Plant-Level Responses to Antidumping Duties:
Evidence from U.S. Manufacturers 1
Justin Pierce 2
Center for Economic Studies, U.S. Census Bureau
April 2009______________________________________________________________________________
Abstract
This paper describes the effects of a temporary increase in tariffs on the performance andbehavior of U.S. manufacturing establishments (plants). Using antidumping duties as an
example of temporary protection, I compare the responses of protected manufacturers to thosepredicted by new models of trade with heterogeneous firms. I find that apparent increases inrevenue-based productivity associated with temporary protection are primarily due to increases
in prices and mark-ups. In fact, antidumping duties lower physical productivity among the set ofprotected plants reporting output data in units of quantity. Moreover, antidumping duties allow
for the continued operation of low-productivity plants that likely would have otherwise ceased
production. As a result, temporary protection slows the process of output rationalization, withless productive plants producing a greater share of total output, leading to a reduction inaggregate productivity growth. Importantly, plants that are denied protection by the government
are no more likely to exit than protected plants. Rather, they adjust by dropping the unprotected
product and producing other, potentially higher-productivity products.
______________________________________________________________________________JEL Classification: F13, F14, D24
Keywords: Antidumping; Temporary Protection; Heterogeneous Firms; Productivity
1

I thank Rod Ludema, Anna Maria Mayda and Brad Jensen for continuous support andguidance. This paper also benefited from helpful suggestions from Peter Schott, Andrew
Bernard, Meredith Crowley and Stephen Redding, as well as seminar participants at Georgetown
University and the Center for Economic Studies. Jim Davis provided timely help with disclosureof results. Any remaining errors are my own. The research in this paper was conducted while
the author was a Special Sworn Status researcher at the U.S. Census Bureau, Center for
Economic Studies. Any opinions and conclusions expressed herein are those of the author anddo not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed
to ensure that no confidential information is disclosed. Support for this research at the
Washington RDC from NSF (ITR-0427889) is also gratefully acknowledged.

24600 Silver Hill Road,
justin.r.pierce@census.gov.

Washington,
1

DC20233,

tel:

301-763-6469,

email:

Section 1: Introduction
What are the effects of temporary tariff protection on U.S. manufacturers? This question
has become increasingly important as antidumping duties have become one of the primary formsof trade protection, in the U.S. and world-wide. Moreover, the answers to this question have
implications that reach beyond antidumping policy. Studying U.S. manufacturers’ reactions toantidumping duties can also provide new insight into the heterogeneous responses of firms tochanges in tariff rates, within the context of a major trade shock in a developed country. This
paper provides the first micro-level evidence on the effects of antidumping duties in the UnitedStates, using a dataset that includes the full population of U.S. manufacturing establishments(plants). Furthermore, through the use of output data measured in units of quantity, I am able todetect substantial differences between the effects of antidumping duties on plants’ physical andrevenue productivities.
While antidumping duty rates can reach into the triple digits and drastically alter tradeflows, there are disagreements about some of their most fundamental implications, including
their effect on firm and plant-level productivity. On one hand, there is a substantial literaturethat suggests that any increase in tariffs should decrease productivity. In Melitz (2003), an
increase in tariffs—or a failure to decrease tariffs—allows for the continued operation of lowproductivity firms that would have otherwise exited, resulting in a decrease in mean firm-levelproductivity. In addition, Bernard, Redding and Schott (2006), describe a channel for withinplant productivity growth during trade liberalization, which arises when plants drop their least
productive products and reallocate resources to their most productive products. Pavcnik (2002)
and Fernandes (2007) (for developing countries) and Bernard, Jensen and Schott (2006) (for the2

U.S.) provide empirical evidence showing that productivity and nominal tariffs are negatively
correlated.
In contrast, there is evidence that tariff protection—particularly temporary protection—
can increase firm or plant-level productivity by increasing the incentive to invest in newtechnology. Matsuyama (1990) was among the first to show that temporary protection can speed
up the time of technology adoption, while noting that the government’s threat to remove
protection if the domestic firm fails to invest is not credible. Similarly, Miyagiwa and Ohno
(1995, 1999) show that protection can induce investment in a fixed cost technology by increasing
the market share of domestic firms. These theoretical models are supported by empirical resultsin Konings and Vandenbussche (2008) showing that revenue-based productivity increased
among E.U. manufacturers receiving temporary antidumping protection. 3 As noted in that paper,
however, increases in revenue productivity can be caused not only by increases in physicalproductivity, but also by increases in prices and mark-ups.
I examine these issues by comparing the behavior of a treatment group of plants thatreceived protection to three control groups of plants in similar industries that did not receive
protection. As described below, these control groups are constructed in a manner that eliminates
two potential sources of bias:

a self-selection bias that exists if industries that apply forprotection differ from those that do not apply and a “government-selection bias” that arises if thegovernment bases its decision of whether to provide protection on variables that are correlatedwith productivity.

I employ a difference-in-difference estimator to estimate the effect of3

Konings and Vandenbussche (2008) find that antidumping duties were associated withincreases in mean plant-level productivity. An important additional result is that antidumping
duties allowed for technological catch-up by the least productive firms, while firms with high exante productivities experienced productivity declines.
3

antidumping protection, which nets out time-invariant differences between the treatment andcontrol groups, as well as macro-level shocks affecting the treatment and control groups
identically. In addition, I examine whether variation in the effective antidumping duty rate
protecting plants leads to heterogeneous responses to protection.
I find that the effect of antidumping duties on plant-level productivity depends cruciallyon whether output is measured in revenue or physical units of quantity. While antidumping
protection is associated with an increase in plant-level revenue productivity, these increases are
driven primarily by increases in prices and mark-ups. 4

Antidumping duties actually lower

physical productivity among the set of protected plants reporting output data in units of quantity.
These results underscore the importance of differentiating between revenue and physicalproductivity—a distinction that has received relatively little attention in the field of international
trade. In fact, this distinction is particularly important when considering the case of antidumping
duties, since increases in prices and markups would likely be taking place at the same time aschanges in physical productivity.
Antidumping duties also provide a useful way of examining some of the best-known
results from the heterogeneous-firm literature. In particular, while most empirical research on
the responses of firms to trade liberalization has focused on developing countries, antidumping
protection can provide an example of a major trade shock in a large, developed country—in thiscase, the United States. Moreover, in many heterogeneous-firm models, trade liberalization
increases aggregate productivity as resources are shifted from less-productive to more-productive

4

I examine the effect of antidumping duties on both prices and mark-ups, since mark-ups will beless responsive to antidumping protection if suppliers are able to extract rents from protected
plants through higher prices.
4

uses. By studying the imposition of antidumping duties, it is possible to examine whether someof these newly-recognized benefits of trade liberalization are eliminated when protection is
imposed.
One well-documented way that trade liberalization reallocates resources from low tohigh-productivity uses is through the exit of the least productive firms.

In the theoretical

literature, exit of low-productivity firms during trade liberalization is a key result of Melitz(2003), Bernard, Eaton, Jensen and Kortum (2003) and Bernard, Redding and Schott (2006).
These theoretical results are also supported by robust empirical evidence. Pavcnik (2002) andBernard, Jensen and Schott (2006) have shown that decreases in trade costs bring about the exitof low-productivity firms and plants, yielding substantial increases in aggregate productivity. To
examine whether antidumping protection slows this process, I compare the probability of exit
among a treatment group of plants that received antidumping protection to that in a control groupof unprotected plants.
Bernard, Redding and Schott (BRS) (2006, 2008) identify an additional channel forresource reallocation and productivity growth during trade liberalization, through productswitching by multi-product firms. BRS (2006) provide models of firms with exporting andproduction, where overall firm productivity is a combination of firm and firm-productcomponents. Trade liberalization yields productivity growth by forcing firms to drop marginallyproductive products and by forcing the least productive firms to exit. 5 But if antidumping
5

BRS (2006) provides a useful framework for examining how multi-product firms react tochanges in trade policy. There are, however, important differences between the framework in
BRS (2006) and the temporary antidumping protection examined in this paper. First, BRS(2006) is based explicitly on a multilateral trade liberalization occurring as two countries movefrom a closed economy to an open-economy equilibrium. In antidumping duty proceedings,
changes in trade policy are unilateral and are targeted against imports from a particular country.
5protection allows low-productivity plants to continue producing low-productivity products, itwill have negative effects on both plant-level and aggregate productivity. I examine the effect ofantidumping duties on plants’ product-switching activities by comparing the probability ofdropping protected products in the treatment group to the probability of dropping products that
did not receive protection in the control group.
I find that antidumping duties allow for continued production by low-productivity plantsthat would have otherwise stopped producing. Importantly, this effect manifests itself not
through decreased plant-level exit, but rather through a reduction in product-switching amongprotected plants. Protected plants are no less likely to exit than those that did not receive
protection. But while low-productivity plants that are turned down for antidumping duties by thegovernment react by dropping products, protected plants are able to continue producing the same
products. As a result, antidumping duties likely decrease the productivity gains that would
otherwise occur as a result of product-switching.
By allowing for continued production by low-productivity plants, antidumping duties
may eliminate the benefits of trade liberalization associated with output rationalization, where
high-productivity plants increase their market share at the expense of low-productivity plants.

Imeasure this effect by decomposing aggregate productivity into mean plant-level productivity
and a term that measures the degree to which higher-productivity plants produce a larger share of
output, as in Olley and Pakes (1996). I find that antidumping protection slows the process of
output rationalization, decreasing aggregate productivity growth. While the degree of output

Second, BRS (2006) focuses on trade liberalization for all products. Antidumping duty
investigations, on the other hand, involve a single product or a set of closely related products.
Third, the trade liberalization in BRS is permanent, while antidumping duties are temporary.
6rationalization is significantly higher among protected plants prior to receiving protection, thecontrol group of unprotected plants steadily increases its level of output rationalization as theantidumping duties set in. By the end of the period of analysis, the control group has overtaken
the treatment group, to exhibit a higher level of output rationalization.
The remainder of the paper is organized as follows. Section 2 describes the data. Section
3 provides a brief discussion of the antidumping investigation process in the United States, aswell as a description of the products typically involved in antidumping investigations. Section 4
describes the empirical strategy and reports results. Section 5 concludes.
Section 2: Data
This analysis uses plant-level and plant-product-level 6 data from the U.S. Census
Bureau’s (Census) Longitudinal Research Database (LRD) for the years 1987 to 1997. Total
factor productivity is calculated using data from the Census of Manufactures (CMF). The CMF
contains plant-level data on output (value of shipments and sometimes quantity), as well as input
data including the number of production and non-production employees, raw material usage,investment, depreciation and book value of capital. The CMF is conducted every five years, inyears ending in two and seven (e.g. 1987, 1992, 1997) and all U.S. manufacturers, regardless of
size, are required by law to respond. 7

6

Plant-product-level data refers to output data for every product produced at every plant. These
shipment data are measured in revenue for all products and in units of physical quantity for asubset of products.

7

The CMF collects a limited set of data from small manufacturers, referred to in the data as“administrative records.” Since input usage data may be imputed for administrative records,
they have been excluded from the analysis. This exclusion of administrative records is standard
in research employing the LRD. See, e.g. Bernard, Redding and Schott (2008).
7

An important benefit of the CMF is the availability of output data measured in units ofquantity for certain products. The availability of quantity-based output data allows for the
calculation of physical productivity—in addition to the standard revenue productivity—as well
as average unit prices and price-cost mark-ups. The ability to examine physical productivity,
prices and mark-ups is extremely important when studying antidumping duties, since changes inphysical productivity are likely accompanied by increases in prices and mark-ups.

These

quantity-based output data have been used in recent studies examining the differences between
revenue and physical productivity, including Foster, Haltiwanger and Syverson (2008).
It is important to define a number of terms that will be used throughout this paper. The
term plant refers to a manufacturing establishment, which is a production facility located at asingle physical location.

Products and industries are 5-digit and 4-digit categories of theStandard Industrial Classification (SIC), respectively. 8 A product group is the set of plants
producing a particular product. Lastly, an investigated product is a product that was involved in
an antidumping investigation, regardless of the outcome of the investigation.
The use of plant-level data is an important innovation of this paper and provides many
advantages over more aggregated data, even including firm-level data. Many firms involved inpetitioning for antidumping protection are large multi-product manufacturers. In fact, somefirms participated as petitioners in multiple antidumping investigations involving multiple
products. Individual plants on the other hand, tend to produce a much narrower set of products
than firms as a whole. The use of plant-level data, therefore allows for much more accurate
matching between the products named in contingent protection investigations and the facilities
that actually produce those products.
8

The 1987 SIC contains 459 four-digit industries and 1,848 five-digit products.
8

The benefits of plant-level matching can be seen clearly by examining the experience of a
specific firm, the integrated steel-maker United States Steel. According to its 2006 annual
report, U.S. Steel operated 24 plants in the United States, producing multiple products including
flat-rolled sheets, tin mill, strip mill plate, galvanized sheets and tubular products. 9 Moreover,
several of U.S. Steel’s products have been subject to antidumping or countervailing duty
protection over the years including Corrosion-Resistant Carbon Steel Flat Products (1993), Cut
to Length Plate (1979, 2003), Seamless Pipe (1995), Oil-Country Tubular Goods (1995), HotRolled Steel Products (2001) and Welded Large-Diameter Line Pipe (2001, 2002). 10 In the caseof U.S. Steel, firm-level data are not sufficient for defining when or in what way the firm
received protection. With plant-level data, however, I am able to identify the plants producing
the specific products covered by antidumping duties applied in specific years.
In addition, I am able to greatly refine the identification of plants that did and did notreceive contingent protection through the use of plant-product-level data contained in the LRD.
These data report the full list of products manufactured at each plant, as well as the value, andsometimes quantity, of shipments attributable to each product. The availability of this plantproduct-level data represents an additional level of disaggregation beyond the “major industry”
codes generally used to identify plants and firms in micro-level datasets.

9

United States Steel at 32. Available online at:

http://www.uss.com/corp/investors/annual_reports/2006-annual-report.pdf
10

United States Steel at 15-16. http://www.uss.com/corp/investors/annual_reports/2006-annualreport.pdf
9The list of products involved in antidumping investigations in the United States is fromversion 3.0 of Chad Bown’s Global Antidumping Database. 11 Products subject to antidumping
investigations are identified using the Harmonized Tariff System (HTS) and products may be
defined from the 4-digit level to the 10-digit level. 12 In addition to a description of the products
involved in each investigation, the antidumping database provides the dates and outcomes ofeach phase of the investigation—e.g. preliminary and final injury and dumping determinations—along with the final remedy. The analysis in this paper considers the effects of antidumping
investigations that were completed during the period from 1988 to 1996. This setup ensures that
I am able to observe plant-level outcomes both before and after the imposition of protection forevery product group. 13 Lastly, because successful antidumping investigations in the UnitedStates almost always result in ad-valorem tariffs—rather than price undertakings or suspension

11

Available online at http://people.brandeis.edu/~cbown/global_ad/.

12

Although the HTS was not effective until 1989, investigations in Bown’s Global Antidumping
Database that ended in 1988 were assigned HTS numbers, ex-post.
13

Because products in antidumping investigations are classified under the HTS, while productsin the LRD are classified under the SIC, it is necessary to concord the two product classification
systems. The matching of HTS codes to SIC codes takes place through a set of SIC Base Codes(SICBase) developed by Census. SICBase codes are a bridge that connects the HTS—where
products are defined solely based on their physical characteristics—to the SIC, where products
are also classified based on their method of production. For this reason, Census assigns a single
SICBase to each HTS10. This SICBase may contain a single SIC5 if the HTS10 is a subset of asingle SIC5, or multiple SIC5s if the HTS10 fits several SIC5 categories. Using a three step
process, I am then able to determine which plants produce products that were involved in
antidumping investigations:
Step 1: SICBase codes are assigned to the HTS10 codes contained in the antidumping dataset(referred to here as BOWN_AD for brevity) using an HTS10-SICBase concordance(HTS_SICBase) published by the Census Bureau.
Step 2: SICBase codes are assigned to each SIC5 in the plant-product-level data in the LRD
using a SIC5-SICBase concordance known as the Principle Differences file (PD). The 1992
principle differences file, which is used for the analysis in this paper can be found online at
http://www.census.gov/epcd/www/intronet.html.
Step 3: The BOWN_AD antidumping dataset is merged to the LRD using the SICBase codes.
10agreements—I am able to study the effect of variation in the antidumping duty rate on
productivity.
Section 3: Antidumping Duties in the United States
Under GATT Article VI and the WTO’s Antidumping Agreement, WTO members are
permitted to impose discriminatory tariffs on goods sold by foreign producers at prices that are
deemed to be less than fair value (LTFV), if these sales result in material injury to the domestic
industry. In the United States, sales are considered to be made at LTFV—i.e. dumped—when aforeign firm sells a good in the United States at a price that is below that offered on comparable
sales in its home market, or below its average total cost (ATC). 14
Antidumping investigations in the United States are initiated by individual firms, tradeassociations or sometimes labor unions, which are referred to in antidumping investigations as
petitioners.

The foreign firms selling allegedly dumped merchandise are referred to as

respondents. Petitioners apply for antidumping protection by submitting a petition to the Import
Administration of the Department of Commerce (DOC) and the International Trade Commission(ITC). The DOC determines whether sales made by foreign firms in the U.S. are being made at
LTFV. The ITC determines whether the U.S. industry has been injured as a result of the
dumping.

14

There are additional subtleties to the LTFV determination. For market economies, thepreferred price comparison is between sales by the foreign producer in the U.S. and its homemarket. If there are insufficient sales in the foreign producer’s home market, U.S. prices arecompared to sales in a third country. If there are insufficient sales in the third country, U.S.
prices are compared to the “constructed value (CV)” of the foreign producer’s merchandise,which is gathered from the firm’s cost accounting system and is essentially ATC. Sales made byfirms in non-market economies are always compared to the “normal value (NV)” the firm’s
merchandise, which is again essentially the firm’s ATC.
11

If the DOC finds that sales have been made at LTFV and the ITC concludes that thesesales have injured U.S. producers, an ad-valorem tariff is placed on imports of goods from therespondents’ home countries. This ad-valorem tariff, which is known as an antidumping duty isequal to the percentage difference between the U.S. price and the home-market price or ATC. I
refer to the magnitude of the antidumping duty as the antidumping duty rate. Because theantidumping duty is applied to all dumped goods, it benefits the petitioners, as well-as nonparticipating producers of the investigated product.
Table 1 reports the types of products involved in antidumping investigations from 1988
to 1996, showing the number of antidumping duty investigations by 2-digit HTS Chapter. The
most frequent seekers of antidumping duties were producers of “Iron and Steel” (Chapter 72) and“Articles of Iron and Steel” (Chapter 73). Other active applicants for antidumping protection
included producers of machinery and appliances (Chapters 84 and 85), inorganic and organic
chemicals (Chapters 28 and 29) and transportation vehicles and parts (Chapter 87). As theseexamples indicate, antidumping duties are primarily used to protect relatively homogenous
manufactured goods.
Table 2 shows the number of antidumping investigations completed, by outcome for theyears 1980 to 2005. The number of antidumping investigations tends to increase during and
immediately following periods of recession, and we see that this phenomenon did, in fact, occur
following the recession of 1990-1991, when the number of new investigations spiked in 1991
and 1992. Aside from this countercyclical trend in new investigations, the period from 1988 to
1996 was typical in terms of the number of investigations initiated.
Section 4: Empirical Strategy and Results
Pre-Estimation Definitions
12

A. Definition of Treatment and Control Groups
To borrow terms from the program analysis literature, I conduct this analysis bycomparing the behavior of plants in a treatment group receiving antidumping protection to plants
in a control group that do not. The treatment group consists of plants producing products thatapplied for and received antidumping protection. Each plant in the treatment group is assigned adate of treatment and an ad-valorem duty rate,15 which comes from the results of theantidumping investigation associated with the product it produces. If a plant produces more than
one product that receives protection, the treatment date and duty are those associated with the
product that accounts for the highest share of its output.
Comparing the behavior of these treated plants to a control group—rather than simply
examining changes in treated plants over time—allows for netting out changes in plant-levelvariables that are independent of the treatment. Using the difference-in-difference frameworkdescribed in more detail below I am also able to net out macro-level shocks that affect all
manufacturers equally.
In the framework being examined in this paper, a natural concern is that any estimatedtreatment effects could be affected by a self-selection bias, because the set of plants that apply
for antidumping protection are almost certainly different from those that do not. For example,
antidumping applicants produce goods that are subject to import competition, perceive
themselves as being injured by imports and operate in industries capable of cooperating to file a
case.

15

Of the 160 antidumping investigations initiated between 1988 and 1996, 5 ended withsuspension agreements. For these cases, no ad-valorem antidumping duty rate was available.
13

To control for this self-selection bias, I use an approach employed in Konings andVandenbussche (2008) to define a first control group. Specifically, I define the control group to
be plants producing products that applied for antidumping duties, but were denied protection by
the government. I will refer to this control group as the termination control group, hereafter. As
with treated plants, plants in the termination control group produce products characterized byhigh import competition, perceive themselves as injured by imports and are able to organize theindustry to file an antidumping petition. Moreover, as shown in Table 3, control plants areconcentrated in the same sectors that successfully apply for protection—especially primary andfabricated metals, and industrial and electronic equipment. 16 In addition, as described in Table
4, plants in the treatment and control groups are comparable in terms of their total value of
shipments, number of employees and capital to labor ratios. Importantly, they also display
nearly identical mean levels of total factor productivity and labor productivity in the pretreatment year of 1987.
Despite the similarity of the treatment and control groups, there is a possibility of anadditional “government selection bias,” if the government only grants protection to petitioners
that meet certain criteria. In particular, the ITC considers variables such as employment andimport penetration when deciding whether to provide protection in antidumping investigation.
Because these variables are likely correlated with productivity, estimates based on thetermination control group may be biased.
16

Observations where the treatment and control groups overlap have been dropped from theanalysis. Overlapping of treatment and control groups can occur for two reasons. First, a single
SIC5 product could receive protection from one antidumping investigation but be denied
protection in another. This is possible if the HTS10 products defined in two different
antidumping investigations both map into the same SIC5. In addition, a single plant could
produce two products, where one product receives protection and the other is turned down forprotection.
14

I control for this potential government selection bias by constructing two alternative
control groups composed of unprotected industries that are similar to protected industries interms of the variables considered by the ITC in antidumping investigations. Specifically, these
two alternative control groups, which I will refer to as the “matched control groups” are formedby estimating a probability of protection based on industry-level independent including lagged
import penetration, lagged employment, GDP growth, labor productivity and price growth. 17
Each matched control group is composed of plants in industries with a high predicted probabilityof protection, but that did not actually receive protection.
The first matched control group—matched control group 1—is formed by estimating theprobability of receiving antidumping protection for all industries. This means that every industry
that did not receive protection has the potential to be included in matched control group 1,
including industries that never applied for protection and those that applied, but were turned
down by the government. Matched control group 1 is formed by estimating a multinomial logit
model where the dependent variable takes a value of 1 if an industry never applied for protection,
2 if it applied and was turned down and 3 if it applied for and received protection. Independent
variables are the determinants of protection considered by the ITC and described above.
Matched control group 1 is then the set of plants in industries that had a probability of protection
greater than the 75th percentile of that in protected industries, but that did not receive
protection. 18

17These variables have been used to explain the probability of receiving antidumping protectionin Blonigen and Park (2004) and Konings and Vandenbussche (2008).

18

This is the same probability cutoff used to form a matched control group in Konings andVandenbussche (2008).
15

Matched control group 2 is formed by only considering industries that applied forprotection. A logit model is estimated where the dependent variable equals 1 for industries thatreceived protection and 0 for industries that applied for but did not receive protection.
Independent variables are the same as those considered when constructing matched control group
1. The control group is composed of plants in industries did not receive protection but that werein the top 75th percentile in terms of their predicted probability of protection. Matched control
group 2 has the attractive property of being composed of plants in industries that applied forprotection—thus controlling for potential self-selection bias—while also being highly similar to
the treated industries, in terms of the variables considered by the ITC.
Results of the multinomial logit and logit regressions used to create the two matched
control groups are reported in Table A.1 of Appendix 1.

Estimated coefficients take theexpected sign and are consistent with results in Blonigen and Park (2004) and Konings andVandenbussche (2008).

Specifically, the probability of receiving antidumping protection

increases with higher levels of import penetration, employment and labor productivity. In
contrast, higher GDP growth and price growth are associated with lower probabilities ofreceiving protection.
B. Productivity Measures
I calculate productivity in two ways. The first is the superlative TFP index from Caves etal. (1982). As described in Aw, Chen and Roberts (2001), this TFP expression measures theperformance of each plant, relative to a hypothetical plant producing the mean level of output
with the mean level of inputs, within an industry, in the base period, 1987. 19 The TFP index
19This measure of total factor productivity is standard in the trade and productivity literaturesand has been used in other studies including Bernard, Redding and Schott (2008).
16

therefore incorporates a plant’s deviation of output and inputs from the industry mean in any
given year, but also from the mean in the base period. This calculation yields a TFP measurethat is comparable across plants and years:t(1) ln TFPpti = (ln Y pti − ln Yt i ) + ∑ ( ln Yt i − ln Yt −i 1 )
s=2

1 i
i
i
− [∑ (S mpt+ S mti )(ln X mpt− ln X mt
)
m 2
t
1
i
i
+ ∑ ∑ (S mti + S mti −1 )(ln X mt
− ln X mt
−1 )]
s =2
m 2

I construct the TFP index expressed in Equation (1) for each plant p in year t using the setof inputs m={Capital, Raw Materials, Production Workers, Non-Production Workers}. Theisuperscript i indicates that mean variables are calculated at the SIC4 industry level. X mpt
is theiexpenditure of plant p in time t on input m and S mpt
is the share of input m in total revenue. I

calculate average input usage and shares at the industry-year-level. Therefore, S mti , ln Yt i andiX mt
are the arithmetic means of industry-level input shares, revenue and input expenditure,

respectively.
The second measure of productivity is a simple, single-factor labor productivity, definedas the real total value of sales (RTVS) per employee:(2) LPpt =RTVS pt
TE pt

where TEpt is the total number of employees at plant p at time t. Labor productivity is usedprimarily as a robustness check for the results based on total factor productivity. All results
reported in this paper hold for both TFP and labor productivity.
17

Semi-parametric estimators, including those developed by Olley and Pakes (1996) and
Levinsohn and Petrin (2003) have been used extensively in recent papers studying the effects ofchanges in trade policy on TFP. 20 As has been established in this literature, these methods canbe useful for correcting the simultaneity bias that arises when plants with high TFP consume
more inputs and the selection bias associated with only observing surviving plants. Thesemethods are not well-suited for use with the economic census data employed in this paper,
however, due to their use of lagged input values in the TFP calculation. While it could be useful
to calculate TFP using one of these semi-parametric methods if annual data were available, I willnote that Van Biesebroeck (2004) finds that TFP measures derived from various methods tend tobe highly correlated.
C. Deflation
Whenever productivity is calculated using either revenue or value-added data—i.e. datathat contain both a price and a quantity component—it is important to separate changes in prices
and mark-ups from changes in true productivity. This separation becomes critically importantwhen mark-ups and productivity could move in the same direction, as in the situation examinedin this paper. While some have suggested that antidumping duties can increase productivity,
through their influence on technology adoption decisions, they almost certainly lead to higher
mark-ups as well. Without an adjustment to account for changes in mark-ups, an increase in
prices resulting from antidumping protection would show up as an increase in observed totalfactor productivity. This means that the results are biased toward finding a positive correlation
between antidumping protection and revenue TFP.

20

See, for example, Pavcnik (2002), Fernandes (2008) and Konings and Vandenbussche (2008).
18

The CMF, which collects output data in units of quantity for a subset of plants is uniquely
suited for separating changes in prices and mark-ups from changes in physical productivity. In
instances in which quantity data are available, physical quantities can be used as a measure ofplant-level output and incorporated into the calculation of physical productivity, without
deflation.
When calculating revenue productivity, I control for changes in mark-ups—to the extent
possible—by deflating revenue using industry-level price indexes, applied to the set of products
produced at each plant. This technique results in a plant-level deflator that is constructed byweighting the industry-level deflators according the share of a plant’s output that is assigned tothat industry. Industry-level output deflators, as well as industry-level deflators for cost of
materials and capital are from the NBER-CES Manufacturing database reported in Bartelsman,
Becker and Gray (2000).
There are at least two ways in which these plant-level deflators are insufficient forcompletely separating changes in mark-ups from changes in true productivity. First, since they
are based on average price indexes, they do not allow for heterogeneity in pricing across plants.
In this sense, plants that charge high prices—due to high local market power, for example—
would be misinterpreted as high-productivity plants. Second, because the price indexes arecalculated at the industry, rather than the product level, they will not fully reflect increases in
product-level prices. This higher level of aggregation means that revenue-based productivity
measures will overstate productivity growth in situations where mark-ups are increasing, as is
likely the case in the situation considered in this paper.
D. Effective Antidumping Duty Rates

19

A single antidumping investigation can be filed against imports from multiple countries
and if the case ends with a determination by the DOC and ITC to offer protection, each country
may be assigned a different ad-valorem antidumping duty. Naturally, imports from certain
countries account for larger shares of U.S. imports of a good than others. In order to account for
the true importance of an antidumping duty on U.S. trade, therefore, I calculate an effective
antidumping duty rate for each product that is assigned an ad-valorem antidumping duty. The
effective antidumping rate is calculated as follows:
Rate gt = ∑ SHAREc , g ,t −1 * AVDc , g ,tcwhere SHAREc , g ,t −1 is country c’s share of U.S. imports of product g in time t-1 and AVDc , g ,t isthe ad-valorem duty applied to imports of product g from country c in time t. A country’s shareis calculated based on imports in time t-1, rather than time t, because antidumping duties often
lead to significant reductions in imports from pre-protection levels. Using a pre-protection share,
therefore, provides a more accurate representation of a country’s importance to U.S. trade.
Research Questions
A. Do Temporary Tariffs Increase or Decrease Plant-Level Productivity?

As discussed above, some have argued that temporary protection can increase withinplant productivity by increasing the incentive to invest in new technology. On the other hand,
temporary protection is also likely to lead to higher prices and mark-ups. Because an increase inrevenue-based productivity that occurs at the time of protection could be caused by either ofthese phenomena, however, it can be difficult to determine what is driving gains in revenue
productivity. Using output data measured in units of quantity, I am able to separate these two
effects by calculating both revenue and physical productivity measures. Moreover, I am able to20

directly measure the effects of antidumping duties on plant-level prices and mark-ups. I find thatapparent growth in productivity associated with antidumping protection is driven primarily byhigher prices and mark-ups, rather than increases in true productivity.
Empirical Strategy

I examine the effect of temporary protection on plant-level productivity, prices and markups using a difference-in-difference approach. As discussed above, the treatment group iscomposed of plants producing products that receive antidumping protection. I will consider
three control groups, including the termination control group composed of plants that applied for,but did not receive protection and the two matched control groups. The goal of the difference-indifference methodology is to isolate the effect of the treatment—antidumping protection—byeliminating time-invariant differences between the treatment and control group, as well as timespecific effects common to both treatment and control. The difference-in-difference estimator,
therefore, measures not simply the change in the dependent variable that occurs following
antidumping protection, but rather measures the difference between the changes in the treatmentgroup and the changes in the control group.
Let T be the set of plants producing products that receive antidumping protection and let
C be the set of plants in a particular control group. Further, define Ig to be the date that theantidumping investigation is initiated for product g. I measure the difference-in-difference effectby estimating Equation (3):(3) Prodpgt = α + β 1Treatment pgt * Post pgt + γ t + δ g + ε pt , where
Treatmentpgt = 1 ∀ p ∈ T and Treatmentpgt = 0 ∀ p ∈ C

21Postpgt = 1 ∀ t > Ig, 0 otherwise 21
Here, Prodpgt is productivity—measured in TFP or Labor Productivity—at plant p, which
produces product g at time t. Year fixed effects capture any macro-level shocks affecting plants
in T and C equally. Similarly, product fixed effects, δ g , capture time-invariant differences
between products. Note that Equation (3) contains product-level fixed effects, rather than a moregeneral Treatment dummy used in the most basic difference-in-difference expressions. This
specification captures time-invariant differences between producers of different products within

T and C. This is likely important when dealing with a diverse set of manufacturers fromdifferent sectors and industries.

Finally, the coefficient β1 on the interaction term is thecoefficient of interest and measures the difference-in-difference effect of antidumping protection
on the plant-level outcomes discussed below.
Equation (3) defines protection with a binary variable—any plant that receives any
antidumping protection is considered to be equally protected. It seems reasonable to expect,however, that plants’ reactions to protection would depend not only on this simple binaryclassification, but also on the level of protection they receive. That is, plants producing productsthat receive high ad-valorem duty rates—such as the 259.17 percent antidumping duty rate on
Aluminum Sulfate from Venezuela—may respond differently than those producing products thatreceive low antidumping duty rates, such as the 2.98 percent rate on Collated Roofing Nails from21

In general, antidumping protection lasts for ten years or more, meaning that almost every
antidumping duty put in place during the sample period considered was still in effect at the endof the period. In 3 of the 198 antidumping investigations considered in this sample, however,antidumping protection began prior to 1992, but ended prior to 1997.
22

Taiwan. As these two examples indicate, the variation in duty rates among cases that receive
protection is large: the mean is 64 percent and the standard deviation is 60 percent.
I measure the effects of heterogeneity in antidumping rates by augmenting Equation (3)with an additional interaction term:(4) Prodpgt = α + β 1Treatment pgt * Post pgt + β 2 Rate pgt * Post pgt + γ t + δ g + ε pt
Here, Ratepgt is the ad-valorem effective antidumping duty rate on product g, which is producedby plant p at time t. By interacting Ratepgt with the Postpgt dummy, I am able to separate theeffect of varying rates of protection from the mean response of all plants receiving antidumping
protection. 22
Equations (3) and (4) provide within-product estimates of the effect of antidumping
duties on plants. It is important to note, however, that these results do not necessarily reflect thewithin-plant effect of antidumping duties. Because equations (3) and (4) are estimated on an
unbalanced panel, coefficient estimates could reflect changes in mean plant-level productivity
due to entry in exit. In order to estimate the within-plant effect of antidumping duties, I reestimate equations (3) and (4) with plant fixed effects for the balanced subsample of plants
producing in all three census years. These estimates provide both a useful robustness check for
the within product-group estimates, as well an explicit estimate of the within-plant effects ofantidumping duties.

22

The Treatment binary variable is redundant in this specification because all treated plants haveeffective duty rates greater than zero. Control plants have effective duty rates that are equal to
zero.
23

Lastly, I will employ the difference-in-difference framework in equations (3) and (4) toexamine the effect of antidumping duties on plant-level prices, as well as mark-ups over average
total cost. Prices are defined as follows:

Ppgt =

TVS pgt
Q pgt

where TVS is a plant’s total value of shipments and Q is the total quantity of units shipped.
Plant-level mark-ups over average total cost are defined as:

PATC pgt =Ppgt
ATC pgt

where ATC pgt =Wages pgt + CM pgt + RTAE pgt
Q pgt

Here, Wages are the wages paid to production workers, CM is the cost of materials and RTAE isreal total value of assets, or capital.

Results
Revenue Productivity
I do find that antidumping protection is associated with increases in revenue productivityof 5 to 8 percent, as shown in Table 5. 23 The first two columns of Table 5 report the results forequation (3) with TFP and labor productivity and the next two report results for the same
specification, with state fixed effects. The final four columns of Table 5 report results fromestimation of equation (4), which includes the interaction term accounting for variation in theeffective antidumping duty rate.

I continue to find a positive and significant relationship

23

Tables 5 and 6 report results based on the termination control group of plants producing
products that applied for, but were turned down for protection.
24

between antidumping protection and revenue productivity when the effective duty rate isincluded in the specification, although the rate term is not significant.
In Table 6, I report the equivalent results when estimating equations 3 and 4 with plant
fixed effects on the balanced subsample. As discussed above, these within-plant estimates areunaffected by changes in the composition of plants in the product-group caused by entry andexit. The results are consistent with those obtained with product fixed effects, with protected
plants exhibiting increases in plant-level productivity of 3 to 6 percent. In sum, the estimatesreported in Tables 5 and 6 appear to support the argument that antidumping duties can bringabout increases in plant-level productivity. These results are also robust to consideration of thetwo matched control groups, as can be seen in Tables 5a and 6a. 24

Physical Productivity
As described above, the use of revenue-based productivity measures can yield misleading
results in situations where prices and mark-ups may also be changing concomitantly.

In

particular, because the imposition of antidumping duties likely allows domestic producers toincrease prices and mark-ups, revenue productivity measures will overstate any potential
productivity gains associated with antidumping protection. Because the CMF contains output
data measured in units of quantity for a subset of products, I am able to calculate measures ofphysical productivity that are unaffected by changes in prices and mark-ups.

24

The increase in revenue productivity associated with antidumping protection—as measured by
the binary protection variable—is a robust result in this analysis. It is somewhat surprising,
however, that revenue productivity appears to be essentially unaffected by changes in theantidumping duty rate. As will be seen in results below, however, this lack of responsivenessappears to be due to decreases in physical productivity associated with higher antidumping duty
rates being offset by increases in prices and mark-ups.
25

The effect of antidumping duties on plant-level productivity is starkly different when
output is measured in units of quantity, rather than revenue. As reported in Table 7, antidumping
duties are actually associated with a decrease in physical productivity among the set of plants
reporting quantity data. In fact, physical productivity actually falls by a greater amount as theeffective duty rate protecting the plant increases. This effect persists when estimating thedifference-in-difference specification with plant fixed effects on the balanced subsample, as
shown in Table 8. Tables 7a and 8a confirm that these results are robust to consideration of thetwo matched control groups, as well.
Bernard, Redding and Schott (2006) provides a plausible reason that plant-levelproductivity may fall in the treatment group, relative to the control group. In this model, tariff
protection allows firms to continue producing low-productivity products that they would haveotherwise stopped producing. Indeed—as will be discussed in more detail below—I do find that
protected plants are less likely to drop investigated products than unprotected plants. This meansthat while plants in the control group focus on their “core competencies” and produce theirhighest-productivity products, plants in the treatment group are able to continue producing lowproductivity products. As a result, within-plant productivity increases more in the control groupthan in the treatment group.
A word of warning in terms of interpreting these results is necessary here. It would beinappropriate based on these results to claim that antidumping duties, in general, decrease plantlevel physical productivity. It is true that antidumping duties were associated with a decline inproductivity among the set of plants reporting output data in units of quantity. However, thisgroup is not necessarily representative of the full set of plants subject to antidumping protection.
First, as can be seen in Table 3, the distribution of plants across 2-digit SIC sectors is somewhat
26different for the set of plants reporting quantity data, than for the overall sample. Second, when I
examine the effect of antidumping protection on the revenue productivity of the subset of plants
reporting output in units of quantity, I find that revenue productivity was unaffected byantidumping protection. This contrasts with the increase in revenue productivity associated withantidumping protection in the full sample. Nonetheless the fact that plants in this sub-sample
experienced a zero effect of antidumping protection on revenue productivity and a large andhighly significant decrease in physical productivity suggests that increases in prices and markups are affecting results based on revenue productivity. 25

Prices and Mark-Ups
The disparity between results showing the effect of antidumping protection on revenue
versus physical productivity suggests that increases in prices and mark-ups are playing a role in
the apparent increase in revenue productivity.

I use the same difference-in-difference

specifications from the productivity analysis to examine the effects of antidumping duties on themeasures of prices and mark-ups over average total cost described above.
As reported in Table 9, I find that antidumping duties are associated with price increasesof 27 to 36 percent. Moreover, these pricing changes are sensitive to the effective duty rate aplant experiences—the higher the effective duty rate, the higher the prices charged by the plant.
These results hold for both within-product-group and within-plant estimators. Table 10 reports
the effects of antidumping protection on mark-ups over average total cost.

25

I find that

An alternative explanation is that plants receiving antidumping protection increase the qualityof the products they produce. This seems unlikely, given that the products for which the Census
Bureau collects quantity data tend to be commodities, with little room for quality improvement.
Nonetheless, exploration of the effect of antidumping duties on product-quality will be aninteresting aspect of my future research.
27

antidumping duties increase mark-ups over average total cost by 7 percent. Moreover, themagnitude of the mark-ups over average total cost increases as the effective duty rate increases
in the within-product estimates. The relationship between the effective duty rate and the level ofmark-ups does not hold in the within-plant specifications, however. These results are robust to
consideration of the two matched control groups, as can be seen in Tables 9a and 10a.
B. Do Temporary Tariffs Discourage Product-Dropping?

Bernard, Redding and Schott (2006) shows that reductions in trade barriers can increase
firm or plant-level productivity by inducing firms to drop their least productive products, while
expanding output of their most productive products. Moreover, product-dropping can yield
increases in aggregate productivity—as defined below—as the least productive plants drop
products. In fact, I do find that antidumping protection decreases the probability of dropping
investigated products.

Empirical Strategy
The effect of antidumping duties on the probability of dropping products is investigatedusing a difference-in-difference specification similar to that employed to study changes in plantlevel productivity, prices and mark-ups. By comparing the probability of product-droppingamong protected plants to the unprotected plants in the three control groups, I am able toestimate the effect of antidumping duties on product-dropping.
An important difference between this product-switching analysis and the plant-levelproductivity regressions described above is that the product-switching data are defined at theplant-product-level. This means that I have dropped the restriction that each plant is assigned toa particular treatment or control product. In doing so, I am able to consider the full set of
products that are involved in antidumping investigations. I employ a linear probability model, to28

allow for the inclusion of fixed effects and clustering of standard errors and estimate thefollowing two equations:′(5) Drop ipgt = α + β 1Treatment ipgt * Post ipgt + β 2 X ipgt + γ t + δ g + ε ipgt′
(6) Drop ipgt = α + β 1Treatment ipgt * Post ipgt + β 2 Post ipgt * Rate ipgt + β 3 X ipgt + γ t + δ g + ε ipgt
Drop is a binary variable that equals 1 if product g is produced by plant p at time t, but not timet+5. X is a matrix of plant-product-level variables found to be determinants of product-droppingin Bernard, Redding and Schott (2008), namely the product-level value of shipments and thenumber of years a product has been produced (tenure). The superscript i denotes that the data areat the plant-product level. To be clear, the variable Drop only takes into account productdropping by continuing plants. Exiting plants are not considered product-droppers.

Results
I find that plants are 6 percent less likely to drop protected products than they are to drop
unprotected products, as reported in Table 11. Moreover, this product-switching behavior issensitive to the value of the effective duty rate applied to a product. I find that the probability ofdropping a protected product decreases as the effective duty rate assigned to that productincreases. In the product-dropping regression, the results are robust to the inclusion of productlevel shipments and product tenure, which are both negative and significant, as expected. These
results make clear that more plants produce a given protected product than would be the case if
the product was unprotected. They are also robust to consideration of the two matched control
groups, as can be seen in Tables 11A and 11B.
This reduction in product-switching brought about by antidumping duties has
implications for both plant-level and aggregate productivity.
29

At the plant level, Bernard,

Redding and Schott (2006) suggests that a reduction in product-dropping resulting from tradeprotection will lower productivity, relative to unprotected plants. While unprotected plants drop
their least productive products to focus on their highest-productivity product-lines, protected
plants continue to produce the protected product, resulting in lower relative productivity. A
reduction in product-dropping among protected plants can also decrease aggregate productivity
growth.

In unprotected product-groups, the least productive producers will either exit

completely, or drop the unprotected product. In the protected product groups, however, these
low-productivity plants are able to continue producing, resulting in lower aggregate productivity
relative to the control group.
C. Do Temporary Tariffs Discourage Plant-Level Exit?

It is a well-known result that trade protection can slow aggregate productivity growth by
preventing the exit of low-productivity plants and firms that would otherwise cease to operate. I
examine this question by comparing the probability of plant-level exit in the treatment group ofprotected plants to that in the control group. I find that antidumping duties do not affect theprobability of exit. Plants that are denied protection by the government are no more likely to exit
than those that receive antidumping duties.

Empirical Strategy
I define a plant as exiting in year t if it appears in the CMF in year t, but not in year t+5.
To be clear, a plant that halts production of the investigated product between year t and year t+5,
but continues to operate, is not counted as an exit. A binary exit variable is defined in this way
for the years 1987 and 1992. The exit variable is missing in 1997 due to a change in productclassification system that makes it difficult to track plant survival from 1997 to 2002.

30

I estimate the relationship between antidumping protection and the probability of exit
using a difference-in-difference framework identical to the specification used to study changes in
product-dropping above. As in the analysis of product-dropping, I employ a linear probability
model, to allow for the inclusion of fixed effects and clustering of standard errors:(7) Exit pgt = α + β 1Treatment pgt * Post pgt + γ t + δ g + ε pt
The binary dependent variable, Exit was described above. The coefficient β1 is the primaryparameter of interest and estimates the effect of receiving antidumping protection on theprobability of exit. As in Equation (3), year and product-group fixed effects are included.
Estimates with robust standard errors and clustering at the product-group level are reported inTable 12.
Next, I expand Equation (7) to include plant-level variables that have been found to beimportant determinants of exit in the large empirical literature on the effects of changes in trade
costs on exit. Using determinants of exit from Bernard, Jensen and Schott (2006), I estimate
Equation (8):(8) Exit pgt = α + β 1Treatment pgt * Post pgt + β ′X pgt + γ t + δ g + ε pt
where X is a matrix of plant-level variables including log of total employment, plant age, log ofcapital-labor ratio, log of average wage and indicators for whether the plant is a multi-productplant, or a part of a multi-unit firm. Lastly, I re-estimate Equations (7) and (8) with theRate*Post interaction variable used to estimate the effect of variation in the effective
antidumping duty rate.

Results

31

Results are reported in Table 12. While I do find in one specification that higher
effective duty rates decrease the probability of exit, the result is not robust. In particular, it
disappears when the additional control variables commonly used in analyses of exit are included.
Moreover, the magnitude of the coefficient is small. A one percent increase in the effective duty
rate decreases the probability of exit by only 0.3 percent. It appears, therefore, that plants thatare turned down for protection are no less likely to exit than those receiving protection.
Combined with the product-dropping result described above, this suggests that U.S.
manufacturers are flexible and dynamic in the face of changes in trade policy. Rather thanexiting, they react to being turned down for antidumping duties by dropping the unprotected
product and shifting resources to other, potentially higher-productivity products. There is also
no effect of antidumping protection on the probability of exit when considering the two matched
control groups, as can be seen in Tables 12A and 12B.
D. Do Temporary Tariffs Decrease Output Rationalization and Aggregate
Productivity?

A number of theoretical models including Melitz (2003) and Bernard, Redding andSchott (2006) predict that tariff increases allow for the continued operation of low-productivity
firms that might otherwise stop production. If antidumping duties create a similar situation, we
should expect the level of output rationalization to increase in the control group relative to thetreatment group. Indeed, I do find that the level of output rationalization rises in the control
group and falls among the protected plants in the treatment group.

Empirical Strategy
I have already shown that antidumping duties allow plants that would have otherwise
dropped the investigated product to continue producing.
32

If these plants that would haveotherwise dropped the product are also low-productivity plants, antidumping duties may have anegative effect on output rationalization and aggregate productivity growth. To compare theproductivity of product-dropping plants to non-droppers, I regress plant-level productivity on abinary variable that equals one in time t if plant p dropped an investigated product between time t
and time t+5:(12) Prodpgt = α + β 1 Drop pgt + γ t + δ g + ε pt
Next, I examine the level of output rationalization directly by decomposing aggregate
productivity as in Olley and Pakes (1996), Pavcnik (2002) and Fernandes (2007).

This

procedure decomposes growth in aggregate productivity into two components, shown below:(13) W gt = ∑ s pgt TFPpgt =TFPtgmean + ( s pgt − s gtmean )(TFPpgt − TFPgtmean )p

The first term of the final expression represents mean plant-level productivity at time t. The
second term is a covariance-like variable representing the degree to which greater output isproduced by higher-productivity plants. s pgt denotes the share of plant p’s output in the total
output of product-group g at time t, while s gtmean is the mean output share of plants producing
product g at time t. Similarly, TFPpgt and TFPgtmean represent the revenue total factor productivityof plant p and the mean TFP of plants in product-group g, respectively. When plants with aboveaverage TFP also capture an above-average market share, the covariance term increases,
indicating a higher level of output rationalization.
The covariance term measuring the degree of output rationalization will be the primaryvariable of interest. Ideally, I would simply examine the effects of antidumping duties onaggregate productivity, Wgt directly. A number of data problems would make this comparison
33

unreliable, however. First, as mentioned above, the use of revenue-based aggregate productivity
measures would overstate productivity gains among protected product-groups, since I haveshown that protected plants respond to temporary protection by increasing prices. Moreover,quantity-based productivity measures are not useful in settings where analysis is taking place atthe product-group level or higher, since quantity data are only available for producers of alimited set of products.
The use of revenue-based productivity measures is less problematic for analyzing output
rationalization. Assuming that prices increase uniformly among all producers of a given productonce it receives protection, the covariance term will still accurately reflect the degree of output
rationalization within a product group. After calculating aggregate productivity, mean plantlevel productivity and the output rationalization term at the product-group-level, I report their
output-weighted means by year, treatment group and a dummy variable indicating whether theantidumping investigation for product g has already taken place.

The results of thedecomposition described are reported in Table 14.

Results
First, I find that plants that drop the investigated product have lower productivities thannon-dropping plants, as reported in Table 13. As a result, the reduction in product-dropping by
low-productivity plants caused by antidumping duties may contribute to a decrease in output
rationalization and aggregate productivity growth among protected product-groups.
Indeed, I do find that antidumping protection decreased the level of output rationalizationin the treatment group, while output rationalization grew in the control group. As reported inTable 14, the treatment group of plants that ultimately receive protection starts with a level of
output rationalization in 1987 that is higher than the control group. As time progresses and34

protection takes effect, however, output rationalization falls in the treatment group—likely due tocontinued operation by low-productivity plants that would have otherwise dropped theinvestigated product—and rises in the control group. By 1997, the control group has overtaken
the treatment group in terms of output rationalization.

By preventing the reallocation of

resources that takes place as a result of trade liberalization, therefore, antidumping duties
contribute to a reduction in aggregate productivity.
Section 6: Conclusions

Antidumping duties have become one of the primary forms of trade protection worldwide, and the large magnitudes of the duties imposed can dramatically alter trade flows. Yet
despite the growing importance of antidumping duties to international trade, there is little
understanding of their effects at the micro level. In addition to increasing our understanding of
an important trade policy, the study of antidumping duties can also provide new insights intosome of the best-known results in the literature examining the heterogeneous responses of firms
to trade liberalization.
Using a difference-in-difference framework, I compare outcomes at plants in thetreatment group that receives protection to those in the three control groups that did not. I findthat apparent increases in revenue productivity associated with antidumping protection are driven
primarily by increases in prices and mark-ups. Physical productivity actually falls among theprotected plants reporting output data in units of quantity. Protected plants are also less likely todrop protected products, although they are no less likely exit. Because antidumping protection
allows for the continued operation of low-productivity plants that might have otherwise dropped
the protected product, antidumping duties decrease the level of output rationalization, with lowproductivity plants expanding their market shares.
35

The results have several implications.

First, for empirical researchers, the results

underscore the importance of differentiating between changes in revenue productivity—which
may be driven by increases in prices and mark-ups—and changes in physical productivity.
Separating these two effects is particularly important in situations where changes in productivity
may be taking place concomitantly with changes in prices, as is the case with antidumping
duties. Second, for theoretical researchers, the results underscore the importance of thinking ofplants and firms as producers of multiple products. While antidumping duties had no effect on
the probability of plant exit, they had a clear impact on plants’ product mix. And finally, forpolicy-makers, the results suggest that antidumping protection does not offer a free lunch in theform of higher plant-level productivity. Offering antidumping protection comes at a cost that isincurred by consumers, in the form of higher prices.

36

References

Amiti, Mary and Jozef Konings (2007), “Trade Liberalization, Intermediate Inputs andProductivity: Evidence from Indonesia,” American Economic Review, 97, pp. 1611-1638.
Aw, B.Y., Chung, S. and Mark Roberts (2000), “Productivity and Turnover in the Export
Market: Micro-Level Evidence from Korea and Taiwan (China),” World Bank Economic

Review, 14, pp. 65-90
Bartelsman, Eric J., Randy A. Becker and Wayne B. Gray (2000) “NBER-CES Manufacturing
Industry Database,” NBER Technical Working Paper 205.
Bernard, Andrew and J. Bradford Jensen (1999), “Exceptional Exporter Performance: Cause,
Effect, or Both?,” Journal of International Economics, 47, pp. 1-25.
Bernard, Andrew, Jonathan Eaton, J. Bradford Jensen and Samuel Kortum (2003), “Plants andProductivity in International Trade,” American Economic Review, 93, pp. 1268-1290.
Bernard, Andrew, J. Bradford Jensen and Peter Schott (2006), “Trade Costs, Firms andProductivity,” Journal of Monetary Economics, 53, pp. 917-937.
Bernard, Andrew, Stephen Redding and Peter Schott (2006), “Multi-Product Firms and Trade
Liberalization,” mimeo.
Bernard, Andrew, Stephen Redding and Peter Schott (2009), “Multi-Product Firms and ProductSwitching,” American Economic Review, forthcoming.
Blonigen, Bruce and Jee-Hyeong Park (2004), “Dynamic Pricing in the Presence of Antidumping
Policy: Theory and Evidence,” American Economic Review, 94, pp.134-154.
Bown, Chad (2007), “Global Antidumping Database 3.0,” mimeo.

37

Caves, Douglas W., Laurits R. Christensen and W. Erwin Diewert (1982), “The Economic
Theory of Index Numbers and the Measurement of Input, Output and Productivity,”

Econometrica, 50(6), pp. 1393-1414.
Crowley, Meredith (2006), “Do Antidumping Duties and Safeguard Tariffs Open or Close
Technology Gaps?,” Journal of International Economics, 68, pp. 469-484.
Fernandes, Ana (2007), “Trade Policy, Trade Volumes and Plant-Level Productivity in
Colombian Manufacturing Industries,” Journal of International Economics, 71(1), pp.
52-71.
Feenstra, Robert, John Romalis and Peter Schott (2002), “U.S. Imports, Exports and Tariff
Data,” NBER Working Paper # 9387.
Goh, A.-T. (2000), “Opportunity Cost, Trade Policies and the Efficiency of Firms,” Journal of

Development Economics, 62, pp. 363–383.
Konings, Jozef and Hylke Vandenbussche (2008), “Heterogeneous Responses of Firms to Trade
Protection,” Journal of International Economics, 76, pp. 371-383.
Konings, Jozef and Hylke Vandenbussche (2005), “Antidumping Protection and Markups ofDomestic Firms: Evidence from Firm Level Data,” Journal of International Economics,
65, pp. 151-165.
Levinsohn, James and Amil Petrin (2003), “Estimating Production Functions Using
Intermediates to Control for Unobservables,” Review of Economic Studies, 70, pp. 317341.
Matsuyama, Kiminori (1990), “Perfect Equilibria in a Trade Liberalization Game,” American

Economic Review, 80(3), pp. 480-492.

38

Melitz, Marc J. (2003), “The Impact of Trade on Aggregate Industry Productivity and IntraIndustry Reallocations.” Econometrica, 71(6), pp. 1695-725.
Miyagiwa, Kaz and Yuka Ohno (1995), “Closing the Technology Gap Under Protection,”

American Economic Review, 85(4), pp. 755-770.
Miyagiwa, Kaz and Yuka Ohno (1999), “Credibility of Protection and Incentives to Innovate,”

International Economic Review, 40(1), pp. 143-163.
Pavcnik, Nina (2002), “Trade Liberalization, Exit and Productivity Improvements: Evidence
from Chilean Plants.” Review of Economic Studies, 69(1), pp. 245-76.
Pierce, Justin (2008), “Trade Liberalization, Productivity and Input Variety: Evidence from
Colombian Plants,” Georgetown University, mimeo, 2009.

Serti, Francesco and Chiara Tomasi (2007), “Self Selection and Post-Entry Effects of Exports.
Evidence from Italian Firms,” mimeo.
United States International Trade Commission (2006), “Import Injury Investigations: Case
Statistics: FY 1980-2005.”
Van Biesebroeck, Johannes (2004), “Robustness of Productivity Estimates,” National Bureau of
Economic Research Working Paper 10303.

39

Table 1: Antidumping Investigations by HTS Chapter, 1988-1996
HTS2
73
72
84
28
85
29
87
90
39
25
81
30
40
56
83
Other
Total

Description
Articles of Iron and SteelIron and Steel
Machinery
Inorganic Chemicals
Electrical Machinery
Organic Chemicals
Transportation Vehicles and Parts
Precision Instruments and Apparatus
Plastics and Articles Thereof
Plastering, Lime and Cement
Other Base Metals
Pharmaceutical Products
Rubber and Articles Thereof
Certain Textiles
Misc. Articles of Base Metal

Investigations
27
20
16
14
13
12
11
8
6
5
5
4
4
4
4
45
198

Notes: This table displays the number of antidumping
investigations by 2-digit Harmonized Tariff System Category.
Investigations involving products in more than one 2-digit HTS
category are counted in each relevant category.

40

Table 2: All Antidumping Cases, by Outcome and SIC2

41

Table 3: Plant-Level Observations, by SIC2

SIC2 Description
Food and Kindred Spirits
Textile Mill Products
ApparelPaper Products
Chemical Products
Rubber Products
Leather Products
Primary Metals
Fabricated Metals
Industrial Machinery
Electronic MachineryTransport Equipment
Measuring Instruments
Misc. Manufacturing

SIC2
20
22
23
26
28
30
32
33
34
35
36
37
38
39

Total Observations
Control Treatment
163
1,462
1,061
891
8,283
1,725
2,602
0
815
3,566
13,681
2,996
2,081
582
468
3,266
13,244
4,318
3,884
16,066
650
7,540
2,869
889
75
3,071
0
413

Total
1,625
1,952
10,0082,602
4,381
16,677
2,663
3,734
17,562
19,950
8,190
3,758
3,146
413

Observations With Quantity
SIC2 Control Treatment Total
20
132
1,096
1,228
22
757
415
1,172
23
928
532
1,460
26
1,065
0
1,065
28
77
652
729
30
170
14
184
32
451
396
847
33
*
1,971
*
34
1,038
500
1,538
35
180
314
494
36
91
35
126
37
723
*
*
38
25
*
*
39
0
88
88

Notes: This table reports the number of plant-level observations in the treatment group (applied andreceived protection) and control group (applied but did not receive protection), by 2-digit SIC (1987)
category. In addition, the table shows the number of plant-level observations where output data werereported in units of quantity by treatment status and SIC2. An asterisk (*) denotes a cell that was
suppressed to prevent the disclosure of confidential data.

42

Table 4: Summary Statistics by Treatment Group, Year

Year
1987
1987
1992
1992
1997
1997

Year
1987
1987
1992
1992
1997
1997

Treatment
0
1
0
1
0
1

Total
Sales
23,59623,437
26,25028,70333,23438,025

No.
Employees
142
165
122
149
119
146

CapitalIntensity
41
53
46
56
52
73

No.
Plants
15,007
14,59817,092
15,588
17,778
16,599Treatment
0
1
0
1
0
1

Revenue
TFP
0.14
0.14
0.11
0.19
0.11
0.29

Revenue
Labor
Prod.
4.68
4.63
4.72
4.72
4.80
4.89

Physical
TFP
-0.52
0.30
-0.02
-0.06
0.26
-0.12

Physical
Labor
Prod.
5.14
5.65
5.48
5.43
5.87
5.45

Qty. Treatment Effective
Share
Share
AD Rate
93%
70%
93%
68%
14%
93%
69%
92%
67%
13%
93%
69%
92%
67%
13%

Notes: This table reports summary statistics by year and treatment status. A treatment of zero (0)denotes the control group and a treatment of one (1) denotes the treatment group. Capital intensity
is the book value of capital divided by the number of employees.

43Table 5: Antidumping Duties and Revenue Productivity – Within Product-Group Estimators
Termination Control Group
Treatment*Post

TFP
0.06**
0.03

LP
0.05**
0.02

TFP
0.06**
0.03

LP
0.05**
0.02

Yes
Yes
No
96,662
0.666

Yes
Yes
No
96,662
0.309

Yes
Yes
Yes
96,662
0.668

Yes
Yes
Yes
96,662
0.313

Post*Rate
Year FE
Product FE
State FE
Observations
R-Squared

TFP
0.08**
0.03-0.001
0.002
Yes
Yes
No
96,6620.666

LP0.06**
0.02-0.001
0.001
Yes
Yes
No
96,662
0.39

TFP
0.08**
0.03-0.001
0.002
Yes
Yes
Yes
96,6620.668

LP0.06**
0.02-0.001
0.001
Yes
Yes
Yes
96,662
0.313

Notes: This table summarizes OLS regression coefficients of plant-level total factor
productivity (TFP) and labor productivity (LP) on the difference-in-difference interaction
term "Treatment*Post" and the effective duty rate interaction term "Post*Rate." Robust
standard errors are reported below each coefficient after adjustment for clustering at the
product-level. ***, ** and * represent statistical significance at the 1%, 5% and 10%
levels, respectively.

Table 5A: Antidumping Duties and Revenue Productivity – Within Product-Group Estimators
Matched Control Groups
Matched Control Group 1
Matched Control Group 2
TFP
LP
TFP
LP
TFP
LP
TFP
LP
Treatment*Post 0.10*** 0.10*** 0.11*** 0.11*** 0.08** 0.06*** 0.10*** 0.07***
0.04
0.02
0.04
0.03
0.03
0.02
0.03
0.02
Post*Rate-0.001-0.001-0.0011 -0.001
0.003
0.002
0.00220.001
Year FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Product FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
131,730 131,730 131,730 131,730 84,857 84,857
84,85784,857
R-Squared
0.797
0.418
0.797
0.418
0.66
0.317
0.66
0.317

Notes: This table summarizes OLS regression coefficients of plant-level total factor productivity(TFP) and labor productivity (LP) on the difference-in-difference interaction term
"Treatment*Post" and the effective duty rate interaction term "Post*Rate." Robust standard errors
are reported below each coefficient after adjustment for clustering at the product-level. ***, **and * represent statistical significance at the 1%, 5% and 10% levels, respectively.

44

Table 6: Antidumping Duties and Revenue Productivity – Within Plant Estimators
Termination Control Group
TFP
LP
TFP
LP
Treatment*Post 0.0487*** 0.0256*** 0.0632*** 0.0312***0.01040.00820.01240.0098
Post*Rate-0.0011**-0.0004
0.00050.0004
Year FE
Yes
Yes
Yes
Yes
Plant FE
Yes
Yes
Yes
Yes
Observations
27,69927,69927,69927,699R-Squared
0.909
0.874
0.99
0.874

Notes: This table summarizes OLS regression coefficients ofplant-level total factor productivity (TFP) and labor productivity(LP) on the difference-in-difference interaction term
"Treatment*Post" and the effective duty rate interaction term
"Post*Rate." Robust standard errors are reported below eachcoefficient after adjustment for clustering at the plant-level. ***,** and * represent statistical significance at the 1%, 5% and 10%
levels, respectively.
Table 6A: Antidumping Duties and Revenue Productivity – Within Plant Estimators
Matched Control Groups
Matched Control Group 1
Treatment*Post

Matched Control Group 2

TFP

LP

TFP

LP

TFP

LP

TFP

LP

0.08***

0.08***

0.10***

0.09***

0.04***

0.02**

0.06***

0.03**

0.001

0.01

0.01

0.01

0.01

0.008

0.01

0.01

-0.004**-0.0001-0.001**-0.001

Post*Rate
Year FE

Yes

Yes

0.001
Yes

0.0005
Yes

Yes

Yes

0.005
Yes

0.0004
Yes

Plant FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

40,37440,37440,37440,37424,471

24,471

24,471

24,471

R-Squared

0.945

0.895

0.945

0.895

0.913

0.874

0.913

0.874

Notes: This table summarizes OLS regression coefficients of plant-level total factor productivity (TFP) andlabor productivity (LP) on the difference-in-difference interaction term "Treatment*Post" and the effective
duty rate interaction term "Post*Rate." Robust standard errors are reported below each coefficient after
adjustment for clustering at the product-level. ***, ** and * represent statistical significance at the 1%, 5%
and 10% levels, respectively.

45

Table 7: Antidumping Duties and Physical Productivity – Within Product-Group Estimators
Termination Control Group
Physical Productivity Measures
TFPQ
LPQ
TFPQ
LPQ
Treatment*Post -0.39* -0.43**
0.22
0.16
0.22
0.21
0.18
0.17
Post*Rate-0.03*** -0.03***
0.005
0.005
Year FE
Yes
Yes
Yes
Yes
Product FE
Yes
Yes
Yes
Yes
Observations
11,526 11,526
11,526
11,526
R-Squared
0.643
0.62
0.646
0.623

Revenue Productivity Measures
TFP
LP
TFP
LP
0.005
-0.03
0.05
0.004
0.070
0.03
0.07
0.042
-0.002 -0.002
0.002 0.002
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
11,526 11,526 11,526 11,526
0.868
0.451
0.868 0.451

Notes: This table summarizes OLS regression coefficients of plant-level productivity on the
difference-in-difference interaction term "Treatment*Post" and the effective duty rate interaction
term "Post*Rate." The first four columns show regression results using measures of physicalproductivity as the dependent variable, for the subset of plants reporting quantity-based output
data. The last four columns show regression results using measures of revenue productivity as
the dependent variable, for the same subset of plants. TFPQ denotes physical total-factorproductivity and LPQ denotes physical labor productivity. Robust standard errors are reported
below each coefficient after adjustment for clustering at the product-level. ***, ** and *represent statistical significance at the 1%, 5% and 10% levels, respectively.

46

Table 7A: Antidumping Duties and Physical Productivity – Within Product-Group Estimators
Matched Control Groups

Treatment*Post
Post*Rate
Year FE
Product FE
Observations
R-Squared

Matched Control Group 1
TFPQ
LPQ
TFPQ
LPQ
-0.43* -0.42*
0.12
0.09
0.26
0.23
0.22
0.21
-0.03** -0.03**
0.01
0.01
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
7,220
7,220
7,220
7,220
0.647
0.612
0.651
0.616

Matched Control Group 2
TFPQ
LPQ
TFPQ
LPQ
-0.40* -0.47**
0.22
0.12
0.24
0.21
0.19
0.18
-0.03*** -0.03***
0.005
0.005
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
10,086
10,086
10,086
10,086
0.639
0.611
0.643
0.614

Notes: These tables summarize OLS regression coefficients of plant-level physical productivity on the
difference-in-difference interaction term "Treatment*Post" and the effective duty rate interaction term
"Post*Rate." TFPQ denotes physical total-factor-productivity and LPQ denotes physical laborproductivity. Robust standard errors are reported below each coefficient after adjustment forclustering at the product-level. ***, ** and * represent statistical significance at the 1%, 5% and 10%
levels, respectively.

47

Table 8: Antidumping Duties and Physical Productivity – Within Plant Estimators
Termination Control Group
Physical Productivity Measures
Revenue Productivity Measures
TFPQ
LPQ
TFPQ
LPQ
TFP
LP
TFP
LP
Treatment*Post -0.29** -0.20* 0.35**
0.37**
0.01
0.03
0.07
0.01
0.11
0.11
0.16
0.15
0.03
0.03
0.04
0.04
Post*Rate-0.04*** -0.03***-0.003** 0.001
0.01
0.01
0.002
0.001
Year FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Plant FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
2,550
2,550
2,550
2,550
2,550 2,550
2,550
2,550
R-Squared
0.896
0.904
0.91
0.98
0.899 0.906
0.899
0.906
Notes: These tables summarize OLS regression coefficients of plant-level productivity on the
difference-in-difference interaction term "Treatment*Post" and the effective duty rateinteraction term "Post*Rate." The first four columns of each table show regression results
using measures of physical productivity as the dependent variable, for the subset of plants
reporting quantity-based output data. The last four columns show regression results using
measures of revenue productivity as the dependent variable, for the same subset of plants.
TFPQ denotes physical total-factor-productivity and LPQ denotes physical labor productivity.
Robust standard errors are reported below each coefficient after adjustment for clustering at
the plant-level. ***, ** and * represent statistical significance at the 1%, 5% and 10% levels,
respectively.

48

Table 8A: Antidumping Duties and Physical Productivity – Within Plant Estimators
Matched Control Groups
Matched Control Group 1
Treatment*Post

Matched Control Group 2

TFPQ

LPQ

TFPQ

LPQ

TFPQ

LPQ

TFPQ

LPQ

-0.60***-0.39***

0.02

0.17

-0.36**-0.31**

0.27

0.26

0.15

0.14

0.17

0.17

0.14

0.14

0.18

0.17

-0.04***-0.03***-0.04***-0.03***

0.01

0.01

0.01

0.01

Post*Rate
Year FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Plant FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

2,013

2,013

2,013

2,013

2,268

2,268

2,268

2,268

R-Squared

0.911

0.919

0.917

0.923

0.911

0.919

0.917

0.924

Notes: These tables summarize OLS regression coefficients of plant-level physical productivity on the difference-indifference interaction term "Treatment*Post" and the effective duty rate interaction term "Post*Rate." TFPQ
denotes physical total-factor-productivity and LPQ denotes physical labor productivity. Robust standard errors arereported below each coefficient after adjustment for clustering at the product-level. ***, ** and * represent
statistical significance at the 1%, 5% and 10% levels, respectively.

49

Table 9: The Effect of Antidumping Duties on Plant-Level Prices
Termination Control Group
Price
Price
Price
Price
Treatment*Post 0.42** -0.16 0.27** -0.35**
0.20
0.17
0.11
0.15
Post*Rate
0.03***
0.04***
0.00
0.01
Year FE
Yes
Yes
Yes
Yes
Product FE
Yes
Yes
No
No
Plant FE
No
No
Yes
Yes
Observations
11,526 11,526 2,550
2,550
R-Squared
0.66
0.66
0.90
0.91
Table 9A: The Effect of Antidumping Duties on Plant-Level Prices
Matched Control Groups
Matched Control 1
Matched Control 2
Price
Price
Price
Price
Price
Price
Price
Price
Treatment*Post 0.38* -0.15 0.36** -0.24 0.44** -0.14 0.32** -0.30
0.21
0.20
0.15
0.18
0.20
0.17
0.14
0.17
Post*Rate
0.03***
0.04***
0.03***
0.037**
0.01
0.01
0.00
0.01
Year FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Product FE
Yes
Yes
No
No
Yes
Yes
No
No
Plant FE
No
No
Yes
Yes
No
No
Yes
Yes
Observations
7,220 7,220
2,013
2,013 10,086 10,086 2,268
2,268
R-Squared
0.63
0.63
0.90
0.91
0.63
0.64
0.91
0.91

Notes: These tables summarize OLS regression coefficients of plant-level price on the
difference-in-difference interaction term "Treatment*Post" and the effective duty rateinteraction term "Post*Rate." Robust standard errors are reported below each coefficient
after adjustment for clustering at the plant-level. ***, ** and * represent statisticalsignificance at the 1%, 5% and 10% levels, respectively. Results with plant fixed effects
are based on the balanced sub-sample of plants that were active in all three census years.

50

Table 10: The Effect of Antidumping Duties on Mark-Ups
Termination Control Group
P/ATC P/ATC P/ATC P/ATC
Treatment*Post 0.06** 0.037 0.07*** 0.048
0.024 0.029
0.024
0.032
Post*Rate
0.001
0.002
0.001
0.002
Year FE
Yes
Yes
Yes
Yes
Product FE
Yes
Yes
No
No
Plant FE
No
No
Yes
Yes
Observations
11,526 11,526 2,550
2,550
R-Squared
0.30
0.33
0.69
0.69

Table 10A: The Effect of Antidumping Duties on Mark-Ups
Termination Control Group
Matched Control 1
Matched Control 2
P/ATC P/ATC P/ATC P/ATC P/ATC P/ATC P/ATC P/ATC
Treatment*Post 0.043 0.024 0.032 0.006 0.06**
0.040
0.06** 0.034
0.034 0.038 0.034 0.037
0.027
0.032
0.028
0.033
Post*Rate
0.001*
0.002
0.0012*0.002
0.00060.002
0.00070.002
Year FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Product FE
Yes
Yes
No
No
Yes
Yes
No
No
Plant FE
No
No
Yes
Yes
No
No
Yes
Yes
Observations
7,220 7,220 2,013 2,013 10,086 10,086
2,268
2,268
R-Squared
0.32
0.32
0.70
0.70
0.31
0.37
0.70
0.70
Notes: These tables summarize OLS regression coefficients of plant-level mark-up over
ATC on the difference-in-difference interaction term "Treatment*Post" and the effective
duty rate interaction term "Post*Rate." Robust standard errors are reported below eachcoefficient after adjustment for clustering at the plant-level. ***, ** and * represent
statistical significance at the 1%, 5% and 10% levels, respectively.

51

Table 11: Antidumping Duties and the Probability of Product-Dropping
Termination Control Group
Drop
Drop
Drop
Drop
Treatment*Post-0.060*** -0.062***-0.023-0.025
0.017
0.016
0.024
0.022
Post*Rate-0.003** -0.003**
0.001
0.001
Product Shipments-0.076***-0.076***
0.002
0.002
Product Tenure-0.119***-0.119***
0.012
0.012
Year FE
Yes
Yes
Yes
Yes
Product FE
Yes
Yes
Yes
Yes
Observations
46,742
46,742
46,742
46,742
R-Squared
0.118
0.204
0.118
0.205

52

Table 11A: Antidumping Duties and the Probability of Product-Dropping
Matched Control Group 1

Treatment*Post

Drop-0.0515***
0.013

Drop
-0.0445***
0.013

Yes
Yes
98,1660.095-0.0475***
0.007-0.1484***
0.008
Yes
Yes
98,166
0.148

Post*Rate
Product Shipments
Product Tenure
Year FE
Product FE
Observations
R-Squared

Drop-0.0150.022-0.0028**
0.001

Yes
Yes
98,1660.095

Drop-0.0070.021-0.0028**0.001-0.0475***
0.007-0.1485***
0.008
Yes
Yes
98,1660.148

Notes: This table summarizes OLS regression coefficients of a binary variableindicating product-dropping (Drop) on the difference-in-difference interaction term
"Treatment*Post" and the effective duty rate interaction term "Post*Rate." Robust
standard errors are reported below each coefficient after adjustment for clustering at
the product-level. ***, ** and * represent statistical significance at the 1%, 5%
and 10% levels, respectively.

53

Table 11B: Antidumping Duties and the Probability of Product-Dropping
Matched Control Group 2Treatment*Post

Drop-0.0383**
0.015

Drop
-0.0397***
0.014

Yes
Yes
41,2890.106-0.0766***
0.003-0.1257***
0.013
Yes
Yes
41,2890.197

Post*Rate
Product Shipments
Product Tenure
Year FE
Product FE
Observations
R-Squared

Drop-0.001
0.023-0.0028**
0.001

Yes
Yes
41,2890.106

Drop-0.0030.020-0.0028**0.001-0.0766***
0.003-0.1258***
0.013
Yes
Yes
41,2890.197

Notes: This table summarizes OLS regression coefficients of a binary variableindicating product-dropping (Drop) on the difference-in-difference interaction
term "Treatment*Post" and the effective duty rate interaction term "Post*Rate."
Robust standard errors are reported below each coefficient after adjustment forclustering at the product-level. ***, ** and * represent statistical significanceat the 1%, 5% and 10% levels, respectively.

54

Table 12: Antidumping Duties and the Probability of Plant-Level Exit
Termination Control Group

Treatment*Post

Exit-0.0018
0.0119Post*Rate

Exit
0.01690.0137-0.0013**0.0006

No. Employees
Plant Age
Capital Intensity
Avg. Wage
Multi-Unit
Multi-ProductYear FE
Product FE
Observations
R-SquaredYes
Yes
62,285
0.059

Yes
Yes
62,2850.059

Exit-0.00240.0119-0.092***0.0031-0.0022***0.0004-0.0168***
0.0025-0.0746***
0.0077
0.0934***0.0072-0.0181***0.0043
Yes
Yes
62,2850.116

Exit
0.00290.015-0.0004
0.0007-0.0919***0.0031-0.0022***0.0004-0.0168***
0.0025-0.0746***
0.0077
0.0934***0.0072-0.0181***0.0043
Yes
Yes
62,2850.116

Notes: This table summarizes OLS regression coefficients of a binary
variable indicating plant exit (Exit) on the difference-in-difference
interaction term "Treatment*Post" and the effective duty rateinteraction term "Post*Rate." Robust standard errors are reported
below each coefficient after adjustment for clustering at the productlevel. ***, ** and * represent statistical significance at the 1%, 5%
and 10% levels, respectively. Control variables are measured at theplant-level. Capital intensity is a plant's book value of capital divided
by the number of employees. Average wage is the average annual
wage paid to production workers. Multi-unit and multi-product arebinary variables that equal one if the plant is part of a multi-unit firm ora producer of multiple products, respectively.

55

Table 12A: Antidumping Duties and the Probability of Plant-Level Exit
Matched Control Group 1

Treatment*Post

Exit-0.01850.0145

Post*Rate

Exit-0.00240.0176-0.00120.0009No. Employees
Plant Age
Capital Intensity
Avg. Wage
Multi-Unit
Multi-ProductYear FE
Product FE
Observations
R-SquaredYes
Yes
85,617
0.068

Yes
Yes
85,6170.069

Exit-0.0186
0.0139-0.1018***0.0055-0.0026***0.0005-0.0147***
0.0027-0.0696***0.01050.0779***0.009-0.024***0.0051
Yes
Yes
85,6170.132

Exit
0.00340.0154-0.0017**0.0007-0.1018***0.0055-0.0026***0.0005-0.0147***
0.0027-0.0697***0.01050.0779***0.009-0.024***0.0051
Yes
Yes
85,6170.132

Notes: This table summarizes OLS regression coefficients of a binary
variable indicating plant exit (Exit) on the difference-in-difference
interaction term "Treatment*Post" and the effective duty rateinteraction term "Post*Rate." Robust standard errors are reported
below each coefficient after adjustment for clustering at the productlevel. ***, ** and * represent statistical significance at the 1%, 5%
and 10% levels, respectively. Control variables are measured at theplant-level. Capital intensity is a plant's book value of capital
divided by the number of employees. Average wage is the averageannual wage paid to production workers. Multi-unit and multiproduct are binary variables that equal one if the plant is part of amulti-unit firm or a producer of multiple products, respectively.

56

Table 12B: Antidumping Duties and the Probability of Plant-Level Exit
Matched Control Group 2Treatment*Post

Exit-0.0038
0.012

Post*Rate

Exit
0.01620.0135-0.0014**0.0006

No. Employees
Plant Age
Capital Intensity
Avg. Wage
Multi-Unit
Multi-ProductYear FE
Product FE
Observations
R-Squared

Yes
Yes
53,741
0.046

Yes
Yes
53,741
0.046

Exit-0.00220.012-0.0926***
0.0032-0.0021***0.0004-0.016***0.0027-0.0714***0.0077
0.0988***0.0077-0.018***0.0042
Yes
Yes
53,741
0.102

Exit
0.00380.0154-0.0004
0.0007-0.0926***
0.0032-0.0021***0.0004-0.016***0.0027-0.0715***0.0077
0.0988***0.0077-0.0179***
0.0042
Yes
Yes
53,741
0.102

Notes: This table summarizes OLS regression coefficients of a binary
variable indicating plant exit (Exit) on the difference-in-difference
interaction term "Treatment*Post" and the effective duty rateinteraction term "Post*Rate." Robust standard errors are reported
below each coefficient after adjustment for clustering at the productlevel. ***, ** and * represent statistical significance at the 1%, 5%
and 10% levels, respectively. Control variables are measured at theplant-level. Capital intensity is a plant's book value of capital divided
by the number of employees. Average wage is the average annual
wage paid to production workers. Multi-unit and multi-product arebinary variables that equal one if the plant is part of a multi-unit firm ora producer of multiple products, respectively.

57

Table 13: Relative Productivity of Product-Droppers
TFP
LP
Drop
-0.0418*** -0.0830***
0.01400.0143
Year FE
Yes
Yes
Product FE
Yes
Yes
Observations
44,38244,382
R-Squared
0.684
0.371

Notes: This table summarizes OLS regression coefficients of revenue-based totalfactor productivity (TFP) and labor productivity (LP) on a binary variableindicating whether a plant dropped an investigated product. Robust standard
errors are reported below each coefficient after adjustment for clustering at the
product-level. ***, ** and * represent statistical significance at the 1%, 5% and
10% levels, respectively.

58

Table 14: Antidumping Duties and Output Rationalization
Year Post Treatment Rationalization Aggregate
1987
0
0
0.100
1.56
1992
0
0
0.075
1.53
1992
1
0
0.187
2.03
1997
1
0
0.171
1.81
1987
0
1
0.163
1.07
1992
0
1
0.176
1.33
1992
1
1
0.210
1.08
1997
1
1
0.166
1.15

Mean
1.46
1.45
1.85
1.64
0.91
1.15
0.87
0.98

Notes: This table reports a decomposition of revenue-based
total factor productivity by year, post-treatment indicator(Post) and treatment status (Treatment). "Rationalization" is aterm measuring the level of output rationalization, as describedearlier. "Aggregate" is aggregate productivity. "Mean" ismean plant-level total factor productivity. "Treatment" equals
1 for plants that applied for and received protection and 0 for
plants that applied for but did not receive protection. "Post"equals 1 for plants that had already been involved in an
antidumping investigation in time t and 0 for plants that had
not yet been involved in an investigation.

59

Table A.1 Results of Multinomial Logit and Logit Models for Matched Control Groups

Matched Control Group 1

Lagged Import
Penetrationln(Lagged Employment)ln(Labor Productivity)
Real GDP Growth
Price Growth
Number of Observations
Pseudo-R Squared
Estimation Technique

Determinants ofProtection
Given Filing

Determinants ofTermination
Given Filing0.246***
0.061
0.387***
0.059
0.210**
0.101
0.044
0.049
-0.053***
0.013
3,423
0.03
Multinomial
Logit-0.2020.133
0.326***
0.058-0.355***
0.109
0.003
0.045
-0.017
0.014
3,423
0.03
Multinomial
Logit

MatchedControlGroup 2
Probabilityof Protection
0.909***
0.280
0.072
0.091
0.740***
0.163
0.024
0.066
-0.029*
0.017
619
0.051
Logit

Notes: This table summarizes estimation results for the multinomial logit andlogit models used to generate the two matched control groups. In themultinomial logit model, the dependent variable takes a value of 1 if anindustry never filed for protection, 2 if it filed but was turned down forprotection and 3 if it applied for and received protection. In the logit model,
the dependent variable takes a value of 1 if an industry applied for andreceived protection and 0 if it applied for, but did not receive protection.
Independent variables are at the industry-year-level. ***, ** and * represent
statistical significance at the 1%, 5% and 10% levels, respectively.

60