Policy Research Working Paper 5223

Self-Enforcing Trade Agreements

Evidence from Time-Varying Trade Policy

Chad P. Bown Meredith A. Crowley

The World BankDevelopment Research GroupTrade and Integration TeamMarch 2010

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Abstract

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Policy Research Working Paper 5223

This paper estimates a model of a government making trade policy adjustments under a self-enforcing trade agreement in the presence of economic shocks. The empirical model is motivated by the formal theories of cooperative trade agreements. The authors find evidence that United States’ use of its antidumping policy during 1997–2006 is consistent with increases in time-varying “cooperative” tariffs, where the likelihood of antidumping is increasing in the size of unexpected import surges, decreasing in the volatility of imports, and decreasing in the elasticities of import demand and export supply.

This paper—a product of the Trade and Integration Team, Development Research Group—is part of a larger effort in the department to evaluate the impact that international institutions have on the market access. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cbown@worldbank.org.

The analysis finds additional support for the theory that some US antidumping use is consistent with cooperative behavior through a second empirical examination of how trading partners responded to these new US tariffs. Even after controlling for factors such as the expected cost and benefit to filing a WTO dispute or engaging in antidumping retaliation, the analysis find that trading partners are less likely to challenge such “cooperative” US antidumping tariffs that were imposed under terms-of-trade pressure suggested by the theory.

1 Introduction

For decades, theorists of international commercial policy have demonstrated how purely economic

incentives affect government tariffs and the decision to enter into trade agreements.1 Long thought

to be more an intellectual curiosity than a determinant of how governments actually set their trade

policies in practice, the terms-of-trade theory has recently become a focus of considerable empirical

research.

Two important contributions document how trade policy formation is determined by economic

incentives in addition to the more generally accepted political economy or income redistribution

motives. Broda, Limao and Weinstein (2008) provide two pieces of evidence broadly consistent

with the idea that countries exploit their market power in trade. First, they estimate disaggregate

foreign export supply elasticities and find that countries that are not members of the World Trade

Organization (WTO) systematically set higher tariffs on goods that are supplied inelastically. Sec-

ond, for a WTO member like the US, they find that trade barriers on products not covered by

the WTO agreement are significantly higher when the US has more market power. In a separate

setting, Bagwell and Staiger (2009) focus on a set of countries newly acceding to the WTO between

1995 and 2005 in order to examine the role of market power in negotiating tariffs for new WTO

members. They find evidence consistent with the theory of the importance of the terms-of-trade

effect; the tariff to which a country negotiates is further below its non-cooperative level, the larger

is its non-cooperative import volume.

The current paper contributes to this empirical literature by estimating whether similar eco-

nomic incentives explain government use of time-varying trade policies. In particular, we study

how countries adjust their trade policies over time in response to economic shocks. The earlier

empirical literature has examined the cross-sectional variation in a country’s tariff level (Broda,

Limao and Weinstein, 2008) or the magnitude of a country’s tariff reduction when moving from

1Irwin (1996) provides a full account of the intellectual history of the terms-of-trade (a.k.a. optimal tariff) theory,

which he finds dates back at least to Robert Torrens in the early nineteenth century. For more recent treatments, see

the seminal work of Johnson (1953-1954) and for integration into the theory of trade agreements, see Bagwell and

Staiger (1990, 1999).

1

a non-cooperative policy to a trade agreement (Bagwell and Staiger, 2009). We focus on a WTO

member’s time-varying tariff increases in the face of trade volume shocks whose influence may vary

due to heterogeneity across import demand and export supply elasticities.

To give our exercise context, it is important to note that while trade agreements like the WTO

do require member countries to accept an upper limit on their permissible applied tariffs, there

are exceptions to WTO rules which allow governments to raise their import-restricting policies

above those upper tariff limits under certain conditions. The most prevalent exception used by

industrialized countries and a number of middle income developing countries is the antidumping

(AD) import-restricting policy, which is the policy forming the basis for our empirical analysis.

One major contribution of this paper is to provide a first formal examination of the empirical

relationship between the terms-of-trade theory and antidumping policy.

The basic intuition behind our empirical approach follows the theoretical model of Bagwell

and Staiger (1990), a repeated game in which two countries trade in the presence of unanticipated

trade volume shocks. Bagwell and Staiger model a self-enforcing trade agreement as the set of

cooperative tariffs that countries can support over time.2 They show that the incentive for a

country participating in such a trade agreement to raise its tariff is greater when import volumes

are larger than expected. In the presence of a positive import volume shock, a self-enforcing

agreement to maintain low tariffs cannot be sustained in sectors with low import demand or export

supply elasticities - either the cooperative tariff supported by the trade agreement will rise or one

country will unilaterally defect from the agreement and impose its higher, noncooperative tariff.

We empirically evaluate the Bagwell and Staiger (1990) model’s ability to explain changes to

US trade policy over the 1997-2006 period. In particular, we examine the extent to which US

antidumping can be understood as an increase in a “cooperative” trade policy - i.e., increasing the

tariff above a trade agreement’s contractual level in order to manage import pressure so as to prevent

the country from permanently defecting from the agreement. We find evidence from this period

2To our knowledge, the only attempt to test implications of the Bagwell and Staiger (1990) approach is Prusa

and Skeath (2002), which is much different from the exercise undertaken here, especially in that they do not look to

formally model the impact of terms of trade motives for antidumping use.

2

that US antidumping use is consistent with increases in time-varying cooperative tariffs, where the

likelihood of antidumping use is increasing in the size of unexpected import surges, decreasing in the

volatility of imports, and decreasing in the elasticities of import demand and export supply. We thus

provide new evidence that purely economic incentives are sizable determinants of US antidumping

use, even after controlling for other political-economic determinants that have been the traditional

focus of research. While a substantial literature examines a country’s use of antidumping policies in

response to macro-economic or industry-level economic fluctuations (e.g., Finger, Hall and Nelson,

1982; Feinberg, 1989; Knetter and Prusa, 2003; Irwin, 2005; Crowley, 2008), ours is the first

to examine the relationship among time-varying trade policies, bilateral trade flows, micro trade

elasticities, and characteristics of the importing country’s domestic industries based on a theoretical

framework in which countries participate in a self-enforcing trade agreement.

While our estimates of US antidumping use are consistent with the influence of terms-of-trade

motives in a cooperative trade agreement, we pause at this stage to more carefully interpret these

results. First, we do not claim that the US imposes antidumping tariffs to take advantage of terms-

of-trade gains, and we also do not claim that US antidumping duties are set as optimal tariffs. To

better understand antidumping use as a theoretically-motivated cooperative tariff increase, consider

the potential trade policy alternative were a similar economic shock to hit a country signed on to

a trade agreement that did not provide access to a policy like antidumping. Because economic

shocks to trade volumes may increase the incentive for governments to defect from the agreement

and impose noncooperative (Nash) tariffs, we interpret some US antidumping use as a “necessary

evil” that prevents the more draconian defection outcome and a potential systemic breakdown of

the agreement.3

Do trading partners view US use of antidumping as such a necessary evil that is part of a

cooperative, self-enforcing trade agreement? In the second half of the paper, we specifically address

3This is consistent with the cooperative tariff interpretation of Bagwell and Staiger (1990, p. 780, emphasis

added) which states “Countries can cooperatively utilize protection during periods of exceptionally high trade volume

to mitigate the incentive of any country to unilaterally defect, and in so doing can avoid reversion to the Nash

equilibrium. Thus, surges in the underlying trade volume lead to periods of “special” protection as countries attempt

to maintain some level of international cooperation.”

3

this question. In particular, suppose under the WTO regime that antidumping tariffs arise in sectors

in which import demand and export supply elasticities are low and import volumes are unexpectedly

high; i.e., where the use of antidumping policy is consistent with a cooperative increase in import

protection to prevent defection from the agreement. How do the adversely affected foreign exporters

respond to US imposition of new antidumping tariffs?

The raw and anecdotal data on WTO trading partners’ responses to US imposing new an-

tidumping tariffs suggests that foreign countries certainly do not always view such tariff increases

as cooperative. Out of roughly 130 US antidumping tariffs imposed between 1997 and 2006, trading

partners formally challeged 27 for removal at WTO dispute settlement (Bown, 2009a).4 Further-

more, trading partners have responded with dozens of other “extra-WTO” challenges to new US

AD tariff increases with their own antidumping retaliation against US exports in the same sector.

The second half of the paper exploits the substantial variation in the foreign trading partner

response to the imposed US AD tariffs. The Bagwell and Staiger (1990) theory suggests that the

response of WTO members to a new US antidumping tariff may vary according to the likelihood

that it was imposed for “cooperative” reasons (motivated by economic shocks). More formally,

our second step is to develop an empirical model to examine the consistency of the data with this

interpretation of foreign challenges to US antidumping tariff increases between 1997-2006. Our

results indicate that foreign trading partners are indeed less willing to challenge US antidumping

tariff increases that are associated with the economic shocks that are significant determinants of the

US trade policy changes. Our estimates indicate that the economic importance of this relationship

is quite sizable, and it holds even once we introduce other control variables that capture the costs

and benefits to the trading partner of WTO dispute settlement use, as motivated, for example,

by the Maggi and Staiger (2008) theory of WTO dispute settlement.5 These results are robust

to extensions of the model in which we extend the trading partner’s policy response action space

4While challenging an imposed AD tariff is quite a common subject for WTO trade disputes, the frequency of

challenges to US tariffs is much larger than the instances facing any other WTO member country.5The second part of the paper fits into a related empirical literature examining determinants of WTO dispute set-

tlement use. Consistent with the results we provide, much of the systematic differences can be explained by expected

political-economic determinants. See, for example, Horn, Mavroidis and Nordstrom (2005), Bown (2004a,b; 2005a,b)

and Busch, Reinhardt, and Shaffer (2008). Nevertheless, one shortcoming of this literature that the current paper

4

to include an additional “extra-WTO” challenge to the US antidumping tariff increase via the

country’s own antidumping retaliation against US exporters in the same sector.

The rest of this paper proceeds as follows. Section 2 briefly reviews the Bagwell and Staiger

(1990) theory that we use to motivate antidumping tariffs modeled as a trade policy change in

a cooperative agreement. There we highlight the theoretical predictions regarding how economic

shocks to trade volumes, in the presence of low export supply and import demand elasticities, af-

fect time-varying trade policies. Section 2 also introduces our empirical model of US antidumping

formation and a discussion of the underlying data used in this portion of the estimation. Section

3 presents our first estimates of the relationship between the terms-of-trade theory and US use of

antidumping over the 1997-2006 period. In section 4 we extend the analysis by examining determi-

nants of whether WTO trading partners view US AD tariffs as acts of “cooperative” adjustment

to trade policy. There we estimate a model of foreign trading partners challenging US AD both

inside and outside the WTO. Finally, section 5 concludes.

2 A Theoretical and Empirical Model of Antidumping Tariffs un-

der a Cooperative Trade Agreement

2.1 The Bagwell and Staiger (1990) theory

Bagwell and Staiger (1990) develop a model in which two countries play a repeated game char-

acterized by a terms-of-trade driven prisoner’s dilemma.6 These countries can support a welfare-

improving equilibrium of lower “cooperative” tariffs with the threat of infinite reversion to the Nash

equilibrium of the stage game. Bagwell and Staiger showed that the lowest cooperative tariff that

can be supported at any time fluctuates in response to unexpected changes in import volumes: in

response to a positive import volume shock, the lowest cooperative tariff that can be supported

increases. Because the importing country’s terms-of-trade gains from deviating from the agreement

ultimately seeks to remediate is that the formation of the policies that are actually challenged may be endogenously

affected by the dispute settlement process.6Bagwell and Staiger (2003) consider additional theoretical extensions to this model.

5

are higher when import volumes are larger, it becomes infeasible to support the tariff originally

negotiated under the trade agreement. In these circumstances, the cooperative tariff supported by

the trade agreement rises for the duration of the import surge.

In developing an empirical model of US antidumping tariff formation within the framework

of US participation in the WTO, we are guided by the predictions of the Bagwell and Staiger

model. Start from the tariffs in the cooperative trade agreement equilibrium. Then according to

the Bagwell and Staiger (1990) model’s equation (28), the incentive to raise tariffs is increasing in

the size of the import volume shock and is decreasing in the export supply and import demand

elasticities.

The model thus provides an explanation for intertemporal and cross-sectional variation in trade

policy changes by a member of the trade agreement that we ultimately take to the data. With

regard to intertemporal variation, a given sector may have low export supply and import demand

elasticities, but the model of self-enforcing trade agreements predicts that we should only observe in-

creases in the tariff when the volume of imports in that sector rises unexpectedly. Cross-sectionally,

if two sectors simultaneously experience unexpected import surges of the same size, the model pre-

dicts that the tariff increase will be larger in the sector with less elastic export supply and import

demand. Intuitively, when export supply is more inelastic, the terms of trade gain from a tariff is

larger. When import demand is less elastic, the domestic efficiency costs of the tariff are smaller.

Furthermore, temporary increases in trade protection will be larger when import surges are more

“unusual.” Equations (19) and (20) of Bagwell and Staiger (1990) together imply that the magni-

tude of the tariff increase is greater for sectors in which import surges are uncommon. For import

surges of the same size in two different sectors, the magnitude of the tariff increase will be larger

in the sector with the lower variance of imports.

To investigate whether the US uses its AD tariff formation policy to respond to these economic

shocks, we use Bagwell and Staiger (1990) to develop the following expression for changes in its

tariff.7 Consider an increase to the “cooperative” US tariff, given by the following expression:

7In particular, see equations (19), (20), and (28).

6

∆τikt = f( 1

σMik(ηxk + ηmk)

[

Mikt − E(Mikt)]

)

(1)

where ∆τikt is the change in the tariff against imports from i in industry k, σMik is the standard

deviation of imports from country i in industry k, ηxk is the elasticity of export supply facing the

US (importer) in industry k, ηmk is the elasticity of US import demand in industry k, Mikt is

imports from country i in industry k at time t, and E(Mikt) is the expected value of imports from

country i in industry k at time t.

To summarize the implications of Bagwell and Staiger (1990) for our approach, in a cooperative

trade agreement equilibrium, the incentive for the US to temporarily raise its tariff is higher if (i)

imports are larger than expected, (ii) the volatility (standard deviation) of imports is low, (iii) the

export supply elasticity is low, and (iv) the import demand elasticity is low.

We estimate empirical models of (1) on a panel of US imports to determine if the US’s use of

antidumping policy is broadly consistent with the US raising its tariff as in a cooperative trade

agreement in which it was confronted by these (measurable) economic shocks. In section 2.2 we

present the empirical model, in section 2.3 we describe the underlying data sources, and in section 3

we turn to the results.

2.2 Empirical model of US antidumping tariff formation

In this section, we present our empirical models of US antidumping tariff formation to determine

if the US is using antidumping policy in a manner consistent with Bagwell and Staiger (1990).

We log linearize (1) and estimate a binary choice (probit) model of whether the US imposed an

antidumping tariff on imports from country i in industry k in year t.

In the binary model, the US government makes a decision of whether to impose an antidumping

tariff according to a latent measure of the potential benefits to the US of imposing a tariff against

country i in industry k. The latent measure d∗ikt

is unobserved, but takes the form d∗ikt

= β′xikt+εikt

where i denotes the foreign country accused of dumping, k denotes the industry in which dumping

is alleged to occur, and t denotes the time period in which the complaint is filed by the US industry.

7

The variables included in xikt are a measure of the import growth into the US from foreign country

i in industry k in year t − 1, the standard deviation of imports from country i in industry k over

the sample period, a dummy indicating if the export supply elasticity facing the US in industry

k is relatively inelastic, a dummy indicating if the US import demand elasticity in industry k is

relatively inelastic, the bilateral real exchange rate between the US and country i at t−1 (measured

in foreign currency over US dollars) and, in some specifications, characteristics of the US industry

at time t − 1.

The US imposes an antidumping tariff according to the rule:

dikt =

1 if d∗ikt

> 0

0 if d∗ikt

≤ 0

(2)

Assuming εikt ∽ N(0, 1), then the likelihood is

L = Π[

Φ(β′xikt)]dikt

Π[

1 − Φ(β′xikt)]1−dikt

(3)

where Φ is the standard normal cdf.

Marginal effects derived from coefficient estimates obtained from maximizing the log of the

likelihood (3) are reported in tables 2 - 8.

2.3 Data used to estimate US antidumping tariff formation

We estimate the empirical model of US antidumping tariff formation on a panel dataset con-

structed from several primary data sources: (1) trade policy data for the US come from the Global

Antidumping Database (Bown, 2009a), (2) US bilateral imports at the industry-level come from

the US International Trade Commission’s DataWeb, (3) industry-level foreign export supply elas-

ticities facing the US come from Broda, Limao, and Weinstein (2008), (4) industry-level US import

demand elasticities come from Broda, Greenfield, and Weinstein (2006), (5) variables describing the

characteristics of US domestic industries come from the US Census Bureau, and finally, (6) annual

8

bilateral real exchange rates in foreign currency per US dollar come from the USDA Economic

Research Service. Summary statistics for all variables in the dataset are reported in table 1.

The ikt panel includes 49 countries denoted i, 283 NAICS 2002 manufacturing industries k at

5 or 6 digits of aggregation, depending upon availability, for the years (t) 1997 through 2006.8

The US trade policy datasets in the Global Antidumping Database provide detailed informa-

tion on the date a petition was filed, the identity of the country accused of dumping, tariff line

information on the products involved, the outcome of the investigation and the magnitude of any

final antidumping tariff imposed by the US against country i. All tariff-line level (HS10 or HS8

digit) trade policy data were concorded to 238 NAICS 2002 5 and 6 digit US industries to merge

into the ikt, foreign country-industry-year panel.

Industry-level foreign export supply elasticities facing the US at the 4 digit HS level were

concorded to 2002 NAICS 5 and 6 digit industries. Because multiple 4-digit HS sectors map into

each NAICS industry, we record the minimum, median, and maximum HS4 digit export supply

elasticity that maps into each NAICS industry and check the robustness of our results to the range

of products produced by each industry. Similarly, import demand elasticities at the 3 digit HS level

were concorded to NAICS industries with the minimum, median and maximum elasticity in each

industry recorded to check the robustness of any results.

The WTO’s Agreement on Antidumping specifies empirical criteria that must be satisfied in

order for a country to impose an antidumping tariff. We included two measures of domestic industry

health to capture the WTO’s injury criteria - the growth of US industry employment and the ratio

of inventories to shipments.

Other industry characteristics are frequently used in the literature to control for political eco-

nomic determinants of an industry’s propensity to obtain import protection through antidumping

tariffs. Because a free-rider problem must be overcome in filing an petition on behalf of the in-

8Data on US manufacturing industries are available at the 5 digit level over the entire sample period. For some

larger 5 digit industries, data is also available at the 6 digit level over the entire sample period. When the more

disaggregated 6 digit industry data were available for all 6 digit industries within a 5 digit industry, we replaced the

more aggregated 5 digit industry data with the less aggregated 6 digit industry data.

9

dustry, more concentrated industries are thought to have a higher propensity to seek antidumping

protection. Thus, we include both the 4-firm concentration ratio (the shipments of the 4 largest

shippers relative to total industry shipments) and the Herfindahl index of industry concentration.

Further, we include a measure of industry size, total employment, because large industries may

be better able to assume the large legal fixed cost of filing a petition. Similarly, we include total

production employment as a measure of an industry’s political importance. The vertical structure

of an industry may matter; industries that are further downstream may file more petitions because

they are more sensitive to industry price changes. We proxy for the vertical structure of an industry

with the value-added to output ratio. Because the current values of industry specific variables and

the choice of whether to petition for protection may be endogenous, we use lagged values of these

variables in estimating the model.

3 Empirical Results: US Antidumping Tariff Formation

The empirical results reported in tables 2-8 suggest that that US imposes antidumping tariffs in a

manner consistent with the theoretical model of Bagwell and Staiger (1990). Table 2 reports that

US antidumping tariffs are more likely to be imposed when past import growth has been strong,

when import growth is less variable, and when import demand and export supply are relatively

inelastic.

Specifically, table 2 presents estimates of the binary model of the US government’s decision to

impose a final antidumping tariff against country i in industry k in year t. Estimates are presented

as marginal effects in which a one-unit increase in a variable is associated with an incremental

increase in the probability that the US will impose an antidumping tariff.

Column (1) presents results for the basic specification of the model. A one-unit increase in the

growth of bilateral imports from country i in industry k in the year before an antidumping petition

is filed is associated with an increase in the probability of an antidumping tariff of 0.04%. This

positive relationship between imports and antidumping tariffs is consistent with the terms-of-trade

theory that hypothesizes that the pressure to raise a cooperative tariff is increasing in the size of

10

the import surge. Proceeding down column 1, we find that antidumping tariffs are less likely when

import flows are highly volatile. A one-unit increase in the standard deviation of imports from

country i in sector k is associated with a fall in the probability of an antidumping tariff of 0.17%.

Turning to the indicator variables for the elasticity of import demand and export supply, a discrete

change for an industry from a relatively elastic to inelastic import demand is associated with a

0.07% increase in the probability of an antidumping tariff, while a discrete change from a relatively

elastic to inelastic export supply elasticity increases the probability of an antidumping tariff by

0.06%. These results are consistent with the Bagwell and Staiger (1990) theory.

Column (2) presents estimates from a model which uses alternative measures of the inverse

export supply and import demand elasticities. Because a wide range of products are produced by

each NAICS industry, several elasticity estimates are available for each US industry. For 5 and 6

digit NAICS industries, some point estimates for the import demand elasticities associated with

these industries ranged from slightly more than 1 to over 30. For export supply elasticities, the

range was even broader with some 5 and 6 digit NAICS industries encompassing point estimates

for export supply elasticities that ranged from close to zero to as high as 150, 185 or almost 300. In

specification (2), we use a dummy variable derived from the median point estimate of the export

supply and of the import demand elasticities to check the robustness of our main result in column 1.

We find that the results regarding the export supply elasticity are highly sensitive to which measure

is used. When we substitute an indicator for the median export supply elasticity, the sign on the

marginal effect changes; the estimated marginal effect is negative suggesting that antidumping

tariffs are more likely in industries facing relatively elastic export supply.

Taking the results from columns (1) and (2) together, it is difficult to definitively conclude what

role the export supply elasticity plays in the imposition of US antidumping tariffs. Antidumping

cases frequently target only a subset of countries that export a particular product to the US (see

Crowley, 2008) or a subset of products of an industry. Unfortunately, the estimated elasticities of

export supply from Broda, Limao, and Weinstein are averaged across both multiple products and

all sources of a product to the US market. The lack of precision in a key variable of interest is

likely to muddy some of the results. One possible explanation is that the US government imposes

11

antidumping tariffs against the individual countries with the least elastic export supply or the

products (within an industry) with the least elastic export supplies, and this is not captured by the

average export supply elasticity variable which does not have variation across countries or products.

The signs of all other variables in column (2) are consistent with column (1).

Specification (3) adds additional industry controls to the basic specification in column (1).

Previous studies (Staiger and Wolak, 1994; Crowley, 2008) have found that domestic industry

characteristics are important determinants of antidumping activity. We find that antidumping

impositions are more likely in more concentrated industries and larger industries.9 Results for all

other variables are robust to the inclusion of these additional controls.

In column (4), we substitute the industry’s level of production employment for the industry’s

level of total employment while keeping the industry concentration ratio. Because a high number of

production workers may be associated with greater political power, one might expect this measure

to be a more important determinant of trade protection than overall employment, but the two

measures have similar positive effects on the likelihood of an antidumping tariff.

Column (5) adds two additional industry characteristics to the specification in column (3) - the

value-added to output ratio and the inventories to shipments ratio. Consistent with the findings of

Crowley (2008) and Staiger and Wolak (1994), a higher value-added to output ratio is associated

with a lower likelihood of receiving an antidumping tariff. A high level of inventories to output

in the period before an antidumping tariff is filed is also associated with a higher probability

of protection. This is consistent with the WTO legal criterion regarding injury to the domestic

industry; rising inventory levels are often used by government agencies as a proxy for injury. Again,

other coefficient estimates are robust with regard to these additional control variables.

Finally, specification (6) uses an alternative measure of the import surge facing the US. The

unexpected import growth variable captures a shock to the import growth rate. It is the import

growth rate at time t−1 less the average of the import growth rates at t−2 and t−3. The sign and

magnitude of this variable are in line with the estimates in specifications (1)-(5). However, with the

9These results are robust to the use of a Herfindahl index of industry concentration in lieu of the four firm

concentration ratio.

12

smaller sample size necessitated by the use of the t− 3 import growth rate, the coefficient estimate

is no longer statistically significant. Estimates for other variables are consistent with specification

(1).

Lastly, in specification (1), a real appreciation of the US dollar is associated with a higher

antidumping tariff. Quantitatively, a one standard deviation increase in the variable increases

the magnitude of the antidumping tariff by 1.14%. This estimate is in line with previous work

by Knetter and Prusa (2003), Feinberg (2005), and Crowley (2008) all of whom find that the

probability of an antidumping tariff is higher when the real dollar appreciates.

Table 3 quantifies the key results of table 2 in terms of their economic significance. In table 3,

we report the predicted probability that the US will impose an antidumping tariff in response to

a one standard deviation increase in each of the continuous explanatory variables. For the dummy

variables, we report that predicted probability of an antidumping tariff if the industry accused

of dumping faces relatively inelastic import demand or export supply. We begin by observing

that antidumping tariffs are rarely used. The last two rows of table 3 present statistics on the

infrequent use of antidumping tariffs in the estimation sample for each specification. The last row

reports the number of observations in the estimation sample while the second to last row reports the

probability of an antidumping tariff being imposed in that sample. For all specifications in table

2, antidumping tariffs were imposed against only sixteen hundredths of one percent of country-

industry-year observations.

How would a change in the magnitude of an explanatory variable affect the probability of a

tariff being imposed? Beginning with specification (1), a one standard deviation increase in the

growth of lagged bilateral imports would increase the probability of an antidumping tariff by 25%,

to 21 hundredths of one percent. Recall that Bagwell and Staiger predict that eruptions of trade

protection are less likely in industries with more volatile imports. Consistent with the Bagwell

and Staiger theory, a one standard deviation increase in the volatility of imports is associated

with a very low probability of an antidumping tariff of only 5 hundredths of one percent. This

is a substantial reduction of 69% in the likelihood of an antidumping tariff relative to its baseline

level in the sample. Continuing with the baseline specification, a discrete change from a relatively

13

elastic to inelastic import demand increases the probability of a tariff by 44%. For the export

supply elasticity dummy, a discrete change from relatively elastic to relatively inelastic increases

the probability of a antidumping tariff by 37.5%.

As discussed earlier, column (2) documents how the export supply elasticity results are sensitive

to the exact measure used. When the elasticity dummies are constructed from a set of point

estimates that include some very large estimates of the export supply elasticity, the results conflict

with the terms-of-trade theory.

Proceeding across columns (3) through (6), the results are quantitatively similar to those in

column (1). Interestingly, the variables that are predicted by Bagwell and Staiger (1990) to be

important determinants of cooperative tariff increases - import growth, import volatility, and the

trade elasticities - all have roughly the same or greater economic significance as the variables tradi-

tionally considered to be important determinants of antidumping activity - industry concentration,

employment, and inventory levels. A one standard deviation increase in the 4-firm concentration

ratio increases the probability of a tariff by about 6% in specifications (3) and (4), while a one

standard deviation increase in the logged level of employment increases the probability of a tariff

by 38% and a one standard deviation increase in the inventories to shipments ratio increases the

likelihood of an antidumping tariff by 19%.

Table 4 introduces a new explanatory variable to proxy for the unexpected import surge in the

Bagwell and Staiger model. Some of the literature on the terms-of-trade theory of trade agreements

emphasizes the importance of the magnitude of the negotiated market access implied by a given

tariff concession rather than the tariff concession itself. In mapping the repeated static (i.e., no

growth) environment of Bagwell and Staiger (1990) to an empirical environment characterized

by underlying domestic economic and trade growth, we interpret negotiated market access as a

commitment to a specified share of the importing country’s market. From this, we define an

import surge at t− 1 as an increase in country i’s share of the US’s market for k between t− 2 and

t − 1.

Moreover, we include an additional explanatory variable on market share to help us understand

how competition among exporters in the US market for k affects US trade policy decisions. The

14

variable, the change in market share for the Rest of World (ROW) at time t − 1, is the change in

the cumulative US market share for all countries that export goods in industry k to the US except

for country i. We include this measure to help disentangle the effects of an aggregate import surge

of k from a country-specific import surge of k from i.

The first column reports our basic specification using the market share variables in lieu of import

growth measures. The results are consistent with those using import growth as the measure of a

bilateral import surge in table 2. A one-unit increase in a country’s change in market share at

time t − 1 increases the probability of an antidumping tariff by 4.5%. Interestingly, the marginal

effect of a change in the cumulative market share of country i’s competitors is negative. A one-unit

increase in the change in the cumulative market share of all countries except country i is associated

with a 1.2 percentage point reduction in the likelihood that country i will face on antidumping

tariff. Although an increase in country i’s market share increases the likelihood that country i will

face an antidumping tariff, this result suggests that it is not unjustifiably grouped with competing

exporters to the US. All else equal, if competing exporters increase their market shares, country i

is less likely to face an antidumping tariff.

Proceeding across columns (2) - (5) of table 4, the results line up with those of table 2.

Table 5 quantifies the results from table 4. The magnitudes of the effects of the explanatory

variables on the likelihood of an antidumping tariff are similar to those reported in table 2. However,

the quantitative effect of a one standard deviation increase in the change in a country’s market

share is smaller than that of import growth, increasing the likelihood of an antidumping tariff by

6.3%. An interesting result is that a one standard deviation increase in the change in the cumulative

market share of competing exporters reduces the probability of an antidumping tariff by 20%.

Table 6 focuses on two industries that are frequent recipients of antidumping protection in

the US - steel and chemicals. While only 1.3% of the observations in our dataset are for the

steel industry, 26.7% of the antidumping tariffs recorded in the dataset are in the steel sector.

Similarly, while only 2.3% of the observations in the dataset are of chemical sectors, 11.8% of the

antidumping policies in the dataset are against chemical exporters. These facts raise the question

of whether the results in tables 2 - 5 driven by antidumping activity in these sectors, and whether

15

US antidumping use treats these sectors differently than other sectors based on the underlying

economic fundamentals?

To address these questions, the specifications in table 6 utilize industry dummies and interaction

terms. Beginning with the first column, the basic specification (1) from table 2 is augmented

to include a dummy for steel industries. The positive coefficient on the steel dummy indicates

a strong bias in favor of antidumping tariffs against steel exporters that goes beyond the basic

economic variables of the Bagwell and Staiger (1990) model; a discrete change from a non-steel

to steel-industry increases the probability of an antidumping tariff by 2.4%. However, even after

controlling for this bias, the results on the other coefficients are largely unchanged suggesting that

the basic results in table 2 are not driven by observations on the steel industry. The second column,

which includes a dummy for chemical sectors, shows a smaller bias against chemical exporters. A

discrete change from non-chemical to chemical products is associated with a 0.43% increase in the

likelihood of an antidumping tariff after controlling for the economic variables of the Bagwell and

Staiger (1990) model.

Columns (3) and (4) of table 6 include terms that interact the volatility of imports with steel

and chemical dummies, respectively. Here the predictions of the Bagwell and Staiger model are

reversed in the steel industry - volatile import growth increases the probability of a steel tariff. In

contrast, for the chemical sectors, more volatile import growth is associated with a reduction in the

probability of an antidumping tariff, as predicted by Bagwell and Staiger. However, the magnitude

of this effect in chemical sectors is much smaller than that reported in our basic specification of

the average across all industries reported in table 2. Finally, the last two columns of table 6 add

domestic industry characteristics - industry concentration, employment, the value-added to output

ratio, and the inventories to shipments ratio - to specifications (1) and (2). After accounting

for these general industry characteristics, the extent of the “bias” against exporters of steel and

chemicals to the US is smaller, but it still economically and statistically significant.

Table 7 presents an analysis of the special status of China in US antidumping investigations.

While China accounts for only 2.5% of the observations in our dataset, it is the target of 42% of US

antidumping tariffs in our sample. As with the steel and chemical industries, we examine whether

16

US imports from China are a special case by augmenting our basic specification from Table 2 with

China dummies and interaction terms.

Beginning with column (1) of table 7, the inclusion of the China dummy in the basic specification

shows a much higher likelihood of antidumping in US imports from China relative to imports from

all other foreign sources; the likelihood of an antidumping tariff increases by 1.79 percentage points

for products imported from China. Interestingly, after including the dummy for China, the effect

of import growth on the probability of an antidumping tariff is smaller and no longer statistically

significant.

Column (2) attempts to identify the source of the higher likelihood of US antidumping against

China by examining the volatility of imports from China through an interaction of the standard

deviation of import growth with a China dummy. The results are similar to those for the steel

industry. While more variable US imports are associated with a lower probability of protection in

general, the pattern is reversed for imports from China; i.e., an antidumping tariff against imports

from China is more likely when imports from China are highly volatile. This result for China

contradicts the predictions of the Bagwell and Staiger model and suggests one possible economic

reason for why antidumping investigations against China often end in new tariffs. Column (3)

focuses on the import demand and export supply elasticities for goods from China. Here the

predictions of the Bagwell and Staiger model are quantitatively much stronger for imports from

China than for imports from other countries. A discrete change from being a non-China exporter

in a sector with inelastic import demand to being a China exporter in the same sector is associated

with a 0.6 percentage point increase in the probability of an antidumping tariff. This is an increase

of 62.5% over the probability of an antidumping tariff in the estimation sample. Similarly, a discrete

change from a non-Chinese to a Chinese exporter in a sector with relatively inelastic export supply

increases the probability of an antidumping tariff by 0.33 percentage points.

Column (4) augments specification (1) by including domestic industry characteristics. The

increased probability of US antidumping decisions against China is smaller after taking into account

features of the domestic US industry, but it is still quantitatively significant. After controlling for

industry characteristics, if a case involves imports from China, the likelihood of an antidumping

17

tariff increases 1.61 percentage points. Column (5) replicates the specification of column (2) but

adds details on the domestic US industry. A similar pattern is observed; the China-specific effect

on the standard deviation of imports is muted when industry details are taken into account, but

the China effect is still substantial. Finally, column (6) adds industry variables to the analysis of

China’s relationship to the import demand and export supply elasticities. The results are essentially

the same as those in column (3), but the estimated magnitudes of the effects are smaller.

The final table of results, table 8, examines changes in market shares for exporters in the steel

and chemical industries and for Chinese exporters. The first three columns of table 8 introduce

dummy variables for steel, chemicals and China to the basic market share specification found in

column (1) of table 4. In all three specifications, the bias toward imposing antidumping tariffs for

these types of cases is present when we use changes in market share rather than import growth as

the measure of an import surge.

More interestingly, columns (4), (5) and (6) interact the relevant industry or country dummy

with the two market share variables. In column (3), the marginal effect of being a steel exporter

and having a change in market share is not statistically significant. A steel exporter with an

increased market share will be more likely to face a US antidumping tariff, but the size of this

effect is the same as in all other industries. However, steel exporters are treated differently when

it comes to competition. For a steel exporter, if competing exporters in other countries experience

an increase in their US market share, all else equal, the steel exporter will be more likely to face an

antidumping tariff. Thus, it appears that steel is an area in which antidumping tariffs penalize some

of the exporters who are not responsible for a US import surge. Column (4) reveals a difference

between the policy treatment of steel and chemical products. If a chemical exporter experiences

an increase in market share, the increased likelihood of an antidumping tariff is much larger than

it is for other industries. However, unlike in steel, exporters in chemical sectors are not grouped

with their competitors if their competitors experience an increase in US market share. Finally,

column (6) reports results for the interaction of China cases with market share variables. First,

when Chinese exporters increase market share, the likelihood of an antidumping tariff is greater

than for non-Chinese exports. Second, China experiences a small negative policy externality when

18

its competitors increase their share of the US market. An increase in the cumulative market share

of non-China countries increases the likelihood that China will face an antidumping tariff. To

summarize, the basic results on market share reported in table (4) are robust to the inclusion of

a steel, a chemical or a China dummy, but there are interesting variations in the effects of market

share and of competitor’s market share for each.

To conclude this section, a large literature has explored the determinants of the antidumping

tariff policy. Our paper is the first to develop an empirical model of antidumping tariffs as a

cooperative tariff increase in a self-enforcing trade agreement subject to unexpected import surges.

We find evidence consistent with the Bagwell and Staiger (1990) theory through our results that

US antidumping tariffs are more likely the larger is lagged import growth, the greater the increase

in the exporter’s share of the US market, the lower the volatility of imports, and the less elastic

are US import demand and foreign export supply.

4 The Trading Partner Response to US Antidumping: Are the

Tariffs Viewed as Cooperative?

Our results on the United States’ use of antidumping indicate substantial evidence consistent with

the Bagwell and Staiger (1990) theory of self-enforcing trade agreements. The main innovation of

our research is to highlight the extent to which US use of antidumping trade policy is associated with

the changing value of the economic incentive to respond to trade volume shocks whose importance

may vary because of heterogeneity across import demand and export supply elasticities (Broda,

Limao and Weinstein, 2008; Bagwell and Staiger, 2009). This particular economic rationale for

changing a US tariff is distinct from the political economy considerations that have been the focus

of much of the previous literature on antidumping as a time-varying trade policy.

Nevertheless, these results introduce a new and related question: if US antidumping policy

use is consistent with the norms of a cooperative and self-enforcing trade agreement, then how

can we explain the variety of foreign policy responses to US tariff increases? Indeed, there are at

least two observable (or measurable) ways by which trading partners have actively responded to a

19

US antidumping tariff increase with their own policy change. First, nine different WTO members

(counting EU states as one WTO member) used the WTO’s dispute settlement procedures to

formally challenge 27 out of the roughly 130 different US antidumping tariffs imposed against

them between 1997 and 2006.10 Second, six WTO members responded to an additional 23 US

antidumping tariffs imposed during this period by initiating their own antidumping activity against

US exporters within the same 4-digit industry; and one interpretation is that such actions were

simply direct antidumping retaliation.11 Combined, this implies that almost 40% of the US tariff

increases under antidumping between 1997-2006 were challenged by its trading partners either

formally at the WTO or informally through unilateral retaliatory action.

On its face, such foreign policy responses seem at odds with the results of the first section of

the paper that US antidumping use is consistent with the theory of cooperative trade agreements.

Our next goal is to attempt to explain the variation in the foreign response to the US raising its

tariffs under its antidumping policy.

The first step is to build from a related literature (e.g., Bown, 2005a; Busch, Reinhardt and

Shaffer, 2008) and examine determinants of whether the foreign exporter responds to the US tariff

increase by initiating a formal WTO dispute settlement challenge that seeks its removal.12 Specif-

ically, we begin by estimating determinants of a binary choice model of whether an exporter hit

with a new US antidumping tariff between 1997-2006 decides to challenge it by initiating a formal

WTO dispute. If the WTO is a cooperative trade agreement in the sense of Bagwell and Staiger

(1990), the theory suggests that formal disputes are not likely to be filed against US trade policy

changes that are imposed because of underlying economic shocks to trade volumes in sectors with

10These WTO members were Brazil, Canada, Ecuador, European Union (on behalf of separate US actions against

exporters from Belgium, Finland, France, Germany, Italy, Netherlands, Spain, Sweden, and the UK), India, Japan,

Korea, Mexico and Thailand.11The WTO members were Canada, China, European Union (on behalf of exporters from Italy and Spain), India,

Mexico and Poland.12See also Horn, Mavroidis and Nordstrom (2005), Bown (2005b), and the literature on determinants of the use

of WTO dispute settlement surveyed in Bown (2009b, chapter 4). By foreign exporter, of course, we refer to the

combined ability of the exporting industry and its government to coordinate the mounting a formal WTO dispute

legal challenge since only governments have standing in WTO disputes.

20

low import demand and export supply elasticities that create an incentive to raise the cooperative

tariff. On the other hand, US tariff increases that appear to be outliers with respect to the esti-

mated rule governing the cooperative trade agreement may be good targets for the foreign trading

partner to mount a formal WTO challenge. In addition to a number of additional determinants

that the previous literature has indicated are likely to affect the decision of a trade partner to

formally challenge the US use of antidumping, our main innovation is to test specifically whether

the predicted likelihood of US antidumping (generated from the first empirical model of the pa-

per) is itself a significant determinant of the trading partner’s policy response to the new US tariff

increase.

As a second step, in section 4.3 we consider a multinomial extension to this first model in which

we allow the foreign trading partner an additional policy instrument with which to respond to

the US tariff increase. In this extension, we consider the possibility that some trading partners

may bypass WTO dispute settlement in favor of more directly using their own antidumping policy

to retaliate against US exporters in the same sector in which their exporters were facing a tariff

increase in the US. In the multinomial model, we give the foreign trading partner three choices of

how to respond to the increased US tariff: challenge it with a WTO dispute, directly retaliate by

using its own antidumping against US exporters in the same sector, or do nothing.

Before proceeding to the formal empirical approach and results, the next section introduces the

data and variable construction used to estimate the models.

4.1 Data used to estimate determinants of the foreign trade policy response

The imposed US antidumping policies result in tariff increases that undoubtedly hurt the foreign

exporter, at least in the short run. What determines whether the foreign exporter will challenge

the US tariff and seek its removal? One potential determinant has already been proposed by the

Bagwell and Staiger (1990) theory; i.e., if the US tariff on h is not a response to an economic shock

that changed the level of the cooperative tariff in the self-enforcing trade agreement. We capture

this possibility through use of the predicted likelihood of the US antidumping tariff on h generated

from model specification 1 of table 2 for each of the antidumping tariffs imposed in our data set

21

during the 1997-2006 period.

To guide our selection of additional potential determinants of the foreign response to the new

US tariff, we draw from the Maggi and Staiger (2008) theoretical model of WTO dispute settlement.

Consider again the perspective of a foreign exporter of product h adversely affected by a new US

antidumping tariff, and the expected costs and benefits that are likely to influence its decision of

whether to formally challenge the new US tariff with a formal WTO dispute. When the costs

of a dispute fall disproportionately on the exporter relative to the importer, one prediction that

comes out of the Maggi and Staiger model is that the importer will be more likely to engage

opportunistically in the first place and thus impose WTO-inconsistent tariffs that will lead to a

dispute. Since 20% of the US antidumping tariffs imposed during this period resulted in WTO

challenges, it is likely that exporter litigation costs are therefore sufficiently high in this sample

of data. In our cross-sectional empirical analysis with a common (US) importer, it is reasonable

to assume that the litigation cost to the US government defending a challenge is similar across all

AD tariffs it imposed. An implication of the Maggi and Staiger theory is that equilibrium WTO

disputes will arise in instances in which the benefits to the exporter from successful resolution to a

dispute - put differently, the opportunity cost of failing to challenge it at the WTO - are also high.

In our empirical model, we use two different variables to capture the opportunity costs to the

exporter for failing to initiate a dispute that would remove the US antidumping tariff. Each variable

is derived from data on the underlying foreign exports of the product h that is the target of the

US antidumping tariff; the variables are constructed to reflect the size of those exports to different

markets.

The first variable is the value of access to the US import market in product h, which is to

proxy for the amount of trade that foreign is likely to lose because of the new US tariff.13 The

larger the amount of pre-antidumping exports to the US market that foreign has at risk, the higher

the opportunity cost of not challenging the US tariff change, and the more likely the exporter will

overcome the fixed cost of filing a WTO dispute.

13Specifically we measure this as the log of foreign’s AD-affected product exports of h to US in t − 1, the year

before the antidumping investigation (that ultimately resulted in the tariff increase) is initiated.

22

The opportunity cost to the exporter for failing to challenge the US tariff may also involve a

second variable that we refer to as the “terms of trade spillover” to third markets in the rest of the

world (ROW). I.e., exporters that ship to multiple markets are likely concerned that the US tariff

will negatively impact their profits associated with sales of product h to ROW, which could occur

through at least two channels associated with a negative terms-of-trade externality. First, for an

unchanged level of import demand in ROW for product h, a shift in foreign’s export supply toward

this market (due to sales shut out of the US market caused by the US AD) will reduce the exporter’s

terms-of-trade in those markets as well, through a “trade deflection” result (Bown and Crowley,

2006, 2007; Chang and Winters, 2002). A second and reinforcing channel may be through ROW

governments engaging in policy learning. Because WTO rules mandate that the US must notify a

new antidumping tariff to the world (via the WTO), this new US tariff in product h reduces the

fixed cost to ROW governments collecting information necessary to ultimately impose their own

new antidumping import restriction on product h from foreign. This would also create a negative

terms-of-trade externality by shifting in the ROW import demand curve.14 The reinforcing nature

of these two effects indicates that the larger is the share of foreign’s total exports of h that it sends

to the ROW, the more likely it will challenge the US tariff increase of h to seek its removal.

In WTO dispute settlement, the expected benefit to a successful outcome to the dispute is also

determined by the likelihood that the new US tariff is removed. While the WTO is a rules-based

organization, a necessary condition for one trading partner to enforce another’s WTO commitments

- e.g., in this case remove or revise a US imposed antidumping tariff that the WTO may determine

was imposed in a way inconsistent with the cooperative trade agreement’s rules - is that foreign has

the capacity to retaliate by threatening new import restrictions against US exporting industries.

One important theory motivating this empirical regularity stems from the underlying interpretation

of the WTO’s guiding principle of reciprocity provided by Bagwell and Staiger (1999). Prior

evidence from other samples of data stemming from different trade policy decisions (e.g., Bown

2004a,b) related to WTO dispute settlement finds evidence that foreign retaliation capacity matters.

Here we measure foreign’s retaliation capacity as simply the log of the value of total US exports

14Maggi (1999) explores alternative implications of the WTO performing an information dissemination role as a

multilateral institution.

23

sent to the foreign country. We expect a positive relationship that larger US exports to foreign

give foreign the necessary capacity to make retaliatory threats to induce the US to comply with

the WTO legal process, increasing the likelihood that foreign will enjoy the benefit of removal of

the US tariff (and restoration of US market access).

In section 4.3 we extend the basic model to also given foreign the policy choice to respond

with a more direct retaliation against US exports through its own antidumping action in the same

sector. In that model we include additional determinants to assess foreign’s capacity to retaliate

directly against the US exporter in the same sector k in which the US imposed the antidumping

tariff. We capture foreign’s AD retaliation capacity as the log of US exports of k sent to foreign.15

We expect the higher US exports of k, the more likely is foreign to respond to the US imposition

of antidumping in k with its own antidumping against US exports of k. In this second model, we

also redefine the retaliation capacity variable intended to influence foreign’s use of WTO dispute

settlement. There we focus on US exports of all other goods (6= k), and we specifically define this

variable as the share of US total exports of these all other goods that are destined for the foreign

market.

To conclude this section, it is important to highlight the important difference in the data used

for this portion of the estimation. Since many of the variables in this section will be defined at the

level of the 6-digit Harmonized System (HS) product h at issue in the antidumping investigation,

the data is much more disaggregated than what we used for the estimation of the antidumping tariff

formation equation, which required access to industry-level data at 5- or 6-digit NAICS industry

level k.16 The product h 6-digit HS data for foreign exports is taken from Comtrade. The data

on US antidumping, WTO challenges, and foreign retaliatory antidumping is again taken from the

Global Antidumping Database (Bown, 2009a).

15One item worth noting is that in this section of the paper the industry k is defined at the 4-digit HS level, which

is different from the first section of the paper in which k was a 5- or 6-digit NAICS industry. Since we do not rely on

industry (production) data in this part of the estimation exercise, we do not bother to concord the HS 6-digit to the

NAICS data, which does have the benefit of reducing some measurement error.16US antidumping investigations are typically carried out at the 8- or 10-digit level, the 6-digit HS level is the most

disaggregated level of trade that is consistently defined across countries, as is required for the variable construction

that we undertake below.

24

Table 9 presents summary statistics of the variables used in the estimation.

4.2 Estimating an empirical model of WTO dispute settlement

Table 10 presents estimates of the marginal effects of the binomial probit model in which the

dependent variable takes on a value of one if the foreign exporter issues a formal legal challenge at

the WTO to the US antidumping tariff imposed against imports from foreign of product h.

Column (1) of table 10 presents our simplest specification that focuses solely on the predicted

likelihood of a US antidumping tariff, where this determinant is generated from the model presented

in the last section. There is evidence of a negative bivariate relationship between these variables,

consistent with the interpretation of the Bagwell and Staiger (1990) framework that US antidumping

tariffs associated with a higher predicted likelihood of a tariff being imposed are less likely to be

challenged through a WTO dispute. By itself this effect is also sizable - a one standard deviation

increase from the mean value of the predicted likelihood of an antidumping tariff decreases the

probability of foreign responding with a WTO dispute by roughly 50%, from 0.186 to 0.108. While

the magnitude of the estimated marginal effect coefficient shown in the table varies slightly in the

additional specifications that we consider and discuss below, the estimated size of this impact on

the probability of a challenge does not.

Column (2) of table 10 is our preferred specification which includes additional variables that

control for other aspects of the expected costs and benefits likely to determine the foreign decision of

whether to challenge the US tariff with a WTO dispute.17 The estimates of these other determinants

all have the theoretically-predicted sign, and only the variable capturing the potential impact of

the US tariff on foreign exports to the ROW (via the “terms of trade spillover”) is not statistically

significant at conventional levels. Importantly, the size and statistical significance of the impact of

the predicted likelihood of the US antidumping tariff is virtually unchanged from specification (1).

Consider next the estimated marginal effects estimates of the other determinants of the foreign

WTO dispute decision reported in specification (2). First, the size of US market access threat-

17The results of this section are consistent with earlier estimates of the impacts of these determinants on other

contexts. See again Bown (2005a) and the survey presented in Bown (2009b, chapter 4).

25

ened by the new US antidumping tariff clearly matters. Foreign is more likely to challenge US

antidumping tariffs imposed on larger (t− 1) US imports of h. Market access is also a very impor-

tant economic determinant in the estimation, as the estimated marginal effect (0.068) implies that

a one standard deviation increase in the amount of US imports at stake more than doubles (from

0.136 to 0.288) the predicted probability of foreign responding with a WTO dispute.

Second, while it is not statistically significant in specification (2), the larger is the foreign

exporter’s affected product h exports to ROW as a share of its total exports of h, the more likely

is foreign to challenge the US antidumping tariff on h at the WTO. The estimated impact is not as

important as the (first order) impact of the direct US market access variable, but it is still sizable,

as the estimated marginal effect (0.170) implies that a one standard deviation increase in the share

of foreign exports of h it sends to ROW leads to a 28.0% increase (from 0.136 to 0.180) in the

predicted probability of foreign responding to the US tariff with a WTO dispute.

Third, the capacity of foreign to make credible retaliatory threats against US exports is also

estimated to positively affect the decision to challenge the US tariff with a WTO dispute. And the

estimated impact (0.057) is also sizable, as a one standard deviation increase in the amount of US

exports sent to foreign leads to a 56.8% increase (from 0.136 to 0.240) in the predicted probability

of foreign responding with a WTO dispute.

Columns (3) and (4) of table 10 present additional robustness checks to the estimates. Spec-

ification (3) is identical to specification (2) except it drops the predicted likelihood of the US

antidumping tariff. The estimated marginal effect estimates on the other determinants are rel-

atively unaffected. Finally, specification 4 includes two additional control variables. The first is

simply the size of the US antidumping tariff, and this is included to address a potential concern that

WTO challenges may be more likely when new US antidumping tariffs are low.18 The estimated

18Including the size of the US tariff controls for two different phenomenon. First, consider the many WTO challenges

to the US use of “zeroing” in antidumping investigations. Foreign may be more likely to challenge low tariffs when

there is zeroing, under the expectation that the WTO dispute may result in the US revising the dumping calculation

without using the zeroing methodology and this calculation could lead to a new antidumping tariff that is below the

de minimus threshold resulting in a complete removal of the antidumping tariff and not simply a downward revision.

For a discussion of zeroing, see Crowley and Howse (2010). Second, the US imposed much higher antidumping tariffs

26

effect is negative, and it is statistically and economically significant as lower US antidumping tariffs

are more likely to be challenged by WTO dispute settlement. Finally, specification (4) also includes

an indicator for whether the foreign target of US antidumping is in a preferential trade agreement

(PTA) with the US. While the estimate is negative, it is not statistically different from zero.

Most importantly, inclusion of these additional determinants does not affect the estimated effects

of the main variables of interest. Even the reduced size of the estimated coefficients of the marginal

effects is of little consequence, as the estimated impact on the predicted probability of a foreign

WTO dispute is virtually unchanged from the levels of economic significance discussed above.

4.3 Estimating an empirical model of WTO disputes, AD industry retaliation,

or no response

As a final exercise, in this section we present the results of an expanded empirical model in which

we allow foreign one additional policy response to a newly imposed US antidumping tariff. Here

we expand the foreign exporter’s choice set so that it also includes responding with a retaliatory

antidumping policy against US exports in the same 4-digit industry k. The motivation for this

exercise stems from the results and conjecture of Bown (2005a), that a contributor to why some

trading partners do not use the WTO to challenge US antidumping is because they have an alter-

native and relatively direct channel to obtain redress - i.e., their own use of antidumping against

the US in the same sector.19

Table 11 presents our empirical results of the marginal effects estimates of the three choice

multinomial probit model. We present three different model specifications; the left column of each

on imports from non-market economies such as China during this time period (Bown, 2010), and after its WTO

accession in 2001 China did not initiate a single WTO dispute over US antidumping through 2006. Thus this variable

may also partially capture a China-specific effect. However, as we explore in the next section, China did actively use

its own antidumping policy against US exporters during this time period.19Since Bown (2005a) did not have access to data on foreign use of antidumping by industry against US firms, that

paper could not explicitly test this hypothesis. The indirect evidence in that paper was the US industries with larger

exports to the foreign country were less likely to have their AD tariffs targeted for a WTO dispute. The resulting

conjecture, that we confirm with evidence below, was that the foreign country was engaging in “vigilante justice” by

simply using reciprocal antidumping instead of WTO dispute settlement where it had the capacity to do so.

27

specification presents the estimates of the determinants of the exporter’s choice to challenge the

imposition of a new US antidumping import restriction through a formal WTO dispute. The middle

column presents estimates of the determinants of the choice to respond via subsequently imposing

a same-industry antidumping action of its own within the next two years after the US imposed

AD in year t. Finally, the right column presents estimates of the determinants of the choice not to

respond with either a WTO dispute challenge or an antidumping retaliation. The determinants are

the same variables used in the binomial probit estimates (see table 10) of the last section, with the

one exception described earlier. In specifications (2) and (3) of the multinomial probit model we

disentangle two components of the foreign retaliation capacity. First, we allow for the exporter’s

capacity to retaliate with a same-industry antidumping action to affect its choice, and we capture

this industry-level retaliation capacity with the log of US exports of 4-digit industry k sent to

foreign in t − 1. The second retaliation capacity variable is US exports of all other goods (6= k)

sent to foreign, which we define as the share of US exports of all other goods sent to all markets.20

Specification (1) of table 11 estimates the foreign choice solely as a function of the predicted

likelihood of the US antidumping tariff from the model of the first section of the paper. The

predicted probabilities of foreign’s three choices based on the estimates in table 11 are 0.181 for a

WTO dispute, 0.182 for antidumping retaliation, and 0.637 for no policy response.21 Just as in the

binomial probit model in the last section, there is evidence of a negative relationship between the

predicted likelihood of this US antidumping tariff and the foreign decision to file a WTO challenge.

Furthermore, the effect is sizable; ceteris paribus, a one standard deviation increase in the predicted

likelihood of AD formation above the sample mean decreases the predicted probability of a WTO

dispute by almost half from 0.181 to 0.098.

Interestingly, specification (1) of the three choice model also suggests that the failure of foreign

not to respond by initiating WTO disputes against instances of high predicted probabilities of US

20These two variables are highly positively correlated in the levels, which is one reason why we define the sec-

ond variable as a share. Nevertheless, the high correlation also makes the estimation results relatively sensitive to

specification choice, as columns 2 and 3 of table 11 illustrate.21This compares to the sample of raw data of outcomes which were 0.195 for a WTO dispute, 0.186 for antidumping

retaliation, and 0.619 for no policy response.

28

antidumping tariffs should not be mistaken for a decision to “do nothing.” Indeed, the estimated

impact of the predicted probability that this determinant has on foreign engaging in direct an-

tidumping retaliation in the same sector is positive (48.953) and statistically significant. The same

one standard deviation increase in the predicted likelihood of a US antidumping tariff that led to a

decrease in the predicted probability of a WTO dispute (from 0.181 to 0.098) leads to a substantial

increase in the predicted probability of foreign antidumping retaliation (from 0.182 to 0.255).

Specification (2) is our preferred specification of the multinomial probit model as it includes

additional control variables. When we include variables controlling for the importance of access

to the US market, the possibility of the US antidumping tariff spilling over to impact foreign’s

terms of trade in ROW, and foreign’s retaliatory capacity, the estimates’ signs on the explanatory

variables are generally consistent with the theory.

First, the size and estimated impact of the negative relationship between the predicted likelihood

of the US antidumping tariff and the WTO dispute choice continues to hold - the higher the

likelihood that the first-stage model predicted for a US antidumping tariff, the less likely that

foreign responds with a WTO dispute. However, while the estimated impact of this determinant

on the antidumping retaliation choice in specification 2 is still positive, unlike specification 1, it is

no longer statistically significant at conventional levels.

Next consider again the determinants associated with the Maggi and Staiger (2008) theory of

the expected costs and benefits to foreign pursuing WTO dispute settlement. First, the US market

access variable is estimated to have a positive and significant impact on the decision to seek removal

of the US antidumping tariff through WTO dispute settlement. Furthermore, unlike the binomial

probit model, the impact on the WTO dispute choice of the “terms-of-trade spillover” variable in

specification (2) of table 11 is also statistically significant and positive, as predicted by the theory.

The retaliation capacity variables of specification 2 also have the theoretically predicted sign,

and they are statistically significant. Increased US exports of k to foreign increase the likelihood

that foreign will respond to the US antidumping tariff with a retaliatory antidumping action in k of

its own (0.053). Furthermore, the higher the share of total US exports of all other goods (6= k) sent

to foreign, the greater is foreign’s capacity to retaliate under the WTO in some other sector, and

29

the higher the likelihood that foreign responds to the US antidumping tariff with a WTO dispute

challenge. In terms of the economic significance of the effects, a one standard deviation increase in

foreign’s antidumping retaliation capacity increases the probability of an AD retaliation from 0.186

to 0.335 with virtually no statistically significant impact on the probability of an WTO dispute.

On the other hand, a one standard deviation increase in WTO retaliation capacity increases the

probability of a WTO dispute from 0.122 to 0.211. Combined these results suggest that retaliation

capacity threats work in predictable ways - large (same) industry k US exports to the foreign

market and relatively small overall US exports to foreign increases the probability of foreign AD as

a response instead of a WTO dispute. Furthermore, this evidence is consistent with the conjecture

of Bown (2005a) that one important determinant for why foreign countries do not use WTO dispute

settlement to challenge AD is because they have access to a separate policy to address the issue -

reciprocal antidumping in the same sector.

Finally, specification (3) of table 11 again includes the additional control variables (the size of

the US antidumping tariff and a PTA indicator) as a final robustness check. While the statistical

significance of some of the estimates changes, for the most part the qualitative pattern to the results

is largely unaltered.

5 Conclusion

This paper empirically examines how governments make trade policy adjustments under a self-

enforcing trade agreement in the presence of economic shocks. Using data on US antidumping

(AD) policy formation between 1997-2006, we find that US antidumping tariffs are consistent with

an increase in “cooperative” tariffs of a Bagwell and Staiger (1990) model of self-enforcing trade

agreements. These results provide empirical support for models of trade agreements that emphasize

the importance of the terms-of-trade motive in tariff setting. We find that both the likelihood of

a US AD as well as the size of the imposed AD tariff are negatively related to the foreign export

supply and the US import demand elasticities. The results are consistent with the optimal tariffs

theory as well as other empirical research (Broda, Limao, and Weinstein 2008; Bagwell and Staiger,

30

2009) in the literature from other settings that the terms of trade affects trade policy formation.

Furthermore, in light of this interpretation of US AD use as a cooperative increase in trade

policy, we empirically investigate determinants of which of these US AD actions are challenged

by trading partners both inside (through dispute settlement) and outside (through antidumping

retaliation) the WTO. After controlling for factors such as a trading partner’s expected cost and

benefit to challenging US antiumping, we find that trading partners are less likely to challenge such

“cooperative” US antidumping tariffs that were imposed under terms-of-trade pressure suggested

by the theory.

31

Table 1: Summary Statistics: US Antidumping Tariff Formation

VariableMean

Standard

Deviation

Dependent Variables

AD imposed by the US against country i industry k in year t(=1) or not 0.0016 0.0395

AD duty against country i industry k in year t 0.7535 0.8791

Explanatory Variables

Measures of bilateral import surge

Growth of imports_ikt-1 0.1021 0.9482

Change in Market Share_ikt-1 0.0002 0.0033

Change in Market Share_Rest of World, kt-1 0.0099 0.0217

Std. deviation of import growth_ik 0.7246 0.6610

Trade elasticities

High Inverse Import Demand Elasticity = 1 0.7273 0.4453

High Inverse Export Supply Elasticity = 1 0.7516 0.4321

Ln (Real Exchange Rate)_it!1 1.9332 2.5510

Domestic Industry Variables

Ln(Four firm concentration ratio)_k 3.4673 0.6080

Ln(US employment_kt-1) 10.3918 0.9730

Ln(US production employment_kt-1) 10.0156 0.9888

Value-added/Shipments_kt-1 0.5127 0.1179

Inventories/Shipments_kt-1 0.1290 0.0630

Notes: 82320 observations of country i exporting goods in industry k to the US between 1997 and 2006.

32

Table 2: US Antidumping Tariff Imposition: Marginal Effects from a Binary Model: Import Growth

Basic

specification

Substitute

alternative

elasticity

measures

Add industry

size and

concentration

to (1)

Substitute

political

strength

for size

Add more

industry

characteristics

Substitute

"unexpected"

import growth

(1) (2) (3) (4) (5) (6)

Measures of bilateral import surge

Growth of imports_ikt!1 0.0004** 0.0004** 0.0003** 0.0003** 0.0003**

(0.0002) (0.0002) (0.0001) (0.0001) (0.0001)

"Unexpected" Import Growth_ ikt!1 0.0002

(0.0002)

Std. deviation of import growth_ik !0.0017*** !0.0017*** !0.0014*** !0.0013*** !0.0012*** !0.0019***

(0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0003)

Trade elasticity dummies

High Inverse Import Demand Elasticity 0.0007*** 0.0006*** 0.0006*** 0.0005*** 0.0006***

(0.0002) (0.0002) (0.0002) (0.0002) (0.0002)

High Inverse Export Supply Elasticity 0.0006*** 0.0004* 0.0004** 0.0004** 0.0004

(0.0002) (0.0002) (0.0002) (0.0002) (0.0002)

Alternative High Inv. Imp Dem Elast 0.0011***

(0.0002)

Alternative High Inv. Exp. Sup. Elast !0.0009***

(0.0002)

Ln (Real Exchange Rate)_it!1 0.0001*** 0.0001*** 0.0001*** 0.0001*** 0.0001*** 0.0001***

(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

Domestic Industry Variables

Ln(Four firm concentration ratio)_k 0.0002** 0.0003** 0.0001

(0.0001) (0.0001) (0.0001)

Ln(US employment_kt!1) 0.0006*** 0.0005***

(0.0001) (0.0001)

Ln(US production employment_kt!1) 0.0006***

(0.0001)

Value!added/Shipments_kt!1 !0.0034***

(0.0007)

Inventories/Shipments_kt!1 0.0042***

(0.0008)

Number of Observations 82320 82320 81990 81902 81922 58964

Log!likelihood !951.361 !936.79 !927.896 !924.089 !910.436 !654.388

Notes: Huber-White robust std errors in parentheses with ***,**, and * indicating statistical significance at the 1%, 5% and

10% levels.

33

Table 3: Predicted Probability of a US AD Tariff for a One S.D. Increase in...

Basic

specification

Substitute

alternative

elasticity

measures

Add industry

size and

concentration

to (1)

Substitute

political

strength

for size

Add more

industry

characteristics

Substitute

"unexpected"

import

growth

(1) (2) (3) (4) (5) (6)

Measure of bilateral import surge

Growth of imports_ikt!1 0.0020 0.0020 0.0019 0.0019 0.0019

"Unexpected" growth of imports_ikt!1 0.0000

Std. Dev. of import growth_ik 0.0005 0.0005 0.0007 0.0007 0.0008 0.0004

Trade elasticity Indicators ! discrete change

Inelastic import demand 0.0023 0.0022 0.0022 0.0021 0.0022

Inelastic export supply 0.0022 0.0020 0.0020 0.0020 0.0020

Alternative inelastic import demand 0.0027

Alternative inelastic export supply 0.0007

Domestic Industry Characteristics

Ln(4 firm concentration ratio_k) 0.0017 0.0018 0.0000

Ln(Total employment_kt!1) 0.0022 0.0021

Ln(Production employment_kt!1) 0.0022

Value!added/Shipments_kt!1 0.0011

Inventories/Shipments_kt!1 0.0019

Prob of a US antidumping duty in estimation sample 0.0016 0.0016 0.0016 0.0016 0.0016 0.0016

Number of Observations in estimation sample 82320 82320 81990 81902 81922 58964

34

Table 4: US Antidumping Tariff Imposition: Marginal Effects from a Binary Model: Change in

Market Share

Basic

specification

Substitute

alternative

elasticity

measures

Add industry

size and

concentration

to (1)

Substitute

political

strength for

size

Add more

industry

characteristics

(1) (2) (3) (4) (5)

Measures of bilateral import surge

Change in MS_ikt!1 0.0448*** 0.0436*** 0.0439*** 0.0441*** 0.0356***

(0.0106) (0.0104) (0.0099) (0.0099) (0.0093)

Change in MS_ROWkt!1 !0.0124*** !0.0100*** !0.0106*** !0.0104*** !0.0096***

(0.0034) (0.0033) (0.0035) (0.0035) (0.0030)

Std. Dev. of import growth_ik !0.0013*** !0.0013*** !0.0010*** !0.0009*** !0.0008***

(0.0002) (0.0002) (0.0002) (0.0002) (0.0002)

Trade elasticities

High Inverse Import Demand Elasticity 0.0006*** 0.0005*** 0.0005*** 0.0005***

(0.0002) (0.0002) (0.0002) (0.0001)

High Inverse Export Supply Elasticity 0.0005*** 0.0004** 0.0003** 0.0003**

(0.0002) (0.0002) (0.0002) (0.0001)

Alternative High Inv. Imp Dem Elast 0.0009***

(0.0002)

Alternative High Inv. Exp. Sup. Elast !0.0009***

(0.0002)

Ln (Real Exchange Rate)_it!1 0.0001*** 0.0001*** 0.0001*** 0.0001*** 0.0001***

(0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

Domestic Industry Characteristics

Ln(4 firm concentration ratio_k) 0.0002** 0.0003*** 0.0001

(0.0001) (0.0001) (0.0001)

Ln(Total employment_kt!1) 0.0006*** 0.0005***

(0.0001) (0.0001)

Ln(Production employment_kt!1) 0.0006***

(0.0001)

Value!added/Shipments_kt!1 !0.0034***

(0.0006)

Inventories/Shipments_kt!1 0.0030***

(0.0007)

Number of Observations 86400 86400 85992 85901 85918

Log!likelihood !917.824 !904.823 !891.546 !886.427 !871.126

Notes: Huber-White robust std errors in parentheses with ***,**, and * indicating statistical significance at the

1%, 5% and 10% levels.

35

Table 5: Predicted Probability of a US AD Tariff for a One S.D. Increase in...

Basic

specification

Substitute

alternative

elasticity

measures

Add industry

size and

concentration

to (1)

Substitute

political

strength

for size

Add more

industry

characteristics

(1) (2) (3) (4) (5)

Measure of bilateral import surge

Change in Market Share_ikt!1 0.0016 0.0016 0.0016 0.0016 0.0016

Change in Market Share_ROW,kt!1 0.0012 0.0013 0.0013 0.0013 0.0013

Std. Dev. of import growth_ik 0.0005 0.0006 0.0008 0.0008 0.0009

Trade elasticity Indicators ! discrete change

Inelastic import demand 0.0021 0.0020 0.0020 0.0020

Inelastic export supply 0.0020 0.0018 0.0018 0.0018

Alternative inelastic import demand 0.0024

Alternative inelastic export supply 0.0007

Domestic Industry Characteristics

Ln(4 firm concentration ratio_k) 0.0016 0.0017 0.0000

Ln(Total employment_kt!1) 0.0020 0.0019

Ln(Production employment_kt!1) 0.0021

Value!added/Shipments_kt!1 0.0011

Inventories/Shipments_kt!1 0.0015

Prob of a US antidumping duty in estimation sample 0.0015 0.0015 0.0015 0.0015 0.0015

Number of Observations in estimation sample 86400 86400 85992 85901 85918

36

Table 6: US Antidumping Tariff Imposition: Import Growth and Industry Effects

Basic

specification

with steel

dummy

Basic

specification

with chemical

dummy

Basic with steel

& std. dev. of

imports

interaction

Basic with

chem &

std.dev. of

imports

interaction

Industry

characteristics &

steel dummy

Industry

characteristics

& chemical

dummy

(1) (2) (3) (4) (5) (6)

Measures of bilateral import growth

Growth of imports_ikt!1 0.0003** 0.0004** 0.0003** 0.0004** 0.0003** 0.0003**

(0.0001) (0.0002) (0.0002) (0.0002) (0.0001) (0.0001)

Measures of Import Volatility

Std. deviation of import growth_ik !0.0014*** !0.0016*** !0.0018*** !0.0018*** !0.0012*** !0.0011***

(0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002)

Steel*Std.dev of imp. growth_ik 0.0027***

(0.0004)

Chemicals*Std.dev of imp growth_ik 0.0015***

(0.0004)

Trade elasticity indicators

High inverse import demand elast_k 0.0003*** 0.0006*** 0.0004** 0.0006*** 0.0002 0.0004***

(0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002)

High inverse export supply elast_k 0.0005*** 0.0005*** 0.0004** 0.0006*** 0.0004*** 0.0003*

(0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002)

Ln (Real Exchange Rate)_it!1 0.0001*** 0.0001*** 0.0001*** 0.0001*** 0.0001*** 0.0001***

(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

Domestic Industry Variables

Ln(Four firm concentration ratio)_k 0.0000 0.0001

(0.0001) (0.0001)

Ln(US employment_kt!1) 0.0003*** 0.0006***

(0.0001) (0.0001)

Value!added/Shipments_kt!1 !0.0020*** !0.0030***

(0.0006) (0.0006)

Inventories/Shipments_kt!1 0.0016** 0.0042***

(0.0008) (0.0008)

Industry indicators

Steel dummy 0.0240*** 0.0139***

(0.0048) (0.0033)

Chemical dummy 0.0043*** 0.0040***

(0.0014) (0.0014)

Number of Observations 82320 82320 82320 82320 81922 81922

Log!likelihood !872.212 !872.212 !911.152 !948.324 !857.884 !897.39

Notes: Huber!White robust std errors in parentheses with ***,**, and * indicating statistical significance at the 1%, 5% and 10% levels.

37

Table 7: US Antidumping Tariff Imposition: Import Growth and China Effects

Basic

specification

with China

dummy

Basic with

China & std.

dev.of imports

interaction

Basic with China

& elasticity

interactions

Industry

characteristics

& China

dummy

Industry

characteristics

with China &

std.dev of import

growth

interaction

Industry

characteristics

with China &

elasticity

interactions

(1) (2) (3) (4) (5) (6)

Measures of bilateral import growth

Growth of imports_ikt!1 0.0001 0.0003* 0.0001 0.0001 0.0002* 0.0001

(0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)

Measures of Import Volatility

Std. deviation of import growth_ik !0.0008*** !0.0016*** !0.0009*** !0.0004*** !0.0011*** !0.0005***

(0.0002) (0.0002) (0.0002) (0.0001) (0.0002) (0.0001)

China*Std.dev of imp. growth_ik 0.0031*** 0.0023***

(0.0005) (0.0005)

Trade elasticity indicators

High inverse import demand elast_k 0.0006*** 0.0005*** 0.0003* 0.0004*** 0.0004*** 0.0002

(0.0002) (0.0002) (0.0002) (0.0001) (0.0001) (0.0001)

High inverse export supply elast_k 0.0005*** 0.0005*** 0.0003* 0.0003** 0.0003** 0.0001

(0.0002) (0.0002) (0.0002) (0.0001) (0.0001) (0.0001)

China*High Inv Imp Dem Elast_k 0.0062*** 0.0041**

(0.0047) (0.0036)

China*High Inv Exp Sup Elast_k 0.0033** 0.0033**

(0.0030) (0.0032)

Ln (Real Exchange Rate)_it!1 0.0001*** 0.0001*** 0.0001*** 0.0001*** 0.0001*** 0.0001***

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Domestic Industry Variables

Ln(Four firm concentration ratio)_k 0.0001 0.0001 0.0001

(0.0001) (0.0001) (0.0001)

Ln(US employment_kt!1) 0.0004*** 0.0004*** 0.0005***

(0.0001) (0.0001) (0.0001)

Value!added/Shipments_kt!1 !0.0025*** !0.0021*** !0.0029***

(0.0005) (0.0006) (0.0005)

Inventories/Shipments_kt!1 0.0032*** 0.0029*** 0.0035***

(0.0005) (0.0007) (0.0006)

China indicator 0.0179*** 0.0161***

(0.0030) (0.0031)

Number of Observations 82320 82320 82320 81922 81922 81922

Log!likelihood !847.766 !909.913 !854.43 !796.305 !863.119 !804.495

Notes: Huber!White robust std errors in parentheses with ***,**, and * indicating statistical significance at the 1%, 5% and 10% levels.

38

Table 8: US Antidumping Tariff Imposition: Change in Market Share and Industry and China

Effects

Market share

specification

with steel

dummy

Market share

specification

with chemical

dummy

Market share

specification

with China

dummy

Market share

with steel

interactions

Market share

with chemical

interactions

Market share

with China

interactions

(1) (2) (3) (4) (5) (6)

Measures of bilateral import growth

Change in Market Share_ikt!1 0.0358*** 0.0443*** 0.0034 0.0396*** 0.0419*** 0.0010

(0.0093) (0.0103) (0.0089) (0.0100) (0.0104) (0.0242)

Change in Market Share_ROW,kt!1 !0.0085*** !0.0110*** !0.0067** !0.0164*** !0.0121*** !0.0141***

(0.0028) (0.0034) (0.0029) (0.0030) (0.0034) (0.0031)

Interactions with Market Share of Country i

Steel*Change in MS_ikt!1 0.0995

(0.2676)

Chemicals*Change in MS_ikt!1 0.4941***

(0.1170)

China*Change in MS_ikt!1 0.0620**

(0.0277)

Interactions with Market Share of Rest of World

Steel*Change in MS_ROW,kt!1 0.1025***

(0.0256)

Chemicals*Change in MS_ROW,kt!1 !0.0003

(0.0246)

China*Change in MS_ROW,kt!1 0.0273*

(0.0162)

Measures of Import Volatility

Std. deviation of import growth_ik !0.0011*** !0.0013*** !0.0007*** !0.0012*** !0.0013*** !0.0013***

(0.0001) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002)

Trade elasticity indicators

High inverse import demand elast_k 0.0002*** 0.0005*** 0.0005*** 0.0005*** 0.0006*** 0.0006***

(0.0002) (0.0002) (0.0001) (0.0002) (0.0002) (0.0001)

High inverse export supply elast_k 0.0005*** 0.0005*** 0.0005*** 0.0005*** 0.0005*** 0.0005**

(0.0001) !0.0002*** (0.0001) (0.0002) (0.0002) (0.0001)

Ln (Real Exchange Rate)_it!1 0.0001*** 0.0001*** 0.0001*** 0.0001*** 0.0001*** 0.0001***

(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

Indicators

Steel dummy 0.0218***

(0.0044)

Chemical dummy 0.0036***

(0.0012)

China dummy 0.0161***

(0.0030)

Number of Observations 86400 86400 86400 86400 86400 86400

Log!likelihood !839.509 !906.301 !826.732 !893.333 !909.026 !826.619

Notes: Huber!White robust std errors in parentheses with ***,**, and * indicating statistical significance at the 1%, 5% and 10% levels. 39

Table 9: Summary Statistics: Foreign Response to US AD

Variable Expected

Sign

Mean Standard

Deviation

Dependent Variables

WTO dispute (=1) or not 0.195 0.398

WTO dispute (=2), AD industry k retaliation (=1), do nothing (=0) 0.576 0.800

Explanatory Variables

Likelihood of US antidumping tariff:

predicted probability† of US AD on imports of h from Foreign in t

[!] 0.003 0.001

US market access:

log of US imports of (AD!product) h from Foreign in t – 1

[+] 10.188 1.729

Terms!of!trade spillover:

share of Foreign’s total US (AD!product) exports h to ROW in t!1

[+] 0.644 0.236

Foreign retaliation capacity:

log of total US exports to Foreign in t – 1

[+] 16.992 1.498

Foreign AD retaliation capacity:

log of US exports of k to Foreign in t – 1

[+] 8.655 2.515

Foreign DSU retaliation capacity:

share of total US exports of all other goods (" k) to Foreign in t – 1

[+] 0.073 0.079

Size of US antidumping duty:

log of 1+US imposed AD duty on h

[!] 0.292 0.290

PTA:

indicator for whether Foreign is in a US preferential trade agreement

[!] 0.085 0.280

Notes: 120 observations of US antidumping duties imposed between 1997 and 2006 against WTO members.

†Prediction from model of table 2, specification 1. Variable h is the 6!digit HS import subject to the US antidumping

tariff, k is the 4!digit HS industry, and t is the year of the beginning of the US antidumping investigation that resulted in

the newly imposed tariff.

40

Table 10: Foreign’s Binomial Probit Model Policy Response to US AD: Marginal Effects Estimates

of Filing a WTO Dispute

Dependent variable = 1 if Foreign target of US

antidumping formally challenges through a WTO dispute

Explanatory variables (1) (2) (3) (4)

Likelihood of US antidumping tariff:

predicted probability† of US AD on imports of h from Foreign in t !64.452*** !62.925*** !! !42.024*

(24.569) (20.780) (21.749)

US market access:

log of US imports of (AD!product) h from Foreign in t – 1 !! 0.068*** 0.061*** 0.050**

(0.021) (0.021) (0.020)

Terms!of!trade spillover:

share of Foreign’s total US (AD!product) exports h to ROW in t!1 !! 0.170 0.167 0.190

(0.148) (0.150) (0.170)

Foreign retaliation capacity:

log of total US exports to Foreign in t – 1 !! 0.057** 0.066** 0.044*

(0.026) (0.028) (0.023)

Size of US antidumping duty:

log of 1+US imposed AD duty on h !! !! !! !0.395***

(0.139)

PTA:

indicator for whether Foreign is in a US preferential trade agreement !! !! !! !0.022

(0.108)

Predicted probability of a WTO dispute (at mean values of the data) 0.186 0.136 0.147 0.107

Observations (number of US AD duties imposed against WTO members) 120 118 118 116

Pseudo R2 0.054 0.217 0.148 0.282

Log pseudolikelihood !56.798 !45.591 !49.577 !41.460

Notes: Marginal effects evaluated at the means of the data. Also estimated with a constant term which is suppressed. Huber!White robust

standard errors in parentheses with ***, **, and * indicating statistical significance at the 1, 5 and 10% level, respectively. †Prediction from model

of table 2, specification 1. Variable h is the 6!digit HS import subject to the US antidumping tariff, k is the 4!digit HS industry, and t is the year of

the beginning of the US antidumping investigation that resulted in the newly imposed tariff.

41

Table 11: Foreign’s Multinomial Probit Model Policy Response to US AD: Marginal Effects Esti-

mates of Filing a WTO Dispute, AD Retaliation, or Nothing

Dependent variable:

Foreign’s choice of...

Dependent variable:

Foreign’s choice of...

Dependent variable:

Foreign’s choice of...

...WTO

...AD

retaliation ...Do ...WTO

...AD

retaliation ...Do ...WTO

...AD

retaliation ...Do

Explanatory variables Dispute (=2) in k (=1) nothing (=0) Dispute (=2) in k (=1) nothing (=0) Dispute (=2) in k (=1) nothing (=0)

(1) (2) (3)

Likelihood of US antidumping tariff: !68.938*** 48.953* 19.985 !57.826** 25.256 32.569 !44.529** 20.754 23.775

predicted probability† of US AD on

imports of h from Foreign in t

(25.172) (25.166) (30.845) (23.360) (27.401) (33.547) (22.096) (29.943) (33.234)

US market access: !!! !!! !!! 0.062*** !0.043* !0.018 0.040** !0.035 !0.004

log of US imports of (AD!product) h

from Foreign in t – 1

(0.023) (0.022) (0.028) (0.019) (0.023) (0.027)

Terms!of!trade spillover: !!! !!! !!! 0.268* !0.161 !0.107 0.197 !0.117 !0.080

share of Foreign’s total US (AD!

product) exports h to ROW in t!1

(0.153) (0.178) (0.211) (0.162) (0.210) (0.243)

Foreign AD retaliation capacity: !!! !!! !!! 0.029 0.053** !0.081*** 0.033** 0.047** !0.081***

log of US exports of k to Foreign in t – 1 (0.017) (0.022) (0.024) (0.016) (0.022) (0.024)

Foreign DSU retaliation capacity: !!! !!! !!! 0.986** !1.166* 0.180 0.469 !1.029 0.561

share of total US exports of all other

goods (" k) to Foreign in t – 1

(0.482) (0.655) (0.759) (0.413) (0.732) (0.778)

Size of US antidumping duty: !!! !!! !!! !!! !!! !!! !0.346*** 0.268** 0.078

log of 1+US imposed AD duty on h (0.131) (0.132) (0.163)

PTA: !!! !!! !!! !!! !!! !!! !0.053 0.156 !0.103

indicator for whether Foreign is in a US

preferential trade agreement

(0.066) (0.208) (0.215)

Predicted probability of outcome (at mean

values of the data)

0.181 0.182 0.637 0.122 0.186 0.693 0.084 0.191 0.724

Observations (number of US AD duties

imposed against WTO members)

118 116 114

Log pseudolikelihood !105.081 !85.857 !80.430

Notes: Marginal effects evaluated at the means of the data. Also estimated with a constant term which is suppressed. Huber!White robust standard errors in parentheses with

***, **, and * indicating statistical significance at the 1, 5 and 10% level, respectively. †Prediction from model of table 2, specification 1. Variable h is the 6!digit HS import subject

to the US antidumping tariff, k is the 4!digit HS industry, and t is the year of the beginning of the US antidumping investigation that resulted in the newly imposed tariff.

42

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