Studies in Cotton Textile Workers, 15 J. Occupational Med. 222
(1973).
36
THE JOURNAL OF LAW AND ECONOMICS
Chairman of the Council of Economic Advisers, requested an informal
review by the Regulatory Analysis Review Group of the cotton dust standards on May 2; however, this was not made public until May 18. In a
May 24 memorandum, Secretary of Labor Marshall asked President Carter not to delay issuance of the final standard, although his memorandum
was not made public until June 8. Carter, Marshall, and Schultze met to
resolve the issue on June 7, with Carter deciding not to delay the standard
to accommodate further review by the White House economists. Schultze
held a news conference later that day to describe the "compromise," but
the news was not published until the next day. Interestingly, on June 7 the
Wall Street Journal published an article (incorrectly) predicting that
Schultze would win the dispute; hence Carter's decision later that day
constituted important news for the textile industry. Since all the news
published between May 2 and June 7 indicated that President Carter might
order the Labor Department to soften its intended standards, we define
this entire period as event 10.13Event 11 stretches from the first published
announcement on June 8 that Marshall had won the dispute to the June 20
announcement of the final regulations. Among other requirements, these
in yarn manufinal rules set three different exposure standards: 200 JLg/m3
facturing, 750 [Lg/m3in slashing and weaving operations, and 500 RLg/m3in
nontextile industries such as cottonseed oil mills and mattress and bedding manufacturers.14
The Senate voted to withhold enforcement funds for the cotton dust
standards on September 25, 1978 (event 12), but the House and Senate
conferees killed the delay on October 8, 1978 (event 13). On October 20,
1978, a federal court of appeals stayed the rules, pending its review of
ATMI challenges to them (event 14). A year later (October 24, 1979) this
court upheld the standards (event 15). On October 6, 1980, the Supreme
Court agreed to hear the case (event 16), eventually sustaining the standards on June 17, 1981 (event 17).
III.
HYPOTHESES
ALTERNATIVE
For the purpose of evaluating the effects of the OSHA cotton dust
standards on the textile industry (more accurately, the effects on shareholders' expectations of events in the development of the standards), it is
useful first to consider industry-wide effects that influence most or all
cotton-producing textile firms and then to examine the differential effects
among firms in the industry.
13 Marshall's May 24 memorandum was not published until after this period.
14 They also include requirements for medical surveillance, employee training, safe work
practices, and respirators.
COTTON DUST STANDARDS
37
Industry-wide Effects
Two recent papers (a trade association report and an article in this
Journal) support the hypothesis that the cotton dust standards benefited
the textile industry or at least portions of it. In the first paper, Eric
Frumin,15 Director of the Department of Occupational Safety and Health
of ACTWU, compares a time series of capital expenditures per employee
from 1978 to 1981 for a group of seven large textile firms to a time series of
average profit rates for the seven firms, both variables having been scaled
by the textile industry averages. He argues that the regulations forced
textile producers to invest in productivity-enhancing equipment that they
should have purchased even without the regulation, making them more
profitable than they would have been without the investments. His graph
shows a tight fit between capital exenditure and profitability, both of
which increased from below the industry averages in 1978 to well above
the averages in 1981 (for the seven firms). Noting that industry capital
expenditures increased about 30 percent from 1978 to 1981 and that by
1982 81 percent of the textile workers were in compliance with the 1978
standards (which required compliance by 1984), he concludes from his
graph that "the economic performance of these (seven) companies
reflects the positive stimulus provided by the standard to the modernization of the textile industry and the consequent improvements in productivity."16
In the second paper, M&M offer two theoretical arguments, both very
different from Frumin's, for why the cotton dust standards may have
benefited the cotton textile industry. First, they demonstrate that the
following set of conditions is sufficient for environmental quality regulation to increase producer wealth, while it also improves the quality of the
environment: (1) entry must be restricted or blocked after the regulation is
imposed but not before; (2) before regulation firms operate and price at
minimum average variable cost (implying zero profit above variable
costs); (3) pollution increases with output; and (4) regulation drives a
wedge between the pre- and postregulation average variable cost curves,
shifting the new minimum to a lower output rate.17 They explain that if the
entire cotton industry gained from the regulation, it was because produc15
See Eric Frumin, The Economic Impact of the OSHA Cotton Dust Standard (unpublished report, Amalgamated Clothing and Textile Workers Union, March 1983).
16
Id. at 5. The caveat to this argument, however, is that they would have been even more
profitable had they purchased only productivity-enhancing equipment without spending any
extra resources to construct cotton dust abatement capabilities.
17
Note that free entry (condition 1) is necessary for the zero-profit preregulation equilibrium described in condition 2.
38
THE JOURNAL OF LAW AND ECONOMICS
ers were able to move up an inelastic demand curve to a higher price,
above the postregulation average variable cost curve (which is higher than
the preregulation curve but not as high as price), without inducing entry.
Second, M&M posit that any profit enhancement in their sample of only
large cotton producers may, alternatively, be due to the intraindustry
transfer effects of the regulation, with large firms gaining in profitability
relative to small firms because of their comparative advantage in unit
compliance costs (a subject to be discussed more fully below).
In contrast to the industry-wide profit-enhancement hypothesis considered by Frumin and M&M (call it Hi), two other hypotheses need to be
considered. The second hypothesis is that the cotton dust standards did
not signficantly affect the profitability of cotton-producing firms (H2).18
The third hypothesis asserts that the standards reduced industry profitability by imposing compliance costs that were not offset by higher selling
prices (H3).19This last hypothesis is consistent with the more traditional
view of the impact of social regulation mentioned above in the introduction.
Differential Effects among Firms
Aside from the overall effect of the regulation on the cotton textile
industry, firms within the industry may be differentially affected due to
any of the following three factors affecting their costs of compliance. All
else being equal, the higher the unit cost of compliance for a firm, the
lower its profits.20 Thus to the extent that unit compliance costs differ
systematically across cotton-producing firms, we should expect to observe differential effects on their profitability produced by the cotton dust
standards.
18
Either the net costs of compliance were low or the industry was able to pass on those
compliance costs through higher prices. The former case of low net compliance costs could
have resulted several ways. The compliance costs could have been incurred jointly with the
costs of otherwise cost-effective, productivity-improving investments. Alternatively, the
compliance costs could have been offset by lower wages (due to a healthier work environment), lower workers' compensation payments, or improved employee productivity through
better morale and less frequent turnover. The latter case of a full pass-through of compliance
costs due to higher prices is only consistent with a horizontal industry supply function (that
is, with no firm-specific factors of production). In this case we would expect to find differential effects across firms only in the unlikely event that the disadvantaged firms exactly offset
the firms that benefited from the regulations.
19
Hypothesis H3 differs from H2 in asserting that the profit reductions are large enough to
be statistically detectable, given the noise in the abnormal returns data.
20 Conceptually, the unit costs of compliance are measured as an average cost of compliance, where total costs are averaged across the various outputs of a firm (with all product
outputs measured in equivalent units of cotton production).
COTTON DUST STANDARDS
39
1. Cotton Use. Unit compliance costs can differ among textile firms if
they devote different proportions of their businesses to cotton production
as opposed to other noncotton textiles (or other products). If OSHA
regulations raise the cost of cotton production but not the cost of production using other fibers, then variations in the percentage of cotton use
across firms would cause variations in the unit costs of compliance.
2. Intraplant Scale Economies. All else being equal, the existence of
economies of scale in compliance within a plant should cause large cottonproducing plants to experience lower unit cost increases than small
plants. Intraplant scale economies might be explained by factors such as
the declining average cost of abatement equipment (for example, for fans
and dust collectors).
3. Interplant Scale Economies. All else being equal, economies of
scale in compliance across plants should lower the unit compliance costs
of multiplant cotton producers. Factors such as the fixed costs of administering a company-wide health and safety program and efficiencies in contracting for a scheduling medical testing would contribute toward interplant scale economies.
The absolute effect of the standards on any individual cotton producer
depends on both the industry-wide effect (H1, H2, or H3) and the effect on
the firm compared with other firms in the industry (caused by the three
factors described above). First, consider industry hypothesis H1 that the
cotton dust regulations enhanced industry profits. Given that all firms
experienced the same regulation-induced price increase, firms with larger
cotton plants or more plants should have realized even greater profit
enhancement from the regulations than those with smaller or fewer
plants. Also, if price increases exceeded unit compliance costs, then firms
with higher cotton proportions should have realized greater profits than
firms with lower cotton proportions.
Next, consider hypothesis H3 that the standards reduced industry
profits. Then the existence of larger plants or more plants should imply
less reduction in profitability relative to other firms in the industry. In
addition to these two effects, if unit costs exceeded price increases, then
firms with higher cotton proportions should realize greater profit reductions than firms with lower cotton proportions.
Finally, consider hypothesis H2 that the cotton dust regulations caused
no overall effect on industry profits. If no cotton firms experienced
significant losses or gains from the regulations, then none of the three
differentiating factors would lead to any variation in profitability. However, even if the average effect is zero, cotton proportions, plant size, and
number of plants could still explain differential profitability effects across
firms in the industry.
40
THE JOURNAL OF LAW AND ECONOMICS
IV.
RESEARCH DESIGN
Our sample of firms includes all companies appearing on COMPUSTAT within SIC 2200 "Textile Mill Products," plus Celanese Corporation.21 Of the forty-five firms so identified, twenty-four are engaged in the
production of cotton textiles, while there is no evidence of cotton production for the remaining twenty-one firms.22
In Section II we identified seventeen potentially important events in the
development of the cotton standard. All these events meet the criteria of
being significant enough potentially to affect investor expectations and of
being precisely dated. All but two of the events were announced shortly
after their occurrence in the Wall Street Journal, the Occupational Safety
and Health Reporter, or the Federal Register. Two events (1 and 3) were
reported several months later in a scientific journal. Event windows for
individual events were determined by starting with the date of the event
itself and ending with its earliest public announcement.23 In addition, we
formed three composite event windows by combining (1) all seventeen
events; (2) all those events that appeared to indicate that the final 200 xLg/
m3 standard was more likely to be promulgated than a more lenient one;
and (3) all those events indicating that the 200 iLg/m3standard was less
likely.24
Excess returns were obtained from the Center for Research on Security
Prices (CRSP) Daily Excess Returns file. A firm's excess return on a
given day is defined as the difference between its actual return and the
21
The listing employed is dated May 1981 and precedes a further breakdown into two SIC
categories. Celanese Corp. is not classified as a textile firm but is involved in the production
of synthetic textile products. We include it in our sample because of its appearance in
M&M's sample.
22 Data sufficient to determine cotton
percentages, as well as information on other variables used in our cross-sectional anlysis (described later), were extracted primarily from
Davison's Textile Blue Book (1975) and SEC 10-K reports. The former source provided us
with (incomplete) information on plants, processes, products, and employees. The latter
gave us further information about raw materials, sales by product line, employees, and
plants.
23 Events 10 and 11 require some judgment in dating the windows according to this rule.
As mentioned earlier, part of the difficulty is that both events are really a series of subevents
that are overlapping in the span of time between their occurrence and public announcement.
The other part is that the decision by President Carter (which followed the request for a
review of the proposed standard) was made on the same day as a Wall Street Journal article
speculating that he would make the opposite choice. Accordingly, any partition of the period
encompassing the two events is somewhat arbitrary. Because of this, we ran the analysis
with that day allocated to either event, without any substantive change in our qualitative
results. Because of the extensive delay in subsequent public disclosure of events 1 and 3, we
defined the event periods as encompassing the days on which the conferences were held.
24 In defining composite event windows 2 and 3, we assume that the market never expected the final standard to be more stringent than 200 ,pg/m3.
COTTON DUST STANDARDS
41
return for a reference portfolio of similar risk securities.25 Not all sample
firms have a complete set of excess returns over the period encompassing
all our events. In particular, just eighteen of the twenty-four "cotton
firms" and nine of the twenty-one noncotton firms have such a set. To test
for industry-wide effects, we relied principally on a sampling rule of using
all firms with excess returns during a given event window to analyze the
market reaction to that event. However, since this rule is no good for
testing for firm-specific effects of composite events, we ran our crosssectional analysis using only those firms with a complete set of excess
returns.
In Section III we identified three firm-specific factors, or characteristics, that might explain differences across firms in the effects of the cotton
dust regulations. The following variables serve as proxy measures for
these factors. Cotton use is measured by two proxies, the percentage of
cotton production to total production (CP) and the ratio of cotton to
noncotton production (CR).26 Intraplant scale economies of compliance,
the second factor, is measured by plant capacity (PC), calculated from the
average of the capacities of all textile plants owned by a given company.27
Third, we measured interplant scale economies in compliance with the
regulations by cotton sales per firm (CS). This variable was constructed
by multiplying the total company sales by an estimate of the proportion of
sales derived from cotton products.
A fourth explanatory variable, firm size (FS) as measured by market
value of common stock, is employed to control for a relationship often
found between security returns and firm size and also to test the hypothesis that large firms gained at the expense of smaller ones.28
25
More specifically, all New York Stock Exchange (NYSE) and American Stock Exchange (AMEX) stocks are placed into one of ten portfolios on the basis of their estimated
risk (Beta). The Beta (risk) values are computed using the methods developed by Myron
Scholes & Joseph Williams, Estimating Betas from Nonsynchronous Data, 5 J. Fin. Econ.
309 (1977).
26
We conducted our own independent analysis of the cotton percentages used by each
textile firm in our expanded sample. Our cotton percentages involved a judicious choice of
what appeared to be the most complete and reliable piece of that information. In calculating
cotton percentages, we adjusted the percentage of textile production involving cotton by the
percentage of total production devoted to textiles. Further details on how our percentages
were calculated are available from the authors on request.
27
Plant capacity is measured in square feet devoted to cotton production. Other measurements available included the number of employees and the number of cotton spindles. Since
data on each of the three variables were incomplete across firms, the missing values for
square feet devoted to cotton production were estimated using a regression of square feet on
the other two variables.
28
See Donald Keim, Size-related Anomalies and Stock Return Seasonality: Further Empirical Evidence, 11 J. Fin. Econ. 13 (1983).
42
THE JOURNAL OF LAW AND ECONOMICS
V.
EMPIRICAL FINDINGS
Evidence of Industry-wide Effects
We estimated daily average excess returns corresponding to each event
by performing the following portfolio regression:
ER, = ao +
kDk, + Ut,
>
(1)
k
where ERt is the excess return on an equally weighted portfolio of cotton
firms for period t; ut is a normally distributed residual with mean zero; Dkt
is a dummy variable taking the value one during event window k; and 6k
measures the daily average change in the value of the portfolio during that
window (net of market-wide effects). Additional estimates were obtained
by redefining ER, as the excess return on the portfolio of cotton firms net
of the excess return on a portfolio of noncotton firms in our sample. If
factors affecting the entire industry masked the effects of each event, and
if the regulatory events did not affect the profitability of the noncotton
textile firms,29then the second portfolio should control for textile industry
effects other than the regulatory events and thus reveal any ER's systematically related to the regulatory events.30 Results of regressions using (1)
are reported in Table 2.
Looking at estimates pertaining to individual events, only one event, 7,
shows a daily average excess return significant at the 90 percent
confidence level for the cotton portfolio, and one event, 9, is significant at
this level for the portfolio of cotton firm returns minus noncotton textile
firm returns. Since at least one significant event could be expected to
occur by chance, no special importance should be attached to events 7
and 9 from these findings. However, when we combine all seventeen
events into a single event window to obtain an estimate of the overall
effect of the regulations, our estimate of the daily average excess return
for the cotton portfolio, less the noncotton portfolio, is negative and significant at slightly below the 90 percent confidence level. Moreover, when
we subdivide events on the basis of our priors on the type of news they
contain, and then combine them, we find significance (at better than the 95
29
This second assumption would have been violated if higher cotton prices due to higher
OSHA compliance costs caused increased demand for noncotton substitute fabrics, thus
raising the profitability of noncotton textile firms. In this situation of both groups benefiting
from the regulations, the ER's for each event would indicate which groups benefited more.
30 The
potential empirical importance of an industry factor has been noted by Eugene
Fama & James MacBeth, Risk, Return and Equilibrium: Empirical Tests, 71 J. Pol. Econ.
607 (1973).
TABLE 2
REGRESSION ESTIMATES OF DAILY AVERAGE EXCESS RETURNS FOR SEVENTEEN EVENT
P
PORTFOLIO OF COTTON FIRMS
EVENT
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
All events
Events increasing
likelihood
Events decreasing
likelihood
LIKELIHOOD
OF STANDARD
Estimate
TnorrPoep
-
Decreased
Decreased
Increased
Increased
Increased
Increased
Increased
Increased
Decreased
Increased
Decreased
Increased
Decreased
Increased
Decreased
Increased
-.001
nn%
t-Value
-1
;R
Prob > Itl
17
Estimate
nnO4
.001
-.000
.002
.002
- .003
.001
- .004
- .001
- .000
- .004
.000
- .002
.003
- .000
-.001
- .294
.347
-.153
1.041
.716
- 1.763
.467
- .948
-.451
-.193
- 1.105
.015
- .438
.667
-.021
-.151
.769
.729
.878
.298
.474
.078
.343
.652
.848
.872
.269
.983
.661
.505
.983
.880
.007
.004
.004
-.002
.000
-.006
-.004
-.017
-.001
-.008
-.014
-.016
.010
.006
.005
-.010
-.001
-.997
.319
-.002
-.001
-.764
.445
-.004
-.001
-.654
.513
- .000
44
THE JOURNAL OF LAW AND ECONOMICS
percent level) for the estimate related to the group of events that a priori
we judged to increase the likelihood of adopting the final 200 ,ug/m3standard rather than a more lenient one. Even for the cotton portfolio alone,
the estimated reaction to all seventeen events combined is negative, although not as significant as for the cotton less noncotton portfolio. These
results provide modest evidence in support of hypothesis H3, that profits
for cotton textile firms were on the average reduced by the cotton dust
standards, and strong evidence for rejecting hypothesis H1, that industry
profits were on the average increased.
Evidence of Intraindustry Effects
While the previous section provides some evidence of a negative effect
of the standards on the profit expectations for the cotton textile industry,
it still remains to determine whether differential effects on the profitability
of individual firms can also be found. In Section IV we developed proxies
for factors that could potentially explain a differential effect of the standards across sample firms. These proxies were used to perform regressions of the form:
ERi = a +
bPil + vi,
(2)
where ERi now represents the daily average excess returns for the ith
cotton firm during the event window of interest, vi is a normally distributed residual with mean zero, Pil is the proxy variable corresponding to
the Ith factor, and bl measures the marginal effect of that factor on excess
returns. Six separate regressions were run for each of the seventeen
events plus the three composite events defined in Section IV.31 Our
findings are reported in Table 3.
The results in all six panels of Table 3 provide no convincing evidence
that any of the three firm-specific factors affected the ER's of the cotton
firms for the seventeen individual events or for the three composite
events. The significant negative coefficient on cotton sales (CS) in panel E
for the composite event "increasing likelihood" can probably be attributed to the collinearity of that variable with firm size (FS), which exhibits
the firm size effect.32 Note that the coefficient of firm size is also signifi-
31 As mentioned earlier, we used the sampling rule of including only those firms in each
regression for which there were both a full set of returns data and an observation on the
relevant proxy variable(s).
32
The estimated correlation between CS and FS is 0.56. The coefficient of CS, when FS is
also included as a regressor, is insignificant, although still negative.
COTTON DUST STANDARDS
45
cant and negative for this composite event in panels C and D. Aside from
the possibility that cotton sales may be a proxy for firm size, the negative
coefficient runs counter to both the profit increase hypothesis, HI, and the
profit reduction hypothesis, H3. Moreover, the negative coefficients on
either cotton sales or firm size also run counter to a profit transfer argument that large cotton textile firms gained from the regulation while small
cotton firms lost.
Several more combinations of the proxy measures were used in additional regressions not reported here, with none of them showing more
significant coefficients than would be expected to occur by chance. Although the failure to find significant complementary cross-sectional relationships raises the possibility that factors other than the cotton dust
standards may be driving the industry-wide effects noted earlier, this
finding is more likely to be due to the lack of statistical power to detect
effects that may be present as well as to the crudeness of the proxy
variables, such as those for cotton use. In addition, the intraplant and
interplant scale economies in compliance may well be small or even negligible.33
There is absolutely no basis in our findings for the hypothesis (H1) that
the imposition of the standard raised the profits of cotton-producing firms.
If anything, the evidence favors hypothesis (H3) that, on the average,
such firms were negatively affected by this regulation. In the absence of
complementary evidence from cross-sectional tests, however, even this
hypothesis lacks strong support. We next consider a disparity between
our findings and those of M&M.
VI.
INTERSTUDY COMPARISON
Difference in Industry-wide Findings
Asserting that the issuance of the NIOSH Criteria Document on September 26, 1974, marked the most important date in the history of the
33
In order to test the validity of our tests on the basis of theoretical distributions, we
derived empirical distributions of t-statistics from our regressions of excess returns from
nonevent two-day periods on cotton mix variables. This procedure provides an indirect test
of the potential influence of contemporaneous cross-dependencies in biasing the tests reported in Table 3. Rejection of the null hypothesis of no association occurs with close to the
predicted frequencies at the 90 and 95 percent confidence levels for CP and at somewhat
lower than predicted frequencies at those levels for CR. Predicted frequencies are eightyfive and forty-three at the 90 and 95 percent confidence levels versus observed frequencies
of eighty-seven and thirty-five for CP and fifty-four and twenty-seven for CR. If there is a
bias in earlier tests, it is likely to be a conservative bias, implying less statistical power than
would be true without such a bias.
TABLE 3
REGRESSION ESTIMATES OF THE EFFECT OF REGULATORY EVENTS ON EXCESS RETURNS
FOR SEVENTEEN EVENT WINDOWS
EVENT
1
2
PANELA.
Estimate-CP
t-value
Prob > Itl
.0162
1.434
.1707
-.0366
-1.188
.2522
PANELB.
Estimate-CR
t-value
Prob > Itl
3
.0045
.667
.5141
-.0046
-.845
.4105
USINGCOTTON
RATIOS
FIRMS
(CR); EIGHTEEN
- .0024
-.299
.7687
.0027
.935
.3637
4
USINGCOTTON
PERCENTAGES
FIRMS
(CP); EIGHTEEN
.0002
.127
.9005
- .0005
-.361
.7232
USING COTTON PERCENTAGES (CP) AND FIRM SIZE (FS);
PANEL C.
FIRMS
EIGHTEEN
Estimate-CP
t-value
Prob > ItI
Estimate-FS*
t-value
Prob > Itl
.0482
1.234
.2360
- .0420
- 1.290
.2164
.0021
.302
.7666
- .0070
-1.313
.2089
-.0114
-.433
.6710
-.0451
-.632
.5371
-.0203
- 1.364
.1962
-.0201
-.1712
.1076
PANELD.
Estimate-CR
t-value
Prob > ItI
Estimate-FS*
t-value
Prob > Itl
USINGCOTTON
RATIOS
(CR) ANDFIRMSIZE(FS);
EIGHTEEN
FIRMS
.0023
.766
.4558
- .0031
-.364
.7212
- .0004
-.203
.8419
-.0010
-.692
.4998
- .0154
-.575
.5738
- .0268
-.362
.7224
- .0222
- 1.502
.1539
- .0179
-1.481
.1592
USING COTTON PERCENTAGES (CP) AND COTTON SALES
PANEL E.
FIRMS
(CS); EIGHTEEN
Estimate-CP
t-value
Prob > ItI
Estimate-CS*
t-value
Prob > ItI
.0187
1.608
.1287
- .0284
- .913
.3758
.0055
.785
.4445
- .0026
- .494
.6282
- .2232
-.967
.3488
- .7462
- 1.205
.2468
- .0932
-.667
.5148
-.1841
- 1.753
.1001
SALES(CS),
PERCENTAGES
USINGCOTTON
(CP), COTTON
PANELF.
AND PLANT CAPACITIES (PC);
Estimate-CP
t-value
Prob > Itl
.0070
.560
.5865
- .0845
Estimate-CS*
-.436
t-value
Prob > Itl
.6714
.0053
Estimate-PCt
.503
t-value
.6248
Prob > Itl
* Coefficientsmultipliedby 1 million.
t Coefficientsmultipliedby 1,000.
FOURTEEN FIRMS
.0351
2.040
.0661
.0021
.263
.7977
.0016
.258
.8014
-.1926
-.722
.4852
-.0865
-.699
.4990
- .0696
-.713
.4909
- .0204
- 1.406
.1873
.0135
2.105
.0691
- .0068
- 1.285
.2253
46
EVENT
5
6
7
PANELA.
.0027
.392
.7002
- .0096
-.603
.5550
- .009
-.216
.8317
PANELC.
- .0017
-.267
.7930
- .0017
-.168
.8683
PANELB.
.0013
.749
.4648
8
9
10
PERCENTAGES
FIRMS
USINGCOTTON
(CP); EIGHTEEN
- .0017
-.142
.8889
- .0015
- 1.017
.3244
RATIOS
USINGCOTTON
FIRMS
(CR); EIGHTEEN
- .0008
-.304
.7651
.0006
.366
.7195
- .0003
- .097
.9237
- .0012
-1.162
.2624
USINGCOTTON
PERCENTAGES
(CP) ANDFIRMSIZE(FS);
EIGHTEEN
FIRMS
- .0002
-.035
.9726
- .0144
-.881
.3923
- .0055
-.524
.6078
- .0019
-.283
.7808
- .0079
-.690
.5009
- .0049
- 1.129
.2767
- .0244
- 1.647
.1204
- .0400
-1.121
.2801
- .0312
- 1.359
.1941
- .0010
-.126
.9011
- .0267
-2.085
.0546
- .0053
-.640
.5316
PANELD.
USINGCOTTON
RATIOS
(CR) ANDFIRMSIZE(FS);
EIGHTEEN
FIRMS
.0007
.416
.6833
- .0018
-.426
.6763
-.0016
-.608
.5525
.0006
-.351
.7301
- .0016
-.546
.5933
-.0014
- 1.278
.2208
-.0229
- 1.573
.1365
-.0352
-.976
.3447
-.0310
- 1.373
.1900
.0002
.028
.9777
-.0259
-2.034
.0600
-.0056
-.680
.5066
PANEL E.
USING COTTON PERCENTAGES (CP)
AND COTTON SALES
FIRMS
(CS); EIGHTEEN
.0062
1.042
.3138
-.0034
-.224
.8254
-.0003
-.029
.9773
.0007
.108
.9156
.0014
.110
.9140
-.0032
- .735
.4738
-.3200
-2.707
.0162
-.5627
- 1.872
.0808
-.1304
-.606
.5536
-.1933
-1.432
.1725
-.1894
-.4660
.4097
-.0616
-.914
.3751
PANEL F.
USING COTTON PERCENTAGES (CP),
COTTON SALES (CS),
ANDPLANTCAPACITIES
FIRMS
(PC); FOURTEEN
.0055
.717
.4881
.0161
.882
.3968
.0042
.525
.6103
- .0039
-.482
.6391
.0029
.167
.8701
- .0058
-1.008
.3349
- .3074
-2.040
.0661
- .3644
- 1.017
.3311
.1445
.908
.3835
-.1769
-1.030
.3249
- .4155
-.139
.8923
- .0410
-.461
.6536
.0018
.280
.7843
- .0072
- .461
.6537
.0091
1.319
.2141
.0042
.599
.5613
- .0098
- .665
.5195
.0042
.876
.3999
(TABLE 3 Continues)
47
TABLE 3 (Continued)
EVENT
11
12
Estimate-CP
t-value
Prob > Itl
.0150
1.047
.3108
-.0020
-.313
.7582
PANELB.
Estimate-CR
t-value
Prob > Itl
.0003
.185
.8552
PANEL C.
14
13
15
FIRMS
USINGCOTTON
PERCENTAGES
(CP); EIGHTEEN
PANELA.
-.0018
-.137
.8930
-.0148
-.650
.5249
.0063
.378
.7105
USINGCOTTON
RATIOS
FIRMS
(CR); EIGHTEEN
.0012
.338
.7399
.0002
.049
.9615
-.0032
-.557
.5852
USING COTTON PERCENTAGES (CP)
-.0006
-.127
.9006
AND FIRM SIZE (FS);
EIGHTEEN
FIRMS
Estimate-CP
t-value
Prob > Itl
Estimate-FS*
t-value
Prob > Itl
Estimate-CR
t-value
Prob > Itl
Estimate-FS*
t-value
Prob > Itl
- .0022
-.325
.7497
.0139
.911
.3766
- .0008
-.057
.9555
- .0023
-.235
.8171
.0053
.308
.7621
-.0016
-.123
.9035
- .0080
-.276
.7865
- .0072
.278
.7848
.0708
- 1.660
.1177
- .0076
-.234
.8178
PANELD.
RATIOS
USINGCOTTON
(CR) ANDFIRMSIZE(FS);
FIRMS
EIGHTEEN
.0003
.175
.8635
- .0004
- .086
.9326
.0014
.420
.6801
- .0008
-.139
.8911
- .0009
-.199
.8447
.0001
.005
.9962
- .0155
-.518
.6117
.0102
.402
.6936
.0718
1.683
.1130
- .0113
-.344
.7355
PANEL E.
AND COTTON SALES
USING COTTON PERCENTAGES (CP)
FIRMS
(CS); EIGHTEEN
Estimate-CP
t-value
Prob > Itl
Estimate-CS*
t-value
Prob > Itl
.0003
.047
.9632
.0099
.685
.5037
- .0027
-.197
.8461
- .0204
- .913
.3759
.0028
.159
.8756
-.0136
-1.353
.1960
.2987
1.318
.2072
.0058
.264
.7955
.3938
1.072
.3006
.1736
.722
.4813
PANELF.
SALES(CS),
USINGCOTTON
PERCENTAGES
(CP), COTTON
AND PLANT CAPACITIES (PC);
Estimate-CP
t-value
Prob > Itl
Estimate-CS*
t-value
Prob > Itl
Estimate-PCt
t-value
Prob > It|
FOURTEEN FIRMS
-.0006
-.077
.9398
.0010
.060
.9536
.0118
.802
.4394
-.0163
- .530
.6065
-.0006
- .024
.9810
-.1472
- 1.164
.2692
.4505
1.765
.1053
.0702
.309
.7631
.5639
1.185
.2609
.3274
1.113
.2896
.0006
.087
.9324
.0199
1.398
.1898
- .0130
- 1.055
.3141
- .0335
- 1.297
.2210
.0153
.838
.4200
48
ALL
EVENT
16
SEVENTEEN
17
EVENTS
PANELA.
.0172
1.196
.2493
.0191
.844
.4110
.0039
.676
.5085
PANELC.
DECREASING
LIKELIHOOD
USINGCOTTON
PERCENTAGES
FIRMS
(CP); EIGHTEEN
PANELB.
.0023
.613
.5487
INCREASING
LIKELIHOOD
- .0019
-.580
.5698
- .0004
-.116
.9088
- .0042
-1.011
.3292
USINGCOTTON
RATIOS
FIRMS
(CR); EIGHTEEN
- .0002
- .234
.8178
.0003
.333
.7432
- .0010
- .936
.3632
USINGCOTTON
PERCENTAGES
(CP) ANDFIRMSIZE(FS);
EIGHTEEN
FIRMS
.0153
1.012
.3278
.0126
.544
.5944
- .0034
-1.070
.3018
- .0024
-.676
.5091
- .0051
-1.152
.2675
-.0134
-.498
.6256
-.0477
- 1.174
.2589
-.0112
- 1.826
.0878
-.0144
-2.149
.0484
-.0062
-.735
.4736
PANELD.
USINGCOTTON
RATIOS
(CR) ANDFIRMSIZE(FS);
FIRMS
EIGHTEEN
.0017
.424
.6777
.0021
.355
.7275
- .0005
-.670
.5129
- .0001
-.156
.8779
- .0020
-1.072
.3005
-.0169
-.613
.5493
-.0494
- 1.203
.2477
-.0106
- 1.690
.1118
-.0135
-1.988
.0654
-.0060
-.711
.4881
PANEL E.
USING COTTON PERCENTAGES (CP)
AND COTTON SALES
FIRMS
(CS); EIGHTEEN
.0144
.946
.3593
.0197
.812
.4295
- .0002
- .069
.9456
.0019
.580
.5703
- .0035
- .796
.4387
.1316
.682
.5055
- .0286
- .093
.9274
-.1017
-2.068
.0564
- .1396
-2.704
.0163
- .0424
- .796
.5532
PANELF.
USINGCOTTON
PERCENTAGES
SALES(CS),
(CP), COTTON
AND PLANT CAPACITIES (PC);
FOURTEEN FIRMS
.0239
1.906
.0831
.0433
1.378
.1957
.0022
.730
.4807
.0039
1.128
.2833
-.0005
-.105
.9185
-.0891
-.576
.5761
.1346
.347
.7349
-.0401
-.867
.4044
-.0628
-1.176
.2644
-.0045
-.061
.9526
.0184
.796
.4432
-.0305
-1.148
.2753
.0008
.319
.7554
-.0002
-.062
.9516
.0023
.581
.5728
49
50
THE JOURNAL OF LAW AND ECONOMICS
regulatory process for the cotton dust standards, M&M examine the impact of this event only. In particular, they used the following "market
model" to estimate the effects of the Criteria Document on security returns of an equally weighted portfolio of fourteen textile firms:
rt = P3rmt +
Dt- + Y(Dtrmt) + t,
(3)
where rt is the portfolio returns for period t, rm, is the market return for
period t, , is a normally distributed residual with a mean of zero, and D, is
a dummy variable taking the value of one during the event period. The
coefficient p measures the systematic risk of the portfolio, 8 measures the
change in the value of the portfolio during the event window (after adjusting for market-wide effects), and y measures the change in the relationship between the market and the portfolio returns during the event period.
They used monthly returns as the unit of measure and selected the fourteen textile firms listed on the New York Stock Exchange (NYSE) during
the ten-year period preceding September 1974, the period over which the
model was estimated. The twelve months preceding and including the
September 1974 transmittal date of the Criteria Document were used as
the event window.34 Their results show positive abnormal returns for the
portfolio during the event period that are significantly different from zero
at the 95 percent confidence level using a one-tailed test.35
By directing their analysis toward return behavior over the twelve
months ending in September 1974, M&M exclude a number of events that
may have been important to investors in assessing the effect of the standards on the expected profitability of affected firms. However, this fact
held aside, there is a question as to whether such a long event window is
suitable for extracting the influence of events during that period on firm
returns.36 The difficulties with a window this long are that statistical
34
They also state that their results are insensitive to the selection of 1965 as the beginning
year or twelve months as the length of the event window. They found comparable results by
beginning in 1950 or any year from 1960 to 1969 as well as by using event windows of
between six and fourteen months.
35 The use of a one-tailed test is
appropriate only if they believed that hypothesis HI (that
the standards benefited the entire industry) is the only alternative to the null hypothesis (H2).
36
Assessing M&M's finding of a significant monthly average abnormal return on descriptive terms alone, we observe that significant estimates may be found in three of nine
nonoverlapping twelve-month windows encompassed within the set of 117 monthly returns
that they employ (two more than we would expect to occur by chance). Specifically, by
replicating their analysis for each of those nine windows considered separately, we obtain
significant coefficients for both the twelve months ending September 30, 1972 (estimate:
-.019; t-value: - 1.889; prob > It: .062), and the twelve months ending September 30, 1969
(estimate: - .021; t-value: - 2.165; prob > ]tl: .033), as well as for the twelve months ending
September 30, 1974 (as reported in Table 4, col. 1, panel A). These results further point
toward the difficulties in interpreting abnormal returns accumulated over event windows as
long as a year.
51
COTTON DUST STANDARDS
TABLE 4
REGRESSION ESTIMATES OF TEXTILE PORTFOLIO MONTHLY AVERAGE ABNORMAL RETURNS
OVERA TWELVE-MONTH
WINDOW
ENDINGSEPTEMBER
30, 1974
M&M's
Portfolio
Returns (NYSE)
Panel A.
Estimate
t-value
Prob > It1
.025
1.963
.052
Panel B.
Estimate
t-value
Prob > Itl
.028
1.630
.106
Cotton
Portfolio
Returns (CRSP)
Cotton Less
Noncotton
Portfolio
Returns (CRSP)
Using Market Returns from an Equally Weighted
Portfolio of NYSE Firms
.024
2.198
.030
-.019
-1.034
.303
Using Market Returns from a Value-weighted
Portfolio of all CRSP Firms
.024
1.481
.142
-.009
- .416
.678
NOTE.-Textileportfoliomonthlyaverageabnormalreturnsare determinedfromall availablereturns
data in a given month.
power may be diminished vis-a-vis market reactions to specific events
contained in this period37 and that other factors pertaining to the textile
industry as a whole could be present in returns even after the removal of
economy-wide effects.38
In order to understand better what may be driving M&M's results on
industry-wide effects, we replicated their estimates of monthly average
abnormal returns for three portfolios of textile firms over the twelvemonth period ending September 30, 1974. The returns employed as the
dependent variables in these regressions included: (1) a portfolio of firms
that made up M&M's sample; (2) an expanded portfolio of all textile firms
engaged in cotton production with returns on the CRSP tapes during this
period; and (3) a portfolio in which returns of the portfolio comprising all
noncotton textile firms on the CRSP tapes are subtracted from those
cotton firms. As reported in Table 4, each of these regressions was run
using a market returns variable determined either from an equally
weighted portfolio of all NYSE firms as used by M&M (panel A) or from a
37
See Stephen Brown & Jerold Warner, Measuring Security Price Performance, 8 J. Fin.
Econ. 205 (1980).
38 See Fama & MacBeth,
supra note 30. M&M attempt to control for such factors by
including oil prices and cotton prices in their analysis. Inclusion of neither of these variables
affects their results in a qualitative sense.
52
THE JOURNAL OF LAW AND ECONOMICS
value-weighted portfolio of all NYSE and American Stock Exchange
(AMEX) firms from the CRSP daily tapes (panel B-the latter representing a more broadly based market index for extracting economy-wide
effects).39
For both the smaller portfolio of fourteen large cotton-producing firms
listed in the NYSE (not shown) and the portfolio of twenty-one cotton
firms listed on the CRSP tapes (panel A, col. 3), subtracting the returns of
noncotton firms from cotton firms causes the sign of the estimate to
change from positive to negative, raising the possibility that abnormal
returns earned by cotton firms are driven by a factor important to noncotton firms as well.
Interpretation of these results depends on the demand substitutability
of cotton and noncotton fabrics. On the one hand, if the two fabrics are
only weak substitutes, then a higher price for cotton would cause little
demand growth for noncotton fabrics and therefore minimal changes in
the price of noncotton fabrics. In this case the positive and larger abnormal returns for the noncotton compared with the cotton textile firms could
not be due to cotton price increases suggested by M&M to have resulted
from OSHA regulation. Rather, it is likely that the positive abnormal
returns for both groups of firms are due to an industry factor unrelated to
the regulation.
On the other hand, if cotton and noncotton fabrics are close substitutes,
then it is evident that M&M's first argument for profit enhancement-that
the regulation created a barrier to entry that enabled existing cottonproducing firms to earn positive rents-is no longer sustainable.
As for their second argument for profit enhancement on the basis of
profit transfers between small and large firms, without barriers to entry
and with close substitutability between cotton and noncotton fabrics, we
would expect to find the larger positive abnormal returns for noncotton
compared with cotton textile firms shown in Table 4. In this case the
compliance costs would force up the prices of cotton and noncotton fabrics, allowing noncotton textile producers to earn higher profits than without the regulations as well as to earn higher profits than the cotton textile
firm (which must bear the compliance costs).
Thus the positive abnormal returns for cotton firms reported in columns
1 and 2 of Table 3 could be due either to profit transfer from small to large
firms, with no significant entry barriers and close substitutability between
39 We also performed the analysis using an equally weighted portfolio of all NYSE and
AMEX firms from the CRSP daily tapes. The coefficients on the event dummy variable were
uniformly smaller in magnitude, and less significant, than when a value-weighted index was
employed.
COTTON DUST STANDARDS
53
cotton and noncotton fabrics, or to some other reason, such as an industry factor unrelated to regulation. In order to discriminate between these
competing arguments, we first consider evidence on foreign imports and
domestic production. We then directly examine the relation between abnormal returns and firm size by extending M&M's cross-sectional analysis to include the latter.
Nonfinancial Evidence
For cotton firms, even large ones, to have benefited from the cotton
dust regulations, it is necessary that they caused the prices of cotton
goods to rise. However, cotton prices could not have risen if the supply of
foreign cotton goods was highly elastic.40 While we do not have accurate
estimates of the foreign supply elasticity, two pieces of indirect evidence
indicate that it was high.
First, while the Multifiber Agreement (MFA) negotiated in 1974 did set
cotton import quotas, the prevailing view appears to be that such quotas
did not create tight constraints on the importation of cotton fabrics and
apparel.41 In particular, a recent International Trade Commission Report
concludes that as of 1980 "[t]otal import levels of cotton broadwoven
fabric and body-supporting garments were probably affected more by
market forces than by MFA restraints."42 Taking cotton broadwoven
fabric as an example, they find that in 1980 only 54 percent of the imports
were from countries party to the MFA, while these countries averaged
only 43.5 percent of their restraint limits, and with no country completely
filling its limits. Similar 1980 statistics are cited for the lack of MFA
constraints on body-supporting garments, women's, girls', and infants'
coats and jackets, and women's knit shirts.43
40
Even if the foreign supply elasticity were low (which the evidence below suggests is not
the case), the extent to which regulation-induced cost increases could be passed on to
consumers in higher prices was limited by the availability of cotton substitutes. Since we
have shown that M&M's profit transfer hypothesis could only be true with close substitutability between cotton and noncotton fabrics, in this case the domestic demand curve would
be elastic. This would have created a severe restriction on the extent to which regulatory
cost increases could be passed on through higher cotton prices.
41 In addition to the MFA, imports of cotton into the United States were subject to ad
valorem tariffs in the 1972-81 period that averaged 21 percent for knit (nonornamented)
garments and 11.4 percent for broadwoven fabrics. However, the existence of tariffs in no
way affects the extent to which costs increases to U.S. producers influence the prices of
cotton goods. Joseph Williams & Robert Wallace, U.S. International Trade Commission,
private communications.
42
U.S. International Trade Commission, The Multifiber Arrangement, 1980-84 (Publication No. 1693, May 1985), at xiv.
43 The MFA did restrict imports of two smaller product classes, gloves and women's
woven shirts.
54
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Since for cotton products, like broadwoven fabric, only about half the
imports came from the thirty-one signatory nations to the MFA, there
were many totally unconstrained sources of foreign cotton supply. Moreover, because the agreements created country-by-country constraints,
countries with import levels near their quotas could export their cotton
goods to a third country with slack in its quota and then export from that
country to the United States. Additionally, the MFA allowed quotas to
carry forward from one year to the next and to carry back to the previous
year, as well as limited switching among specific commodity quotas. And,
finally, we note that the MFA cotton quotas increased every year, and
usually by more than the rate of increase of domestic cotton textile and
apparel consumption.
The second piece of indirect evidence about the import supply elasticity
is that cotton imports, in fact, rose 108 percent over the decade during
which the standards were imposed (1970-81), and the rate of increase has
escalated since then. This rapid rise in import levels would have been
impossible with binding import quotas, and it suggests that the elasticity
of import supply was high.
While the above indirect evidence about foreign import supply elasticities suggests that the cotton dust standards did not lead to large price
increases, they would have caused profit reductions for cotton producers
only if the compliance costs were large. Two pieces of nonfinancial data
are consistent with the conclusion that those costs were indeed large.
First, domestic cotton production fell from 44 percent of domestic textile
production in 1970 to 28 percent in 1981. Second, the market share of
cotton imports rose over the same period from 11.2 percent of U.S. cotton
consumption to 29.1 percent.44
Difference in Intraindustry Findings
Before directly testing the role of firm size, we first reconsider M&M's
cross-sectional model that also includes the cotton percentage and unexpected earnings variables. Since a firm's return represents an average
over all its lines of business, M&M further reasoned that, if industry-wide
hypothesis HI were true, then the abnormal returns attributable to the
regulation for a textile firm would be larger the greater the percentage of
its products made from cotton. For the thirteen firms in their sample, they
regressed monthly average abnormal returns (AR) on a variable C1
44 These data were obtained from the Cotton Analysis Division of Cotton, Inc., Raleigh,
North Carolina.
COTTON DUST STANDARDS
55
defined as the percentage of cotton to total fiber used in production.45
Four functional forms of the relationship were tried and reported, with the
equation
AR = a + b
C
1 - C1
(4)
providing the best fit. Their estimate of b in equation (4) was positive and
significant at a one-tailed 95 percent confidence level, a result that led
them to conclude that "cotton . . . users were made better off by the
regulation."
In order to examine this finding further, we next replicated M&M's
cross-sectional analysis of equation (4). While the cotton percentages (CI)
were not reported by M&M in their article, the authors graciously made
their data available to us on all but three firms: Spring Mills, Reigel, and
WestPoint Pepperell.46 A subset of our results is shown in Table 5. Panel
A of that table displays estimates of the coefficient b of the cotton percentage variable under two sampling rules: (1) M&M's sample of thirteen
firms using their cotton percentages where available and ours for Spring
Mills and Reigel and (2) our expanded sample of twenty-one firms with
our cotton percentages.
Note that in panel A the estimates and related t-values for the first
sampling rule are very similar to those reported by M&M, our estimate of
the coefficient b being slightly higher and more significant than M&M's
(recall that our cotton percentages differ for two firms). However, when
we expand the sample to include all firms with returns data available, the
relation between the cotton ratio, C1/(1 - C1), and monthly average abnormal returns (AR's) actually becomes negative.47
45 The fourteenth firm (WestPoint Pepperell) was excluded by M&M due to the unavailability of data. They also report similar results for a sample that excludes another firm,
Texfi.
46 M&M advised us that their data on
Spring Mills and Reigel had been received in
confidence. In their paper they explain that the cotton percentage datum for WestPoint
Pepperell was unavailable.
47 This dramatic change in the cotton percentage coefficient prompted further analysis.
We repeated the analysis using the following additional sampling rules: (1) M&M's sample of
firms with our cotton percentages; (2) our expanded sample of firms using M&M's cotton
percentages where available; (3) M&M's sample excluding Cone Mills; (4) M&M's sample
with our cotton percentage for Cone Mills; and (5) our expanded sample excluding Cone
Mills. (Cone Mills is the firm with the largest difference between M&M's estimate of Cl, 90
percent, and our estimate of C1, 57 percent.) For each sampling rule, we also repeated the
analysis using C1 instead of the cotton percentage variable noted in equation (4), C1/(1 C1). The coefficient on the cotton percentage variable is highly sensitive to the treatment of
Cone Mills and, to a lesser degree, to the specification of the cotton percentage variable and
the inclusion of the additional firms from AMEX. The only time that the cotton percentage
56
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TABLE 5
REGRESSION
OFTHEIMPACT
ONMONTHLY
ESTIMATES
OFTHECRITERIA
DOCUMENT
AVERAGE
ABNORMAL
OVERA TWELVE-MONTH
ENDINGSEPTEMBER
RETURNS
WINDOW
30, 1974
M&M's Sample and Cotton
Percentages (Thirteen Firms)
Panel A.
Estimate
t-value
Prob > Itl
.052
2.283
.043
Panel B.
Expanded Sample, Revised
Percentages (Twenty-one Firms)
Using Cotton Ratios (CR)
- .012
-.182
.858
Using Cotton Ratios (CR), Firm Size (FS),
and Earnings Changes (EC)*
Estimate for CR
t-value
Prob > t|t
.066
3.256
.012
-.096
-1.615
.126
Estimate for FS
t-value
Prob > Itt
- .862
-.318
.759
- 5.817
- 1.653
.138
Estimate for EC
1973
t-value
Prob > Itl
.332
3.410
.009
.208
2.134
.049
Estimate for EC
1974
.009
.192
.092
2.429
t-value
.027
Prob > Itl
.929
* Percentagechangein earningsper shareduringthe fiscal year indicated.
variable is significant (at a two-tailed 90 percent level or better) is when C, for Cone Mills is
set equal to 90 percent and the specification of the cotton percentage variable is C1/(1 - C1),
as in equation (4). When Cone Mills is deleted, or when C1 for Cone Mills is set equal to 57
percent (as we estimated it to be), the coefficient on cotton percentage does not even
approach significance (the t-values range from -0.682 to 1.179). The exclusion of Cone
Mills can be further supported by an analysis of influential observations suggested by David
A. Belseley, Edwin Kuh, & Roy E. Welsch, Regression Diagnostics (1980). (The value for
two of the overall influence diagnostics, HAT and COVRATIO, and the diagnostic for the
coefficient on the cotton percentage variable, DFBETA, are higher for Cone Mills than for
any other firm.) When Cone Mills is deleted, the largest positive t-value for the cotton
percentage variable is 0.491. In addition, we compared the thirteen NYSE firms to the
additional eight AMEX firms along several dimensions: firm size (FS), cotton percentage
(C1), and twelve-month excess returns (the dependent variable). Using both parametric and
nonparametric analyses, the only variable that reflected a significant difference between the
two groups was FS, with the NYSE firms being larger, as expected.
COTTON DUST STANDARDS
57
In panel B we report the same regressions as in panel A except that the
model now includes the additional variables of firm size (FS) and percentage change in earnings (EC) from the preceding year (both 1973 and 1974
since M&M's window spans parts of each) as a proxy for unexpected
earnings.48 The rationale for including EC is to obtain a better-specified
model, given the evidence in the literature that abnormal returns are
sensitive to such a variable.49 Observe that the negative association between cotton ratio (CR) and abnormal returns found in panel A for the
larger sample is further strengthened in panel B, the cotton ratio
coefficient now bordering on significance at the 90 percent level.50 Furthermore, the two unexpected earnings variables are significant in this
regression.5
Returning to the profit transfer hypothesis, it is evident that the inclusion of firm size as a regressor provides a direct test of the claim that large
firms benefited from the regulation at the expense of small firms. Similar
to the results from our study of seventeen events reported in the last
section, the estimated coefficient of this variable is negative rather than
positive, as the profit transfer argument described by M&M would seem
to recommend. While this result does not completely rule out the possibility that a profit transfer occurred, it implies that if a profit transfer from
small to large firms were present, it was small enough to have been more
than offset by the small firm effect noted earlier. In light of the findings in
this and the preceding sections, we find it difficult to accept the conclusion that the cotton dust standards permitted large firms to gain in
profitability at the expense of smaller cotton producers.
48
See Ross Watts & Richard Leftwich, The Time Series of Annual Earnings, 15 J. Acct.
Research 253 (1977).
49 See
Ray Ball & Philip Brown, An Empirical Evaluation of Accounting Income Numbers, 6 J. Acct. Research 159 (1968). There was no need to include forecast errors in
earnings in the earlier analysis inasmuch as the shorter windows tend to reduce the likelihood of picking up any drift that can be associated with such errors. In addition, the shorter
windows allow greater diversification in earnings announcement dates, thereby diminishing
any systematic influence of those announcements (or their anticipation) on returns.
50
As noted in note 47 supra, we ran the analysis using several additional sampling rules as
well as an alternative specification of the cotton percentage variable. As before, the results
were very sensitive to the treatment of Cone Mills. Under the various specifications the tvalues for the cotton percentage variable ranged from - 3.061 to 1.531. When either Cone
Mills was deleted or its value of Cl set to 57 percent, the coefficient on the cotton percentage
variable was generally negative, with the largest positive value being 0.618.
51 As an alternative to using the two annual unexpected earnings measures, we also ran
the analysis using the percentage change in the four quarters of earnings ending closest to
September 30, 1974 (the end of the return measurement period). The use of this alternative
measure of unexpected earnings would not alter any of the above statements concerning
cotton percentages.
58
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VII.
CONCLUSIONS
An analysis of seventeen separate events in the development of the
OSHA cotton dust standards suggests that individually they did not affect
investor expectations enough to be significantly related to changes in the
share prices of textile firms, given the most powerful tests we could
design. However, when combined into three composite event windows,
the results suggest that investors expected the industry to be adversely
affected by the cotton dust standards or, at least, to be unaffected by
them. The cross-sectional analysis indicates that the returns of larger
firms producing cotton textiles were lower during the period encompassed
by composite event windows than the returns of smaller producers, a
conclusion totally unsupportive of the hypothesis that the standards
created a comparative advantage for larger cotton firms.
Undoubtedly, the differences between our event study methodology
and that of M&M contribute to the difference in conclusions drawn from
the two studies. While M&M used monthly returns and focused on a
single twelve-month window during the regulatory process, we used daily
returns and considered seventeen shorter event windows spanning the
entire regulatory process. However, even utilizing their approach, our
further analysis of their event window leads to a different conclusion from
theirs. By expanding the sample of cotton-producing firms and adjusting
for a possible industry effect, this analysis reveals a zero or possibly
negative effect on expected average industry profitability during this
twelve-month period. Furthermore, neither the evidence on foreign imports and domestic production nor tests on firm size in the direct crosssectional regressions are supportive of the hypothesis that the regulations
benefited large cotton producers at the expense of small ones.
The Occupational Safety and Health Administration estimated the present value of the stream of future after-tax compliance costs of the cotton
dust standards to be about 37 percent of the 1978 market value of all
cotton-producing textile firms,52 while M&M attribute a one-half billion
dollar (25 percent) increase in market value to the standards. Subject to
52
The Occupational Safety and Health Administration estimated the annualized costs to
the textile industry of the final standards to be $171 million, reflecting capital costs of $550
million and operating, maintenance, energy, and other variable costs of $61.5 million per
year. The estimate also assumes a nine-year lifetime for equipment and a 10 percent real
discount rate. On adjusting these annual costs to reflect a 50 percent marginal tax rate and a
10 percent investment tax credit, as well as using the same 10 percent discount rate, we
calculated the present value of the stream of future after-tax compliance costs to be $745
million. This figure amounts to 37 percent of the $2 billion market value of the NYSE and
AMEX textile firms in 1978, the year of promulgation of the standards.
COTTON DUST STANDARDS
59
the limitations of the statistical power of event studies in general, our
analysis of the composite window consisting of all seventeen events suggests that the market expected the net effect of the cotton dust standards
on the textile industry to be somewhere between an insignificant loss and
the original OSHA estimate, with our best point estimate being that market value of cotton firms fell approximately 23 percent.53
53
This figure is obtained by multiplying our estimate of daily average excess returns of
the cotton minus the noncotton portfolio (-.00228) for all seventeen event windows by the
total number of days in those windows (100).