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After protection: The impact of import barriers on corporate equity values and profit expectations

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AFTER PROTECTION: THE IMPACT OF IMPORT BARRIERS ON
CORPORATE EQUITY VALUES AND PROFIT EXPECTATIONS
by
Erik Nils Dohlman
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment o f the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(POLITICAL ECONOMY AND PUBLIC POLICY)
May, 1999
Copyright 1999 ERIK NILS DOHLMAN
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UMI Number: 9933713
Copyright 1999 by
Dohlman, Erik Nils
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UNIVERSITY OF SOUTHERN CALIFORNIA
THE GRADUATE SCHOOL
UNIVERSITY PARK
LOS ANGELES. CALIFORNIA 90007
This dissertation, written by
Erik Nils Dohlman
under the direction of Wv?. Dissertation
Committee, and approved by all its members,
has been presented to and accepted by The
Graduate School, in partial fulfillment of re
quirements for the degree of
DOCTOR OF PHILOSOPHY
H ate Studies
Date .. A p .? .il..2 3 1.29.9
DISSERTATION C O
Chairperson
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ACKNOWLEDGEMENTS
A number of people have contributed to the successful completion of this dissertation, and I
am indebted to them for their support and guidance. I first thank my principal dissertation advisors,
Dr. Jonathan Aronson and Dr. Peter Rosendorff, for their careful reading of my dissertation and
sound advice on how to improve its content. Their time, interest, and efforts will always be
appreciated. I also thank Dr. Doug Joines, who carefully pointed out the strengths and potential
limitations of the event study method. Although not a formal member of my dissertation committee,
Dr. Chia-Shang (James) Chu, provided invaluable assistance at the early stages of my dissertation,
particularly with the econometric methods that I employed. Other members of the USC community
also deserve recognition: Dr. John Elliott, founder and Chair of the Political Economy and Public
Policy (PEPP) program; Dr. Faride Motamedi, whose dedication to the program and its students are
legendary, friends and student-colleagues in the PEPP program; and the Graduate School, which
provided financial support with a Pre-Doctoral Merit Fellowship. Non-USC affiliated event study
practitioners John Binder, Glenn Henderson, and D. Scott Lee were also kind enough to respond to
my questions and offer advice on how to improve my study.
On a final note, I also want to thank those who pointed me in the right direction, kept me on
path with their unwavering support, and provided me with role models to admire. My parents, Dr.
Claes and Carin Dohlman, have always wanted the best for their children, and have always given
their best to help us succeed. My siblings: Lena, Jan, Ebba, Henrik, and Peter have lent me their
experience and have always been ready sources of encouragement and good advice. My brother
Peter, in particular, went well above and beyond the call of duty with all of his assistance. And to my
wife, Deepa, I dedicate this dissertation. Her strength, faith, and commitment are a source of
inspiration and I thank her for adding so much to my life.
ii
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS
I X
LIST OF FIGURES AND TABLES
VI
ABSTRACT
vn
Chapter
1. INTRODUCTION
1
Motivations
Chapter Synopses:
2. U.S. TRADE POLICY
10
I. Introduction
n. The Welfare Effects of Protection
m . Evolution of U.S. Protection - The Rise o f Special Protection
IV. Objectives of Special Protection: Theory and Practice
A) Economic Efficiency Arguments
i) Strategic Trade
ii) Infant Industry Argument
B) Political Models of Trade Policy Determination
C) U.S. Trade Policy in Practice
V. Conclusion
in
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3. INVESTIGATING THE VALUE OF PROTECTION TO INDUSTRY:
A CRITIQUE OF EMPIRICAL LITERATURE
I. Introduction
II. The Logic o f Protection: The ‘‘ Breathing Space” Rationale
HI. Empirical Literature: The Two Approaches
A) Survey Studies
B) Event Studies
IV. Conclusion
4. CAPITAL MARKET EVENT STUDY: MEASURING THE
IMPACT OF PROTECTION
I. Introduction
Overview of Event Studies
II. Case Selection and Data Sources
A) Industry Case Selection
B) Firm Selection and Data Sources
HI. Methods
A) Modeling Expected Returns
B) Estimating Excess Returns During the Event Period(s)
C) Aggregating Excess Returns: Cumulative Excess Returns (CER)
D) Hypothesis Testing: Measuring the Significance of Excess Returns
E) Concerns with the use o f Daily Stock Data - Non-normal
Distribution and Biased and Inconsistent OLS Parameter
Estimates
F) Concerns with Estimates o f Variance - Autocorrelation, Cross-
Sectional Correlation, and Increases in Variance
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IV. Selection o f Events, Event Dates, and Event Intervals
A) Identification of Events
B) Selection o f Event Dates
C) Event Intervals and the Cumulation o f Excess Returns
V. Results
VI. Conclusion
5. CONCLUSION 102
I. Summary o f Results
n. Implications o f Results
in. Heuristic Value
REFERENCES 109
APPENDIX I 117
v
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LIST OF FIGURES AND TABLES
FIGURES
2.1 Distributional Effects o f Trade B arriers 13
2.2 Welfare Effects of Protection (1990) 14
2.3 Consumer Costs of Special Protection (Selected Years) 16
2.4 Trade Petitions (Selected Years) 17
TABLES
4.1 Industries Selected (by date of petition) 67
4.2 Event Dates 86
4.3 Total Cumulative Excess Returns (TCER): 5 Day Interval for
Each Event 91
4.4 Cumulative Excess Returns (CER): 2 Weeks Prior to Petition and
Injury Findings to 2 Weeks Following Final Action 95
4.5 Cumulative Excess Returns (CER): Petition and Start of ITC
Investigation 97
4.6 Cumulative Excess Returns (CER): Injury, Remedy, and Final Action 98
VI
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Abstract
The Jones-Neary specific factors model asserts that import barriers will raise the return to
owners of sector-specific capital. However, empirical studies using the “event study” method reveal
that protection is expected to have an inconsequential or even negative impact on future profit flows
to industries granted Section 201 Escape Clause relief. The policy implication is that trade sanctions
designed to facilitate adjustment, and perhaps revitalize domestic industries, are ineffective or
counter-productive. Prompted by concerns about the methodological accuracy of prior studies, this
dissertation reinvestigates the issue, using a substantially revised and more precise event study
approach to detecting changes to shareholders’ profit expectations in twelve industries granted
temporary import relief.
By using greater precision in event-dating and more accurate selection of industry data, I
demonstrate that industries shielded from increased, but fairly traded, imports do expert protection to
enhance their profit outlook, but not universally. Of the twelve industries studied, five experienced
positive and statistically significant “excess returns” to share prices over the intervals immediately
surrounding each of the key decisions leading to protection. Differences in outcomes appear to be
linked to the type of trade measure selected, with those protected by unilaterally imposed tariffs or
global quotas faring better than industries protected by voluntary export restraints (VERs). VERs
may be perceived as less valuable than unilaterally imposed barriers, particularly tariffs, due to the
opportunity for circumvention or quality upgrades by exporters. Another possibility is that quota
rents devolve to foreign governments or firms rather than being captured by domestic import-
competing industries (firms) who are themselves importers. Alternatively, the difficulty of assigning
precise event dates to voluntary agreements negotiated over a lengthy interval may make these cases
less amenable to the detection of significantly positive excess returns using the event study approach.
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There is no question in my mind but that some companies will not survive.
They will go down the tube and never be competitive. But, on the other hand,
there are some . . . that we can give a respite. If they prove that they can be
world competitive again, we will give them an opportunity to try it but with a
lim ita tio n on it. We do not give relief in a haphazard way to companies that
would not be world competitive at the end of the process.1
- Senator Bentsen: on the 1988 Omnibus Trade Reform Bill.
CHAPTER ONE - INTRODUCTION
The infamous Smoot-Hawley Tariff Act of 1930 demonstrated to U.S. trade policy-makers
what economists already knew.2 Impediments to trade harm not just consumers, but reduce net
national welfare as well. Following the dismal aftermath, the U.S. responded by pursuing
multilateral tariff reduction agreements, which gradually lowered the average U.S. tariff on dutiable
items from 20 percent in 1947 to under 4 percent today. Despite the considerable progress in tariff
reduction, recent decades have witnessed the substantial growth of so-called “special” barriers to
trade as the U.S. economy has become more exposed to international competition. Prominent
examples include voluntary export restraints (VERs) on the steel, automobile, and machine tool
industries, and “escape clause” (Section 201) relief from footwear, television, and heavyweight
motorcycle imports. In 1990, U.S. consumer welfare losses associated with special protection
exceeded $30 billion (Hufbauer & Elliott, 1994), but as with many policies that adversely affect one
group, others are expected to profit. Industry representatives, policy-makers, and economic theory
agree that capital owners of sheltered industries are rewarded by trade restrictions.
The conventional wisdom, however, has been challenged by several empirical studies that
have gauged the response of industry shareholders to the imposition of temporary restraints on fairly
1 133 Congressional Record S1898-99 (daily edition, February 5, 1987)
2 Almost one thousand economists signed a petition urging Congress and the President not to enact
these tariffs. Dobson (1976) estimated that average duties approached sixty percent in the years
following the enactment of the Smoot-Hawley Act (p.34).
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traded imports. By measuring asset (security) price changes at the time import relief was granted,
these “event studies” have found that industry shareholders expect protection to have an
inconsequential (Hartigan et al., 1986) or even negative (Canto et aL, 1986) impact on future profit
flows. The results imply that import barriers do nothing to enhance the prospect of industry
revitalization and that trade policies designed to do so may even be counter productive.
Although the event study method is well established in Finance and Accounting literature,
there are several good reasons to believe that the findings of these particular studies are flawed.
First, under the reasonable and empirically supported (Magee, 1980) assumption of short-term
(physical) capital immobility, economic theory (Neary, 1978) supports the contention that trade
barriers will raise profits to existing firms, and that expectations of these profits will be rapidly
incorporated by capital owners. Second, the results of prior studies are likely to have been distorted
by the use of imprecise methods ill-suited to the detection of a positive market response. Based on
these concerns, this dissertation therefore reinvestigates the issue, using a substantially revised and
more precise (event study) approach to detecting changes in shareholders’ profit expectations.
Twelve industries shielded from increased, but fairly traded imports by Section 201 escape clause
relief are the focus of this study.
The principal innovation is that, rather than using stock price data encompassing the entire
trade petition process as the previous studies do, this paper focuses on security price changes in the
short intervals immediately surrounding each of the key administrative decisions (events) leading to
the successful trade petition. This approach provides a greater chance of capturing the reaction of
shareholders to protection by increasing the power of hypothesis tests, and limiting the impact of
non-protection related events on the results. In addition to serving as a test of robustness for the
existing literature, then, a new empirical study has the practical role of clarifying whether
shareholder reactions to protection are consistent with the objectives of industry representatives and
2
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policy-makers. In the following sections, I present the motivations for a new empirical investigation
and a brief synopsis of each chapter.
Motivations
Economic theory predicts that, when physical capital is immobile (sector specific) in the
short run, trade barriers will increase profits to the domestic import-competing firms (Neary, 1978).3
The efficient markets/rational-expectations hypothesis also asserts that any expected change to future
earnings will be immediately incorporated into current security prices. The share price change
therefore represents the present discounted value of anticipated future gains (or losses) to the
industry. Event studies have demonstrated the validity of this hypothesis by documenting significant
and unexpected changes to share prices in response to the announcement of numerous firm and
industry related events: earnings announcements, regulatory changes, buyout proposals, as well as
other trade related events.
Grossman & Levinsohn (1989), for example, report that “unanticipated, positive shocks to
import prices cause[d] higher than normal stock market returns” 4 in each of six import competing
industries studied. And Thompson (1994) shows that a stock market reaction can be captured in
firms faced with trade liberalization.5 Moreover, Hartigan et al. (1989) discover that the routine, and
far less costly to consumers, administration of antidumping (AD) duties on unfairly traded imports
also lift industry profit expectations.6 Consequently, the exceptional approval of an industry request
3 Magee (1980), for example, provides empirical support for the assumption that capital owners (and
workers) engage in industry-specific lobbying.
4 Grossman & Levinsohn (1989). P. 1065. The price shocks were not caused by trade barriers.
5 Canadian firms with a comparative disadvantage vis-a-vis the United States (small scale and
capital-intensive firms) experienced negative and significant returns in response to the U.S.-Canada
free trade agreement
6 AD duties affected about $3.5 billion of imports in 1991, whereas the value of imports in 21 other
specially protected industries was over $53 billion in 1990 (Hufbauer & Elliot, 1994). pp. 15, 118.
Single industry studies by Lenway et al. (1990, 1994) also show that steel protection stemming from
unfair trade cases raised profit expectations.
3
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for fair trade (e.g. escape clause) relief should also generate an immediate and positive change to
share prices in that industry. The findings of event studies focusing on such barriers, however, are
inconsistent with both theoretical expectation and the findings of this associated literature.
The prospect that industries do not benefit from special trade barriers should be disturbing to
policy-makers as well as industry representatives. The trade laws governing such remedies,
particularly Section 201 (escape clause relief), clearly favor firms that have expressed the willingness
to use the opportunity afforded by protection to engage in new research, investment, and other steps
to improve productivity and competitiveness. The U.S. International Trade Commission (ITC), for
example, regularly cites the prospect of such actions in its decisions to recommend import barriers in
rather than trade adjustment assistance. Of course, the duration and magnitude of protection, the
possibility of circumvention, and other factors will determine the extent to which trade relief is
valued by capital owners. But the complete absence of a positive shareholder response to barriers
against fairly traded imports points to some potential methodological shortcomings in the literature
examining these cases.
Concerns about the methodological approach used by Canto et aL (1986) and Hartigan et al.
(1986) cento* on two main issues. The most important is imprecision in event dating. The rational
expectations/efficient-markets hypothesis, supported by empirical work by Hilmer & Yu (1979),
asserts that security markets respond rapidly to new information. That is, share prices will adjust
within days, if not hours, to firm or industry related events. Event study simulation literature (e.g.
Brown & Warner, 1985) also demonstrates that the ability to detect statistically significant market
responses quickly diminishes as the event interval (the period being examined for “excess returns”)
expands. If an event is known to have occurred on a certain day, the event interval should therefore
be defined as narrowly as possible around that day. Hartigan et al. (1986), however, define the event
interval as the period encompassing the entire escape clause petition process, which stretched over
4
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forty weeks in some cases. Canto et aL (1986) defined three separate event intervals, each lasting no
less than twelve months. The lengthy intervals severely compromise the ability to detect a
statistically significant market reaction and increases the likelihood that results are distorted by
“confounding” (non-protection related) events.7 Methods of more precisely dating and isolating the
event(s) within the trade petition process are described in Chapter Four.8
A second concern common to both studies is the selection of firm stock return data used to
represent each industry. Indications are that the affected industries were defined too broadly (Canto
et aL) or too narrowly (Hartigan et al.). That is, the firms included for analysis either did not produce
the protected category of goods and therefore should have been omitted, or affected firms for which
data was available were excluded. These and other methodological issues are discussed in more
detail in Chapter Three.
In summary, a well-constructed event study measures the perceptions of shareholders to
important firm or industry events by calibrating the impact on industry equity values. In the realm of
trade policy, the method can be used to verify whether trade sanctions create expectations of higher
fixture earnings among capital owners and therefore improves industry adjustment prospects. In
addition, differences in the perceived efficacy of various trade instruments (e.g. tariffs vs. voluntary
export restraints) can be gauged and the results used to inform policy decisions. A more precise
event study approach enhances the ability to detect these effects. The following sections provide a
brief synopsis of each chapter.
7 Canto et al. (1986) also exert a downward bias on their results by selecting an inflated expected
return measure based on the average “bull market” returns. This is discussed in Chapter Three.
8 I define each of the administrative actions leading to protection as events and examine a narrow
interval surrounding each event for excess returns. I also examine and test the statistical significance
of the aggregated excess returns for all events affecting each of the twelve industries.
5
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Chapter Two — U.S. Trade Policy
The theme of this chapter is the prominent role selective, or “special,” protection has come
to play in the U.S. trade policy arsenal in recent decades. Orthodox trade theory is used to examine
the general distributional effects of trade barriers, and empirical estimates of the consumer and net
national welfare losses associated with selective protection are given. The estimates reveal that, in
1990, special protection of just twenty-one industries accounted for almost half of all consumer costs
and nearly the entire net welfare loss stemming from all U.S. trade barriers (Hufbauer & Elliott,
1994). Factors leading to the growing popularity and “success” rate of fair (escape clause) and unfair
(countervailing duty and antidumping) trade petitions are discussed, and the policy rationale behind
their use is explored. Although economic theory does offer insights to how national welfare may
improve with trade intervention, I argue that two possible justifications for protection—the infant
industry and strategic trade theory—provide neither the motivation nor concrete guidelines for most
instances of U.S. trade sanctions. The public choice approach to understanding the determinants of
trade policy formation is also evaluated. The last section elaborates on the stated aims of U.S. trade
laws, emphasizing the fair trade laws designed to create financial incentives with which previously
competitive industries can re-equip for improved future performance. Uncertainty surrounding the
efficacy of these incentives are also noted.
Chapter Three — Investigating the Value o f Protection to Industry: A Critique o f Empirical Literature
This chapter examines the reasoning behind and potential criticisms of the common
“breathing space” argument for protection, and explains how the event study method captures the
anticipated change in future earnings brought about by protection. Empirical studies assessing the
value of protection or its role in industry adjustment to import competition are then evaluated.
Survey studies of protected industries are discussed to underscore the potential contribution of event
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studies to these inquiries, and methodological shortcomings of prior event studies investigating
protection are detailed.
Trade barriers are expected to increase industry earnings and create a “breathing space” from
foreign competition, so a commonly held view is that protection creates new financial incentives that
will be used by industries to improve efficiency and performance. The analogy is that the industry is
like a prize fighter caught off guard by a stunning blow. The contestant simply needs some time to
gather strength, reevaluate strategy, and then reengage the challenger after a sufficient time has
passed. Critics of the “breathing space” argument contend that, even in the absence of trade barriers,
private capital markets will supply funds for worthy projects, or that protection may even reduce
incentives to engage in cost-reducing projects.
Empirical efforts assessing the value of protection to industry have taken two approaches,
one (survey studies) evaluating the adjustment and performance of industries following protection
and the other (event studies) concentrating on the capital market effects of trade barriers. The
advantage of the event study approach is that, rather than inferring the creation of financial incentives
as survey studies do, they directly calibrate the impact of protection on firm equity values and profit
expectations. The presence or absence of “excess returns” (altered profit expectations) then permit
inferences about the influence of trade sanctions on an industry’s adjustment to be made. Thus, event
studies can be useful in verifying whether protection is seen as valuable to affected industries in
general, and can explain different outcomes for industry adjustment by indicating which industry or
firms have secured increased access to capital. Event studies examining the impact of temporary
trade barriers have employed imprecise methods and are ill-suited to the accurate detection of
shareholder responses, however. The potential flaws associated with these studies are enumerated.
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Chapter Four—Capital Market Event Study: Measuring the Impact o f Protection
Chapter Four presents the methods and results of this study. Using a revised Capital Market
Event Study approach, findings reveal that protection is expected to benefit targeted industries, but
not universally. Five of the twelve industries experienced returns significantly greater than expected
(excess returns) when returns were cumulated over the interval immediately surrounding each of the
key events leading to protection. None of the industries recorded significantly negative excess
returns. Different outcomes appear to be tied to the type of trade barrier selected. Five of the seven
industries exhibiting no significant shareholder response were protected by voluntary export
restraints (VERs or OMAs), whereas each of the industries expected to benefit from protection were
shielded by unilaterally imposed tariffs and/or global quotas. The absence of a significant
shareholder response may also be tied in some cases to the difficulty of defining a precise and narrow
event interval.
The findings of this study contrast sharply with those of Canto et al. (1986), who concluded
that protection was viewed negatively by shareholders, and more modestly with those of Hartigan et
al. (1986), who found the impact to be predominantly insignificant In Chapter Four I conclude that
more precise event dating and, in some cases, selection of firm data explain the differences with
Hartigan et al. I also argue in Chapter Three that the negative results found by Canto et al. are related
to these same factors as well as to a biased approach to modeling expected industry returns. The
discussion of results is preceded by a description of industry case selection and the choice of firm
data; an outline of methods used to calculate and conduct hypothesis tests on the excess returns for
each industry; and the identification and dating of key events within the trade petition process.
Chapter Five — Conclusion
The concluding chapter summarizes results, discusses implications, and suggests areas of
further research. The findings of this study can be used as a stepping stone for more detailed
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investigation into the conditions under which protection is expected to raise industry profits and the
role trade barriers play in industry adjustment to import competition. Because the presence of excess
returns is not by itself proof that industries will take steps to become more competitive, or that they
will be successful, future research should be engaged with identifying industry or firm level
characteristics that are associated with excess returns, and exploring the connection between these
returns and changes to industry performance.
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CHAPTER TWO - U.S. TRADE POLICY
I) Introduction
U.S. trade policy since 1945 has been characterized by the successful effort to reduce
general tariff levels both at home and abroad. In recent decades, though, new and selectively
imposed trade barriers have achieved growing prominence. In 1990, “special” protection of just
twenty-one U.S. industries accounted for nearly half of the estimated $70 billion consumer welfare
loss associated with all import barriers (Hufbauer & Elliott, 1994). In the absence of unambiguous
theoretical support for such protection on social welfare grounds, policy-makers have justified relief
to troubled industries as a means to boost profits, and to secure a "breathing space" during which
efforts to improve competitiveness can be undertaken. Despite the costs to society, it is unclear
whether capital owners of protected firms actually regard protection as beneficial. Existing “event
study” literature reveals that recipients of temporary protection expect trade barriers to have an
inconsequential (Hartigan et al., 1986) or even negative (Canto et al., 1986) effect on future profit
flows. Such findings confound theoretical expectations, and those of policy-makers and industry
representatives seeking protection.
Because the results of prior literature are counter-intuitive, I reexamine the issue using the
event study method to test the impact of selective trade intervention on twelve industries. Event
studies are used to evaluate shareholders’ perceptions of firm or industry related events—such as the
imposition of trade barriers— by examining deviations from expected share prices at the time of the
event. If share prices deviate significantly from expected values, it can be attributed to a change in
expected future earnings that is incorporated into the present value of the stock. In addition to being
counter-intuitive, findings that show protection to be of no expected value to industry may be an
artifact of imprecise methods and data selection. Consequently, a new empirical study can establish
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whether previous results are robust and if shareholder reactions to protection are consistent with the
objectives of industry representatives and policy-makers.
In this chapter, the empirical work is previewed with a discussion of the increasingly
prominent role of selective protection in the U.S. trade policy arsenal in recent decades. The policy
rationale for such intervention is also explored.1 Contrary perhaps to public perception, the United
States still protects many of its markets, and has shown the willingness to erect new barriers as the
pressures of international competition (and corporate lobbying) have become intense. These trade
barriers are neither justified as nor theoretically grounded in arguments favoring protection as a
national welfare enhancing solution, however. Although economic theory does provide insights to
how national welfare may improve with trade intervention, two common justifications for
protection—the infant industry argument and strategic trade theory—offer little practical guidance
for U.S. trade policy-makers. In any event, trade practices are often guided, not by national welfare
concerns, but by the motives and behavior of the political actors—voters, politicians, and interest
groups. At the same time, trade laws governing the types of trade barriers examined here are
officially sanctioned as a means to stabilize and potentially revitalize industries weighed down by
import competition.2 That the prospect of industry revitalization is realistic, though, depends to a
large extent on whether protection is associated with the expectation of higher industry profits.
The chapter is divided into the following sections. In section two, a brief theoretical
description of how trade barriers affect producer, consumer, and net national welfare is given.
* Selective (or special) protection is defined by Hufbauer & Rosen (1986) as “exceptional restraints
on imports, implemented through high tariffs, quotas, or other limitations that go well beyond normal
tariff or border restrictions.” P. 5. A review of the main channels of obtaining such protection as well
as main industries covered is contained in Appendix I.
21 refer principally to import barriers brought about by Section 201 (escape clause) petitions, Section
232 (national security), and voluntary export restraints (VERs), which are designed to aid industries
suffering from increased—but fairly traded—imports.
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Recent empirical estimates of these effects show that special protection accounts for a major share of
welfare losses stemming from all protection. Section three focuses on the growing profile of
selective trade barriers in recent decades and examines some of the factors leading to their
popularity. In section four, I discuss two theories that can be used to justify industry protection on
national welfare grounds, but argue that they do not form a sound basis for U.S. trade policy making.
The public choice approach to understanding the determinants, rather than the consequences, of trade
policy decisions is also evaluated. I then show that that U.S. trade laws are still intended to yield
some benefits by promoting “positive” industry adjustment and revitalization, but contend that a
great deal of uncertainty surrounds the effectiveness of these laws. Section five concludes.
II) The Welfare Effects of Protection
From an analytical perspective, orthodox trade theory can be used to describe how trade
barriers distribute gains and losses among producers and consumers, and what their impact will be on
net national welfare. Tariffs, quotas, or other trade restrictions drive the domestic price of an
importable good higher, which benefits existing domestic producers by stimulating greater output
and a larger producer surplus/ At the same time, import restrictions not only reduce consumer
welfare but the overall economic welfare of the importing nation.4 This applies even if the
government captures and redistributes the revenues generated by the import restriction (tariff
revenues or the revenues from the sale of quota licenses), because the loss of consumer surplus still
exceeds the combined gains in government revenues and producer surplus.
J Producer surplus is captured by existing producers if entry is difficult.
4 The well-known exceptions are the optimum tariff argument, the infant industry argument, and the
more recently developed theories of strategic trade.
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The reason for the net loss is twofold. First, the price increase induces some consumers to
stop purchasing the good, which eliminates their consumer surplus entirely without providing
compensating gams in government revenues or producer surplus. Second, the price increase does
elicit higher domestic output, but the marginal cost of the additional units exceeds the price of the
imports that they replace. Thus, the imposition of protection results in efficiency losses to both
consumers and producers, and these combine to produce the net "deadweight" loss to society. In
addition, the revenues generated by the sale of quota licenses, especially in voluntary restraint
arrangement (VRA) cases, are often collected by foreign governments, thereby increasing the net
national welfare loss.5 The welfare effects of a tariff are demonstrated in Figure 2.1.6 Lines DD and
SS are the domestic demand and supply curves of the importable. Assuming a perfectly elastic
foreign supply, the pre-tariff domestic price is P* and the post-tariff domestic price is Pr-
Figure 2.1: Distributional Effects of Trade Barriers
P
Pr
P *
Qty of Importable
5 The term VRA, used interchangeably with Voluntary Export Restraints (VER), refers to agreement
by the exporting nation to restrict export of a good to a certain level for a specified time period.
6 Source: Vousden, Neil. 1990. The economics o f trade protection. Cambridge: Cambridge
University Press: p. 29.
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The gain in producer surplus from the tariff is represented by the area PT EGP* and the gross
consumer loss from the tariff is given by the area PT FJP*. Assuming the tariff revenue (area EFIH) is
redistributed to consumers, the net loss to society is given by the consumer and producer
“deadweight” loss triangles (area EGH plus area FU) and the net loss to consumers is the sum of the
deadweight losses and the producer surplus gain. To give some idea of their impact on the U.S.
economy, a number of studies have estimated the welfare costs of U.S. trade barriers or, similarly,
the welfare gains expected from their removal (Tarr & Morkre, 1980, 1984; Hufbauer, Berliner, &
Elliott, 1986; Hufbauer & Rosen, 1986; de Melo & Tarr, 1992).
A recent and comprehensive study by Hufbauer and Elliott (1994) uses a computable partial
equilibrium model and classical assumptions (e.g. perfect competition, perfectly elastic foreign
supply) to estimate the consumer, producer, and net national welfare effects of protection.
F Ig u re 2 .2: W s Ifa re E ffe cts o f P ro te c tio n (19 9 0)
I S p e c i a l Protection
IA11 P rotection
Producer Gain C o n su m er Loss Net W elfare Loss
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As shown in Figure 2.2, potential gains to U.S. consumers from the removal of all tariffs and
quantitative restrictions amounted to $70 billion, or about $1,000 per family, in 1990.7 This includes
$32 billion in consumer gains from removing restrictions on twenty-one specially protected
industries included in their study, and roughly $38 billion from removing tariffs on all other goods.
The net national welfare gain would amount to almost $10 billion for the twenty-one industries,
whereas the removal of all other trade barriers would only contribute an additional $300 million gain.
Over $7 billion of the net national gain could come from quota rents recaptured from foreign
exporters and producers. In summary, the decision to grant trade relief to a small number of
industries nearly doubled consumer welfare losses in 1990 and accounted for virtually the entire
national welfare loss.
HI) Evolution o f U.S. Protection - The Rise o f Special Protection
The economic costs of protection can be considerable, as demonstrated above.
Consequently, the post-war thrust of U.S. trade policy has been to negotiate multilateral tariff
reduction agreements. These pacts have reduced the average U.S. tariff on dutiable items from about
20 percent in 1947 to less than 4 percent today and helped double the share of trade in U.S. GNP
since I960.* At the same time, growing exposure to international competition has led to a
considerable increase in the application of "special" import relief measures in the 1970s and 1980s.
Figure 2.3 shows the proportion (by value) of imports affected and consumer costs of special
protection for selected years.9
7 Figures based on Hufbauer & Elliott (1994). pp. 6-10.
8 From 9.4% in 1960 to 21.4% in 1991.
9 In Figure 2.3 and accompanying text, the figures prior to 1990 are based on Hufbauer & Rosen
(1986). Figures for 1990 are based on Hufbauer & Elliott (1994). It should be noted that the figures
from 1990 are based on a revised methodology that reduces estimates of consumer loss for that year.
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Figure 2.3: Consumer costs of special protection (selected years)
Consumer cost of Special Protection
Percentage of total imports covered by
Special Protection_________________
1975 1 980 1984 1990
Y e a r
In 1975, special import barriers covered 8 percent of all imports and cost consumers about
$13 billion in higher prices. But by 1984, a flood of new petitions for trade relief had pushed the
proportion of imports covered by these special barriers to 21 percent, with consumer losses of almost
$54 billion. The percentage of imports affected by special trade barriers tapered off to 10.5 percent
in 1990, due primarily to the declining effectiveness of VERs on automobiles and steel and the
phasing out of escape clause (Section 201) protection cases initiated in the early to mid-1980’ s. Still,
special protection took $32 billion out of the pockets of consumers in 1990, more in real terms than
the value in 1975.1 0
Consequently, the numbers are not strictly comparable.
1 0 These figures are based on Hufbauer & Elliott (1994), and Hufbauer & Rosen (1986) and may
underestimate the overall value of imported goods covered by special protection as they do not cover
industries with domestic markets of less than $1 billion.
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The rise in special protection coincided with the growth of U.S. trade deficits and economic
exigencies of that period. But administrative changes to U.S. trade law in the 1970s and 1980s also
contributed by relaxing procedures and enhancing the prospects for success. The Trade Act of 1974,
for example, lowered the standard of proof for demonstrating that an industry had been injured by
increased—but fairly traded—imports in escape clause cases. Similar changes were made in
antidumping (AD) and countervailing duty (CVD) statutes in 1979 and again in the 1980s. The
result was a surge in both the number of trade relief petitions filed and the success rate of petitioners.
Figue 24; Trade Ftetitions (Selected Yfeas)
0 -
1 9 6 5 - 1 9 6 8 1 9 6 3 - 1 9 8 9 1 9 3 4 - 1 9 6 8 19 8 0 - 1 9 8 9 1 9 6 2 - 1 9 7 4 1 9 7 5 - 1 9 8 6
U pet i t i ons fled 3 7 1 438 1 9 1 345 32 59
QrtrrbersujoesdU 1 2 1 7 3 30 135 4 13
CV D CSses A D Cases
R r a p a n a w p Q i w
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Figure 2.4 reflects the growing popularity of trade measures among troubled domestic
industries, and the heightened responsiveness of government agencies. In the decade following the
Trade Act of 1974, for example, the number of escape clause petitions filed and the percentage
ending with import relief nearly doubled compared with the previous decade. And although they still
accounted for a far smaller share of the overall consumer losses stemming from protection, the
“success” rate of AD and CVD petitions rose dramatically in the 1980s when compared with
previous decades.1 1
In addition to administrative changes in U.S. trade law that streamlined the procedure for
obtaining import relief and increased its attractiveness, resolution of trade disputes through political,
rather than purely administrative, means has also become an important feature of U.S. trade policy.
Political solutions to trade disputes, such as the negotiation of Voluntary Export Restraints (VERs),
have been undertaken by the government to supersede fair and unfair trade petitions already initiated
by industry representatives. This occurred in 1982 when the U.S. negotiated the steel VER following
the submission of 150 AD and CVD petitions against foreign steel makers, and again in 1984 when
President Reagan rejected an affirmative International Trade Commission (TTC) escape clause
determination for the steel industry and replaced it with a more restrictive VER.
The ability of large industries to bypass normal institutional channels— even when those
institutions reject their petitions— has also demonstrated that they need not validate their case under
current trade statutes to obtain protection. The 1981 voluntary restraint arrangement (VRA) on
automobiles from Japan, for instance, followed a negative determination by the ITC on the domestic
automobile industry's escape clause petition. Other prominent examples of politically negotiated
trade barriers include the 1986 VRA on Machine Tools, which was prompted by national security
1 1 Figures on AD and CVD petitions come from Destler (1992), pp 141-154. Figures on escape
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concerns, and the 1986 Semiconductor Agreement which followed both antidumping and Section
301 petitions against Japanese producers.
Although the imposition of new trade barriers has waned in recent years, the threat of
resurgence remains. A nationwide poll conducted in 1995 indicates that almost 70 percent of
Americans favor the imposition of new tariffs on products from countries with a trade imbalance
with the United States.1 2 As large trade deficits persist, and are aggravated by distress in the East
Asian economies, the incentive to alleviate the hardship of specific industries with selective
protection remains strong.
IV) Objectives o f Special Protection: Theory and Practice
Orthodox trade theory and empirical investigation demonstrate that trade barriers are a
costly, and still common, form of economic assistance to industry. Troubled industries nevertheless
seek protection in order to raise prices and profits, and perhaps to enhance the prospect of industry
revitalization. But what objectives do policy-maker’s hope to serve by granting protection? In less
developed countries, tariffs and other trade barriers have traditionally been used to raise government
revenue and to foster nascent industries. For developed economies, which typically protect mature
(“sunset”) industries and rely little on tariff revenues, the answer is less clear. Consequently, the
motivation for using protection as an economic policy tool should be publicly scrutinized, especially
if the value of protection to the industry can be questioned.
From a theoretical perspective, there are two main arguments that can support industry
protection as optimal from a national welfare perspective: the infant industry argument and the
clause cases are from Rosenthal & Gilbert (1989), pp.405-406.
1 2 L.A. Times, December 14, 1995. p. Dl.
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theory of strategic trade.1 3 Nevertheless, I conclude that they supply neither the motivation nor
concrete guidelines for the implementation of most U.S. trade actions. Although the literature
describes how protection can enhance national as well as industry welfare, their relevance to U.S.
trade policy-making is undermined by several weaknesses. In particular, the conditions that favor the
implementation of these policies either do not apply to the U.S. (in the case of infant industry), or
strict assumptions incorporated into theoretical models (in the case of strategic trade) limit the
opportunity to develop consistent policy recommendations. The determinants of trade policy
formation are more easily explained by referring to the economic incentives and behavior of political
actors within a public choice framework.
Given the ambiguity that surrounds the economic efficiency arguments for protection, their
relevancy for U.S. trade policy-making is uncertain. In the following sections, I review and critique
the two theories to point out difficulties inherent to their use as policy tools in the U.S. I then discuss
the contribution made by public choice literature to our understanding of the trade policy decision
making process. Finally, I explore the official motives and justifications for trade barriers of the type
I examine here (Section 201 escape clause relief) in order to underscore the importance of a new
empirical investigation of their efficacy.
A) Economic Efficiency Arguments
i) Strategic Trade
The theory of strategic trade, which has achieved growing prominence in recent years, marks
a departure from orthodox trade theory by abandoning the strict assumptions of perfect competition
and constant returns to scale in production.1 4 It suggests that in markets characterized by increasing
131 do not discuss the optimal tariff argument.
1 4 For theoretical treatments of strategic trade theory, see: J. Brander & B. Spencer. 1981. "Tariffs
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returns to scale, imperfect competition, and positive externalities in production, governments can
intervene in trade to elevate both industry performance and national welfare.
Acceptance of the theoretical precepts of strategic trade theory thus implies that government
can play an important role in enhancing the competitiveness of firms and national welfare.
According to Krugman (1987), two prominent rationales for government intervention are that
"government policy can tilt the terms of oligopolistic competition to shift excess returns (profits or
wages) from foreign to domestic firms" and "government policy should favor industries that yield
externalities, especially generation of knowledge that firms cannot fully appropriate."1 5 The profit
(or rent) shifting argument provides the basis for most of the strategic trade literature, although the
externality argument is also cited by advocates of "managed trade," such as Tyson (1990).1 6
The profit or rent shifting argument is based on the well-known contribution of Brander and
Spencer (1984), who model a Cournot (quantity setters) duopoly with one foreign firm and one
domestic firm. They conclude that, in the presence of pure economic profits, a tariff can cause the
foreign firm to cede both market share and profits to the domestic firm. The source of the national
gain is increased producer surplus and tariff revenues that overwhelm the loss in consumer surplus.
and the Extraction of Foreign Monopoly Rents under Potential Entry," Canadian Journal o f
Economics 14, no.3: 371-389; Krugman, Paul, "Import protection as export promotion: International
competition in the presence of oligopoly and economies of scale." In H. Keirzkowski (ed).
Monopolistic Competition in International Trade. Oxford: Oxford University Press: 180-193. For
the pros and cons of strategic trade policy, see Krueger, Anne, and Tyson, Laura, in Lawrence &
Schultze (eds.). 1990. An American Trade Strategy: Options for the 1990's. Washington: The
Brookings Institution.
1 5 Krugman, Paul. 1987. "Is Free Trade Passe?" In King, Philip (ed.). 1990. International Economics
and International Economic Policy: A Reader, p.95.
1 6 Tyson, Laura D'Andrea. 1990. "Managed Trade: Making the Best of the Second Best" In
Lawrence, R. and Schultze, C. (eds.) An American Trade Strategy: Options for the 1990's.
Washington: The Brookings Institution.
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The literature on strategic trade has developed so rapidly and introduced so many variations
«
that a comprehensive review is beyond the scope of present inquiry. Instead, I focus on some general
features of the literature that limit the application to trade policy. The main complication is the
specific and rigid nature of the assumptions incorporated into these models. Assumptions are an
integral part of any model but, as even strategic trade theorists recognize, the modest modification of
any one can lead to alterations or complete reversals of policy prescriptions. Eaton and Grossman
(1986), for example, note that strategic trade theory
(provides] examples in which interventionist trade policy can raise national
welfare in imperfectly competitive environments. Yet each makes special
assumptions about the form of oligopolistic competition, the substitutability of
the goods produced, and the markets in which the goods are sold. It is difficult
to extract general principles for trade policy from this analysis.1 7
This difficulty is illustrated by the diverging policy recommendations that emerge from
different assumptions of firm behavior. For instance, whereas Brander and Spencer (1985) conclude
that an export subsidy is the optimal policy for a domestic monopolist engaging in Cournot
competition with a foreign firm, Eaton and Grossman (1986) find that an export tax is optimal if the
firms engage in Bertrand price competition (in which price, not quantity, is chosen). Moreover,
Vousden (1990), echoed by Corden (1992),1 8 point out that "both Cournot and Bertrand conjectures
are unlikely descriptions of actual firm behavior because they implicitly assume that firms do not
learn from their mistakes."1 9
1 7 Eaton, Jonathon and G. Grossman. 1986. "Optimal Trade and Industrial Policy under Oligopoly."
In Grossman, Gene (ed). 1992. Imperfect Competition and International Trade. Cambridge: MIT
Press, p. 122.
1 8 Corden, Max. 1992. International Trade Theory and Policy. Brookfield, VT: Edward Elgar
Publishing Company, p.288.
1 9 Vousden, Neil. 1990. The economics o f trade protection. Cambridge: Cambridge University
Press, p. 136.
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An additional complication arises when more realistic assumptions of industry structure are
incorporated. Most models of strategic trade policy assume monopoly or duopoly, with no entry.
When market structure becomes mutable, in the sense that entry and exit occurs, new possibilities
emerge. For example, Dixit and Kyle (1985) conclude that a production subsidy can be optimal even
if it entails no profit shifting among a given set of firms if the entry of domestic firms induces the exit
of foreign firms.2 0 In contrast, Horstmann and Markusen (1984)2 1 demonstrate under similar
circumstances that a production subsidy can induce inefficient entry, which raises industry average
cost and dissipates the gains from profit shifting. A tax on production may then be appropriate even
when a subsidy would be optimal given an exogenous market structure.
Finally, several additional problems hinder the implementation of such policies. First, there
remain empirical and theoretical questions regarding the presence and appropriability of externalities,
and of the existence of excess returns to factors of production.2 2 Second, there is an identification
problem, similar to that of the infant, industry argument, with regard to the targeting of specific firms
for promotion. Third, the likelihood of retaliation in what are sure to be high profile cases introduces
the possibility that such policies may ultimately be counterproductive.
In summary, strategic trade theorists have addressed important shortcomings of orthodox
trade theory and support their selection of a new framework of analysis with convincing empirical or
2 0 From Eaton, Jonathon, Grossman, Gene; "Optimal Trade and Industrial Policy Under Oligopoly."
In Grossman, Gene (ed). 1992. Imperfect Competition and International Trade. Cambridge: MIT
Press, p.132.
2 1 Ibid, p.132
2 2 See, for example, Katz & Summers (1988) who conclude that "capital owners in the American
economy receive few monopoly rents."
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anecdotal evidence of trade under imperfect competition.2 3 Recognition of these shortcomings
represents an important step towards including in analysis the role that government and firms
themselves play in industrial development and growth, rather than treating competitiveness as
exogenously determined. The difficulty of drawing practical policy conclusions is still well
recognized within the literature, however. According to Vousden, the contrasting conclusions of
similar models "highlights just how sensitive many of these models are to assumptions about entry of
firms, the type of market (segmented or integrated), the properties assumed for demand curves and so
forth. The protectionist debate will presumably lead to closer scrutiny of these assumptions, and
hopefully the outcome will be a less bewildering array of models and results.... [In any event] the
rent-shifting argument for protection is now thought to be neither theoretically robust nor empirically
significant"2 4 The caution exhibited within the literature serves as a clear warning about the
potential hazards of pursuing policies grounded in strategic trade theory.
ii) Infant Industry Argument
A second, more traditional, argument linking protection and enhanced national welfare is the
infant industry argument2 5 It rests on the idea that a nation’s potential comparative advantage may
go unrealized due to a lack of know-how and an inefficiently small scale of production during an
industry’s nascent stage. Consequently, protection may be a welfare enhancing solution in the long
run by nurturing the industry as it overcomes its initial inefficiencies. According to Corden (1974),
2 3 See Tyson, Laura D’ Andrea. 1992. Who's Bashing Whom. p.17.
2 4 Vousden, Neil. 1990. The economics o f trade protection. Cambridge: Cambridge University
Press, p. 146.
2 5 The infant industry argument can be traced back to Alexander Hamilton, Friedrich List, and J.S.
Mill.
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the modem variant of the argument can be built along either of two lines of analysis: dynamic
internal economies or dynamic external economies.2 6
Dynamic internal economies refer to the reduction of costs, improved quality, marketing,
and distribution that can occur over time as knowledge and experience are accumulated. For
government intervention to be justified, there must be an imperfection of the capital market or of
private information which hinders investment in an industry characterized by dynamic internal
economies. A capital market imperfection, such as a general bias against long-term investment, due
to a divergence between private and social time preference, may result in financing inadequate to
cover the initial losses faced by the infant industry. Similarly, if the private sector lacks information
or the ability to properly assess it, Ionger-range projects may be seen as excessively risky. In both
cases, government intervention can induce investment that would have taken place in the absence of
these imperfections.
When production by one firm creates an invisible capital asset, such as job skills, the
benefits of which eventually go to another firm, there are dynamic external economies. The case
for infant industry protection arises when there are reciprocal external economies such that
production of each firm creates assets that ultimately benefit the other. The result of the
accumulation of the two assets will be that in later years the costs of both firms will fell. Protection
can be justified so long as the reciprocal dynamic external economies are being created and the
industry still feces import competition.
The infant industry argument is primarily used as a justification for protection in developing
countries, where capital markets and information may be insufficient, but can also apply to
developed countries when positive spillovers that are external to the firm are present. Nevertheless,
2 6 Corden, W.M. 1974. Trade Policy and Economic Welfare. Oxford: Clarendon Press. Chapter 9.
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the infant industry argument appears either unsuitable to the U.S., given the structure of its economy,
or inapplicable to most examples of trade intervention by the U.S. The case resting on dynamic
internal economies is especially weak for developed economies such as the United States, which
have well-developed capital markets and information is quickly disseminated. Informational
asymmetries between the public and private sector are unlikely in this case. Even if it were
theoretically appropriate, the record also shows that most instances of special protection target
mature (e.g. steel, automobiles) rather than emerging industries whose comparative advantage is yet
to be unveiled. Similarly, there is the additional practical problem of identifying, ex ante, which
industry or industries possess a potential comparative advantage or generate dynamic external
economies. And finally, as with most other arguments for protection, the source of the problem is a
domestic distortion, such as a capital market failure. It is recognized that the "first-best" solution is
to overcome the distortion with some form of production subsidy rather than with protection, which
introduces an additional consumption distortion.
In summary, the selective protection of certain industries based on the idea of fostering their
early development may be justified from a national welfare perspective under some conditions.
Nevertheless, the infant industry argument is least appropriate for economically advanced nations,
such as the U.S., with well-developed institutions and infrastructure. Nor does it support the
protection of mature industries typically seen in the U.S. And although seemingly more applicable to
circumstances freed by the United States, the theories of strategic trade receive little support within
the trade theory literature for the active pursuit of policies based on these ideas.
B) Political Models of Trade Policy Determination (Endogenous Trade Theory)
To this point, the discussion of U.S. trade policy has remained within the traditional domains
of empirical and theoretical trade analysis. That is, the redistributive effects of actual or potential
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trade intervention have been evaluated, but the mechanism by which societal preferences are
translated into policy actions have been ignored. Building on the public choice approach to
economic policy determination, more recent literature on endogenous tariff (or trade policy)
formation has sought to explain this link. In particular, the literature addresses the apparent paradox
of democratically arrived at policy decisions that are known to lower a nation’s collective economic
welfare. As a predominantly empirical work measuring the anticipated producer gains from
protection, this study still leaves the process by which policy decisions are made firmly within the
“black box.” Nevertheless, this section is included to highlight the explanatory power of the public
choice approach as well as to underscore some of its limitations.
One of the strengths of public choice literature is that it provides insight to how societal
preferences are conveyed into specific policy actions in the trade policy arena. By treating
politicians as well as voters as economic actors, the literature models behavior that explains trade
policy outcomes as a process responding to the self-interested behavior of individuals or interest
groups. The analytical approach is particularly useful for understanding policy shifts as a natural
reflection of changing economic conditions — long term changes in factor endowments, for example,
or shorter-term macroeconomic fluctuations. The literature also illustrates how individual industries
or pressure groups can obtain exclusive economic privileges despite opposition from the greater
majority. To explain other phenomena, however, the literature must reach beyond its initial
assumptions and recognize that motivations other than pure self-interest often guide behavior. I
argue that voting behavior, the behavior of political party'’s, and a certain policy initiatives cannot be
explained within a pure public choice framework, but the literature nevertheless does provide useful
insights to the existence of “safeguard” measures such as the Section 201 escape clause cases
examined in this study.
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Public choice literature is characterized by the application of modem neoclassical economic
thought to the “analysis of the operation of the political process.”2 7 As such, the basic units of
analysis-politicians, voters, and interest groups—are treated as rational, self-interested utility
maximizers in the political arena, and the examination of their behavior is based on the additional
assumption that “all individuals’ welfare depends only on the goods and services a person consumes
directly.”2 * Consequently, politicians are expected to act as political entrepreneurs whose only goal
is to gain office and, one there, to maximize personal utility subject only to the constraint of periodic
elections. Voter choice is explained by their rational calculus of which candidate or party will meet
their personal policy preferences, and interest group behavior is seen as motivated solely by their
desire to obtain exclusive economic benefits from elected officials. As a positive theory, public
choice seeks to explain policy outcomes, especially those that have redistributive income effects, on
the basis of these assumptions. The approach is motivated, in particular, by the desire to address
what its proponents see as a widespread misconception of how the political process actually operates.
One common explanation for the presence of selective trade barriers (such as the Section
201 cases examined here), for instance, is that they are tolerated by policy-makers as a necessary, but
unfortunate, evil. That is, although protectionist policies are generally regrettable, temporary barriers
serve a “safety valve” (Barton and Fisher, 1986) function of reducing unwanted pressure to adopt
even more damaging policies. Alternatively, policy-makers and those seeking protection often cite
goals based on social or interpersonal effects—value laden explanations such as the preservation of a
national “way of life” or reducing adjustment costs on equity grounds (Corden’s conservative social
welfare function). The assumption is that elected officials, with the consent of voters, design trade
policies to serve the general public interest while at the same time compensating those who suffer
2 7 Kelman (1987). P.80
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most from such policies. Public choice theorists dismiss such explanations and observe that this
“public interest” perspective imputes a different set of motives and behavior to political actors than is
elsewhere apparent in human interaction. In other words, public choice theorists rightly question
whether “there is something in the nature of market organization, as such, that brings out the selfish
motives in man, and something in the political organization, as such, which in turn suppresses these
motives and brings out the more ‘noble’ ones... ”2 9
By recognizing that political actors do operate in much the same way as they do in the
marketplace, then, public choice theory suggests an explanation for policy outcomes which are sub-
optimal for national welfare despite (or because of) the rational behavior of all those involved. The
literature posits, for example, that even though the majority of citizens in a democratic society are
likely to gain from free trade, they may rationally choose not to contest trade restrictions because the
cost of acquiring information, voting, or lobbying for free trade outweigh the expected individual
benefit from doing so.3 0 In contrast, interest groups and voters likely to gain significantly from a
protectionist outcome are impelled by their cost/benefit calculus to vote or lobby for that result.
Thus, the assumption of rational utility (income) maximization, supplemented by the notions of voter
costs and imperfect knowledge, provide public choice theory with a plausible and simple explanation
for the “undersupply of public goods”3 1 such as free trade, and for the existence of protectionist
measures favoring powerful and concentrated industries like steel, automobile, and textile
manufacturers.
Within the endogenous trade theory literature, formal models generally classify these
outcomes as the result of either majority decisions (the adding machine or direct majority voting
2 8 Baldwin (1985). P.2
2 9 Buchanan, from Kelman (1987). P.85
3 0 Mayer (1984)
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models), or the focused efforts of well-organized pressure groups or lobbies (pressure group model).
Although less realistic about the political process, the direct majority voting models are well-suited to
explaining broad trends in trade policy, whereas the pressure group models are adept at clarifying
what would otherwise be considered anomalies under direct majority voting, such as the protection
of a single industry employing a minority of voters.
One of the original expositions of tariff formation under direct majority voting is by Baldwin
(1976), who postulates a collective decision on tariff levels by voters in a small country. Assuming
costless voting and free and perfect information, Baldwin suggests that the optimal free trade policy
can be selected, regardless of the nation’s underlying factor endowment, so long as gainers
compensate losers from this policy choice. Noting that this outcome is rarely if ever observed in the
real world, Mayer (1984) develops a more rigorous model of tariff formation under direct voting that
offers one view of why this is so.
Employing the two goods-two factors Heckscher-Ohlin model, Mayer shows that a nation’s
equilibrium tariff rate depends on its relative factor endowments and the distribution of factor
ownership among voters. With each individual owning one unit of labor and a non-negative (but not
evenly distributed) share of capital, utility is a function of income from these two factors as well as
from his/her share of any tariff revenue. In the absence of voting costs or eligibility requirements,
then, the equilibrium tariff (subsidy) rate at any given time will be that which maximizes the utility
of the median voter. Regardless of factor endowments, the optimal policy for a small country is free
trade, but a positive tariff rate on capital-intensive imports in, say, a labor abundant economy will
still be adopted if the median voter’s capital-labor ratio exceeds the economy-wide average.
Incorporating more realistic assumptions about voter eligibility rules and voter costs does not change
3 1 Olson (1965), from Barry (1978). P. 24
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the central conclusions. The equilibrium tariff (subsidy) rate will reflect the preferences of the
m ftHian voter, but these conditions, particularly in the past, would have skewed the policy outcome in
the favor of capital owners.
The approach used by Mayer thus explains the presence of tariffs in an economy that would
gain from their elimination, but the analytical framework can also be directed to understanding
longer term trends in trade policy, “especially in relationship to changes in voter eligibility rules,
voter costs, or overall factor-ownership distribution.”3 2 Mayer notes, for example, that the relaxation
of voter eligibility rules directed against workers and small capital owners should be associated with
declining tariffs on capital-intensive imports and with increased tariffs on labor-intensive imports.
Though he finds empirical support for this hypothesis not fully conclusive, evidence that changing
economic conditions does affect voter preferences and policy outcomes is fairly strong.
Gallarotti (1985), for example, finds a generally strong and significant correlation between
levels of economic activity and the direction of tariff changes in Germany (1853 to 1914), and in the
United States and Great Britain (1800 to 1914 in each case). Severe recessions were associated with
rising tariffs and periods of strong growth with diminishing ones. Separately, Magee and Young
(1987) demonstrate that tariff levels in the United States (1900 to 1984) exhibited a lagged response
to changes in exogenous macroeconomic variables such as the rates of unemployment and inflation,
and to the terms of trade. They also revealed evidence of a long-term association between changing
factor endowments and tariff levels in the U.S. over this period. Although not within a direct
majority voting framework, Baldwin (1985) also finds statistical support for the hypothesis that the
voting behavior of Congressional representatives is “sensitive to the import-competition problems of
3 2 Mayer (1984). In Bhagwati (1991). P.340
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industries w ithin their districts,”3 3 a result he finds consistent with the adding machine (majority
voting) model.
The evidence presented by Baldwin and Magee and Young thus bolsters the public choice
contention that policy-makers are in feet policy-mediators responding, if imperfectly, to the shifting
economic preferences of societal actors. The simple Heckscher-Ohlin model of direct majority
voting, however, appears much less powerful when confronted with the frequently observed
phenomena of individual industry’s gaining tariff protection even though the vast majority of eligible
voters do not benefit from such a policy. That is, the direct majority voting model can reasonably
explain policy changes that reward the simple majority that prefers them, but not how minority
groups are able to acquire exclusive economic privileges. Why does labor rich India, for example,
with an extremely skewed distribution of capital and relatively high voter participation rates, protect
so many of its capital-intensive industries? Nor does this model explain why a capital abundant
nation such as the U.S. should choose to impose barriers on capital-intensive imports (such as the
auto, specialty steel, and machine tool industries examined in this study) in the first place, or why in
feet the U.S. has erected tariffs on both capital and labor-intensive goods (such as non-rubber
footwear and textiles).
A more satisfactory explanation for these phenomena comes from the common interest or
pressure group models (Brock and Magee, 1978; Findlay and Wellisz, 1982).3 4 Rather than focusing
directly on the preferences of voters, the pressure and interest group models of tariff formation
consider how political decision makers (governments, political parties) mediate contests between
3 3 Baldwin (1985). P.68
3 4 Mayer does extend his model to a multi-sector specific-fectors economy, and concludes that
owners of specific factors may succeed in obtaining protection for their industry if the magnitude of
voting costs for those who oppose these barriers exceeds their income loss from the adoption of such
a policy. Although the specific-fectors model more accurately depicts the structure of the economy,
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societal groups collectively engaged in the pursuit of their common interests. Baldwin (1985)
captures the essence of these models by noting that industries often “organize for the purpose of
raising funds through voluntary contributions and then use these funds to lobby for import protection
by disseminating information favorable to their case and by providing campaign contributions to
office-seekers who support their position.”3 5 Successful groups are those that, for reasons elaborated
by Olson (1965), overcome free rider problems to lobby effectively (buy support from other voter or
politicians) for their cause.
Although it is somewhat tautological to argue that prevailing groups are the ones best able to
advocate their interests, the literature does suggest criteria that can be used to evaluate why groups
are successful, and can therefore attempt predictions or explain the success/failure of group
initiatives based on these criteria. In any event, casual observation attests to the ability of large
and/or concentrated industries (steel, automobiles, textiles and apparel) to acquire exclusive
economic privileges, and empirical evidence in support of the pressure group model is mixed, but
generally supportive (Baldwin, 1985). Baldwin, for instance, concludes that the “market structure
variables stressed in the pressure group model do not seem to be significant in determining which
industries are most successful in securing protection,” but he does find Congressional voting
behavior on the 1974 Trade Act and the pattern of tariff cuts agreed to by the U.S. during the Tokyo
Round trade negotiations to be consistent with the pressure group model. In the first case, members
of Congress representing import sensitive districts strongly opposed to the Trade Act—because it
gave greater discretionary power to the President in tariff cutting matters—were more likely to
oppose the legislation. That politicians are sensitive to interest group pressures is also supported by
evidence showing that duty cuts agreed to during the Tokyo Round tended to be lower for industries
direct majority voting is clearly unrepresentative of how the political process actually functions.
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represented by a large number of employees and for products whose industry representatives lobbied
against the Trade Act of 1974.
Given that conditions favor concentrated industry groups advocating protection rather than
dispersed consumer interests opposed to it, why then is protection not more prevalent? At first
glance, the public choice literature does not appear to have a satisfactory response to this question.
Baldwin cites institutional changes made after 1930 that have insulated policy-makers from pressure
group activity. But this observation neglects to explain, within a public choice framework, why
policy-makers would be willing to cede decision-making authority to other institutions, except by
referring to behavior that lies outside of most models’ core assumptions. More recent literature,
however, offers an explanation that remains faithful to the public choice approach. Destler and Odell
(1987), and Milner (1987) argue that anti-protectionist forces—multinationals, highly dependent
import-users, exporters, and retailers—have become much more active and effective in countering
prctection-seekers. That is, protection-seeking groups continue to compete for economic spoils in
the political arena but the rewards are by no means guaranteed since their success creates clear
economic incentives for new pressure groups to form and contest their actions.
In summary, the public choice approach to analyzing the political process has considerable
intuitive appeal, and is supported by anecdotal and empirical evidence of political behavior that is
guided by, or responds to, the economic interests of individuals or groups that represent them. At the
same time, overly rigid adherence to its central assumptions opens the public choice literature to
criticism on a number of fronts. It is valid to assume that human behavior remains consistent
between the economic and political arenas, but the flaw lies with the assumption that “economic”
behavior precludes the pursuit of social or interpersonal goals. Why do people engage in volunteer
3 3 Baldwin (1985). P. 11
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work, for example, or accept lower paid jobs with social or non-profit organizations? Why do non
profit organizations exist and what explains charitable giving? Although it is a useful approximation
to assume that all individuals’ welfare depends only on the goods and services that they consume
directly, there are aspects of voting behavior and numerous examples of policy decisions that are
difficult to explain within this framework.
Public choice theory asserts, for instance, that potential voters often choose to remain
“rationally ignorant,” and therefore opt not to vote because the costs of gathering information and
disutility of voting outweigh the expected benefits from electoral participation.3 6 By the same logic,
the decision to vote in any particular election would depend on the personal gains, in terms of goods
and services, the voter may expect from influencing the election. This, in turn, depends on the
“perceived probability that a vote for one alternative rather than others will make some significant
difference [in the outcome].”3 7 In other words, the decision to vote is based on the rational
calculation of determining which party is likely to meet the voter’s preferences and by estimating the
probability that the voter’s ballot will have a decisive impact on the outcome of the election. But
since the probability of any single citizen casting the decisive vote in a general election is
“vanishing ly small,’”8 the costs of educating oneself on the anticipated advantages and disadvantages
of various candidates and issues is likely to far exceed the expected gains from casting that vote.
One would therefore expect to see a massive free rider problem in terms of voting behavior.
Empirical evidence on the economic determinants of voting behavior also undermines the
public choice approach. Kinder et al. (1989) demonstrate that there is “essentially no [statistical]
connection” between changes in voters’ personal financial situation and changes in voting behavior
3 6 Vaubel (1986). P.42
3 7 Campbell, from Barry (1978). P. 19
3 8 Barry (1978). P. 101
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in U.S. presidential elections. In contrast, a strong statistical correlation between voter behavior and
national economic conditions (sociotropic voting) has been established, which suggests that voters
are motivated either by some investment in group identification or by some enlightened self interest.
An additional, related, flaw in the public choice approach lies with the assumption that
politicians “act solely in order to attain the income, prestige, and power which come from being in
office.... [They] never seek office as a means of carrying out particular policies; their only goal is to
reap the rewards of holding office per se [and] treat policies purely as a means to their private
ends.”3 9 This view of the politician fails to explain why candidates or parties should take contrasting
stands on various policy issues and further reduces the voter’s expectation that her vote choice will
yield any personal advantage. In addition, if politicians act as Downs believed, and are more or less
informed on the preferences of voters, there would presumably be virtually no difference between the
policy platforms of competing candidates or parties. Downs’ assumption about the behavior of
politicians may, to some extent, explain the tendency for political party’s—particularly in the two
party system—to move towards the center, but cannot explain their enduring ideological differences
such as the party based voting patterns by USITC commissioners (Baldwin, 1985).
Furthermore, it is well known that salaries of politicians are often well below that which they
could make in comparable positions of responsibility in the private sector. And although most know
enough not to cite personal gain as a motivation to enter civil service, only eighteen percent of senior
civil service managers said that “salary was a strong reason for staying” at their jobs whereas
seventy-six percent cited the “opportunity to have an impact on public affairs.”4 0 In any event, if a
candidate’s political stance on a policy issue can be expected to change at any time in response to
3 9 Downs (1957), from Kelman (1987). P. 82
4 0 Kelman (1987). P.92
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capricious voter preferences or their own selfish motives, individual incentives to stay informed and
vote would be even further reduced.
A final shortcoming of the public choice literature is the inability to explain ideologically or
altruistically based changes to public policy that have been evident in past decades. As noted by
Kelman, “how could a self-interest account of the political process explain, except through the
grossest of distortions, the vast increases in spending for the poor that occurred in the 1960’s and
early 1970’s? The poor were not an electoral majority, nor were they well organized into interest
groups.”4 1 And although he acknowledges that the political behavior of many interest groups
represents “the most important exception to the argument that public spirit is important in accounting
for the viewpoints that citizens take in the political process,”4 2 he also cites new health, safety, and
environmental legislation as well as the deregulation of well organized airline and trucking industries
as strong anecdotal evidence that short term self-interest is not always the paramount determinant of
political decisions.
Baldwin also concedes that “such considerations as equity, social justice, and patriotism may
also affect public policy choices”4 3 and that these attitudes are not directly related to the goods and
services a person hopes to consume. Rather, they may instead be motivated by such considerations
as interpersonal relationships, satisfaction from helping others, and enlightened self-interest. For
example, although his statistical investigation revealed congressional voting behavior to be consistent
with the pressure group (“common interest” and majority voting “adding machine”) models of
endogenous trade theory, he also notes that “the feet that the import sensitive sectors generally feced
adjustment pressures because of import competition, and in many cases employed comparatively
4 1 Kelman (1987). P. 86
4 2 Ibid. P. 91
4 3 Baldwin (1985). P. 19
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high proportions of low-wage, unskilled workers, makes the results consistent with the status quo
and social change models.”4 4 The latter models, which take account of social and interpersonal
concerns, and considerations of equity, have traditionally been excluded from public choice theory.
Even if such considerations are simply taken as a “moralistic restraint on utility maximization rather
than part of one’s utility function,”4 5 it then becomes impossible to distinguish the extent to which
policy outcomes are determined by self interest and how far they are determined by the moralistic
restraint. Thus, Baldwin’s empirical examination of the political behavior of Congress supports the
notion that politicians often conform to the preferences of concentrated constituent interest groups
rather than acting towards a clearly established public interest (free trade in this case), but he usefully
extends the public choice framework to account for behavior that otherwise could only be considered
irrational.
The apparent flaws of orthodox public choice theory notwithstanding, it can still be usefully
applied to understanding why “safeguard” provisions, such as the Section 201 escape clause cases
examined here, exist in the first place. Under a pure public interest approach to policy making, these
provisions would not be necessary since public-minded decision-makers would resist new trade
barriers despite the electoral consequences, if any, of doing so. Sykes (1991), however, argues that
self-interested policy-makers included safeguard clauses in the GATT code to protect themselves
from the uncertain, and possibly adverse, consequences of making trade concessions. Although
political conditions may be favorable to liberalizing agreements at the time, politicians recognize that
locking these agreements in represents a politically risky strategy should economic conditions
change in the future. Rather than acting as a “safety valve” to limit pressures for increased
protection, then, self-interested politicians regard safeguards as a type of political insurance that
4 4 Baldwin (1985). P.68
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allows them to adjustto circumstances “in which the political rewards to an increase in protection (or
the political costs of an irrevocable commitment to reduce protection) are great.” Sykes also
concludes that there are favorable normative implications to this strategy since the knowledge that
trade concessions are escapable “facilitates initial trade concessions and may reduce the social costs
of protection over time.”4 6
In summary, the public choice literature and theories of endogenous trade policy formation
extend our understanding of how political preferences are formed and how policies take shape. By
examining the economic motives of various players in the political arena, the determinants of trade
policy are identified and can be used to explain and make predictions about trade policy outcomes.
The analytical approach is particularly useful for understanding the evolution of trade policy based
on changing factor endowments and macroeconomic fluctuations. It also gives insight to the ability
of small but concentrated and influential interest groups to acquire exclusive economic privileges.
Despite the difficulty public choice theory has explaining cither phenomena, it nevertheless provides
a plausible reason for policy-makers to include an escape hatch to liberalizing trade agreements that
appear politically popular at the time they are made.
C) U.S. Trade Policy in Practice
In the United States, the official justification for restricting trade clearly does not rely on the
theoretical arguments just evaluated. Rather, policy-makers claim that import barriers are used to
support either of two more immediate goals. The first goal is to counteract or retaliate against
"unfair" foreign trading practices such as subsidization or dumping at below cost of goods exported
to the United States. In this case, there is no presumption that the industry would require significant
4 3 Phelps, from Baldwin (1985). P. 20
4 6 Sykes. P.259
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adjustment in the absence of unfair foreign trade practices. The intent is to reestablish "fairness" in
trade so that actual competitiveness is accurately reflected in trade flows. In any event, the impact of
these cases are relatively insubstantial as they affected only about $3.5 billion worth of imports in
1991.4 7 The second motive, which represents something of a special exception in U.S. trade law, is
to provide an industrial safety net, or “breathing space,” for industries harmed by increased, but fairly
traded imports. Industries as large and diverse as the automobile, machine tool, specialty steel, and
color television industries have been granted temporary trade relief with the aim of reversing a
competitive decline.
The most clearly articulated example of U.S. trade laws intended to help restore the
competitiveness of industries harmed by increased, but fairly traded, imports is the escape clause
(Section 201-203) statutes.4 * The Trade Act of 1974 cites two possible courses by which escape
clause relief can ease industrial adjustment to import competition 4 9 One is to slow a declining
industry’ s contraction and ease the costs associated with the transfer of Iess-than-perfectly mobile
resources to other sectors of the economy. It has become evident, however, that escape clause relief
4 7 Hufbauer & Elliott, 1994. P.l 18.
4 8 Section 201 of the Trade Act of 1974 stipulates that an industry may receive temporary protection,
upon presidential approval, by demonstrating to the ITC that increased imports were a "substantial"
cause of serious injury or the threat of serious injury. Relief granted is temporary “in order to
encourage adjustment of the industry to increased import competition.” U.S. Congress, House of
Representatives Committee on Ways and Means, Sub-Committee on Trade. 1984 (December).
Overview o f Current Provisions o f U.S. Trade Law. Washington: U.S. Government Printing Office.
P.65
4 9 Similarly, Section 232 of the Trade Act of 1974 authorizes the President to impose trade barriers
on imports that threaten to undermine an industry vital to national security. Factors considered in
such decisions include ‘the impact of foreign competition on the economic welfare of domestic
industries; and any substantial unemployment, revenue declines, loss of skills or investment, or other
serious effects resulting from displacement of any domestic products by excessive imports.” Ibid,
p.89.
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has been used to promote “positive”5 0 adjustment in the sense that the domestic industry will be “able
to compete successfully with imports”5 1 upon termination of protection, and therefore create “greater
social benefits than costs.”5 2 Both the legislative intent and the pattern of implementation by the U.S.
International Trade Commission (TTC) has made it clear that the goal is not to prolong an inevitable
demise, but rather contribute to the industry’s vibrancy by providing a "breathing space" to re-equip
and enhance long-term competitiveness.5 " A General Accounting Office (GAO) report, for example,
notes that:
The legislative history in reference to this section indicates that it is not the intent
of the escape clause to protect an industry which has not acted to help itself
become more competitive. This section names specific efforts, such as
reasonable research and investment, steps to improve productivity, and other
methods that competitive industries must continually undertake.5 4
ITC escape clause investigations and related documents also indicate that the capacity of the
domestic industry to rebound is an important determinant of the decision-making process and one of
the reasons why import relief rather than Trade Adjustment Assistance (TAA) is recommended. The
5 0 The Trade Act of 1988 defines "positive adjustment to imporfcompetition" as follows:
(1) a positive adjustment to import competition occurs when;
(A) the domestic industry—
(0 is able to compete successfully with imports after actions taken under section 204 terminate,
or
(ii) the domestic industry experiences an orderly transfer of resources to other productive
pursuits; and
(B) dislocated workers in the industry experience an orderly transition to productive pursuits.
(2) The domestic industry may be considered to have made a positive adjustment to import
competition even though the industry is not of the same size and composition as the industry at the
time the investigation was initiated under section 202(b). Cited from RosenthaL, Paul and R. Gilbert.
1989. “The 1988 Amendments to Section 201: It isn’t just for Import Relief Anymore.” The
International Law Journal o f Georgetown University Law Center 20, no. 3. p.428.
5 1 Rosenthal, Paul and R. Gilbert (1989). p.428.
5 2 Sykes (1991). P.260
5 " The GAO (1981) notes: “The legislative history in reference to this section [201] indicates that it is
not the intent of the escape clause to protect an industry which has not acted to help itself become
more competitive.” p. 18.
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ITC rarely conditions trade relief on concrete adjustment plans, but investigations typically include
an analysis of “efforts of U.S. producers to compete with imports” and, in successful petitions, the
ITC has cited company plans for investment or the reopening of idle facilities rather than the “orderly
transfer of resources to other productive pursuits” in remedy recommendations.3 5 In one case
(Carbon and Certain Alloy Steel Products, 201-TA-51) several ITC Commissioners argued that relief
to the steel industry be conditioned on an industry representative’s proposal to include “investment
commitments, labor concessions, and productivity improvements...” in the ITC remedy
recommendation.5 6
Section 203 ofthe 1974 Trade Act also specifies that the ITC should review the progress and
specific efforts made by the industry to adjust to import competition, but it is evident from the GAO
report that the ITC’s monitoring and enforcement of industry efforts to promote positive adjustment
have been inadequate, even when this goal has been made fairly explicit with specific
recommendations (e.g. the industrial fastener case). Other independent efforts to assess the impact of
protection on industry performance also conclude that litde is known of these effects. Krueger
(1994), for example, argues that there remain "important questions about the efficacy of protectionist
trade policies, even in assisting the industries that seek protection," and adds that "to the extent that
5 4 U.S. General Accounting Office. 1981 (August). Changes Needed in Administering Relief to
Industries Hurt by Overseas Competition. Washington: Government Printing Office, p. 18.
5 5 In Investigation TA-201-56 (Wood Shakes and Shingles), Commissioners noted that the industry
“clearly seeks reiief under the . . . basis” of preventing or remedying serious injury or threat to the
industry ratherthan seeking a more orderly exit from the industry (pp.75-76). ITC Investigation TA-
201-39 (Porcelain-on-steel cooking ware) cited plans by the remaining domestic producer to
purchase, modify, and reactivate a former competitors plant (p.A-31). The Special Representative
for Trade Negotiations announced that trade relief would be sought for the footwear industry in order
to assist its efforts “to become more competitive.” (Press Release #247, Office of The Special
Representative for Trade Negotiations, April 1, 1977).
5 6 USITC Publication 1553. Cited from Rosenthal, Paul. 1986. “Industrial Policy and
Competitiveness: The Emergence of the Escape Clause.” The International Law Journal o f
Georgetown University Law Center 18, no. 4. p.773.
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trade barriers give producers false assurances, they may indeed be counterproductive from the
industry's perspective in the long run."5 7 The absence of official monitoring as well as skepticism
toward the anticipated benefits of protection underscore the importance of further research into the
issue.
In summary, there is a considerable degree of uncertainty regarding the compatibility of the
goals and outcomes of special protection. Nor does the government regularly provide the
mechanisms by which such evaluations can be made. In Chapter Three, I assess examples of
empirical literature that have evaluated industry adjustment to protection but find that they offer little
to support the idea that protection actually aids in this process.
V) Conclusion
The presence of special trade barriers imposes a considerable cost upon the nation's
consumer welfare. Nonetheless, the imposition of new trade barriers on behalf of specific industries
has been a steady feature of U.S. trade policy in recent decades. As with any economic policy that
adversely affects one group, there are expected to be attendant benefits. Economic theory provides
insights to the circumstances in which national welfare may improve with trade intervention, but the
more proximate goal of U.S. policy— theories of endogenous trade policy formation notwithstanding-
-is the creation of financial incentives to be used by the industry to enhance future competitiveness.
In conjunction with the inadequacy of official monitoring, however, the results of existing event
studies have created considerable uncertainty as to what, if any, benefits are captured by protected
industries. Consequently, the careful construction of a new event study provides an opportunity to
test whether protection is in fact associated with the expectation of higher future earnings, and can
5 7 Krueger, Anne. 1994 (May). “Implications ofthe Results of Individual Studies.” NBER Working
Paper 4445 (The Political Economy of U.S. Protection), p.9.
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reveal whether the costs to consumers are accompanied by the intended gains to industry. The
results can also show whether the choice of trade instrument has any bearing on the perceived value
of protection to industry shareholders. The intention is to inform policy-makers, industry
representatives, investors, and analysts concerned with the consequences of protection.
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CHAPTER THREE
INVESTIGATING THE VALUE OF PROTECTION TO INDUSTRY: A CRITIQUE OF
EMPIRICAL LITERATURE
I) Introduction
Trade barriers are expected to raise industry earnings and create a “breathing space” from
foreign competition, so a commonly held view is that protection generates new financial incentives
for industries to enhance efficiency. Numerous empirical studies have demonstrated that selective
protection has a negative impact on consumer and net national welfare, but are these losses
accompanied by the expected gains to industry competitiveness?1 Empirical literature on the subject
is characterized by substantial disagreement. Various survey studies, for example, have evaluated
whether protection leads to improved industry performance, but have failed to establish a meaningful
consensus as to the effectiveness of trade measures, the importance of protection in relation to other
factors affecting industry health, and the desirability of outcomes.2 Alternatively, several event
studies have posed a more direct question: Does protection raise industry profit expectations and
thereby create the financial incentives required for efficiency enhancing investments?
Event studies measure the expected change in future earnings brought about by protection
(the event) by capturing its effect on security prices. Deviations from expected share prices (excess
returns) at the time protection is granted represent the present discounted value of anticipated future
gains or losses to the industry. The price response therefore indicates whether protection is seen as
potentially valuable to the industry, and offers a distinct starting point for analyzing the influence
protection can have on future industry performance. Nevertheless, results from the existing event
1 Tarr & Morkre (1980, 1984); Hufbauer, Berliner & Elliott (1986); Hufbauer & Rosen (1986);
De Melo & Tarr (1992); Hufbauer & Elliott (1994).
2 Congressional Budget Office (1986, 1991); International Trade Commission (1982); Hufbauer
& Rosen (1986); Hufbauer & Elliott (1994); Tonelson (1994).
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study literature reveal an interesting empirical puzzle. Unexpected import price shocks (Grossman &
Levinsohn, 1989), trade liberalization (Thompson, 1994), and trade restraints stemming from
“unfair” trade complaints (Hartigan et al., 1989; Lenway et al., 1990, 1994) produce measurable
changes to security prices in the expected direction. The response to trade barriers arising from
“fair” trade (e.g. escape clause) cases, however, is either undetectable or move opposite of the
expected direction (Canto et al., 1986; Hartigan et al., 1986)/ That is, these barriers are viewed by
shareholders as inconsequential or even damaging to the profit outlook of affected firms.
These results are puzzling. Trade theory (Neary, 1978) predicts that higher import prices
will increase the profits to import competing industries when capital is immobile.4 The efficient
markets/rational-expectations hypothesis also asserts that expectations of higher profits will be
immediately incorporated into current share prices. Consequently, there is no a priori reason to
expect that protection stemming from fair trade cases should not be considered valuable and that the
value to industry will not be reflected as higher asset prices. The findings are also ironic because
barriers against fairly traded imports (e.g. escape clause) account for a major share of the costs of
protection, and are supposed to be something of a special exception for troubled but presumably
salvageable industries. The unexpected results may reflect methodological shortcomings in the fair
trade event studies, however. The opportunity therefore exists to contribute to the literature through
the careful construction of a new study.
J Unfair trade cases involve accusations of dumping (exporting at below cost) or foreign subsidies
and are referred to as antidumping (AD) or countervailing duty (CVD) cases. Industries injured
by increased but fairly traded imports may petition for protection under the escape clause
provision.
4 Neary, J. Peter. 1978. “Short-run Capital Specificity and the Pure Theory of International
Trade.” The Economic Journal 88:488-510. The assumption of capital immobility in the short
run is a reasonable one in most cases. Magee (1980), for example, found that capital owners, as
well as labor, supported industry specific interests while lobbying for the 1974 Trade Act.
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Methodological concerns can be divided into two main areas: 1) methods are skewed
towards yielding negative or insignificant results by imprecise event dating and/or improper
modeling of expected returns; and 2) data selection inaccurately represents the affected industries.
My methods, described in the following chapter, address these deficiencies by better representing
each industry, properly modeling expected industry returns, and more precisely isolating the impact
of protection by focusing only on key dates (events) within the trade petition process.
In this chapter, the reasoning behind and potential criticisms of the “breathing space”
argument are explored, and an explanation of how the event study method captures anticipated
changes in future earnings is given. Survey studies examining the pattern of industry adjustment
following protection are briefly evaluated to clarify the contribution event studies can make to such
inquiries. Finally, methodological shortcomings in the fairtrade studies are enumerated.
II) The Logic of Protection: The "Breathing Space" Rationale
According to Sykes (1991), an important motive of safeguard (e.g. escape clause) actions is
to “provide ailing firms with an increase in profits, enabling them to invest in new technology and
modem equipment, which will later allow them to compete successfully in the international
marketplace.”5 Import restrictions can support the efforts of domestic producers by improving
access to financial resources and creating a “breathing space” that can be used to re-equip for future
competition. Even if trade relief is temporary, as it is in most fair trade cases, higher anticipated
profits still generates two immediate incentives for increased investment: 1) by lowering perceived
risks to outside capital suppliers; and 2) by increasing the value of internally held stocks among
public companies. Consequently, firms need not wait to realize higher earnings in order to engage in
productivity enhancing investments because the cost of capital is reduced. The U.S. trade
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representative to President Reagan, for instance, defended restrictions on Japanese automobiles by
arguing that they would help the domestic industry raise $12 to $14 billion for new investment
projects needed to meet future Japanese competition.6 The application of an event study is an
effective way to determine whether protection does present such incentives.
Event studies measure the impact of protection on domestic producers by examining the
immediate effect of policy changes on an industry's security prices. Underlying the method is the
efficient markets/rational-expectations hypothesis, which asserts that security prices immediately
reflect all available information. Unanticipated changes in information or policy (events) therefore
“result in a current change in security prices, and the price change is an unbiased estimate of the
value of the change in future cash flows to the [affected] firm.”7 The security price change is
measured by the computation of “abnormal” or “excess” stock returns at the time of the event by
comparing actual event period returns with the returns that would be expected in the absence of any
policy change.8 A test statistic is then constructed to measure the significance of the abnormal
returns under the null hypothesis that they equal zero.
In summary, event studies can verify whether necessary, if not sufficient, pre-conditions for
efficiency enhancing investments are present. A positive and statistically significant change in
security prices (“excess returns”) indicates a favorable market assessment of protection. By
5 Sykes, Alan (1991). P.263.
6 The USTR was Bill Brock. New York Times. May 3, 1981. P.L7.
7 Schwert, William (1981) pp.121-122.
8 More specifically, the method entails the following steps, as described by Bowman (1983) and
Henderson (1990): 1) Identify the event of interest; 2) define the date upon which the market
would have received the news; 3) model the returns ofthe individual companies in the absence of
the news; 4) estimate the excess returns during the event period; 5) aggregate the excess returns
across firms and across time; 6) statistically test the aggregated excess returns to determine
whether they are significant.
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equipping domestic firms with the time and financial incentives to make cost reducing investments,
adjustmentto foreign competition can be facilitated.
One general criticism ofthe "breathing space" rationale for protection is the assumption that
these industries will be more likely to engage in productivity enhancing capital investments.
Trebilcock, et al. (1990) point out that if an adequate return could be made on new fixed assets, then
the private capital market would provide the funds required to make the investment.9 Of course, one
of the central arguments of proponents for protection is that private capital markets will be
discouraged from such lending, except at inflated rates, due to the perceived risks. A Congressional
Budget Office report notes, however, that trade restraints may also reduce incentives to make new
investments:
A firm would realize the higher revenues resulting from protection regardless of
whether it invested in new technologies. Indeed, protection can actually reduce
a firm's incentives to invest.... If imports from lower-cost producers are
restricted, a firm may be less inclined to make a major and risky investment in a
cost-reducing project1 0
In fact, one of the criticisms of the steel industry was that, rather than reinvesting in its steel
operations, it used import barriers as an opportunity to diversify into the oil and other manufacturing
industries.1 1 Acknowledgement of and skepticism towards the breathing space argument are both
well represented in the empirical literature evaluated in the following sections. But in any event, if
9 The authors argue that the most logical course of action for distressed firms unable to cover
variable costs is to allow fixed assets and long term capacity to run down. Trebilcock, Michael,
Chandler, M., and R. Howse. 1990. Trade and Transitions: A Comparative Analysis o f Adjustment
Policies. London: Routledge. p.22.
1 0 U.S. Congressional Budget Office. 1986 (November). Has Trade Protection Revitalized
Domestic Industries? Washington: Government Printing Office, p.8.
1 1 Although not explored here, one plausible explanation for why protection may diminish the
incentive to make efficiency enhancing investments is the impact on managerial efficiency. See
Corden, W.M., "The efficiency effects of trade and protection," 1970. In Corden, Max. 1992.
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equity holders themselves do not expect protection to generate new financial resources, the
likelihood of cost-saving investments is clearly diminished. Consequently, it is important to verify
that the anticipated industry benefits are present before causal inferences about the role of protection
in industry adjustment are made. This shortcoming is evident in the first category of literature
examined in the following sections.
HI) Empirical Literature: The Two Approaches
Empirical efforts to assess the value of protection to industry have taken two approaches:
survey studies that evaluate the adjustment and performance of industries in the years following
protection; and event studies, which investigate the capital market effects of protection. The first
group analyzes protected industries by tracing trends in output, employment, investment, and
profitability. Although detailed and comparative, survey studies struggle to distinguish the influence
of protection from other factors that affect the health of an industry over time. Lacking a uniform
standard to measure and assess the effects of protection on each industry, they are not well designed
for testing hypotheses that help explain variations in industry adjustment. In contrast, the event study
literature establishes a basis for evaluating and comparing prospects for industry adjustment by
directly calibrating the impact of protection on firm equity values and profit expectations. They can
therefore be used to verify whether protection creates the financial incentives to embark on a ne
investment program. Nevertheless, event studies examining fair trade cases have employed
imprecise methods that are ill-suited to the accurate detection of shareholder responses.
International Trade Theory andPolicv. Brookfield, VT: Edward Elgar Publishing Company, pp.
240-243.
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A) Survey Studies
The Congressional Budget Office (CBO, 1986, 1991), International Trade Commission
(ITC, 1982), Hufbauer and Rosen (1986a), Hufbuaer et al. (1986b), and Tonelson (1994) each
examine key indicators of industry performance for a number of protected industries.
Unfortunately, few generalizable conclusions about the role of protection in the adjustment
process emerge from this body of literature. Some of this is a natural reflection of the different
industries and varying time spans being considered. Different industry characteristics, market
conditions, and the level and type of protection affect the adjustment process. But more
importantly, the lack of agreement stems from different assessments of the effectiveness of trade
barriers and their role in promoting industry adjustment.
The ITC (1982), for example, contends that protection played little or no role in
facilitating adjustment of five industries even though outcomes varied considerably.1 2 Despite
meeting strict guidelines requiring that imports be the “major” cause of injury,the ITC
concluded that trade relief was an ineffective solution for these industries because “so much of
the firms’ injury was caused by non-import-related factors, or because the decline of imports
following relief was small.”1 4 In other words, the adjustment pattern of these industries was
largely unrelated to import relief.
In contrast, Tonelson (1994) asserts that protection directly contributed to improvements
in quality, productivity, and production in the steel, automobile, machine tool, semiconductor,
1 2 The industries studied include carpets and mgs, sheet glass, watches, stainless steel flatware,
and bicycles.
1 3 The Trade Act of 1974 stipulates that imports need only be a “substantial” cause of injury,
meaning not less than any other cause.
1 4 U.S. International Trade Commission. 1982 (March). The Effectiveness o f Escape Clause
Relief in Promoting Adjustment to Import Competition. USITC Publication 1229. Washington:
USITC. p.86.
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and textiles and apparel industries. Echoing the breathing space argument, he contends that
protection facilitated industry revitalization by increasing profitability and improving access to
capital markets. It also signaled to executives that “their industries could indeed have a future
and that retooling stood a real chance of being rewarded.”1 5 Striking a more intermediate posture
are the CBO (1986, 1991) and Hufbuaer et al. (1986a, 1986b) who find that protection was
generally effective in curbing imports but question the extent to which it aided industry
competitiveness.
The scholarship on the effectiveness of protection therefore remains divided. The ITC
argues that protection was ineffective in curbing imports or that other factors exerted a more
powerful influence. Tonelson finds that protection was both effective and made a major
contribution to the revitalization of industry. Other studies conclude that while protection did
stem imports, the outcomes were nonetheless disappointing.
A shared characteristic of the survey studies is the absence of a common method for
evaluating the expected contribution of protection to industry resources. Such studies are unable
to distinguish the influence of protection from other factors that act upon industry performance.
Another complication is that not all protection is created equal. It takes different forms (tariffs,
quotas, VERs) and varies in magnitude from case to case. Survey studies are not equipped to
differentiate between cases that yield the anticipated financial rewards and those that do not.
With no consistent basis for comparison, it is difficult to attribute the pattern of industry
adjustment to the impact of protection. Event studies, in contrast, establish from the outset
whether the chosen barriers were perceived as sufficient to bring about higher earnings and the
1 4 U.S. International Trade Commission. 1982 (March). The Effectiveness o f Escape Clause
Relief in Promoting Adjustment to Import Competition. USITC Publication 1229. Washington:
USITC. p.86.
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incentives to adjust in a “positive” fashion. In the next section I examine the small body of event
study literature using this approach, and focus specifically on the methods of two fair trade
studies whose findings indicate that protection is perceived as ineffective or damaging to
recipient industries.
B) Event Studies
Applications of the event study method have generated a sizeable literature in the Finance
and Accounting fields since the late 1960s. Extensions of the method to examine the wealth effects
of trade restrictions are relatively novel, however. Canto et al. (1986), Hartigan et al. (1986, 1989),
and Lenway et al. (1990, 1994) have contributed to this small literature.1 6 Although the studies are
not strictly comparable due to different case selection and somewhat varying methods, one consistent
and p uzzling result emerges. Whereas trade restrictions stemming from “unfair trade” complaints
yield positive wealth effects to import-competing industries, those intended to benefit industries
harmed by fairly traded imports are viewed as ineffective or even harmful.
Hartigan et al. (1989), for example, finds that the filing of non-steel antidumping (AD)
petitions in the early 1980's was associated with positive and significant "cumulative abnormal
returns" to petitioning firms when examined as a group. Lenway et al. (1990,1994) extends these
results to show that the 1977 trigger price mechanism (TPM) and 1984 voluntary export restraint
(VER) on steel also generated positive and significant abnormal returns to the domestic industry.1 7
,5. Tonelson, Alan. 1994. “Beating Back Predatory Trade.” Foreign Affairs 73, no. 4. pp. 123-134.
1 6 Similarly, Thompson (1994) employs the event study method to analyze the impact of the
Canada-United States Free Trade Agreement on manufacturing firms in Canada.
1 7 Lenway et al. (1994). Each of these trade settlements arose from the filing of antidumping and/or
countervailing duty petitions by the steel industry. Of other steel trade cases examined, the 1982
VER produced insignificant results and the 1980 TPM produced negative and significant abnormal
returns, although Lenway et al. (1990) found these to be insignificant.
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Further evidence that protection is likely to be seen as beneficial to domestic industries is provided
by Grossman and Levinsohn (1989), who demonstrate that industry excess (abnormal) returns are
positively and significantly correlated with unexpected price shifts of competing imports in each of
six unprotected industries.
These results are not sustained by two studies focusing on fair trade laws, which are
expressly designed to offer temporary respite for troubled but presumably salvageable industries.
Hartigan et al. (1986) found that only one of seven industry’s obtaining escape clause protection
(Citizens Band radio) experienced positive and significant abnormal returns during the petition
process. Canto et al. (1986) concluded that the value of protection was not just trivial, but actually
viewed as potentially damaging to each of four industries (Footwear, Color TV, Auto, Steel)
examined.1 *
The conflicting results could signify that temporary protection in fair trade (escape clause)
cases is simply not seen as valuable to capital owners of affected industries, despite the potential of
increased profits in the short run. Because escape clause protection is temporary and diminishes each
year it is in effect, it may be viewed by markets as less valuable than CVD or AD duties, which are
open-ended. In addition, Prusa (1992) contends that even AD petitions that are withdrawn could
benefit domestic industries because the process permits settlements to be negotiated between the
domestic and foreign parties. This may facilitate collusion between foreign and domestic producers
in ways not possible with escape clause cases. Nevertheless, I argue that the results of fair trade
studies are suspect because they employ a methodological approach that is imprecise, potentially
biased, and inaccurately represents protected industries. Since their results do not conform to
IS Three of the four cases originated as escape clause cases. The other (steel) experienced negative
but insignificant excess returns.
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theoretical expectation or to related empirical work, I therefore pay particular attention in the
following sections to methodological and data issues that condition the findings of Canto et al.
(1986) and Hartigan et al. (1986). I argue later that studies focusing on unfair trade cases largely
avoid the shortcomings that I specify.
Using monthly data, Canto, Dietrich, Jain, and Mudaliar (1986) examine the impact of
protection on four industries (leather shoes, color televisions, automobiles, and steel), and find that
protection in each case is associated with lower than expected stock prices.1 9 Based on these results,
the authors conclude that shareholders in affected firms expected protection to reduce industry
profits. If these findings proved to be robust, the implication is that protection is not only detrimental
to consumer welfare, but also to the industries seeking protection. Methodological problems in the
following categories could undermine the validity ofthe results, however. The method of calculating
abnormal returns is likely to be biased toward yielding negative results, the composition of industry-
specific indexes may inaccurately represent the protected industry; and the duration of event intervals
obscures the impact of trade specific events.
The negative bias stems from the authors' use of the Mean Value Model to calculate
expected returns. This model calculates abnormal (excess) returns as the difference between a firm's
(industry’ s) event period returns and the average market return (expected return) for some other
period. Canto et al. define expected returns as the average S&P return of six different "bull market"
periods ranging from 1953 to 1982.2 0 This measure sets a high standard for expected returns that is
not likely to be matched by a troubled industry in the process of seeking protection. A more
1 9 The results for steel were not statistically significant.
"° A bull market period is defined as starting with a six month continuous rise in the six month
moving average of the S&P 500 and ending with a six month continuous decline of the six month
moving average.
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appropriate and commonly used approach to defining expected returns is to establish a firm’s (or
industry’s) normal relationship with a market index using the ordinary least squares (OLS) method.2 1
The statistical significance of their results may also be overstated by failing to adjust the test statistic
to reflect the cross-sectional correlation induced by event and industry clustering.2 2 Simulation
literature has demonstrated that without such an adjustment the likelihood of falsely rejecting the null
hypothesis (Type I errors) increases.
Another complication arises from the data used by Canto et al. to represent each industry.
For each industry, the authors selected those firms whose Standard Industrial Classification (SIC) on
the Center for Research in Security Prices (CRSP) database corresponded with the protected
categories. There are two possible problems with this approach. First, for three of the industries
(steel, color televisions, and footwear), firm data was drawn using only the three-digit level of SIC
disaggregation. Given that most protected goods are found in the four, or even five, digit level of the
SIC, their industry analysis almost certainly included data from a number of firms not producing the
protected category and should therefore have been omitted. Second, the CRSP database containing
historical stock data lists firms according to their most recent SIC codes. Consequently, firms that
were not producing the protected category during the period of investigation may have been included
in the analysis.2 3 As a result, the analysis likely includes an unrepresentative sample of firms for
three ofthe four industries examined.
2 1 The OLS Market Model establishes a statistical relationship between movements in stock index
returns and firm (industry) returns. The relationship is established using data immediately prior to
the event. Another approach, the Mean Adjusted Returns method, uses the firm’s (industry’s) own
market performance from a preceding period to measure expected returns, but it can be misspecified
when event and industry “clustering” is present (Brown & Warner, 1980).
2 2 Event clustering refers to events that affect each industry on the same date. Industry clustering
refers to events affecting firms in the same industry.
2 3 Use of the CRSP database confirms that overlooking such considerations can result in the
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Finally, the authors define event intervals in such a way that they contain a great deal of data
lying outside of the defined event date (announcement of protection). While it is possible that the
market anticipated the (negative) impact of protection over a sustained period,2 4 that the excess
returns were consistently negative for each ofthe event intervals (EM-12 to EM-1, EM-12 to EMO,
and EM-12 to EM+6, where EM is the event month) is somewhat surprising. Event study
simulations have demonstrated that extended event intervals yield much less powerful results than
precisely dated ones (Brown & Warner, 1980,1985; Dyckman et al., 1984).2 5 By failing to precisely
and narrowly define the event interval, the findings may also reflect the impact of other industry-
related events occurring in the time span. Ultimately, Canto demonstrates that these industry stocks
performed worse than the market would be expected to during growth periods, but it is difficult to
attribute these results to the impact of protection. Rather, it is more likely that they reflect the bias in
expected returns referred to previously.
Using weekly data and the ordinary least squares (OLS) capital market method to calculate
abnormal returns, Hartigan, Perry, and Kamma (1986) analyze the impact ofthe protection-seeking
process on nineteen industries that sought escape clause relief between 1975 and 1980. Their results
show that ofthe industries ultimately granted protection, only one (CB Radios) experienced positive
inappropriate inclusion of a number of firms. A test extraction of firms using the four digit SIC still
yielded a number of firms not producing the product of interest, either because the product fell within
a higher level of disaggregation or business reference guides (e.g. Moody's Manual) indicated the
firm did not produce the product during the period investigated.
2 4 The petition process in these cases typically last between six and twelve months. During this
period, the outcome can be signaled by various interim decisions or by public pronouncements if
a trade agreement is being negotiated under Presidential authority.
2 3 Using daily data and the OLS method, abnormal returns (known to be present on day 0) were
detected only 13% ofthe time (at the 1% level of confidence) using an eleven day interval around the
event date, whereas they were detected 80% ofthe time when the event date was precisely identified
(Brown & Warner, 1985). Similar but less disparate results apply to Dyckman et al. (1984) using
other methods.
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and significant abnormal returns during the petition process.2 6 In contrast to the conclusions of
Canto et al., none of the industries, including television receivers and automobiles, showed
significantly negative abnormal returns.2 7 Hartigan et al. examine more cases and avoid the negative
bias ofthe Canto study by establishing a more precise relationship between the firm and market price
using the OLS capital market model. But several data selection and methodological problems
hindered this study as well.
First, of the nineteen industries studied, only seven succeeded in their quest to obtain
protection. Six petitions were denied by the USITC and six more by Presidential decision.
Consequently, for twelve of the industries, the study captures the effect of the (unsuccessful)
protection-seeking process rather than the effect of protection itself. Since the profit outlook is not
changed by the rejection of petitions, insignificant results are expected in these cases.
Furthermore, the likelihood of detecting abnormal returns (if present) in the remaining cases
is diminished by the method used to cumulate abnormal returns. Hartigan et al. calculated the
Cumulative Average Residual (CAR)— used for hypothesis testing— by summing each industry’s
average weekly residual (abnormal return) over the entire petition process, a period lasting from
twenty-five to forty-two weeks. As with the Canto et al. study, there are two problems associated
with this method: the cumulation of abnormal returns over this time period is likely to reflect the
impact of non-trade related events; and the significance of abnormal returns is difficult to detect
when long event intervals are used.
2 6 Stainless steel flatware experienced significantly positive abnormal returns for the petition process
despite a negative Presidential decision.
2 7 The automobile cases are not strictly comparable since Hartigan (1986) measures the effects of the
unsuccessful escape clause petition whereas Canto (1986) sought to measure the effects of the
subsequently negotiated VER with Japan.
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In the first case, earnings announcements, corporate restructuring or other “confounding”
events may obscure the specific impact of the trade related event. Cumulating excess returns over
the entire petition process greatly enhances the chance of including the impact of some other event in
their results. A more accurate approach is to select data from the periods immediately surrounding
each of the key events (i.e. filing of petition, USITC decision, Presidential decision) within the
petition process.2 8 This method of cumulating abnormal returns around key decision dates is used by
Hartigan et al. (1989) in the study of antidumping cases, which may explain why AD petitions in that
study are shown to be valuable to import-competing firms while escape clause petitions are not.
Second, event study simulations reveal that precise dating of events greatly improves the
ability to detect significant abnormal returns. Brown and Warner (1985), for example, show that
statistically significant abnormal returns (known to be present on day zero) were detected only 13%
ofthe time using an eleven day interval around the event date, but were found 80% ofthe time when
the event date was precisely identified.2 9 The importance of defining narrow event interval is also
accentuated by the work of Hillmer and Yu (1979) and Penman (1982), who demonstrate that stock
prices react very quickly to new information.
Finally, there are several problems with the selection of firm data. For some industries, the
number of firms chosen to represent each industry fall well short of the number of firms for which
2 8 The impact of these “confounding” events on the significance of abnormal returns is uncertain
without detailed information on each of the events. Hartigan et al. (1986) later examine the
industry abnormal returns for the week surrounding the final USITC decision and the Presidential
decision, but find no positive and significant results. This may be attributable to the difficulty of
properly identifying event dates, inaccurate firm selection, or neglect of other important events,
including the announcement date of specific sanctions which sometimes follows the presidential
announcement date. Approaches to circumventing these potential problems are described in
Chapter Four.
2 9 Using the OLS capital market model and a 1% significance level.
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data is available.3 0 This raises the possibility that an unrepresentative sample of firms has been
selected. Furthermore, in at least one case that led to protection (non-electric cookingware), firms
whose products were subsequently excluded from the petition by the ITC were included in the
industry analysis. This may have biased the results against the finding of significant positive
abnormal returns. Thus, while Hartigan et al. (1986) yield results less subject to biased outcomes,
the methodology and data are still not well-suited to revealing the existence of abnormal returns (if
present) in industries afforded protection.
This discussion of the event study literature has centered on the empirical puzzle presented
by studies that have examined fair trade and unfair trade cases. Although positive and significant
excess returns cannot be expected in all cases, I have focused on the methods employed by the fair
trade studies because the general findings are counter-intuitive. I should note that the methods used
in studies examining unfair trade cases largely avoid the shortcomings I have emphasized. For
example, Hartigan et al. (1989) and Lenway et al. (1990, 1994) each use the OLS capita! market
model and so escape the potential negative bias introduced by Canto et al.’s use of the Mean Value
Model. In addition, the potential problem of measuring non-trade related events is minimized by
their selection of narrower event “windows” than used by either Hartigan et al. (1986) or Canto et al.
(1986)/1 Finally, data selection appears to have been conducted in such a way as to more accurately
represent each industry examined. Hartigan et al. (1989) include useable data from “all firms that
were judged (by the USITC) to be potentially affected by the alleged dumping, not just the firm or
j0 Hartigan et al. use three or less firms per industry in fifteen of the nineteen cases. In most cases,
this study uses between five and fifteen firms per industry.
Jl Lenway (1990) uses an eleven day event period and Hartigan (1989) uses data from the two
weeks surrounding each event.
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group of firms that filed the complaint.’”2 And Lenway et al. were able to include data from all the
integrated steel mills and most of the larger minimills in their 1994 study, which expanded on their
1990 study of steel antidumping cases. I conclude that the methods and data used in the unfair trade
studies are well-suited to detecting the presence of abnormal returns and have provided accurate
results.
If used properly, the event study method is a useful technique for measuring the presence
and magnitude of anticipated changes in future industry profitability caused by protection. At the
same time, studies of fair trade barriers yield results inconsistent with theoretical expectation and
associated work on unfair trade cases. Although there may be intrinsic differences in the value of
protection obtained through the different channels, I argue that the discrepancy can be attributed to
methodological shortcomings by Canto et al.(1986) and Hartigan et al. (1986). Specifically, the
negative or insignificant results can be tied to a biased measure of predicted stock returns, a lack of
precision in event dating, and the possibility that results are skewed by the influence of unrelated
events. Finally, the selected data may inaccurately represent each industry by under-representing the
number of firms in each industry or by the inclusion of unaffected firms.
IV) Conclusion
The “breathing space” argument for protection is theoretically grounded and intuitively
plausible. By increasing expected returns to import-competing firms, trade barriers can create
incentives to engage in cost-cutting investments that might improve the industry outlook. Literature
on the subject is characterized by substantial disagreement as to whether industries actually benefit
from import relief, however. Survey literature, for example, is filled with conflicting conclusions
regarding the effectiveness of various trade measures to reduce imports, the importance of protection
3 2 Hartigan et al. (1989), p.184
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in relation to other factors affecting industry health, and the favorability of outcomes. Moreover,
inferences about the contribution to industry adjustment are hampered by the inability to verify the
presence of incentives created by increased expected returns.
Alternatively, event studies can be useful in verifying whether protection is seen as valuable
to affected industries in general, and can explain different outcomes for industry adjustment by
indicating which industry or firms have secured increased access to capital. Nevertheless, existing
literature indicates that, in fair trade cases, protection is expected to have an inconsequential or
negative impact on future earnings. I argue that shortcomings in the method and data selection
employed by Canto et al. and Hartigan et al. (1986) are likely to conceal the true impact of trade
restrictions on the industries they study. I therefore reexamine the issue by selecting data that better
represents each industry, more accurately modeling expected returns, and precisely isolates the
impact of protection by focusing only on key dates within the trade petition process. In Chapter
Four, I present the data and method used and the results of my study.
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CHAPTER FOUR
CAPITAL MARKET EVENT STUDY: MEASURING THE IMPACT OF PROTECTION
I) Introduction
In this chapter I empirically test die value of temporary protection by measuring its impact on
industry asset values. Specifically, the Capital Market Event Study approach is used to determine
whether trade sanctions are associated with “excess returns” to the common stock of industries granted
temporary (escape clause) relief.1 The presence of positive and statistically significant excess returns
indicates that protection is expected to benefit the industry by raising profits.
Two main considerations motivate the choice of this approach. First, escape clause protection
is based on the idea that there is an acceptable trade-off between assisting troubled industries and the
losses to consumer welfare and efficiency that accompany such a policy. Industries are granted escape
clause protection rather than trade adjustment assistance because the sun has ostensibly not set on
America’s comparative advantage in these products. Detecting excess returns clarifies the trade-off by
revealing market perceptions of government efforts to aid specific industries with trade policies. Second,
previous event studies have shown that there is no positive side to the trade-off. Protection is either
unaccompanied by the anticipated benefits to industry (Hartigan et al., 1986) or is expected to have
significantly negative short term consequences (Canto et al., 1986). Escape clause protection is therefore
considered an ineffective form of relief or prior event studies have provided an inaccurate account of the
impact protection has on firm or industry wealth.
1 Excess returns are those greater or less than expected given the firm's normal relationship with the
market. The terms “excess,” “unexpected,” and “abnormal” returns are used interchangeably.
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My findings reveal that protection is in feet expected to benefit targeted industries, although not
universally. Five of the twelve industries I examine experienced positive and significant excess returns
when the returns are cumulated over the interval immediately surrounding each of die key events leading
to protection. The value of protection is less apparait when excess returns are cumulated over the entire
petition process, with only two industries amassing significant positive returns in that span. Differences
in the perceived value of protection to shareholders appear to be linked to the type of trade measures
selected. Industries protected by unilaterally imposed tariffs and/or global quotas fere better than those
sheltered by voluntary export restraints (VERs).
The chapter is arranged as follows: after presenting an overview of evait study methods, section
two describes the selection of industry cases and data used in the event study. Section three outlines the
methods used to calculate excess returns, the aggregation of excess returns to form a cumulative excess
return (CER) measure, and hypothesis testing. In section four I discuss the identification and dating of
key events within the trade petition process, and explain the choice of intervals over which excess returns
are cumulated. Section five presents the results and I conclude in section six.
Overview of Event Study Methods
Event studies estimate the effect of an event (protection) on the value of a firm or industry’s
common stock. The impact is measured by the presence of “excess” (unexpected) stock returns, which
are the difference between actual event period returns and those expected in the event’s absence. A test
statistic is then constructed to measure the significance of the excess returns under the null hypothesis
that they equal zero. More broadly, the method entails six steps: identify the event of interest; define the
date upon which the market would have received the news; model the expected returns of the individual
2 Or orderly market agreements (OMAs), which are similar to VERs.
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industries in the absence of the event; estimate the daily excess returns during the event period(s);
aggregate the daily excess returns across time for each industry; and determine the statistical significance
of the industry excess returns (Bowman, 1983; Henderson, 1990).
II) Case Selection and Data Sources
A total of twelve industries, whose petitions for protection were initiated between 1973 and
1985, are examined. Hie main selection criteria are that protection is temporary and designed to counter
increased, but fairly traded, imports. Consequently, all but one case are drawn from industries protected
under or initiated as “escape clause” relief cases/ Although uncommon in comparison to unfair (AD
and CVD) cases, escape clause cases have been disproportionately costly to U.S. consumers.4 They are
also exceptional in that they represent the clearest example of a U.S. trade policy whose central objective
is to rejuvenate domestic industries. An event study examining these industries offers a good test of how
the market perceives the government's ability to target industries considered capable of doing so.
I omit otherwise significant examples of industries protected from fairly traded imports, such as
textiles and apparel, because long-standing trade barriers are not amenable to the detection of excess
returns using the event study method. For similar reasons, I exclude cases—primarily affecting
agricultural industries—that are ‘Triggered” automatically by declining import prices or exceeding pre-
established import-to-domestic consumption ratios (Section 22 of the Agricultural Adjustment Act of
1933).5 With these industries, protection exerts a permanent influence rather than a discretely timed
temporary lift to industries thought capable of rejuvenation. Precise event dates are also difficult to
identify. Escape clause petitions, on the other hand, offer the advantage of a clearly defined and
J The exception was Machine Tools, initiated as a Section 232 national security case.
4 Antidumping cases have also been adequately treated by Hartigan et al. (1989).
5 Trade Adjustment Assistance (TAA) cases are not examined since they directly benefit workers,
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relatively closed administrative process that facilitates the identification of key events and event dates.
No other special criteria for case selection are inposed other than each industry must be represented by
at least one firm for which daily stock return data is available for the required interval.
A) Industry Case Selection
The initial sample of cases is drawn from escape clause (Section 201) petitions filed with the
International Trade Commission (TTC) between 1974 and 1995. In that time, sixty-five petitions for
products ranging from birch-door skins and apple juice to motor vehicles and color televisions were
initiated. Of those petitions, the ITC found in thirty-four cases that imports were a “substantial” cause
of injury or threat of injury and recommended import tariffs, quotas, or adjustment assistance. Acting on
the ITC’s recommendations, the President ordered tariffs, quotas, or the negotiation of orderly marketing
agreements (OMAs) for twelve industries, adjustment assistance for six, and price supports for one
industry. Three of the twelve cases resulting in escape clause protection were omitted due to a lack of
data on publicly traded firms in those industries.6
In addition to the nine remaining cases, three were later added. The first, ball bearings, was
initiated under escape clause provisions that existed prior to the Trade Act of 1974, which established the
ITC and revised the trade petition process. The second, motor vehicles, was opened as an escape clause
case in 1980 but the ITC found that imports were not a “substantial” cause of injury and recommended
no relief. Nevertheless, the administration subsequently negotiated a voluntary export restraint (VER)
with Japan and I treat the case as a successful petition for temporary trade relief. Finally, the machine
tool industry was included because it obtained import relief under a provision similar to the escape clause
designed to protect industries in the interest of national security (Section 232). Table 4.1 lists each of the
and not capital owners. The event study method is unlikely to capture these effects.
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selected industries, the number of firms used in compiling data for each industry, and the type and
duration of protection each industry received.
Table 4.1: Industries Selected (by date o f petition)
Industry Number Trade Barrier Duration of Protection
of Firms
Ball Bearings 6 TARIFF 05/74-05/78
Stainless and Alloy
Tool Steel
13 OMA-QUOTA
(GLOBAL)
06/76 - 02/80
Non-Rubber
Footwear
13 OMA
(KOREA, TAIWAN)
07/77-07/81
Television Receivers 6 OMA (JAPAN) 07/77 - 07/82
CB Radio
2 TARIFF 04/78-04/81
High-Carbon
Ferrochromium
2 TARIFF 11/78-11/82
Clothespins 1 QUOTA (GLOBAL) 02/79 - 02/82
Industrial Fasteners 8 TARIFF 01/79-01/82
Non-Electric
Cookware 1 TARIFF 01/80-01/84
Motor Vehicles 4 VER (JAPAN) 04/81-01/87
Stainless and Alloy
Tool Steel
11 TARIFF-QUOTA
(GLOBAL)
01/83*-01/87
Machine Tools 7 VER 01/87-12/93
(JAPAN, TAIWAN,
______________________________ SWITZERLAND, GERMANY)_________
* retroactively imposed Sources: ITC: Derrick. C. (1988);
VER: Voluntary Export Restraint Hufbauer, Berliner. & Elliott (1986)
OMA: Orderly Marketing Agreement
6 Wood shakes and shingles, canned mushrooms, and heavyweight motorcycles.
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B) Firm Selection and Data Sources
Seeking to develop a comprehensive and accurate portfolio of firms to represent each industry,
the following approach to firm selection was used. Initially, I selected each firm named as a petitioner or
producer from the ITC investigation reports. Petitions frequently came from industry associations whose
membership was oftai difficult to ascertain, however. Consequently, I also drew upon firms mentioned
in secondary sources and, when possible, received lists of industry association members to include for
consideration. Next, I performed a search on the Carter for Research in Security Prices (CRSP) data
base by four digit standard industrial classification (SIC) code for a list of firms producing the product
category of interest. These steps yielded a preliminary list of firms for each industry. Thai, using the
Moody's Manual for the year each portion was filed, I examined each firm’s product list and excluded
those not manufacturing the good in question.7 Finally, any firm for which stock return data was missing
or incomplete for any part of the estimation or event period was also excluded. This omitted data from
firms that merged, were bought out, went out of business, or were delisted for any other reason. The
results are industry portfolios ranging in size from one to thirteen firms. Industries represented by four
or fewer firms include all producers for which data is available.8
Data for the Capital Market Event Study comes from the Center for Research in Security
Prices (CRSP) which provides daily continuously compounded return data for firms listed by
primary SIC code in the combined AMEX/NYSE and NASDAQ stock exchanges. All statistical
estimation is conducted using SAS statistical software.
7 Unless the firm was specifically named as a petitioner.
8 Recall that in their event studies, Canto et al. (1986) and Hartigan et al. (1986) may have skewed
their results by under-representing each industry, or misrepresenting them by including firms not
manufacturing the protected good at the time of the petition. Hartigan (1986), for example,
selected only those firms mentioned in the TIC investigation reports, and Canto (1986) simply
included all firms listed under a (sometimes overly broad) SIC code.
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Ill) Methods
A) Modeling Expected Returns
In order to calculate excess returns during the event period, the industry’s expected returns must
first be established. This is done by modeling an industry portfolio’s normal relationship with a broad
market index in the time preceding the event. Although die event study literature has employed a number
of models to estimate expected returns, the ordinary least squares (OLS) capital market model has
proved to be reliable and is commonly used.9 The OLS capital market model establishes the industry’s
normal relationship with the market in the following equation:
R i , t = + ( 1 )
where
Ri,t is the continuously compounded rate of return for industry portfolio i at time t
C ti is a constant
Bi is the systematic risk of industry i
Rm,t is the continuously compounded rate of return for the market
portfolio (proxied by the return on an value weighted
portfolio made of all stock on the New York Stock Exchange
(NYSE) and American Stock Exchange (AMEX)) at time t
Ui,t is a disturbance term.
Rit is constructed as the equally weighted average (portfolio) daily return of firms in each
industry. This equation is estimated for each industry portfolio for the period beginning thirteen calendar
9 An extensive event study “simulation” literature has examined the power and specification of
alternative methods for calculating abnormal returns. Results show that, under various conditions,
the single index OLS market model yields results less subject to misspecification and of equivalent
power to several commonly used alternatives, including multi-index OLS market models. See
Brown and Warner (1980, 1985), Brenner (1979), Binder (1997, unpublished manuscript).
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mouths prior to the event and aiding one calendar month prior to die event (the estimation period), giving
approximately 250 observations (or trading days) for each industry. Data from the event period and the
month preceding it are not included in order to avoid contaminating the estimates with event related
effects.1 0 The resulting parameters, as and are ordinary least squares estimates of industry i’s market
model parameters based cm stock returns data taken from this estimation period. The parameters
establish a benchmark of the expected relationship between the industry portfolio and the market that is
then used to calculate excess returns during the event period.
B) Estimating Excess Returns During the Event Period(s)
Excess returns for each industry are measured as the difference between the industry portfolio’s
actual return during the event period and the expected return established by the parameters in equation 1.
E R i, t = R i,t — ((Xi " i “ B iRm, f ) (2)
Where,
ERi.t is the excess return for industry portfolio i on event day t
Ri,t is die industry portfolio return on event day t
C X i , B i are parameter estimates from the estimation period in equation 1
Rm,t is the return to the market (NYSE/AMEX) portfolio on event day t.
Excess returns (ERa), then, are equivalent to the prediction errors of the OLS model in equation
one. These excess returns can, if significantly different from zero, be attributed to the event in question.
1 0 Binder (1997, unpublished manuscript) notes: “if the event period is included in the period used
to estimate the market model parameters, the coefficient estimates are biased because the
disturbances (which contain the effects of the event and related occurrences) are not mean zero.”
p.5.
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C) Aggregating Excess Returns: Cumulative Excess Returns (CER)
Because we are interested in the overall excess returns earned by each industry during the event
period(s), an industry portfolio excess return measure for each event day was used. Event studies
commonly calculate OLS parameters and excess returns for each firm as in equation (1) and equation (2)
and then aggregate and average these excess returns to establish the industry’s average excess return
(AER) for each event day. Since each industry event simultaneously affects every firm, an industry
portfolio return measure (Rn) was instead used in the above equations. One advantage of this approach
is that it avoids the problem of cross-sectional correlation among each firm’s market model residuals,
which complicates hypothesis testing by reducing power and increasing the likelihood of Type I (falsely
rejecting the null hypothesis) errors. Methods and other concerns associated with hypothesis testing are
discussed further in the next section.
As previously noted, equation 2 provides each industry’s excess return measure for each evert
day t. Excess returns are then aggregated over the defined event days to form a cumulative excess return
(CER) measure:
CER = Y JE R j, t (3)
where t is the number of days in the event interval.1 1
The CER provides information on the cumulative effect of an event on an industry’s security
prices and simplifies analysis of excess returns when the event interval is longer than several days.
Choosing longer event intervals is necessary when the date of market reaction to news of an event is
uncertain, or when the cumulative effect of a series of non-overlapping events is measured. In my study,
1 1 Bowman (1983) notes that “Regardless of the more sophisticated...methods, the CAR [CER]
methodology has become firmly established as the commonly used approach.” p.571.
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I identify and define five different actions within the trade petition process as events and then conduct
hypothesis tests on the CER for each event and on their sum (total cumulative excess returns: TCER).
The TCER is a comprehensive figure measuring the net impact of the five distinct events encompassing
the successful escape clause petition. Since the identification of events and event dates is somewhat
elaborate, I defer to section three (Selection of Events, Event Dates, and Event Intervals) a more detailed
discussion of this process.
D) Hypothesis Testing: Measuring the Significance of Excess Returns
Testing the significance of the CER or TCER for each industry is based on the null
hypothesis that the sum of event period excess returns are equal to zero.1 2 If the CER or TCER is
found to be significantly different from zero, then the alternative hypothesis that protection induces
a positive (or negative) wealth effect can be accepted. Significance of the CER is measured by the
“t-test” statistic, which is calculated as the ratio of the CER to its estimated standard deviation.
The estimated standard deviation is derived from the time series of the OLS equation used to
establish the market model parameters during the estimation period (equation one).'J If the
assumptions commonly made about OLS equations are not violated,1 4 the t statistic would be:
1 2 Because the principles of hypothesis testing are the same for CERs and the TCER, I simply refer
to the CER in this section to simplify notation.
lj Canto et al. (1986), p.40
The following assumptions are normally made with the OLS model: residuals are a) normally
distributed with a mean of zero; b) not serially correlated; c) not cross-correlated with residuals
from other firms; d) have a constant variance; and e) are not correlated with the explanatory
variable (Rm .t). Violations of these assumptions are discussed with the exception of e). For reasons
given by Henderson (1990, p.294), violations of this assumption are not expected here, and a
corrective technique introduced by Patell (1976) does not perform well using the market model
when event clustering (each firm simultaneously affected by the same event) is present.
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where
n
y ERi is the CER over the number of days (n) in the event interval,
r=I
and
S 2(ERi) = ^r= e t
f t= c 2
(e2 — el — l)
which, when summed to reflect the number of event days, is an estimate of the variance of the
the numerator is the error sum of squares from the estimation period.
Using such a statistic raises several concerns about violating statistical assumptions which,
if ignored, can result in misspecification and alter the power of hypothesis tests. The first concern
stems from the properties of daily stock return data; the second involves the estimation of variance.
Pursuant to these concerns, which are discussed in more detail in the ensuing sub-sections, I use
the Newey-West (1987) heteroskedasticity and autocorrelation consistent estimator (hace) of
variance following Chu (unpublished manuscript).1 5 The t-test used to measure the significance of
CERs takes the following form:
1 5 Chu, Chia-Shang James, Assistant Professor of Economics, University of Southern California.
Undated. “Operational Manual for HAC.”
CER, where el is the beginning of the estimation period, el is the end of the estimation period and
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t =
CER
hT h a ce
r=l
(5)
where
hace =
e2 — ei — l
ez — ei — 2
and
T(0)
f(l)
f ( A / - l )
and
M
is the estimation period variance from equation 1
is the first order covariance of estimation period residuals
is the covariance of estimation period residuals of order (M — 1)
is the integral value (whole number) of the truncation lag parameter, which places
a lower weight on autocorrelation of higher order and limits the weighting scheme
to autocorrelation of the order M .
M is calculated as follows:
M = (1 A41)[a(ei - e: - 1)]I/3
where
4 - p 1
a ------ —
a - p - y
and
is an estimated autoregressive coefficient of the first order [AR(1)] obtained
by regressing the equation 1 residuals on the residuals from time (t-1).
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Most of the concerns addressed in the ensuing two subsections are either accounted for in
the test statistic I employ or present no special difficulties.
E) Concerns with the Use of Daily Stock Data - Non-normal Distribution and Biased and
Inconsistent OLS Parameter Estimates
Because the power of hypothesis testing using daily return data is substantially greater
than with monthly or weekly data, its use in event studies is preferred (Brown and Warner, 1980,
1985). This is particularly true when the event period can be precisely identified. However, use of
daily data has raised misspecification concerns due to its non-normal distribution, which can
increase the probability of Type I errors. Moreover, Scholes and Williams (1977) and Dimson
(1979) have reported that use of daily data may produce biased and inconsistent estimates of the
OLS parameter Pi due to non-synchronous trading.1 6 In an effort to circumvent potential
misspecification due to parameter bias, Canto et al. (1986) and Hartigan et al. (1986) used
monthly and weekly stock return data, respectively, in their studies.
The feet that daily stock returns are highly non-normal (skewed and kurtotic) has been
documented by a number of studies (Brown and Warner, 1985; Dyckman, Philbrick, and Stephan,
1984; Berry, Gallinger, and Henderson, 1990). Moreover, several of these studies have reported
that OLS market model residuals and market model excess returns are skewed and kurtotic as well,
with the implication that standard parametric test statistics are poorly specified. However, there
are several factors that mitigate against this concern. Brown and Warner (1985) and Dyckman et
al. (1984), for instance, demonstrate that the distribution of OLS residuals and mean excess returns
approach normality when portfolios are used. Simulation studies using portfolios of twenty-five
1 6 Nonsynchronous trading refers to the mismatching of values for Ray and Ru caused by
infrequent trading of certain securities (Henderson, 1990).
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firms (Berry, Gallinger, and Henderson, 1990), and ten firms (Corrado and Zivney, 1992;
Dyckman, Philbrick, and Stephan, 1984) have concluded that the “Student t statistic is an
appropriate test statistic for event studies with daily (nonnormal) data.”1 7 Brown and Warner
18
(1985) also report reliable t-test specification for portfolios with as few as five firms. And
according to Corrado and Zivney (1992), “the adverse effects of the significant skewness and
kurtosis [in their results] on t-test specification only became apparent in 1-percent level tests.”
That is, rejection rates at the 5 percent level were within the ninety-five percent confidence interval
but were not at the one percent nominal test level.1 9 They also report that the use of longer (e.g.
more than 100 day) estimation periods also tends to improve specification.2 0
Despite the findings of the simulation literature noted above, the non-normal distribution of
test statistics can remain an important issue when portfolio size is very small, as it is for several of
the industries used in this study. However, even for hypothesis tests on single firm abnormal
returns, the incidence of Type I errors may not be especially severe. In simulation studies that
examine the characteristics of individual firm data, Thompson (1988) and Berry, Gallinger, and
Henderson (1990) do verify that daily stock return data is highly non-normal,2 1 but conclude that
the residuals (prediction errors) from the OLS market model are distributed in an “essentially
1 7 Berry, Gallinger, and Henderson (1990). P.78. Dyckman et al. (1984) conclude that the t-test is
“an accurate test for the presence of abnormal performance despite the non-normal distribution of
daily returns.” P. 26
1 S p. 14. Brown and Warner (1985) state that “standard parametric [t-tests] for significance of the
mean excess return are well specified [and] the tests typically have the appropriate probability of
Type 1 errors.” P.25
1 9 With a portfolio size of 10, and using a 250 day estimation period (as I do), the null hypothesis
was rejected 2 percent of the time on upper tail tests and 1.4% of the time on lower tail tests.
2 0 My tests use approximately 250 days in the estimation period.
2 1 Berry et al. find that, in a sample of 2000 firms, skewness is detected in 54% and kurtosis in
70% of the return series at the 95% significance levels.
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normal” fashion.2 2 For example, although Berry et al. detect skewness and kurtosis in
approximately one third of the two thousand residual series (at the 99% significance level) with a
commonly used procedure, additional tests suggest that the residuals are “reasonably” normally
distributed. Both the Kolmogorov-Smimov (KS) and Schapiro-Wilk (SW) tests (which the authors
regard as a more powerful test of nonnormality) reject the hypothesis of normality about as often
as the preset significance level.2 3 Thompson (1988) also concludes that the OLS method produces
prediction errors (residuals) with a distribution that is not significantly distorted, but finds that t-
tests on individual firm abnormal returns may in some cases be misspecified. Nevertheless, the
level of misspecification was not especially severe, with rejections of the null hypothesis only 1.4
percent more frequent than chance alone would have predicted (3.84% versus 2.25% in right tailed
tests using the OLS market model).2 4 Berry et al. also suggest that nonparametric alternatives to
the t-test, while more powerful, are more likely to be misspecified.
Furthermore, although evidence indicates that standard OLS estimates of the parameter p,
are biased and that non-synchronous trading induces serial (auto) correlation in daily excess
returns, there are good reasons not to employ alternative methods of parameter estimation
suggested in the literature. The commonly used Scholes-Williams (SW) and Dimson techniques,
for example, appear to confer no advantage in detecting abnormal returns. Reinganum (1982),
Ruback (1982), Theobold (1983), and Brown and Warner (1985) all find that parameter estimates
“ Berry et al. (1990). P.76
The KS test showed that “only about 5 percent to 10 percent of the residuals are nonnormal at
the 95 percent significance level...” and the SW statistic rejected the hypothesis of normality 4.8
percent of the time at the 95 percent significance level. P. 75
According to Thompson, the proportions of Type I errors differ significantly from the preset
significance level of 0.025 (in a two tailed test) in right tail tests but not in left tail tests. The
actual proportion of rejections of the null hypothesis with no abnormal returns introduced was
0.0222 for left tail tests, and 0.0384 for right tail tests (with a sample of 465 firms, using the
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using these procedures were not significantly different from those obtained using standard OLS
estimation. Nor do the alternative procedures alter the power of hypothesis tests in simulation
studies (Brown and Warner, 1985; Dyckman, Philbrick, and Stephan, 1984) or remove
autocorrelation from excess return measures (Brown and Warner, 1985; Berry, Gallinger, and
Henderson, 1987).2 5 In addition, Brown and Warner (1985) find that “failure to take into account
non-synchronous trading in estimating market model coefficients does not result in misspecification
of event study methodologies using the OLS market model... .”2 6
Thus, the direct consequences of parameter bias are neither especially grave for hypothesis
testing nor are they ameliorated by commonly used alternatives of parameter estimation. In my
tests, the Scholes-Williams technique yielded parameter estimates virtually indistinguishable from
those obtained using the standard OLS procedure. The non-normal distribution of test statistics is
of some concern for hypothesis tests using smaller portfolios, and the results for these industries
should be treated with some caution. However, more recent evidence suggests that skewed and
kurtotic distribution of individual firm residuals (using the OLS market model) are less common
than previously believed, and the incidence of Type I errors due to this problem is not especially
severe. There are also several reasons, as discussed in the next section, to believe that the methods
used in this study provide conservative estimates of the true significance levels of industry excess
returns. With these concerns in mind, daily return data is still preferable to the monthly and
market model and continuously compounded returns). P. 80-81
2 3 Dyckman et al. (1984) explain the failure of the SW and Dimson techniques to improve detection
of abnormal performance with thinly traded stocks as due to a bias towards more frequently traded
stocks on the CRSP database and the elimination of less actively traded stocks in their simulations
because of missing data. Since my data is drawn from CRSP and will exclude firms with missing
data, these conclusions should be generalizable to my study.
2 6 Brown and Warner (1985). p. 16.
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weekly data used by Canto et al. (1986) and Hartigan et al. (1986) due to the enhanced power
given by more precise identification of event dates.
F) Concerns with Estimation of Variance - Autocorrelation, Cross-Sectional Correlation, and
Increases in Variance
Accurate hypothesis testing is complicated by several issues related to the estimation of
variance, which can affect the power and specification of the test with both daily and monthly data.
One concern is that both the time series of event period excess returns and the OLS residuals from
equation one (used to derive the estimated variance and standard deviation used in hypothesis
testing) may be serially dependent (autocorrelated) due to non-synchronous trading (Scholes and
Williams, 1977; Ruback, 1982). The variance estimator used for hypothesis testing may be
undervalued, particularly when the event period encompasses a large number of consecutive days.
Although procedures to reflect first-order autocorrelation in the time series of excess
returns are not uncommon in applied event studies, the extent to which autocorrelation affects
event study methodologies is debated.2 7 Nevertheless, I take a conservative approach to the
problem. First, I use the heteroscedasticity and autocorrelation consistent estimator (hace) of
variance to account for first and higher-order autocorrelation in the series of estimation period
residuals. Second, I adjust the variance estimate to reflect the number of days in the test interval in
a spirit similar to that of Lee (1992).2 8 In my tests of total cumulative excess returns (TCER), I
modify the estimated standard deviation to reflect autocorrelation for the sum of event days despite
2 7 The simulation study by Brown and Warner (1985) revealed that autocorrelation in the time
series of excess returns did not greatly affect the specification of the tests (p. 19). In addition,
Lenway et al. (1990) found no evidence of autocorrelation in their event study on the effect of
protection on the steel industry; and Henderson (1990) notes that “...efficient market tests
document the limited autocorrelation in security returns. Autocorrelation in the residuals is even
smaller and appears to pose little problem for event studies.” p.293.
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the fact that the events I examine are (typically) divided into five non-overlapping time periods.
The resulting variance (and standard deviation) estimates should yieid t-tests on the conservative
side.2 9
Similarly, evidence indicates that daily and monthly security returns of individual
securities exhibit cross-sectional (contemporaneous) correlation, particularly when securities are of
the same industry and key event dates are the same for all firms — a problem known as industry and
event date clustering (Beaver, 1981; Schwert, 1981). The consequence of cross-sectional
correlation, as described by Collins & Dent (1984), is that the variance of the mean excess return
measure is underestimated, resulting in Type I errors/0 However, Brown and Warner (1985) note
that using portfolio returns, as I did, to estimate variance automatically takes into account cross-
sectional dependence in the security-specific residuals/1 Consequently, cross-sectional correlation
presented no special difficulty for hypothesis testing in this study.
Finally, Hilmer and Yu (1979), Kalay and Lowenstein (1983) and Christie (1983) have
found evidence that the variance of a security’s return increases substantially around event
periods.3 2 Including only data from the pre-event period to estimate the standard deviation can
result in a higher probability of Type I errors when heteroscedasticity is present.
2 8 D. Scott Lee (1992). p.1070.
2 9 Cowan (1991) shows that when the number of event days is small relative to the number of days
used in the estimation period, the uncorrected test statistic will be very close to the corrected
(autocorrelation adjusted) test statistic. When the number of event days is 5 and the estimation
period 100 days, the uncorrected test statistic exceeds the corrected one by only 1.6 percent. In my
tests, the estimation period is approximately 250 days and, in most cases, the number of event days
is 25 or less, divided into non-overlapping intervals. Thus, correcting for autocorrelation in the
manner I have described will exert a downward bias on the t-test. Results from Cowan (1991)
cited from Binder (1997, unpublished manuscript).
J° When using monthly data, this effect is detected more frequently using the CRSP value weighted
index as a market proxy rather than the equally weighted index (Brown and Warner, 1980, p.235).
Jl Brown and Warner (1985). p.8.
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One way to correct for this is to use before and after event period data in equation one to
reflect any variance shift that occurred. Unfortunately, doing so would reduce the sample size of
firms in the majority of industries because of incomplete data in the post event period. While the
estimated variance (hace) used for hypothesis testing does account for heteroscedasticity in the time
series of estimation period residuals, I make no corrections to reflect event period increases in
variance. If variance has increased during the event period, failure to account for this does
modestly increase the risk of Type I errors but also reduces the power of hypothesis tests." But if
the event is really a non-event, as under the null, this should not be the case. And in conducting
hypothesis tests, the relevant distributions for significance calculations are those under the null.
In summary, the use of daily data and the proper estimation of variance complicate
hypothesis testing but present no insurmountable difficulties. Simulation studies using daily data
show that results, even with smaller industry portfolios, are well specified and better suited to
detecting the presence of abnormal returns than monthly data. The impact of autocorrelation and
cross-sectional correlation has been accounted for in my methods. Ignoring potential variance
shifts during the event period increases the risk of Type I errors but also reduces the ability to
detect abnormal returns. In addition, there are several reasons to believe that my results will be on
the conservative side. First, I use a value-weighted index as a proxy for market returns rather than
the equally-weighted index commonly used. Brown and Warner (1980) have shown that using the
value-weighted index makes detection of excess returns less likely than with equally-weighted
returns. Second, Thompson (1988) argues that using continuously compounded returns, as I do,
j2 Christie (1983) and Kalay & Lowenstein (1983) were referenced by Brown and Warner (1985).
" Assuming a doubling of variance during the event period, the frequency of Type I errors is about
twice that expected when there is no abnormal return, but detection of abnormal returns when
present is reduced (Brown and Warner, 1985. Table 9, p.23).
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makes the detection of effects expected to produce a positive abnormal return more difficult iu
comparison to the use of simple returns.3 4 Finally, Corrado and Zivney (1992) and Dyckman et al.
(1984) both reveal that the power of hypothesis tests diminish as the size of the industry portfolio
decreases. Dyckman et al., for example, finds that with the five day event interval used in my tests,
a three percent level of abnormal returns are detected only twenty-two percent of the time with a
portfolio of ten firms, whereas they are detected eighty-six percent of the time using a portfolio of
one hundred firms.3 5 The industry portfolios used here range in size from one to eleven firms.
IV) Selection of Events, Event Dates, and Event Intervals
In addition to concerns about statistical estimation, a critical component of any event study is
precisely identifying and dating the action or actions that define the event, hi particular, consideration
must be given to three issues: the key actions or announcements associated with the event should be
identified in advance; the date(s) upon which news of the event first became public must be accurately
identified; and excess returns surrounding each event should be calculated over an appropriate interval
(event span). Ambiguity with or inattention to these details is likely to hinder detection of excess returns
or lead to erroneous attribution of excess returns to the event in question.
Event studies frequently focus on events characterized by a single well-defined action or
announcement, such as a product recall announcement, stock split, or earnings announcement. In such
* * Thompson (1988) reasons: “...since the natural logarithm transformation used to obtain
continuously compounded returns reduces positive simple returns and increases in absolute value
negative simple returns, positive event effects should be easier to detect using simple returns.”
p.78. Thompson also reports that the “proportion of Type I errors for continuously compounded
returns are closer to the preset significance level than the proportion of Type I errors for simple
returns.
3 5 P. 11. Corrado and Zivney introduce a one percent level of abnormal performance to the event
date and detect abnormal returns 93.8% of the time with a portfolio of 50 firms and 28.9% of the
time with a 10 firm portfolio (using a standard t-test and no event date uncertainty). P.473
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cases, the event and the event date are often easily identified and the main concern is choosing an
appropriate event interval over which to cumulate excess returns. But when the event is characterized by
a series of actions, as with a corporate merger or regulatory change, it can be difficult to determine ex
ante which action is likely to elicit a market response and when the information is incorporated into
market expectations. Approval of a merger by shareholders and antitrust authorities, for example, may
be widely anticipated. Regulatory changes involve not just a series of legislative steps, but media
scrutiny, intense lobbying, and speculation that make the identification of key events difficult/6 The
potartial risk is that the event date will be misidentified or the event date is chosen ex post based on the
researcher’s results.
Alternatively, uncertainty about the importance or timing of certain “sub-events” can be
handled by lengthening the event span to encompass all possible dates upon which the market
reacted to news. However, doing so introduces additional noise from non-event related occurrences
and considerably lessens the power of hypothesis tests, as discussed in Chapter Three. When the
key events can be precisely identified and dated, the literature is uniform in advocating cumulation
of excess returns over a short event span. In the following sub-sections, I identify and define the
key actions that make up each event, assign dates to each, and explain the choice of event intervals.
A) Identification of Events
The escape clause petition process is a well-defined and relatively closed administrative
procedure, making the identification and dating of key events straightforward. So, rather than
3 6 Binder (1985), for example, examined twenty regulatory changes instituted between 1887 and 1978
and detected abnormal returns only about as often as chance alone would have predicted. Binder
concluded that the number of announcements associated with each regulatory change, their public
nature, and the lengthy time span from first announcement to implementation made the stock market
effect difficult to isolate, either because it was anticipated or was hidden in the noise of the period.
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aggregating excess returns over the entire petition, which can last close to a year, I identify five actions in
the petition process that act as market signals and define each of them as events. I then cumulate excess
returns surrounding each event to form its CER, and aggregate these to calculate the total cumulative
excess return (TCER) for the petition. Because information from any one or all of these events may be
incorporated into current security prices, the TCER provides the best measure of the net impact of a
successful petition. The five events are: the receipt of the petition by the ITC; the start of the ITC
investigation (a pro forma event that may still have informational content); the ITC injury determination
vote; the ITC remedy determination vote; and the Presidential decision to accept, reject, or modify the
ITC’s recommendations. In cases resolved through the negotiation of an orderly market agreement
(OMA) or voluntary export restraint (VER), the date these are announced become an additional event.
Later I discuss modifications to the event interval that are required for some of these cases.
The petition for an escape clause investigation may be initiated by an industry representative
(including an association, a firm, a union, or group of workers), the President, U.S. Trade
Representative, House Committee on Ways and Means, Saiate Finance Committee, or on the ITC’s own
motion. After receipt of the petition, the ITC initiates an investigation within several weeks, whereupon
the ITC Commissioners decide in an open vote whether imports are a “substantial” cause of serious
injury, or threat thereof to the domestic industry. Following an affirmative injury vote, the Commission
conducts another open vote recommending its trade remedy to the President/7 The ITC can recommend
tariffs, quotas, some combination of the two, or trade adjustment assistance. The ITC’s findings and
recommendations are submitted to the President within six months of the investigation’s starting date and
the President then has sixty days to accept, reject, or amend the ITC’s recommendation. The President
may also defer a decision by ordering the negotiation of an orderly market agreement (OMA).
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Other than the petition itself, each of these events (their outcome) is likely to be relatively
well-guarded and difficult to anticipate. The main exceptions lie with those cases resolved through
the negotiation of an OMA or VER, which become subject to media speculation and public
pronouncements from the negotiating parties. In such cases, the general details of an agreement
may be anticipated well in advance of any final agreement and require a longer event interval.
B) Selection of Event Dates
Two general sources of information were used to identify and verify the appropriate dates
associated with each event: the ITC and newspaper media. For each industry, the petition date and start
of investigation date were obtained from the ITC Investigation reports submitted to the President. The
dates of Presidential action were listed in a summary of ITC escape clause investigations provided by the
ITC and the outcome, but not the dates of ITC injury and remedy votes were contained in dated ITC
press releases found in the Federal Register. Relying exclusively on the dates provided by the ITC or
Federal Register carried a substantial risk of misidentifying the true event dates, however.
A search of the Wall Street Journal (WSJ) and New York Times (NYT) indexes for first
mention of each event revealed that media reports often preceded the dates given by the ITC, in some
cases by up to several weeks. The ITC, for example, listed June 9, 1978 as the receipt-of-petition date
in the Industrial Fasteners case, but the Wall Street Journal reported on May 4, 1978 that the House
Ways and Means Committee would submit a request for an investigation. A search of the newspaper
indexes also demonstrated that the timing of the ITC injury and remedy votes could not be accurately
ascertained from the ITC press releases found in the Federal Register. The ITC reported the results of
the footwear (ITC investigation #18) injury and remedy votes in a press release dated February 8, 1977,
j7 Occasionally, the remedy vote is conducted on the same day as the injury vote.
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whereas the votes took place on December 28, 1976 and January 6, 1977 and were reported the
following days by die Wall Street Journal.3 8 Consequently, newspaper reports were used to verify die
timing of each evaxt and the earliest of the two dates was chosen as the event date.
Table 4.2 lists each industry and the date of each event given by the ITC. Below each is
the date upon which the event was first reported in the Wall Street Journal or New York Times. If
only one date is given, it is the one provided by the ITC unless otherwise noted. The injury and
remedy votes were occasionally held on the same day and when the petition was initiated on the
ITC’s own motion, the petition date and start of investigation date are the same.
Table 4.2 — Event Dates
INDUSTRY
PETITION
DATE
INVESTI
GATION
START
INJURY
VOTE
REMEDY
VOTE
PRESIDENTIAL
DECISION
BA LL
BEARINGS
n/a a) 01/31/73
b) 02/05/73*
a) 07/30/73
b) 07/31/73
a) 07/30/73
b ) 07/31/73
a) 03/29/74
b) 04/01/74
STAINLESS
AND ALL O Y
T O O L ST E E L
a) 07/16/75
b) 07/18/75*
a) 08/05/75 a) 01/15/76
b) 01/17/76
a) 01/15/76
b ) 01/17/76
a) 03/16/76
b) 03/17/76
OMA A nnounced;
a) 06/08/76**
NON-RUBBER
FOO T W EA R
a) 09/28/76
(Senate
Finance
Committee)
a) 10/05/76
b) 10/06/76
a) 12/28/76
b) 12/29/76
a) 01/06/77
b ) 01/07/77
a) 04/01/77 (rejected
pending OM A)
b ) 04/04/77
OMA A nnounced;
a) 05/17/77
TELEVISION
R E C E IV E R S
a) 09/22/76
b ) 09/23/76
a) 10/21/76 a) 03/08/77
b) 03/09/77
a) 03/14/77
b ) 03/15/77
a) 05/18/77 OM A
in lieu of m andatory
sanctions
3 8 Thanks are owed to the ITC staff member who provided the dates of each vote.
8 6
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Table 4.2 (cont)
CB RADIOS
a) 08/02/77
b) 08/04/77*
a) 08/10/77
b) 08/12/77*
a) 01/20/78
b) 01/23/78
a) 01/27/78
b) 01/30/78
a) 03/28/78
b) 03/29/78
H IGH-CARBON
FERRO -
C H R O M U JM
a) 06/09/78
b) 06/08/78
***
a) 06/21/78 08/21/78
08/24/78*
a) 08/30/78
b) 08/31/78
a) 11/02/78
b) 11/03/78
CLOTHES-PINS a) 07/27/78
(ITC motion)
a) 07/27/78 a) 11/16/78 a) 11/28/78
b) 11/29/78
a) 02/08/79
b) 02/09/79
INDUSTRIAL
FASTENERS
a) 06/09/78
b) 05/04/78
****
a) 08/03/78 a) 10/26/78 a) 10/31/78
b) 11/01/78
a) 12/22/78
b) 12/27/78*
NON-ELECTRIC
C O O K W ARE
a) 05/04/79 '
b) 05/09/79*
a) 05/15/79 a) 10/24/79
(WSJ)*****
a) 11/02/79
(WSJ)*****
a) 01/02/80
b) 01/03/80
M OTOR
VEH ICLES
a) 06/12/80
b) 06/13/80
a) 06/30/80 a) 11/10/80
(negative
vote)
b) 11/12/80*
a) 11/21/80
(Congress
authorizes
VER)
a) 05/01/81
(VER agreement)
b) 05/01/81
STAINLESS
AND ALLOY
TO O L STEEL
a) 11/23/82
(petition
received)
b) 11/15/82
(investigation
requested)
a) 12/09/82 a) 03/24/83
b) 03/25/83
a) 04/27/83
b) 04/28/83
a) 07/05/83
b) 07/06/83
M ACHINE
TOO LS
a) 03/10/83
(filed with
Commerce
Department)
§ i) 05/21/86
ii) 11/13-11/21/86
iii) 12/17/86
Notes: The UC injury and remedy votes in the Ball Bearings case took place on My 25. 1973. but under rules in
effect prior to the T rade Act of 1974 the votes were held in closed session. The outcome of the vote was revealed
on My 30, 1973 and this is used as the event date. The petition date could not be found.
* Denotes a lapse of more than one day between the event and first appearance in the W SJ or NYT.
** OMA signed on June 11.1976 b ut details of the agreement were reported on June 8, 1976.
*** The ITC received the petition on 06/09/78 but the Wall Street Journal (W SJ) announced on
06/08/78 that the House W ays and Means (HW &M ) Committee requested a new investigation.
**** The petition was received on 06/09/78 but the W SJ announced the H W & M Committee's request
for a new investigation on May 4, 1978.
***** The injury and remedy vote outcomes were reported in the W S J. IT C staff could not verify vote d ates.
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§ The machine tool case was initiated as a section 232 (national security) case on M arch 10, 1983. and
follows different procedures from escape clause cases. Due to the lapse between the start of investigation
and a final agreement I use only the following three dates as events: i) 05/21/86 - Reagan defers a
decision and requests a Voluntary Export Restraint by four largest suppliers; ii) 11/13-11/21/86 -
news of pact with J ap an reported over this span; in) Limits imposed on imports from Switzerland and
the Federal Republic of G erm any, and details of agreement with Japan and Taiwan are announced.
All dates are from W S J , NYT, or Washington Post news reports.
C) Event Intervals and the Cumulation of Excess Returns
The choice of event interval involves the balancing of two concerns: ensuring that the market
reaction to the event fells within the designated event interval, and limiting the number of event
days to enhance the power of the tests. When the date that the new information reaches the market
is known, an interval of several days around the event date should be sufficient to capture the effect
of the news announcement. Beyond enhancing the power of the tests, one advantage of using short
event intervals is that it substantially lowers the possibility of including the reaction to
“confounding” events. That is, non-protection related events that could have an impact on the firm
or industry stock return. Although such events are still possible even with the shorter intervals
used here, I still contend that it represents an improvement over previous event studies, which have
used intervals as long as nineteen months.3 9 In the results section, I explore the impact non
protection related events may have had on my findings by examining Wall Street Journal and New
York Times indexes for articles pertaining to the four industries each represented by two or fewer
firms. Investigating the impact of confounding events on all industries, even with the shorter
intervals used here, would be impractical, however. It would require a search of several thousand
index entries (74 firms times 15-35 event days) and involve numerous subjective judgements as to
the expected impact of the event and appropriateness of excluding data.
j9 Used by Canto et al. (1986) with monthly data.
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In my tests, cumulative excess returns were calculated for each event over several different
intervals. First, CERs were calculated for the five trading days surrounding and including the
event, where day “0” is defined as the earliest event day given in table 4.2.
r= -2
CERj = ^ ERi, j (6)
c = - Z
where j is the event selected.
The CER was also calculated for two shorter intervals (equations 7 and 8), with day “0” defined
as the date of first mention in the WSJ or New York Times or, if not reported, the same event day as
used in equation 6.4 0
CERj ^ E R . j (7)
r= -l
:=-2
CERj = y m . j (8)
r=0
Thai, the CERs from equation six were summed to provide the TCER, or Total Cumulative
Excess Return for each industry.4 1
TCER = CER, (9)
j= i
where k is the number of events (usually 5) being summed.
4 0 Two exceptions to the intervals used in equations 6 through 8 were made. For automobiles, the
same intervals were used for the petition, start of investigation, and injury finding dates. Since the
ITC injury determination was negative, the date Congress authorized the negotiation of a VER was
counted as an event, with the same intervals. Due to the media speculation leading up to the VER,
I used a 15 (trading) day interval (04/20/81 - 05/08/81). Three events were used for machine tools
(described in the notes to Table 2). The first and third use the same intervals as equations 6
through 8 and the second event spans seven (trading) days (11/13/86 - 11/21/86) for each equation.
4 1 Note that when two events (e.g. the ITC injury and remedy votes) occur on the same day, the
event CER is not double counted in calculating TCER.
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In addition, cumulative excess returns were tallied over two longer intervals. The first
measures the CER from two weeks prior to the filing of the petition to two weeks following the
final action (Presidential action or negotiation of OMA or VER). The second measures the CER
from two weeks prior to the injury finding to two weeks following final action.
V) Results
At the start of the chapter I discussed motivations for examining the consequences of
protection using the event study method. One was to clarify the trade off between the benefits to
industry from protection and the losses to consumer welfare and efficiency that result from it. The
negative consequences of protection have been chronicled, but few studies have sought to document
the reaction of equity markets to determine whether expectations of excess returns are in
accordance with policy goals and theoretical expectation. Neary (1978) postulates that when
capital is immobile in the short run, higher import prices induced by tariffs or quotas raise the
return to capital of import-competing domestic producers. Moreover, the efficient
markets/rational-expectations hypothesis asserts that security prices will immediately reflect the
expectations of higher prices and profits stemming from protection. Nevertheless, existing event
study literature has shown the market reaction to be significantly negative or inconsequential when
the imports were fairly traded. I reexamine the issue and find that protection does benefit recipient
industries, although not universally.
Consider Table 4.3, which presents the total cumulative excess returns (TCER) and t-
statistics for each industry. Recall that the TCER is the sum of the CERs for each of the events
leading to protection and is the most comprehensive of the measures I use to detect the overall
impact of protection on industry equity values. The TCER is significantly positive for five of the
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twelve industries and positive in two others. None of the industries experienced significantly negative
returns. Take the non-electric cookware industry, for example. A TCER of 0.3743 represents a return
thirty-seven percent higher than would be expected (given die industry’s normal relationship with the
market) in the days immediately surrounding each of the events leading to industry protection.
Table 4.3: Total Cumulative Excess Returns (TCER): 5 day interval for each event
CER = Y jERs.j ; TCER = J^CERj
t= -2 7=1
Industry TCER t-statistic no. of events (i) event davs
Ball Bearings 0.0277 0.720 3 1 5
Stainless and Alloy
Tool Steel
-0.0033 -0.007 5 25
Footw ear -0.0277 -0.559 6 30
Television 0.0404 1.509 5 24
C B R adio 0.2529 3.131*** 5 25
High-Carbon
Ferrochrom ium -0.0353 -0.895 5 25
Clothespins 0.1326 2.686*** 4 20
Industrial Fasteners 0.0853 2.429** 5 23
Non-Electric
Cookw are 0.3743 2.403** 5 25
M otor Vehicles -0.0561 -0.571 5 35
Stainless and Alloy
Tool Steel 0.0911 2.238** 5 25
M achine Tools -0.0298 -0.677 3 17
** = Significant at the 5% level (2-tailed test)
*** = Significant at the 1 % level
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Using shorter intervals as in equations 7 and 8 demonstrate that results are not especially
sensitive to the choice of event span so long as excess returns are cumulated in die days immediately
surrounding the event. The same five industries, for example, experience significantly positive TCERs
42
using four day intervals for each event (equation 7).
Noticing that three (CB radios, Clothespins, and Non-electric cookware) of the five industries
experiencing significantly positive excess returns were represented by two or fewer firms, I considered
the possibility that the results had been distorted by some non-protection related event affecting these
firms during the event intervals.4 3 Although this possibility exists for the other significantly positive
cases as well, the feet that these industries were represented by eight (Industrial fasteners) and eleven
(Specialty steel) firms, respectively, reduces the likelihood that individual firm events would exert a large
influence on the overall industry results.4 4 In any case, to explore the potential impact of such events cm
the three smaller industries, I examined Wall Street Journal and New York Times indexes for all aitries
pertaining to each of the affected firms cm the selected evert dates. No firm specific articles, other than
those concerned with the escape clause petitions, were found for the CB radio and Non-electric cookware
industries cm these dates, suggesting that the results for these cases reflect only the anticipated effects of
- * 5
protection.
4 2 These results are not shown. Using the three day interval (equation 8) reduces to three the
number of industries with significantly positive TCERs. I attribute this to the feet that day “0” in
this interval is the day the event was first reported by the WSJ or NYT, which follows the event by
one or more days. This suggests that markets react very quickly to new information as the efficient
markets/rational-expectations hypothesis asserts.
4 3 Thompson (1988) has also examined the possibility that “extraneous” firm related events during
the estimation period increases the variance of a firm’s returns and therefore decreases the power
of hypothesis tests. His simulation results demonstrate that such events appear to have no impact
on the power and specification of hypothesis tests.
4 4 Recall that the daily portfolio industry returns were composed of the equally weighted average of
all firms in the industry.
4 5 The CB radio industry was represented by two firms: E.F. Johnson and Motorola. The Non-
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It appears, however, that an event coinciding with die ITC injury determination for Clothespins
may have exerted an upward bias on the results for that industry. On November 16, 1978, the same day
as the ITC injury determination, the Wall Street Journal reported that shareholders of Diamond
International, Corp.—the lone publicly held maker of clothespins in the U.S.—and those of LMF Corp.
had approved the previously announced merger of LMF into Diamond.4 6 Shareholder approval of a
proposed merger is viewed as a positive endorsement of that action and can be accompanied by a rapid
and positive share price revision if the outcome was unanticipated. A closer look at the daily excess
returns around this event date also shows that they spiked upward on the day it was reported by the Wall
Street Journal. In feet, of the twenty event days used for this case, the abnormal returns reached their
highest values on that and the following day, and the cumulative abnormal return for the five days
surrounding the ITC injury determination was highly significant, with a t-statistic of 3.57. Although it is
difficult to disentangle the relative importance of the merger approval from the ITC injury determination,
the feet that none of the other petition related events produced significantly positive cumulative excess
returns points to the conclusion that my results for this case may be unreliable 4 7 At the same time, this
conclusion is by no means certain. The highest daily excess return during this period was recorded on
the day following the ITC injury determination, but two days following the shareholder decision. The
assumption that news is rapidly incorporated into share prices leaves open the possibility that the very
high excess return (ER = 0.101) for that day reflects the impact of the more recently occurring event (the
electric cookware industry was represented by the one remaining domestic firm manufacturing this
Droduct: General Housewares Corporation.
P. 45. A press release from the Office of the Special Representative for Trade Negotiations
(February 8, 1979) reported that there were five firms employing less than 400 workers in the
domestic clothespins industry. Diamond International Corp. was the only publicly traded company
among these firms and therefore the only one included in my analysis.
4 7 Excluding the results from the injury determination, the TCER still came in as positive (TCER =
.045), but the t-statistics for each of the remaining events were insignificant (petition t = 1.04; ITC
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ITC injury determination).4 8 It is also possible, as I argued earlier, that the outcome of the vote could be
anticipated and the expected impact of the merger was already incorporated by shareholders.
With these concerns in mind, my findings still contrast sharply with those of Canto et al. (1986),
who detected significantly negative returns in three of four industries (footwear, color TV, and motor
vehicles). To a lesser extent, they also contrast with those of Hartigan et al. (1986), who found that only
one (CB radio) of nineteen industries examined experienced significantly positive returns. I argued in
Chapter Three that Canto et al.’s (1986) are biased towards the negative by their method of modeling
expected returns. But the difference between my findings and Hartigan et al. (1986), whose methods are
otherwise similar to mine, are almost certainly a consequence of my choice of narrower event intervals.
This is evident from Table 4.4, which contains the CER and t-statistics what excess returns are
cumulated over the same interval used by the Hartigan study (two weeks prior to the petition to two
weeks following die final outcome).
When this longer event interval is used, I detect significantly positive CERs in only two
industries (CB radio and the second stainless steel case) while that of the footwear industry was
significantly negative. And when the five cases that our studies’ have in common are compared,
the results are virtually identical in three cases (the first stainless steel case, CB radios, and
clothespins). The differences in the other two industries can be tied to firm selection. For example,
I find a higher CER for cookware because I excluded the firms whose products were removed from
the ITC investigation. The use of daily rather than weekly returns and my choice of variance
estimator do not seem to have been a major factor in accounting for different results.
remedy t = 1.40; presidential decision t = -0.63).
4 8 Fama (1991) notes that “typical results in event studies on daily data is that, on average, stock
prices seem to adjust within a day to event announcements.” P. 1601
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Table 4.4: Cumulative Excess Returns (CER): 2 weeks prior to petition and injury
findings to 2 weeks following final action
from petition from injury finding
Industry CER t-statistic CER t-statistic
Ball Bearings -0.1787 -1.036 -0.0098 -0.074
Stainless and Alloy
Tool Steel
0.1093 0.730 0.1596 1.563
Footwear -0.2581 -2.054** -0.2020 -1.959*
Television -0.0579 -0.799 0.0621 1.469
C B R adio 0.4263 1.962** 0.1363 1.080
High-Carbon
Ferrochrom ium 0.0640 0.772 0.0934 1.556
Clothespins 0.1672 1.237 0.2056 2.210**
Industrial Fasteners 0.0800 0.836 0.0954 1.987**
Non-Electric
Cookware 0.4996 1.209 0.5090 2.203**
Motor Vehicles 0.1703 0.673 0.1718 0.948
Stainless and Alloy
Tool Steel 0.3851 3.705*** 0.1671 2.310**
Machine Tools -0.0806 -0.608 0.0182 0.316
* = Significant at the 1 0 % level (2-tailed test)
** = Significant at the 5 % level
*** = Significant at the 1 % level_______________________________________________________
Note: The tune spans for machine tools are as follows: The first TCER measures the interval from tw o weeks
prior to Reagan’ s decision to seek a VER (05/21/86) to one week following the agreement with Switzerland
and G ermany (12/17/86). The second m easures the interval spanning two weeks prior to the agreem ent with
Japan (11/21/86) to one week following the agreement with Switzerland and G erm any (12/17/86).
In table 4.4 we also see that when excess returns are cumulated from the ITC injury finding
rather than from the petition date, the number of significantly positive CERs jumps to four from two,
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while the CER for footwear remains significantly negative. The t-statistics also become larger (or less
negative) in ten of the twelve cases, suggesting that foe filing of foe petition and start of investigation are
viewed with equanimity by equity holders. This makes sense given foe greater uncertainty of obtaining
protection before foe affirmative ITC injury determination is made. From 1974 to 1995, only eighteen
percent of escape clause petitions resulted in trade protection whereas thirty-five percent of those
receiving an affirmative injury finding were ultimately protected.
In tables 4.5 and 4.6, I examine this possibility by presenting the CERs for each of foe
events comprising foe TCER The expectation is that the CERs (and t-statistics) should become
progressively larger as the process moves forward and foe imposition of protection becomes more
certain. The exception may be with the start of foe ITC investigation which is a pro forma event
following each petition.
Table 4.5 contains foe CERs and t-statistics for foe petition and start of investigation
dates. The petition generated significantly positive CERs for only two industries and foe start of
foe investigation only one. The two industries with significantly positive CERs for these events
also were among those with significantly positive TCERs. Somewhat unexpectedly, the injury,
remedy, and final actions (table 4.6) produced no more than two significantly positive CERs for
any action and foe ITC injury vote resulted in significantly negative CERs in two cases. The result
for motor vehicles is not unexpected since foe ITC vote was negative but the injury CER for
industrial fasteners was strongly negative despite an affirmative finding. It is possible that the
negative reaction to the affirmative injury vote for industrial fasteners reflected skepticism about
foe industry’s prospects. Two previous escape clause petitions by foe industry had been rejected,
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the most recent by presidential decision, and shareholders may have viewed the ITC decision as
confirmation that the industry was in real disrepair.
Table 4.5: Cumulative Excess Returns (CER): Petition and Start of ITC Investigation
Petition Start of Investigation
Industrv CER t-statistic CER t-statistic
Ball Bearings n/a n/a -0.033 - 1.47
Stainless and Alloy
Tool Steel -0.003 -0.13 0.020 0.93
Footwear -0.021 -1.02 -0.006 -0.31
Television 0.001 0.12 0.002 0.15
C B Radio 0.035 0.97 0.005 0.13
High-Carbon
Ferrochrom ium -0.008 -0.43 0.005 0.29
Clothespins 0.026 1.04 same as petition date
Industrial Fasteners 0.070 4.29*** 0.058 3.55***
Non-electric
Cookware 0.033 0.48 -0.088 - 1.26
Motor Vehicles 0.050 1.34 0.000 0.01
Stainless and Alloy
Tool Steel 0.031 1.69* -0.015 -0.80
Machine Tools n/a n/a
* = Significant at the 10% level (2-tailed test)
** = Significant at the 5% level
*** = Significant at the 1 % level
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Table 4.6: Cumulative Excess Returns (CER): Injury, Remedy, and Final Action
Iniurv Remedv Final Actionfs)
Industrv CER t-statistic CER t-statistic CER t-statistic
Ball Bearings 0.036 1.62 same as injury a) 0.024 1.10
Stainless and Alloy 0.025
Tool Steel
1.14 same as injury a)-0.028
b) -0.014
-1.31
-0.64
Footwear -0.011 -0.53 0.033 1.64 a)-0.001
b)-0.022
-0.07
-1.08
Television 0.012 1.01 0.010 0.83 c) 0.014 1.18
CB Radio 0.012 0.34 0.012 0.33 a) 0.189 5.23***
High-Carbon
Ferrochrom ium
-0.005 -0.26 -0.007 - 0.39 a)-0.021 -1.21
Clothespins 0.088 3.57*** 0.035 1.40 a)-0.016 -0.64
Industrial
Fasteners
-0.094 5.29***
-0.027 -1.62 a) 0.010 0.64
Non-electric
Cookware
M otor Vehicles
0.028
-0.071 -
0.40 0.027 0.39
VER Authorization
1.92* -0.035 -0.94
a) 0.375 5.38***
VER Announcement
a) 0.028 0.43
Stainless and
Alloy Tool Steel
0.025 1.39 0.025 1.35 a) 0.025 1.37
Machine Tools
Request for VER
-0.038 - 1.58 b )
VER with Japan
-0.023 -0.81
VER expanded
b) 0.031 1.29
* = Significant at the 10% level
** = Significant at the 5% level
*** = Significant at the 1% level
(2-tailed test) a) President's decision
b) OMA or VER announced
c) OMA in lieu of m andatory sanctions
Although results in tables 4.5 and 4.6 indicate that no one event in the petition process is viewed
as particularly important, the pattern conforms more to expectations when the average t-statistics for
each event are examined. Excluding the machine tool industry, which was not an escape clause case, the
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average t-statistic for the petition was 0.76 and fell to 0.18 for the start of investigation. The average
climbs to 0.34 for the injury finding date (excluding the negative determination for motor vehicles) and
would have been 0.96 except for the strongly negative figure for the industrial fastener industry. The
remedy date produced a figure of 0.67, and when the President chose to accept ITC remedy
recommendations rather than order the negotiation of a voluntary export agreement, the average was
1.69, which is significant to the ten percent level. Interestingly, when the trade issue was settled through
the more protracted negotiation of a VER or OMA, the average t-statistic for the periods surrounding
these decisions was only 0.06.4 9 This indicates either disappointment with a settlement less restrictive
than the one recommended by the ITC or the market anticipated the agreement prior to the chosen event
interval.
In summary, the results laid support to the notion that equity holders view the imposition of
protection favorably, and incorporate the expectation of higher prices and profits stemming from
protection very quickly. Whereas previous studies found little evidence of a positive market response,
five of the twelve industries I examine experienced positive and significant excess returns, and no
significantly negative excess returns, when they are cumulated in the period immediately surrounding
each of the key evaits leading to protection. Moreover, the market reaction is strongest for the action
leading immediately to the imposition of protection; namely, the Presidaitial decision to impose
mandatory sanctions. This is not surprising given the relative uncertainty of outcome that accompanies
escape clause petitions. It is interesting to note that the seven industries not experioicing significantly
4 9 The footwear, motor vehicle, machine tool, and first stainless steel case were resolved through
the negotiation of VERs or OMAs following a Presidential decision to defer trade sanctions until
such an agreement could be made. The President’s decision in the color television case was to
accept an OMA rather than impose mandatory sanctions. The average t-statistic figure for the
VERs and OMAs includes both machine tools agreements but does not include t-statistics from the
President’s decisions to defer a decision, which were all negative.
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positive excess returns include the five cases resolved through die negotiation of OMAs or VERs.
Perhaps voluntary quantitative restrictions, particularly ones that are not global in reach, are not viewed
as valuable to domestic industries due to the possibility of circumvention or quality upgrades that
dimmish the impact of these barriers.5 0 And since domestic producers sometimes also import the
protected category of good, VERs may disappoint producers who stood to collect quota rents on these
imports. Such rents typically devolve to foreign exporters in voluntary export agreements. A final
possibility is that these agreements are viewed favorably by the market, but the absence of significantly
positive excess returns reflects the difficulty of detection when longer event intervals are used, or the
market reaction occurred prior to the chosen event span.
VI) Conclusion
Escape clause protection rep resorts a special exception in U.S. trade law. Industries are granted
temporary respite from imports with the intention of enhancing prices and profits and affording the
industry an opportunity to become newly competitive. Nevertheless, existing event study literature has
documented that markets react negatively or inconsequentially to these measures despite theoretical
expectations to the contrary. By using a revised methodological approach, I reveal that protection is
viewed as beneficial to import competing industries and expectations of higher profits are incorporated
by the market very quickly. When excess returns are cumulated over the days immediately surrounding
each of the key events leading to protection, five of the twelve industries examined experienced returns
significantly greater than expected. Moreover, the markets respond most positively to a decisive
presidential decision to impose mandatory and unilateral sanctions rather than resolve the dispute with
the negotiation of a voluntary export restraint. Each of the five industries experiencing significantly
5 0 See Feenstra (1984), who discusses such quality upgrades of imported Japanese cars following
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positive excess returns was protected, without negotiation, by global quotas, tariffs, or a combination of
the two. Cases resolved by VERs or OMAs, on the other hand, may be viewed as unimportant by
shareholders due to the chance of circumvention or quality upgrades. Another possibility is that the
market reaction may indeed have been positive, but difficult to detect when such agreements are widely
anticipated. In the next chapter, I summarize the results, discuss their implications, and suggest avenues
for further research.
the automobile VER. Aw and Roberts (1986) conduct a similar study on the footwear OMA.
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CHAPTER FIVE - CONCLUSION
The principal question posed by this dissertation is whether industries benefit, as intended by
industry representatives and policy makers, from special import barriers. Economic theory suggests
that they should. Trade barriers are expected to yield higher prices and profits for domestic import-
competing industries, and also present them an opportunity to improve their longer-term outlook.
Despite ample literature documenting the consumer and net welfare losses associated with
protection, there is however scant empirical evidence to support the notion that industries actually
reap the expected rewards. Event studies examining the impact of fair trade barriers have concluded
that capital owners expect no resulting increase in profits, or find that protection is counterproductive
to industry revitalization efforts. Prompted by concerns about the methodological soundness and
accuracy of results in these studies, I therefore reinvestigated the issue using a revised and more
precise event study approach to explore the impact of protection on twelve industries. The findings
are intended to verify whether protection serves the interests of those who seek it, and to act as a first
step to informing policy makers of the conditions under which protection is considered most
effective. In this chapter, I summarize results, discuss implications, and suggest avenues for further
research.
I) Summary of Results
The central finding of this study is that exceptional barriers to fairly traded imports can be
shown to raise industry profit expectations, but not universally. Of the twelve industries examined,
five experienced security price returns significantly greater than expected. This can be taken as
evidence that shareholders in these industries expected protection to raise future profits. Although
results for the remaining industries were insignificantly different from zero, this information reveals
a second important finding. Differences in outcomes can be tied to the type of trade measure
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selected. Each of the five industries with a significantly positive share price response was protected
by unilaterally imposed barriers: tariffs, global quota, or global tariff-quota. Of the unaffected
industries, five were shielded, after some negotiation, by voluntary export restraints (VERs) or (also
voluntary) Orderly Marketing Agreements (OMAs). Whether this demonstrates that such trade
barriers are ineffective is uncertain, however. The opportunity to circumvent export limits may
indeed reduce the perceived efficacy of voluntary restraints, or the event study is not well suited to
detecting significant excess returns over the long event intervals used in these cases.
In the first case, VERs, particularly ones not global in reach, can be dodged via third-party
transshipments, or new suppliers rush to fill in the gap from affected suppliers. Quality upgrades by
exporters may also diminish the effectiveness of these barriers. This phenomenon has been
documented in the automobile and footwear industries, for example. An additional possibility
emerges when one considers that many import-competing firms are themselves importers.
Quantitative restrictions generate economic rents (excess profits) that can be captured by the
importing firm. But one outcome of VERs or OMAs is that export restraints are typically assigned
or managed by the exporting country, and the quota rents therefore devolve to that country rather
than to the importer. This may explain why the unilaterally imposed global quota on clothespins and
1983 global tariff-quota on stainless steel were perceived as beneficial by shareholders, whereas
VERs or OMAs in other cases (including the earlier global OMA on stainless steel) were not.
A second important possibility to consider is that the nature of voluntary agreements simply
makes excess returns difficult to detect. VERs and OMAs are often the outcome of protracted
negotiations, with a great deal of media speculation, leaks of information, and so on. Consequently,
the provisions of a deal may be anticipated well in advance of a final agreement. The risk of mis-
identifying the correct event date complicates the event study by necessitating the use of a longer
event interval. Recall that longer event intervals present two problems for the accurate detection of
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excess returns. Events unrelated to the one being investigated (“confounding” events) are more
likely to distort the findings, and the power of hypothesis tests are substantially reduced when event
intervals are expanded to account for event date uncertainty. In any case, ambiguity surrounding the
value of voluntary restraints to domestic producers implies that the choice of restrictive trade
measure can be an important consideration to capital owners of affected industries.
H) Implications of Results
What lessons do the findings enumerated above have for policy-makers and industry
representatives considering protectionist barriers? The key contribution is that the findings (partially)
corroborate theoretical expectations about the effects of protection, and reaffirm assertions of
protection-seekers who argue that trade barriers can provide a boost in the arm to troubled industries.
Whereas previous event studies had cast doubt on the potential importance of protection, I
demonstrate that in an appreciable number of cases, the imposition of protection is accepted as a
positive signal among capital owners. As discussed in Chapter Two, the policy of granting
temporary barriers to select industries is largely predicated on the expectation that protection raises
industry profits and creates incentives to make productivity enhancing investments. The presence of
significantly positive excess returns verifies that protection can support these efforts by reducing
borrowing costs; first, by lowering perceived risks to outside capital suppliers and second, by
increasing the value of internally held stocks among public companies. Moreover, the results
suggest that the most reliable approach to obtaining the desired reaction is with the imposition of
tariffs or mandatory global quotas. In sum, the results do not by themselves prove that industries will
take steps to become more competitive, or that they will be successful, but they do show that the
prospect exists.
Although geared toward assisting policy-makers in decision making, the results can be
informative to industry representatives considering trade petitions as well. In seeking protection,
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industries will balance the (rent seeking) costs of protection with the potential gains. Since voluntary
trade measures may be structured, inadvertently or otherwise, in such a way that they are
circumventable or forfeit potential quota rents to foreign governments or suppliers, tariffs or
unilaterally imposed global quotas should be the most desirable outcome from industry’s perspective.
A final contribution of this study is that it reemphasizes to event study practitioners the
importance of stringent attention to methodological detail, particularly with regard to precision in
event dating. If the results of empirical studies using this approach are to have any practical
relevance to businesses or policy makers, the results of any event study must be reproducible and
robust to further research. The final section discusses future avenues of research that can assist
efforts to gauge the importance of protection to industry and its role in promoting their recovery.
Ill) Heuristic Value
The results of this event study can be used as a stepping stone to further research in several
areas. The first is to investigate industry or firm characteristics and other factors that are correlated
with the existence of significantly positive excess returns. That is, what variables are most strongly
associated with the expectation of higher profits? A second avenue is to look more deeply into the
longer-term adjustment of industries following protection by using variations in excess returns as an
initial hypothesis for explaining different outcomes. The aim is to refine understanding of the
conditions that are most (least) favorable to the creation of financial incentives to improve future
competitiveness, and to better understand whether protection plays a transitory role in the adjustment
process or exerts a more permanent influence.
In the first area, I have already suggested that the selection of trade measures is an important
factor in determining the perceived value of protection to shareholders. Unilaterally imposed
sanctions achieve the most positive response. Other factors and industry (or firm) characteristics that
may condition the reaction of capital holders, such as the magnitude of protection, market structure,
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source of imports, and degree of capital specificity have been taken as exogenous, however. A cross-
sectional study with a binary (dummy) dependent variable measuring the presence (absence) of
excess returns could be used to explore the relative importance of these other factors and serve as
empirical tests of trade theory.1
One obvious extension is to verify that the magnitude of the tariff (equivalent) increase is
positively correlated with profit expectations. A more intriguing question is to explore whether the
comparative capital/labor ratios of the exporting nation (industry) to the United States exerts any
influence over outcomes. Exports from less developed economies are presumably based on a
comparative advantage in the production of labor-intensive goods (shoes, for example), whereas
those from similarly developed nations are based on different product characteristics. The question
is whether shareholders consider the prospect of higher profits, and industry recovery, to be greater in
those product categories where the U.S. maintains a theoretical comparative advantage, or
“equivalence” (autos, specialty steel, machine tools). Another industry characteristic that can be
examined is the degree of industry concentration. By facilitating collusive practices, more
concentrated industries may derive greater benefits from protection than dispersed industries.
Even more promising are tests on firm level characteristics. An empirical test of Neary’s
(1978) capital-specificity hypothesis can be conducted using firm data on fixed capital, or some other
proxy, to measure whether capital (im-)mobility is associated with higher excess returns. Capital
owners of existing firms characterized by capital immobility and difficult entry would be expected to
reap higher returns than those with high capital mobility and easy entry. Finally, I have argued that
one reason VERs (or OMAs) are perceived as less valuable than equivalent unilaterally imposed
quotas is that some protected firms are themselves importers. VERs assign quota rents from
protection to exporters rather than being captured by these firms. A cross-sectional study with firms’
1 A standardized (by the standard deviation) excess return measure as the dependent variable could
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importing status as an explanatory variable could unveil significant variations in firm share responses
even if the net effect on the industry is insignificant. The footwear industry, with many firms who
are (were) also importers is a good prospect for examining this hypothesis.
The central difficulty with these areas of proposed research is limitations in sample size.
Examining firm level variables should expand sample size to sufficient proportions, but twelve
industry cases does not provide enough variation when industry level characteristics are being
explored. Research on industry characteristics is further complicated by uncertainty about the cause
of insignificant results in VER and OMA cases. If the results reflect imprecision in event dating
rather than the other factors considered, the outcome of any cross-sectional study could be spurious.
Although they play a lesser role in U.S. trade policy (in terms of value of imports affected), one
possible solution is to expand the scope of study to include antidumping and countervailing duty
cases.2 Another is to use cases from outside the United States.
The second avenue of research pertains to the longer-term influence of protection on
industry adjustment. From a public welfare perspective, U.S. trade policies are not intended to
simply delay the inevitable demise of a fading industry. Instead, they are aimed at creating the
financial incentives, and a “breathing space,” for industries to re-equip for improved
competitiveness. As suggested earlier, though, the presence of significantly positive excess returns
cannot by itself be taken as evidence that industries will take steps to become more competitive, or
that they will be successful. Further research should be targeted at revealing whether the financial
incentives are exercised and that they can make an appreciable difference to industry adjustment.
Among other explanatory variables, the success or failure of industry petitions for protection, and the
resulting excess returns, can be used to examine changes in domestic market share, productivity,
also be used.
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earnings, or other dependent variables reflective of industry performance/ In conjunction with the
findings of this study, I hope that further research into these areas will contribute to a better
understanding protection’s consequences and inform decision making. As a final word, I should
emphasize that my findings do not represent a justification for increased protectionism. This study
measures only the anticipated benefits to industry from protection and not the losses to consumer
welfare and economic efficiency that accompany it. The failure to detect any potential benefit to
industry would make the use of trade barriers indefensible from any perspective.
2 A review of Section 232 (national security) cases reveals that, other than machine tools, only
several oil related petitions have been approved since 1964. Section 406 (market disruption by
imports from Communist States) cases have been equally sparse.
3 Industries awarded protection but failing to gain significantly positive excess returns, or industries
found by the ITC to be injured by imports but denied trade relief can be used as one set of cases.
Those obtaining protection and significant excess returns would form the other set.
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United States. Brookings Papers on Economic Activity: Microeconomics. Marin N. Baily, Peter C.
Reiss, and Clifford Winston (Eds.), Washington D C. The Brookings Institution. 1994: 51-118.
Staiger, Robert and F. Wolak. 1992. “The effect of domestic antidumping law in the presence of foreign
monopoly.” Journal o f International Economics 32: 265-287.
Strong, Norman. 1992. “Modelling Abnormal Returns: A Review Article.” Journal o f Business Finane
& Accounting 19, no. 4: 533-553.
Sykes, Alan O. 1991. “Protectionism as a ‘Safeguard’: A Positive Analysis of the GATT ‘Escape
Clause’ with Normative Speculations.” The University o f Chicago Law Review 58, no. 1 : 255-305.
Tarr, David G. and MJE. Morkre. 1984. Aggregate Costs to the United States o f Tariffs and Quotas on
Imports: General Tariff Cuts and Removal o f Quotas on Automobiles. Steel. Sugar, and Textiles.
Washington: Bureau of Economics, Federal Trade Commission.
Theobald, Michael. 1983. "The Analytic Relationship Between Intervalling and Nontrading in
Continuous Time." Journal o f Financial and Quantitative Analysis 18: 199-209.
Thompson, Aileen. 1994. 'Trade liberalization, comparative advantage, and scale economies: Stock
market evidence from Canada." Journal o f International Economics 37: 1-27.
Thompson, Joel. 1989. "An Alternative Control Model for Event Studies." Journal o f Business Finance
& Accounting 16, no. 4: 507-513.
Thompson, Joel. 1988. “More Methods That Make Little Difference in Event Studies.” Journal o f
Business Finance & Accounting 15, no. 1 : 77-86.
Tonelson, Alan. 1994. “Beating Back Predatory Trade.” Foreign Affairs 73, no. 4: 123-134.
Trebilcock, Michael, Chandler, M., and R. Howse. 1990. Trade and Transitions: A Comparative
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Tyson, Laura D’Andrea. Who's Bashing Whom? Trade Conflict in High-Technology Industries.
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Tyson, Laura D’Andrea. “Managed Trade: Making the Best of the Second Best.” In Lawrence, R. and
Schultze, C. (eds) 1990. An American Trade Strategy: Options fo r the I990's. Washington: The
Brookings Institution.
114
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U.S. Congress, House of Representatives Committee on Ways and Means, Sub-Committee on Trade.
1984 (December). Overview o f Current Provisions o f U.S. Trade Law. Washington: U.S. Government
Printing Office.
U.S. Congressional Budget Office. 1991 (December). Trade Restraints and the Competitive Status o f
the Textile, Apparel, and Nonrubber-Footwear Industries. Washington: Congressional Budget Office.
U.S. Congressional Budget Office. 1986 (November). Has Trade Protection Revitalized Domestic
Industries? Washington: Government Printing Office.
U.S. Department of Commerce. 1994 (November). Section 232 Investigations: The Effect ofImports on
the National Security under the Trade Expansion Act o f 1 962 as amended. Washington: Bureau of
Export Administration.
U.S. General Accounting Office. 1981 (August). Changes Needed in Administering Relief to Industries
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1377. Washington: USITC. (Investigation TA-20I-48).
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Promoting Adjustment to Import Competition. USITC Publication no. 1229. Washington: USITC.
U.S. International Trade Commission. 1980. Certain Motor Vehicles and Certain Chassis and Bodies
Thereof. USITC Publication no. 1110. Washington: USITC. (Investigation TA-201-44)
U.S. International Trade Commission. 1979. Non-Electric Cookware. USITC Publication no. 1087.
Washington: USITC. (Investigation TA-201-39).
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USITC. (Investigation TA-201-36).
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Publication no. 0924. Washington: USITC. (Investigation TA-201-37).
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no. 0852. Washington: USITC. (Investigation TA-201-29).
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115
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Vousden, Neil. 1990. The Economics o f Trade Protection. Cambridge, England: The Cambridge
University Press.
1 1 6
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APPENDIX 1
Special protection in the United States can be secured through several different channels.
Though the methods used to obtain special protection and types of protection implemented are
diverse, the objectives are essentially the same - to preserve or promote an industry harmed by
import competition. The following section further details the channels through which protection is
obtained; changes in the frequency and success with which they are employed; and important
industries affected.
U.S. Trade Law - Routes to Special Protection
What makes certain types of protection "special?" According to one definition, special
protection is characterized by "exceptional restraints on imports, implemented through high tariffs,
quotas, or other limitations that go well beyond normal tariff or border restrictions."1 Most examples
of special protection fall into one of four broad categories: exceptionally high tariffs; fair trade
remedy; unfair trade remedy; and presidential use of inherent constitutional powers and statutory
frameworks for discretionary protection.
i) Exceptionally high tariffs
Consisting primarily of industries granted high tariff protection under the Fordney-
McCumber Tariff Act of 1922 and the Smoot-Hawley Tariff Act of 1930, certain industries have
managed to evade the post-war tariff concessions accorded to other industries, either by gaining
exemption from the President's tariff cutting authority or by being designated an "import sensitive"
industry. Representative industries include Benzenoid Chemicals, Rubber Footwear, Glassware,
1 Hufbauer & Rosen, 1986. p.5.
117
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Ceramic Articles and Tiles, and Orange Juice. Tariffs on these products ranged from 14% to 44% in
1986 and affected over $12.5 billion of imports in 1990.2
ii) Fair Trade Remedy - Escape Clause Relief
Originally adopted in the 1942 Reciprocal Trade Agreement with Mexico in 1942, the
escape clause has been included in all subsequent U.S. trade agreements." The escape clause,
currently administered under sections 201-203 of the Trade Act of 1974, permits the imposition of
special protection for domestic industries injured, or threatened with injury, by increased but fairly
traded imports. Such protection may include the withdrawal or modification of previous trade
concessions and/or the imposition of new duties or quantitative restrictions on the imported article.
The procedure for obtaining escape clause relief may be initiated by an industry
representative, including a trade association, firm, or group of workers, or upon request by the
President, U.S. Trade Representative, Senate Finance Committee, or House Ways and Means
Committee. Following such a petition or request, the U.S. International Trade Commission (ITC)
conducts an investigation under section 201(b) to determine whether increased imports have been a
"substantial" cause of injury, or threat of injury, to a domestic industry. With an affirmative finding
by the ITC, the President may, if he finds it to be in the national economic interest, grant protection
to the affected industry for a period of up to five years. The President may, under section 203,
2 Ibid, pp. 7-9; Hufbauer & Elliott (1994), pp. 4-5.
" Crawford, Carol. 1993. "Section 201 of the Trade Act of 1974: The Intersection of Fair Import
Relief and Competitiveness Policy." Washington: International Trade Commission.
118
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extend protection for an additional three years at a level no higher than that in effect immediately
prior to the scheduled termination of protection.4
In the period leading up to the Trade Act of 1974, the success of industries seeking escape
clause relief was constrained by the provision under the 1962 Trade Act requiring that relief be
granted only if injury to domestic firms harmed by increased imports resulted "in major part" from
previously granted trade concessions. Consequently, between 1962 and 1974, only 4 of 32 cases
brought before the ITC led to trade protection (a 12.5% "success rate").5 With the Trade Act of
1974, the link between previous concessions and injury was eliminated and in 1979 the relationship
between increased imports and injury was reduced from "the major factor" to a "substantial cause."
From 1975 to 1986, fifty-nine cases were initiated. Of those, thirteen industries ultimately
received import relief (other than trade adjustment assistance), for a 22% "success rate." Another 7
cases resulted in expedited trade adjustment assistance. Between 1986 and 1990, however, only
three cases were initiated, all of which received negative determinations from the ITC.
Significant examples of industries receiving protection under the escape clause and their
year-end trade coverage include Ball Bearings ($199 million in 1978); Specialty Steel ($404 million
in 1984); Nonrubber Footwear ($2,480 million in 1981); Color Televisions ($1,543 million in 1982);
Mushrooms ($110 million in 1983); Heavyweight Motorcycles ($124 million in 1984); CB Radios
($54 million in 1981); Bolts, Nuts, and Large Screws ($311 million in 1982); and Ceramic Articles
($498 million in 1984).6
4 U.S. Congress, House of Representatives Committee on Ways and Means, Sub-Committee on
Trade. 1984 (December). Overview o f Current Provisions o f U.S. Trade Law. Washington: U.S.
Government Printing Office, pp. 62-65.
5 Rosenthal, Paul and R _ Gilbert. 1989. “The 1988 Amendments to Section 201: It isn’ t just for
Import Relief Anymore.” The International Law Journal o f Georgetown University Law Center 20,
no. 3. pp. 405-406.
6 Hufbauer & Rosen, 1986. p. 11.
119
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iii) Unfair Trade Remedy - Antidumping, Countervailing Duty,
and Section 301 Laws
In contrast to escape clause relief action, which witnessed brief but waning popularity
among industries seeking special protection, resort to unfair trade remedy laws has increased
dramatically in recent decades, especially since 1980.
Most commonly, unfair trading refers to the practice of dumping, or selling at "less than fair
value," products to certain export markets, or to the export of subsidized goods. Section 731 of the
Tariff Act of 1930, as amended by the 1979 Trade Agreements Act, directs the United States
government to impose an antidumping duty should the following two conditions be met. First, the
Department of Commerce finds that "a class or kind of foreign merchandise is being, or is likely to
be, sold in the United States at less than its fair value." Second, that the ITC has determined that "an
industry in the United States is materially injured, or is threatened with material injury, or the
establishment of an industry in the United States is materially retarded, by the reason of imports of
that merchandise."7 In this event, the duty shall be equal to the margin between the estimated foreign
market value of the good and the import price of the good.
Similarly, section 701 of the Tariff Act of 1930, as added and amended by the Trade
Agreements Act of 1979 and Trade and Tariff Act of 1984, respectively, provides that a
countervailing duty be imposed on imported merchandise should two conditions be met. First, the
Department of Commerce finds that a subsidy is being provided "with respect to the manufacture,
production, or exportation of a class or kind of merchandise imported into the United States."
7 U.S. Congress, House of Representatives Committee on Ways and Means, Sub-Committee on
Trade. 1984 (December). Overview o f Current Provisions o f U.S. Trade Law. Washington: U.S.
Government Printing Office, p.40.
120
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Second, the same injury test used in dumping cases is met. Countervailing duties are imposed in the
amount of the net subsidy as determined by the Department of Commerce.
Although the Trade Agreements Act of 1979 required a "material injury" test for
antidumping and countervailing duty cases, several legal and administrative changes between 1974
and 1988 made it progressively easier and more attractive for petitioners to prove dumping or
subsidization by their foreign competitors. In particular, by reducing the elapsed time between the
submission of a petition and a final determination, changing the way dumping margins are
calculated, and requiring importers to submit a deposit prior to a final determination, antidumping
and countervailing duty petitions became the prevalent method of acquiring protection among
industry representatives. Furthermore, the appeal of unfair trade remedies as opposed to the escape
clause route is strengthened by the lower standard for injury ("material" vs. "serious") of
antidumping and countervailing duty cases.
The impact of these changes is observed in both the increase in the number of petitions filed
and in the success rate of these petitions, as noted earlier. The trade coverage of all antidumping
duty orders in effect in 1991 is estimated to be $3,212 million.8
Another tool in the U.S. trade policy arsenal designed to remedy unfair foreign trading
practices, Section 301, was established with the Trade Act of 1974. Section 301, amended and
strengthened in subsequent Trade Acts, marks a departure from other unfair trade laws by granting
the President authority to enforce U.S. rights under international trade agreements and to respond to
any "foreign act, policy or practice which is unjustifiable, unreasonable, or discriminatory and
burdens or restricts U.S. commerce."9
8 Hufbauer & Elliott, 1994. p. 118
9 U.S. Congress, House of Representatives Committee on Ways and Means, Sub-Committee on
121
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Investigations may be self-initiated by the USTR or following a petition by any interested
person. If the petition is not rejected or withdrawn and the dispute is not resolved under international
(e.g. GATT) dispute settlement mechanisms within a specified period of time, the President is
directed to take "all appropriate and feasible action within his power,"1 0 and may include retaliation
in the form of suspension or withdrawal of trade agreement benefits, imposition of tariffs, fees or
other import restrictions. From 1975 through 1984, 48 investigations were initiated, with 10
terminated due to resolution of the dispute, and only one concluding in retaliatory action. Another
thirty-five cases were filed from 1985 to 1991. In contrast to ADD and CVD orders, the target of
retaliatory measures remains at the President's discretion and need not involve protection of a
petitioners industry. For this reason, Section 301 is not a direct route to special protection in the
U.S., but rather one designed to dismantle foreign trade barriers.
iv) Presidential Use of Inherent Constitutional Powers and Statutory Frameworks
for Discretionary Protection
Normally witnessed in high profile cases, the President can supersede other channels to
import relief by negotiating VERs directly with foreign governments in order to limit exports to the
United States. The intent of such agreements is to provide import relief to domestic industries while
limiting the diplomatic damage that could be caused by unilaterally imposing new trade barriers.
VERs also permit the United States to avoid the offering of compensatory tariff cuts on other
products as would be required under escape clause relief cases. Although VERs violate, in spirit, the
principle of non-discrimination, the "voluntary" nature of these agreements allow the United States to
Trade. 1984 (December). Overview o f Current Provisions o f U.S. Trade Law. Washington: U.S.
Government Printing Office, p. 146.
1 0 Ibid, p. 57
122
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limit exports from the most important source countries without violating GATT codes directly.
Foreign governments accede to these agreements either because they are a less restrictive alternative
to trade sanctions being considered or because the scarcity quota rents devolve to the foreign
governments rather than to the U.S. treasury. Significant examples of bilaterally negotiated restraint
agreements and their trade coverage in selected years include Carbon Steel: Phase I ($4,830 million
in 1974); Carbon Steel: Phase III ($10,206 million in 1984); and the voluntary export restraint (VER)
on automobiles from Japan ($29,260 million in 1984).1 1
Several other laws regulating imports provide the President with the statutory frameworks
under which he can negotiate additional restrictions on imports. Prominent examples of such laws
include Section 204 of the Agricultural Act of 1956, under which the various Multifibre
Arrangements (MFAs) restricting imports of textiles and apparel have been negotiated ($34,720
million in 1990); Section 22 of the Agricultural Adjustment Act of 1933, designed to prevent imports
from interfering with USDA efforts to stabilize or raise agricultural commodity prices (trade
coverage in 1984 of approximately $1,800 million for dairy, peanuts, and sugar); and Section 232 of
the Trade Expansion Act of 1962 restricting imports on national security grounds, used to restrict
petroleum imports between 1962 and 1973, and Machine Tools from 1986 to 1994 (approximate
trade coverage on affected suppliers of $600 million in 1990).1 2
1 1 Hufbauer and Rosen, 1986.
1 2 Figures for Machine Tools derived from Hufbauer & Elliott (1994); all other figures from
Hufbauer & Rosen (1986).
123
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Creator Dohlman, Erik Nils (author)

Core Title After protection: The impact of import barriers on corporate equity values and profit expectations

Degree Doctor of Philosophy

Degree Program Political Economy

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Economics, Commerce-Business,OAI-PMH Harvest

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Advisor Aronson, Jonathan (committee chair), Joines, Doug (committee member), Rosendorff, Peter (committee member)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c17-434785

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