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Federal Reserve Bank of Chicago
Self-Enforcing Trade Agreements:
Evidence from Time-Varying
Trade Policy
Chad P. Bown and Meredith A. Crowley
REVISED
May 2012
WP 2009-17
Self-Enforcing Trade Agreements:
Evidence from Time-Varying Trade Policy
Meredith A. Crowley∗
Chad P. Bown
The World Bank
Federal Reserve Bank of Chicago
This version: May 2012
Abstract
The Bagwell and Staiger (1990) theory of cooperative trade agreements predicts new tariffs (i) increase with imports, (ii) increase with
the inverse of the sum of the import demand and export supply elasticities, and (iii) decrease with the variance of imports. We find US
import policy during 1997-2006 to be consistent with this theory. A
one standard deviation increase in import growth, the inverse of the
sum of the import demand and export supply elasticity, and the standard deviation of import growth changes the probability that the US
imposes an antidumping tariff by 35%, by 88%, and by -76%, respectively.
∗
Bown: The World Bank, Development Research Group, Trade and International Integration, 1818 H Street NW, Mailstop: MC3-303, Washington, DC 20433, cbown@worldbank.org.
Crowley: Federal Reserve Bank of Chicago, Economic Research, 11th floor, 230 S. LaSalle,
Chicago, IL 60604, meredith.crowley@chi.frb.org. For helpful feedback, we thank three
anonymous referees, Pol Antras, Kyle Bagwell, Emily Blanchard, Marc Busch, Robert
Feinberg, James Harrigan, Bernard Hoekman, Doug Irwin, Nuno Lim˜o, Rod Ludema,
a
Giovanni Maggi, Petros Mavroidis, Rachel McCulloch, Bob Staiger, and seminar participants at American, Brandeis, BLS, Chicago Fed, ETSG, Georgetown, SEA Meetings,
UVA, and the World Bank. Aksel Erbahar, Adam Hogan, Xi Luo, and Christine Ostrowski provided excellent research assistance. Any opinions expressed in this paper are
those of the authors and do not necessarily reflect those of the World Bank, the Federal
Reserve Bank of Chicago, or the Federal Reserve System. All remaining errors are our
own.
JEL Codes: F12, F13
Keywords: trade agreements, terms of trade, anti-dumping, safeguards, WTO
In an influential paper, Bagwell and Staiger (1990) develop a model of a
cooperative trade agreement between two large countries.1 They show that,
in a dynamic, repeated trade policy-setting game, a cooperative trade policy
equilibrium characterized by relatively low trade taxes can be sustained by
the threat of infinite reversion to a Nash equilibrium of high trade taxes.
Governments optimally choose low cooperative tariffs so that they can reap
the benefits of greater trade. This cooperative equilibrium is characterized
by a positive correlation between unexpected increases in import volumes
and import tariffs. That is, when the import volume rises in response to
an output shock, the lowest import tariff that governments can sustain as
the cooperative equilibrium in the infinitely repeated dynamic trade policy
game must rise. Our paper provides the first empirical investigation of
the intertemporal and cross-sectional predictions of the Bagwell and Staiger
model.
Import tariffs in the Bagwell and Staiger model generate terms-of-trade
gains and thus vary intertemporally and cross-sectionally according to observable characteristics. The model first predicts that increases in an import
tariff are more likely when import volumes increase. Second, conditioning
on a positive import surge, the gains from (and thus the likelihood of) a
tariff increase are rising in the inverse of the sum of the export supply and
import demand elasticities. Thus, in the cross-section, a tariff increase is
more likely for an import surge of a given size if import demand and export
supply are more inelastic. Third, the gains from (and thus the likelihood of)
maintaining a cooperative equilibrium with low trade taxes are increasing
in the mean and variance of the underlying free trade volume. Therefore,
conditional on an import surge of a given magnitude, an increase in a tariff
for a product is more likely, the smaller is the variance of imports of that
product in the cross-section.
To analyze the model, we use data on increases in US import tariffs
against 49 countries under the US’s antidumping and safeguard laws over
the 1997-2006 period.2 Although trade agreements like that embodied by
1
Bagwell and Staiger (1990) helped influence a rich body of theory to understand the
trade policy choices of countries that voluntarily submit to the rules of international trade
agreements and their associated institutions (Bagwell and Staiger, 1999; Maggi, 1999;
Ossa, 2011).
2
We therefore examine whether antidumping and safeguard tariffs are consistent with
the conditions under which trade volume shocks increase the incentive for a government
to raise cooperative tariffs in order to continue participating in a self-enforcing trade
agreement. Such an interpretation is consistent with Bagwell and Staiger (1990, p. 780,
emphasis added), which states “[c]ountries can cooperatively utilize protection during
periods of exceptionally high trade volume to mitigate the incentive of any country to
1
the World Trade Organization (WTO) require member countries to establish
an upper limit on the tariff for their imported products, there are exceptions
to WTO rules which allow governments to exceed those upper tariff limits
under certain conditions. Antidumping and safeguards are two of the most
important policies that major WTO economies use when they seek to implement higher import tariffs. Furthermore, these policies are economically
important; e.g. the United States subjected 4-6% of its imported products
at the 6-digit Harmonized System level to these policies during our sample
period (Bown 2011; Prusa, 2011).
Our empirical results confirm a number of theoretical predictions from
the Bagwell and Staiger model. In our baseline specification, we find that
a one standard deviation increase in the recent growth of bilateral imports
increases the probability of an antidumping tariff by 35%. We also find that
the probability of an antidumping tariff increases as import demand and
export supply become less elastic; a one standard deviation increase in the
inverse of the sum of the import demand and export supply elasticities —
the variable formally derived from the theory — increases the probability
of an antidumping tariff by 88%. Finally, a one standard deviation increase
in the standard deviation of import growth reduces the likelihood of an
antidumping measure by 76%. Expanding our analysis to include safeguard
tariffs as well as antidumping tariffs, we find that one standard deviation
increases in these variables changes the predicted likelihood of a new timevarying tariff by 22%, 106% and -75%, respectively.
We investigate the robustness of our results to alternative explanations
for time-variation in tariffs. In particular, we extend the empirical model
to include political economy measures that have been widely utilized in the
large literature on the use of antidumping and safeguard tariffs (e.g., Finger,
Hall and Nelson, 1982; Feinberg, 1989; Knetter and Prusa, 2003; Crowley,
2011). We confirm that the quantitative importance of the key theoretical
determinants generated from the Bagwell and Staiger (1990) model — import growth, import variance, and the trade elasticities — is similar to or
greater than that of the traditional political-economy measures — industry
concentration, employment, and inventory levels — that previous research
has shown to be important determinants of these forms of time-varying tariff
protection. Most significantly, inclusion of these political economy measures
in our augmented empirical model does not affect our key findings.
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.”
2
The terms-of-trade motive for trade policy plays a critical role in the
Bagwell and Staiger (1990) theory.3 Our empirical investigation of these
economic forces complements two other recent empirical contributions documenting how trade policy formation is determined by economic incentives
in addition to political economy and income redistribution motives. Broda,
Lim˜o and Weinstein (2008) provide two pieces of evidence broadly consisa
tent with the idea that countries exploit their market power in trade. First,
they find that countries that are not members of the WTO systematically
set higher tariffs on goods that are supplied inelastically. Second, they find
that trade barriers on products not covered by the WTO agreement are
significantly higher when the importing WTO member has greater market
power. In a separate setting, Bagwell and Staiger (2011) 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 terms-of-trade effect;
the tariff to which a country negotiates is further below its non-cooperative
level, the larger was its import volume before accession negotiations began.
The current paper contributes to this empirical literature by exploiting
intertemporal and cross-sectional variation to explain government use of
time-varying trade policies. In particular, we study how countries adjust
their trade policies over time in response to shocks to trade flows and how
these adjustments vary cross-sectionally according to industry structure.
The earlier empirical literature has examined the cross-sectional variation in
a country’s tariff level (Broda, Lim˜o and Weinstein, 2008) or the magnitude
a
of a country’s tariff reduction when moving from a non-cooperative policy
to a trade agreement (Bagwell and Staiger, 2011). Our paper departs from
this literature by focusing on one important 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.
The rest of this paper proceeds as follows. Section 1 briefly reviews the
Bagwell and Staiger (1990) theory before introducing our empirical model
of US antidumping and safeguard tariff determination. Section 2 presents
a discussion of the data used in the estimation. Section 3 presents the
estimates of the model of US tariff formation over the 1997-2006 period.
Finally, section 4 concludes.
3
Irwin (1996) provides a full account of the intellectual history of the terms-of-trade
(or “optimal tariff”) theory, which he finds dates back at least to Robert Torrens in the
early nineteenth century. More recent treatments include the seminal work of Johnson
(1953-1954).
3
1
1.1
Tariffs under a Cooperative Trade Agreement
The Bagwell and Staiger (1990) theory
Bagwell and Staiger characterize the most cooperative trade policy equilibrium in a two country partial equilibrium model of trade. In this model,
stochastic output leads to fluctuations in the volume of trade over time
that provide an incentive for countries to adjust the level of trade policy restrictiveness. We focus on the empirical predictions of Bagwell and Staiger’s
extension of their model which examines trade policy under more general import demand and export supply functions, M (k ∗ , P ∗ ) and X(k, P ). Specifically, P is the (domestic) exporter’s price, P ∗ is the (foreign) importer’s
price, and k and k ∗ are general shift parameters such that ∂M (k ∗ , P ∗ )/∂k ∗ >
0 and ∂X(k, P )/∂k > 0. Letting V f designate the free trade volume of imports and exports, assume an increase in either shift parameter causes an
increase in the volume of trade, i.e., dV f /dk ∗ > 0 and dV f /dk > 0. Bagwell
and Staiger analyze the choice of a specific import tariff, τ ∗ , and a specific
export tax, τ , where P ∗ − P = τ ∗ + τ in equilibrium.
The national welfare for each country is defined as the sum of consumer’s
surplus, producer’s surplus and tariff or tax revenue and can be denoted
W (k, k ∗ , τ, τ ∗ ) for the domestic (exporting) country and W ∗ (k, k ∗ , τ, τ ∗ ) for
the foreign (importing) country. The Nash equilibrium in the one-shot trade
∗
policy setting game is characterized by an import tariff, τN (k, k ∗ ), and an
∗ ), that are each inefficiently high.4 Bagwell and Staiger
export tax, τN (k, k
use their stochastic output model to prove that, provided the discount factor
is not too high, a cooperative equilibrium characterized by an import tariff,
∗
τc , that is lower than the Nash equilibrium tariff and an export tax, τc ,
that is lower than the Nash equilibrium export tax can be supported by the
threat of infinite reversion to the Nash equilibrium in a dynamic infinitely
repeated game.5
For the most cooperative equilibrium to exist, both countries must benefit from cooperation. The “no defection” condition requires that, for every
possible volume of trade, the discounted present value of gains from cooperation to the foreign importing country, defined as ω ∗ (·), exceeds the
within-period gain of defecting from the cooperative agreement, defined as
4
Our analysis focuses on the interior solution to the one-shot game with positive trade
taxes. We rule out prohibitive trade taxes or taxes that reverse the natural direction of
trade.
5
A maintained assumption is that output follows an i.i.d. process. This, in turn,
implies that trade volume shocks are i.i.d. Bagwell and Staiger (2003) describe a richer
environment with serially correlated shocks.
4
Ω∗ (·).6 If the incentive to defect, Ω∗ (·), increases, equation (1) implies that
∗
the cooperative trade policies, τc and τc , must rise in order to maintain the
7
inequality.
∗
∗
Ω∗ (k, k ∗ , τc (k, k ∗ ), τD (k, k ∗ , τc (·)) ≤ ω ∗ (τc (k, k ∗ ), τc (k, k ∗ ))
(1)
Consider the special case of two countries that start from a most coopera∗
tive trade policy equilibrium of free trade, τc = 0, τc = 0, P ∗ (·) = P (·) = P f .
What incentive is there for the importing country to deviate from this cooperative policy? The gains to the importing country of defecting to a policy
∗
τD from a cooperative equilibrium of free trade can be written:
∗
∗
∗
Ω∗ (k, k ∗ , 0, τD ) =[P f − P (k, k ∗ , 0, τD )]M (k ∗ , P ∗ (k, k ∗ , 0, τD ))
∫ P ∗ (k,k∗ ,0,τ ∗ )
D
∗
−
[M (k ∗ , P ∗ ) − M (k ∗ , P ∗ (k, k ∗ , 0, τD ))]dP ∗
Pf
(2)
Equation (2) states that if the importing country defects to its best
∗
response tariff, τD , and the exporting country maintains a cooperative policy
of free trade, τc = 0, then the change in the importing country’s welfare in
the period in which it defects is equal to its terms-of-trade gain (the first
term) less the efficiency loss associated with distorting the consumption
price in its economy away from the free trade price and reducing the import
volume to an inefficiently low level (the second term).
Further, Bagwell and Staiger have shown, by direct calculation, that the
incentive to defect from a cooperative free trade equilibrium is increasing in
positive shocks to trade volume if and only if the efficiency loss of the tariff
policy is sufficiently small:
dΩ∗ (·)
∂M (k ∗ , P f ) [ P f ]
> 0 iff
>
f
f
dk ∗
∂k ∗
ηx + ηm
∫
P ∗ (k,k∗ ,0,τD )
∂M (k ∗ , P ∗ ) ∗
dP ,
∂k ∗
Pf
(3)
f
f
where ηx is the export supply elasticity evaluated at free trade and ηm is
the import demand elasticity evaluated (positively) at free trade.
6
Defection from the cooperative agreement by the foreign importing country consists
∗
of the importing country choosing its unilateral best response, τD (k, k∗ , τc (·)), to the
∗
domestic exporting country’s most cooperative trade policy, τc (k, k ).
7
Symmetry implies a similar “no defection” expression for the exporting country.
5
Equation (3) provides the basis for the Bagwell and Staiger result that
the most cooperative tariff increases in response to a positive import volume
shock under fairly general conditions. Intuitively, if the most cooperative
tariff fails to rise, the importing country will defect because the within-period
gain from defecting exceeds the discounted present value of infinite reversion
to the Nash equilibrium. This expression provides our first set of testable
empirical predictions. An increase in import volume raises the incentive
to defect provided that import demand and export supply are sufficiently
f
f
inelastic, i.e., 1/(ηx + ηm ) is large. Thus, the likelihood of a tariff increase
rises with an increase in import volume. Moreover, equation (3) indicates
that, for a given increase in import volume, ∂M/∂k ∗ , the likelihood of a
tariff increase is increasing cross-sectionally in the inverse of the sum of
the import demand and export supply elasticities. For highly competitive
sectors with highly elastic import demand and export supply, the inverse of
the sum of the export supply and import demand elasticity will approach
zero, providing no incentive to defect, even for large increases in import
volume.
Next, we turn to the incentives to maintain cooperation. In any period,
the gains to the importing country of maintaining cooperation can be written
as:
∗
ω ∗ (τc (k, k ∗ ), τc (k, k ∗ )) ≡
δ
∗
[EW ∗ (k, k ∗ , τc (k, k ∗ ), τc (k, k ∗ ))
1−δ
∗
− EW ∗ (k, k ∗ , τN (k, k ∗ ), τN (k, k ∗ ))]
(4)
∗
where τc (k, k ∗ ) is the cooperative import tariff, τc (k, k ∗ ) is the cooperative
∗
export tax, τN (k, k ∗ ) is the Nash equilibrium import tariff, and τN (k, k ∗ )
is the Nash equilibrium export tax. Equation (4) indicates the gains to
cooperation are equal to the discounted present value of the difference between expected welfare under cooperative trade policies and expected welfare under Nash equilibrium trade policies. While the gains to a country
of defecting from a cooperative agreement vary period-by-period with the
realization of the within-period free trade volume, the discounted present
∗
value of the expected gains to maintaining a cooperative equilibrium (τc , τc )
8
is time-invariant.
To develop empirical predictions, we consider the special case of the
Bagwell and Staiger model with linear import demand and export supply,
8
Because trade volume shocks are assumed to be i.i.d., expected welfare is timeinvariant.
6
M (k ∗ , P ∗ ) = k ∗ − aP ∗ and X(k, P ) = k + aP .9 Further, we restrict our
attention to symmetric trade policy functions in both the static and dynamic
∗
∗
games, τN (·) = τN (·) and τc (·) = τc (·) . We present the cooperative trade
∗
policies as functions of the underlying free trade volume, τc (V f ) = τc (V f ).
Starting with equation (4), direct calculation of the gains to cooperation,
where punishment involves infinite reversion to the interior Nash equilibrium
of the static game, yields:
ω ∗ (τc (V f )) = ω(τc (V f ))
) a( 2
)}
δ { 5 ( 2
=
σV f + [EV f ]2 − στ c + [Eτ c (V f )]2
1 − δ 12a
4
(5)
2
where EV f and σV f are the mean and variance of the underlying free trade
2
volume and Eτ c (V f ) and στ c are the mean and variance of the cooperative
tariff function. From equation (5), it is clear that the implications from
Bagwell and Staiger regarding the stochastic output model are preserved in
the special case of linear import demand and export supply. In particular,
the expected future gains to cooperation are increasing in the mean, EV f ,
2
and variance, σV f , of the underlying free trade volume, holding the cooper∗
ative trade policy, τc (V f ) = τc (V f ), fixed. Further calculation reveals the
following rule for the most cooperative trade policy:
{
f
0
if V f ∈ [0, V ]
∗
f
∗
f
∗
τc (V , ω ) = τc (V , ω ) = 1
(6)
f
f
(V f − V ) if V f ≥ V
2a
√
f
where V = 6aω ∗ , the cutoff value of trade volume below which the most
cooperative policy is free trade.10
As in Bagwell and Staiger, equations (5) and (6) imply that, in the crosssection, a given increase in imports above the expected value will result in
a higher cooperative tariff for the sector that has the smaller variance of
imports.11 In other words, an increase in the tariff is more likely when an
∗
For this special case, V f = (k + k∗ )/2 and τN = τN = (k + k∗ )/4a.
Note that while we treated ω ∗ as a constant for the purpose of calculating (6), ω ∗
is the function given in equation (5). Using the fixed point argument in Bagwell-Staiger,
∗
ω ∗ (·) and, thus, τc (·), can be expressed as functions of the model’s exogenous parameters.
11
The cross-sectional implications from the single sector model of Bagwell and Staiger
(1990) come from equations (19) and (20) which together imply that the magnitude 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.
9
10
7
import surge in a sector appears to be unusual. The final empirical prediction that we take to the data is therefore that a tariff increase is more likely
in sectors in which the standard deviation of that sector’s imports is lower.
Nevertheless, it is worth highlighting that the interpretation that we adopt
for our empirical specification below does rely on the single sector set-up of
the Bagwell and Staiger model. Our approach implicitly assumes that, in a
game played between countries with multiple sectors, the retaliation threat
to deviation in a single sector is localized to that sector. Empirically, this
assumption seems reasonable because governments incur non-trivial administrative costs in order to change tariffs and most retaliation threats made
under the WTO system have been limited to small sets of goods.12
1.2
An empirical model of time-varying US tariffs
Our empirical strategy is to aggregate the comparative static predictions of
equations (3), (5) and (6) into a single estimating equation. Equation (3)
indicates that the incentive to defect will vary intertemporally with changes
in import flows and cross-sectionally with the elasticities of import demand
and export supply. In particular, the terms-of-trade theory implies that a
change in imports will only affect the incentive to defect, and hence raise
cooperative tariffs, if export supply and import demand are relatively inelastic. Thus, the empirical specification must allow for an interaction between
imports and elasticities. Equations (5) and (6) together indicate that, in the
cross-section, cooperative tariff increases will be more likely and/or larger in
sectors with less volatile imports. Combining these predictions, we estimate
the following equation:
(
)
(
)
1
1
m
+ β3 Mikt ∗
+ β4 σik + εikt ,
ηxk + ηmk
ηxk + ηmk
(7)
where yikt is a measure of a trade policy change imposed against country i
for products of sector k in year t, Mikt is a measure of the change in imports
of k originating from country i in year t, 1/(ηxk + ηmk ) is the inverse of the
yikt = β0 + β1 Mikt + β2
12
More generally, in a multi-sector model in which the incentive constraints are pooled
across sectors, the associated welfare loss due to the breakdown in cooperation could reflect
the variance of trade volume aggregated across all sectors. We thank a referee for pointing
out this possibility; we leave the empirical investigation for future research. Maggi (1999)
is one theoretical approach that examines the pooling of incentive constraints in a multicountry, multi-sector model. However, his model focuses on multiple trading partners
and emphasizes the role of multilateral cooperation, rather than extending a two-country,
one-sector model to multiple sectors.
8
sum of the export supply and import demand elasticities for product k, and
m
σik is a measure of the variance of imports of product k from country i. We
augment (7) to include the change in the bilateral real exchange rate between
the importing country and country i to control for aggregate relative price
changes.
Empirically, changes in the incentive to defect can be interpreted as
affecting the probability of a tariff increase or as determining the magnitude
of a cooperative tariff increase. Our primary approach is to examine how
variation in the data affects the probability of an antidumping or safeguard
tariff across time, countries and industrial sectors. We report estimates
from both a probit model and a logit model of tariff imposition. As a
robustness check, we also use a censored Tobit model to determine the size
of antidumping tariffs that are imposed, interpreting yikt as an antidumping
tariff.
2
Data used to estimate US tariff formation
We estimate the empirical model of US antidumping and safeguard tariff formation on a panel dataset constructed from several primary data sources:
(1) trade policy data for the US come from the World Bank’s Temporary
Trade Barriers Database (Bown, 2010b), (2) US bilateral imports at the industry level come from the US International Trade Commission’s DataWeb,
(3) industry-level foreign export supply elasticities facing the US come from
Broda, Lim˜o, and Weinstein (2008), (4) industry-level US import demand
a
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 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 1.13
The ikt panel includes 49 countries denoted i, 283 North American Industry Classification System (NAICS) 2007 manufacturing industries k at
the 5- or 6- digit level of aggregation, depending upon availability, for the
years (t) 1997 through 2006.14
13
Table 1 includes footnotes which describe how some variables are scaled by factors
ranging from 1/100 to 1/10,000 prior to estimation. Our discussion of all quantitative
empirical results fully accounts for this scaling.
14
These 49 countries are: Argentina, Australia, Austria, Bangladesh, Belgium, Brazil,
Canada, Chile, China, Colombia, Costa Rica, Denmark, Ecuador, Egypt, El Salvador,
Finland, France, Germany, Greece, Hong Kong, Hungary, India, Indonesia, Ireland, Is-
9
The Temporary Trade Barriers Database provides detailed information
on US antidumping and safeguard tariffs including the date a petition to restrict imports was filed, the identity of the country accused of dumping, the
identity of countries included in the safeguard tariff, 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 (8- or 10- digit Harmonized System (HS)) trade policy data were concorded to 283 NAICS (2007 version) 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 4digit HS level were concorded to NAICS 5- and 6- digit industries. Because
multiple 4-digit HS sectors can sometimes map into each NAICS industry,
we record the median 4-digit HS export supply elasticity that maps into
each NAICS industry as the elasticity for an industrial sector. Similarly,
import demand elasticities at the 3- digit HS level were concorded to NAICS
industries with the median elasticity in each industry used as the elasticity
in the sector. To address a concern that some observations with extremely
high or low import demand or export supply elasticities could be affecting
our results, as a robustness check, we experiment with estimating our model
on a smaller sample of data for which we drop observations in which either
the inverse import demand or inverse export supply elasticity is in the top
5% or bottom 5% of the distribution of our primary estimation sample.
The leading alternative explanation for changes in tariffs over time is
that political economy concerns lead governments to protect certain sectors of the economy. Other industry characteristics are frequently used in
the literature to control for political economic determinants of an industry’s propensity to obtain import protection. We follow Staiger and Wolak
(1994) and Crowley (2011) in the choice of domestic industry characteristics
to include in our analysis. Because a free-rider problem must be overcome in
filing a request for import protection on behalf of the industry, more concenrael, Italy, Japan, Kenya, Malaysia, Mexico, Netherlands, New Zealand, Norway, Peru,
Philippines, Poland, Portugal, Singapore, South Africa, South Korea, Spain, Sweden,
Switzerland, Taiwan, Thailand, Trinidad, Turkey, United Kingdom, and Venezuela. Data
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. Because we require
two years of lagged data for our explanatory variable, we estimate the model on policy
data from 1999-2006.
10
trated industries are thought to have a higher propensity to seek and to be
awarded antidumping or safeguard tariff protection. Thus, we include the
4-firm concentration ratio (the shipments of the 4 largest shippers relative
to total industry shipments). 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 an antidumping or safeguard petition. Total
employment also serves as a measure of an industry’s political importance.
The vertical structure of an industry may matter; upstream industries producing simpler commodities 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. Finally, because the current
values of industry-specific variables may be endogenous to the antidumping
or safeguard tariff, we use lagged values of these variables in estimating the
model.
Furthermore, the WTO’s Agreement on Antidumping and the WTO’s
Agreement on Safeguards specify empirical “injury” criteria that must be
satisfied in order for a country to impose a special antidumping or safeguard tariff (Finger, Hall and Nelson, 1982; Feinberg, 1989; Knetter and
Prusa, 2003; Crowley, 2011). In some specifications, we include the ratio of
inventories to shipments to capture the WTO’s injury criteria.
3
Empirical Results: US Tariff Formation
The empirical results reported in tables 2-4 provide evidence that the United
States uses time-varying tariffs as predicted by the theoretical model of Bagwell and Staiger (1990). We examine 49 of the US’s trading partners and
find that the likelihood of an antidumping (antidumping or safeguard) tariff rises by 35% (22%) in response to a one standard deviation increase in
bilateral import growth, rises by 88% (106%) in response to a one standard
deviation increase in the inverse sum of the elasticities of export supply and
import demand, and falls by 76% (75%) in response to a one standard deviation increase in a measure of the variance of import growth. Because
the terms-of-trade theory describes the trade policy choices of large countries, we also report results for a sample limited to the US’s top ten trading
partners by import volume and find results that are quantitatively larger
for some variables.15 Analysis of the magnitude of antidumping tariffs also
aligns with the theoretical predictions of the Bagwell and Staiger model.
15
These 10 countries are Canada, China, France, Germany, Italy, Japan, Mexico, South
Korea, Taiwan, and the United Kingdom.
11
Finally, we show that our results are robust to augmenting our empirical
specification to include variables that have been widely used in the political
economy literature on antidumping and safeguard policy.
We begin by describing the reported estimates of the binary model of
the US government’s decision to impose a final antidumping (or safeguard)
tariff against country i in industry k after an investigation begun in year t.
Estimates from a probit model 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 or safeguard
tariff. From our estimating equation (7), the marginal effect of a change in
bilateral import growth, Mikt , on the probability of a tariff works through
the direct effect of( change in this variable as well as indirectly through the
a
)
1
interaction term, Mikt ∗ ηxk +ηmk . Thus, for each specification, we report
only the total marginal effect of bilateral import growth as:
(
(
))
1
∂P r(yikt = AD|x)
= ϕ(β ′ x) β1 + β3
∂Mikt
ηxk + ηmk
(8)
where we use the sample averages of β ′ x and (1/(ηxk + ηmk ) in all calculations. ϕ(·) is the standard normal density and is used in all probit specifications. Similarly, the marginal effect of a change in the inverse sum of
the elasticities of export supply and import demand works through a direct
effect and the interaction term. An analogous formula is used to calculate
the marginal effect of a change in the elasticity measure.16
3.1
Baseline Results
Turn next to the results in Table 2, which analyzes the imposition of antidumping tariffs. Consistent with the theory, new US antidumping tariffs
are more likely to be imposed when there has been a surge in past import
growth, import demand and export supply are relatively inelastic, and import growth is less volatile.
Column (1) of Table 2 presents results for the basic specification of the
model. First, the marginal effect of the growth of bilateral imports from
16
For Table 2 specification (5) we report the marginal effects of the logit model and use
[1/(1+exp(−β ′ x))]∗[1−(1/(1+exp(−β ′ x)))] for the density ϕ(·). For Table 3 specification
(5) we report the coefficients from a Tobit model. Thus, ϕ(·) is replaced with a 1 in
calculating the interactions terms. The standard error of the marginal effect of a change
in bilateral import growth on the probability of a tariff for the probit specifications is given
)2
(
1
ˆ ˆ
ˆ
ˆ
+ 2Cov[β1 , β3 ])1/2 . The logistic density is used
by ϕ(β ′ x) ∗ (V ar[β1 ] + V ar[β3 ]
ηxk +ηmk
in lieu of the normal density for calculating the standard error in Table 2 specification (5).
12
country i in industry k in the year before an antidumping petition is filed is
estimated at 4.44 and is statistically different from zero. In our discussion
of results for this model, we focus our interpretation on the increase in the
probability above the mean value, calculated by multiplying the estimated
marginal effect (e.g., 4.44) by a one standard deviation change in the explanatory variable (e.g., the lagged value of import growth of 0.947 × 10−4 ,
from Table 1). In this case, the growth of bilateral imports is associated
with an increase in the probability of an antidumping tariff of 0.04 percentage points. In the bottom panel we use our estimated probit model to
predict the probability of an antidumping tariff for a one standard deviation
increase in bilateral import growth when all other variables are evaluated at
their means. The predicted probability of 0.23% represents a 35% increase
in the likelihood of an antidumping tariff relative to its mean value.
Our second result from specification (1) is that antidumping tariffs are
more likely in sectors in which the export supply and import demand are
relatively inelastic. 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. Empirically, a one
standard deviation increase in the log of the inverse sum of the export supply
and import demand elasticities increases the probability of an antidumping
tariff by 0.09 percentage points. In the lower panel, the predicted probability from the probit model for this change in the elasticity measure is 0.32%,
an 88% increase in the likelihood of a tariff.
The other two explanatory variables in the baseline specification in Table 2 are the standard deviation of import growth and the percent change
in the bilateral real exchange rate. The marginal effect on the standard
deviation of import growth of -0.16 indicates that the likelihood of a tariff is decreasing cross-sectionally as import growth becomes more volatile.
In other words, increases in trade protection are more likely for sectors in
which an import surge is relatively unusual. A one standard deviation increase in the standard deviation of import growth reduces the probability of
an antidumping tariff to 0.04% from a sample mean of 0.17%, a decline of
76%. Finally, a real appreciation of the US dollar increases the likelihood of
an antidumping tariff. Quantitatively, a one standard deviation increase in
the bilateral real exchange rate yields a modest increase in the probability
of an antidumping tariff to 0.20%, an 18% increase relative to the mean
in the sample. This finding is in line with previous work by Knetter and
Prusa (2003) and Crowley (2011), all of which find evidence from other time
periods that the probability of an antidumping tariff is higher when the real
dollar appreciates.
13
Column (2) presents our first robustness check by using the inverse of
the sum of the export supply and import demand elasticities instead of the
natural log of its value. This is exactly the measure of market power used
in the Bagwell and Staiger model without transforming the data for this
variable to create a more normal-shaped distribution. All marginal effects
have the same signs as those reported in column (1). Quantitatively, the
predicted probabilities associated with a one standard deviation increase in
each of the variables of interest are virtually identical to those reported in
column (1).
Specification (3) provides a second robustness check to examine the sensitivity of the results to outliers in the distribution of import demand and
export supply elasticities, a concern noted in Broda, Lim˜o and Weinstein
a
(2008). For this specification, we start with the estimation sample in column (1) and drop those observations for which the inverse import demand
elasticity is in the top 5% or the bottom 5% of the distribution of inverse
import demand elasticities and the observations that are in the top 5% or
bottom 5% of the distribution of the inverse export supply elasticities. Restricting the sample in this way produces small increases in the magnitudes
of the estimated marginal effects for all variables. This generates modest
increases in the quantitative impact of each variable of interest on the predicted probabilities. A one standard deviation increase in import growth
increases the likelihood of a antidumping tariff by 42% and a one standard
deviation increase in the elasticity measure raises the probability of a tariff
by 84%. Increasing the standard deviation of import growth by one standard deviation reduces the chance of a tariff by 79%. Lastly, the predicted
probability of an antidumping tariff increases by 11% with a one standard
deviation appreciation in the real exchange rate.
Table 2 column (4) focuses on the US’s top ten trading partners by import volume. This is an important sample for examining the Bagwell and
Staiger theory as their model describes the policy choices of large countries
that are assumed be capable of influencing the terms-of-trade. In this sample, the likelihood of an antidumping tariff is more than two and a half times
larger than in the full sample. For this sample, a one standard deviation
increase in lagged bilateral import growth increases the probability of an antidumping tariff by 50%. This is modestly larger than the increase observed
in the full sample of 49 countries. A one standard deviation increase in the
elasticity measure increases the likelihood of a tariff by 59%. Increasing the
standard deviation of import growth by one standard deviation reduces the
likelihood of protection by 52%. Finally, the effect of an increase in the bilateral real exchange rate by one standard deviation is slightly larger among
14
the top 10 trading partners; it increases the likelihood of an antidumping
tariff by 33%.
The final specification of Table 2 examines the standard errors of our
estimates by implementing the variance estimator of Cameron, Gelbach and
Miller (CGM) (2011) in a logit model.17 The CGM procedure constructs
a variance estimator that allows two-way nonnested clustering. In our application, one might be concerned that errors are correlated with industry
groups, k, and within country groups, i. The marginal effects reported in
column (5) from the logit model are similar to the marginal effects from the
probit model reported in column (4) and have no discernable quantitatively
different effect on the predicted probabilities reported in the bottom panel
of Table 2. However, the CGM variance estimator yields standard errors
that are larger than the Huber-White robust standard errors reported for
the probit specifications in columns (1) - (4). In terms of hypothesis testing,
using the CGM standard errors, the marginal effect of the growth of imports
is statistically significant at the 1% level, but the statistical significance of
estimates on the natural log of the inverse sum of the export supply and
import demand elasticities and of the bilateral real exchange rate declines
to the 10% level. Using the CGM procedure, the estimate of the marginal
effect of the standard deviation of import growth is no longer statistically
different from zero.
3.2
Robustness Checks: Market Share, Safeguards, and China
Table 3 introduces a new explanatory variable to proxy for the unexpected
import surge in the Bagwell and Staiger model. Some of the papers in the
literature on the terms-of-trade theory of trade agreements (Bagwell and
Staiger, 1999; Ossa, 2011) emphasize the importance of the market access
implied by a negotiated tariff rate over tariff rates and import volumes.
In mapping the repeated static environment of Bagwell and Staiger (1990)
to an empirical environment characterized by domestic economic and trade
growth, in Table 3 we use country i’s share of the importing country’s market
as our measure of expected import volume. 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.
The first column of Table 3 reports our basic specification using the
market share variable in lieu of the import growth measure. The results
are consistent with those of the baseline specification (1) of Table 2. A one
17
Judson Caskey provided the STATA code for the CGM variance estimator in a logit
model.
15
standard deviation increase in a country’s change in US market share at
time t − 1 increases the probability of a US antidumping tariff by 18%. A
comparison of the estimates for the impacts of the other variables included
in the column (1) specifications of both Table 2 and Table 3 reveals that
they are virtually identical.
The remaining specifications in Table 3 explore the robustness of our
results through additional sensitivity analyses. Specification (2) reports estimates on a subsample of data made up of the top 10 foreign sources of
US imports during this period. It provides additional evidence that the
estimated impact of these explanatory variables is economically important.
In specification (3), we redefine the dependent variable to allow our timevarying trade policy to reflect safeguard tariffs in addition to antidumping
tariffs. While there were many fewer instances compared to antidumping in
which the United States used its safeguard policy during this time period,
a focus on antidumping alone does miss out on one particularly important
trade policy change that took place. In 2002, the United States used its
safeguard tariff to restrict imports of steel in product lines that covered
roughly $5 billion in annual US imports. Inclusion of these steel safeguard
tariffs and a few other US safeguard policy actions during 1997-2006 does not
change the qualitative nature of our results. Compared to specification (2),
the results reported in column (3) suggest a slightly larger impact (relative to
the predicted probability at the means) of the elasticities, standard deviation
of import growth, and real exchange rate.
Specification (4) presents an analysis of China, the most frequent target
of US antidumping tariffs during this period (Bown, 2010a) and an increasingly important source of US imports. While China accounts for only 2.5%
of the observations in our baseline sample of data, it is the target of 44% of
US antidumping tariffs in our sample. With the exception of the variable
capturing the change in US market share (for which the marginal effect is
positive, though not statistically different from zero), the estimated marginal
effects are of the theoretically-predicted sign and are statistically significant.
Furthermore, as the lower half of Table 3 indicates, the economic magnitudes
of their estimated impact on the probability of US tariff formation during
this period are also sizeable.18
Finally, specification (5) redefines the dependent variable as the size of
the imposed US antidumping tariff and re-estimates the model on the top 10
18
Because the sample of data in specification (4) consists of only one trading partner,
Huber-White robust standard errors should correct the variance estimator for correlated
errors within industries. This is an alternative way to address a concern that correlated
errors might be non-nested in both country groups and industry groups.
16
trading partner sample of data using a Tobit model that is censored at zero.
To interpret the quantitative significance of the estimates of the Tobit model,
we start with the observation that the mean value of the antidumping tariff
in this sample, defined as ln(1 + antidumping tariff), is reported in Table
1 as 0.0030. The antidumping tariff is reported in percentage points, thus
the value 0.0030 can be expressed as a mean tariff of 0.3%.19 Using the
estimated coefficient of 2800.09 in the top row of column (5), we find that
a one standard deviation increase in country i’s market share leads to an
increase in the dependent variable of 0.168. Adding this to the sample mean
tariff and transforming yields an increase in the antidumping tariff rate of
18.39 percentage points associated with a one standard deviation increase
in the change of country i’s US market share. A similar calculation finds
that a one standard deviation increase in the natural log of the inverse of
the sum of the export supply and import demand elasticities is associated
with a 45.27 percentage point increase in the antidumping tariff. A one
standard deviation increase in the variability of import growth reduces the
antidumping tariff rate by 26.76 percentage points. Finally, a one standard
deviation increase in the growth of the bilateral real exchange rate increases
the tariff rate by 27.41 percentage points. In summary, the results from the
Tobit model confirm the Bagwell and Staiger predictions regarding changes
in the cooperative tariff.
3.3
Model Extensions: Domestic Industry Characteristics
and Political Economy
Table 4 presents a final set of robustness checks in which we extend the
baseline model to include additional industry level covariates that the previous literature has suggested are significant determinants of time-varying
antidumping and safeguard tariffs. We first establish the benchmark by reestimating the baseline model for the full sample of trading partners with
the dependent variable now defined as an indicator for whether the United
States implemented an antidumping or safeguard tariff. Specification (1)
indicates that the size of the marginal effects for the variables motivated by
the Bagwell and Staiger theory, as well as their estimated impact on the
predicted probability of a new tariff, are consistent with the results found
thus far.
Specification (2) extends the model by adding four new industry level
19
Recall that most observations in our sample face an antidumping tariff of 0% while a
small number of observations face large positive values. The mean tariff in the sample of
the US’s top 10 trading partners, conditional on a positive duty, is 116.7%
17
covariates, three of which also have intertemporal variation. The estimated
impact of each variable for this sample of data and these trade policies is
statistically significant and consistent with our expectations based on evidence from previous research - the probability of new tariffs is increasing in
industry concentration, the number of employees in the industry, and the
ratio of inventories to shipments, whereas the probability is decreasing in
the ratio of value-added to shipments. Most relevant for our purposes is
that inclusion of these industry-level covariates does not change the sign
and the statistical significance, and it does not significantly affect the size
of the estimated marginal effects for the main variables of interest. Furthermore, it also worth noting that one standard deviation changes to the
variables motivated by the Bagwell and Staiger theory generate changes to
the predicted probability of new import tariffs that are frequently of similar or greater magnitude than these political-economic covariates that have
been the emphasis of the traditional literature. Specifically, a one standard
deviation change to the elasticities increases the predicted probability of a
new tariff by 97% to 0.63. Specification (2) indicates that the most economically important domestic industry covariate is employment; a one standard
deviation change to the number of workers in the industry increases the
predicted probability of a new tariff by 94% to 0.62.
Our final robustness check of Table 4 re-estimates specification (2) with
the inclusion of sector-level indicator variables for industries which produce
steel or chemicals products. 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 steel. Similarly, while only 2.3% of the observations
in the dataset are of chemicals, 11.8% of the antidumping policies in the
dataset are against chemical exporters. Nevertheless, the results presented
in specification (3) indicate the determinants of new US antidumping and
safeguard tariffs are robust to the introduction of special controls for these
sectors. First, the positive coefficient on the steel (chemical) indicator is
strong evidence in favor of new tariffs against exporters from these sectors
that goes beyond the basic economic variables of the Bagwell and Staiger
(1990) model; a discrete change from a non-steel (non-chemicals) to a steel
(chemicals) industry increases the probability of an antidumping tariff by 4
percentage points (1 percentage point), a large effect given that the probability of a new tariff for a non-steel, non-chemical sector is less than 1 percent.
However, even after controlling for these sectors, the estimates of the other
marginal effects are mostly unchanged, suggesting that the basic results are
not driven by observations from the steel and chemical industries. The sole
exception is the reduced impact of the elasticities variable; after controlling
18
directly for steel and chemicals in specification (3), a one standard deviation
change to the elasticities increases the predicted probability of a new tariff
by only 22% to 0.39. Nevertheless, even in this specification the result is
economically important and statistically different from zero.
To conclude this section, a large literature has explored the politicaleconomic determinants of US antidumping and safeguard tariff policy. Our
paper is the first to develop an empirical model of US tariff formation in
which antidumping and safeguard policies are treated as time-varying cooperative tariffs in a self-enforcing trade agreement. Our evidence is consistent
with the Bagwell and Staiger (1990) theory, as we find that US antidumping
and safeguard 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
variance of imports, and the less elastic are US import demand and foreign
export supply.
4
Conclusion
Our paper generates supportive evidence for the Bagwell and Staiger (1990)
model of self-enforcing trade agreements. More generally, we show that
the theory of cooperative trade agreements provides an empirically useful
framework for understanding important trade policies like antidumping and
safeguard tariffs. Using data from 1997-2006, we find that these new US
tariffs are consistent with an increase in the incentive to raise “cooperative”
tariffs as in the Bagwell and Staiger (1990) model of self-enforcing trade
agreements. This paper presents three pieces of evidence supportive of this
theory: the likelihood of these new import tariffs is increasing in the size of
import surges, decreasing in the elasticities of import demand and export
supply, and decreasing in the standard deviation of import growth. A one
standard deviation increase in each of these variables is economically important, changing the probability that these tariffs will be imposed by 35%,
by 88%, and by -76%, respectively. Our results are robust to restricting our
analysis to the US’s top ten trading partners and to analyzing the imposition of antidumping and safeguard tariffs. The results provide empirical
support for models of trade agreements that emphasize the importance of
the terms-of-trade motive in tariff setting, and they complement other empirical research (Broda, Lim˜o, and Weinstein 2008; Bagwell and Staiger,
a
2011) on trade policy formation.
This empirical investigation of US trade policy raises additional questions for future research. The use of antidumping and safeguard policies
19
has proliferated since the early 1990s; currently these policies are frequently
used by a number of major emerging economies in the WTO such as India,
China and Brazil. This use has been especially endemic to the global economic crisis of 2008-10 (Bown, 2011). To what extent does the theoretical
model of Bagwell and Staiger (1990) apply to these economies’ use of timevarying tariffs, and what other roles might such policies play in supporting
cooperative trade agreements between these economies in the WTO system?
Finally, and perhaps most importantly, a more thorough understanding of
the use of such policies would also better inform us as to the potential limits to cooperation between sovereign nations through trade agreements, an
ongoing sticking point in trade negotiations.
20
Table 1: Summary Statistics: US Antidumping and Safeguard Tariff Imposition
Full sample
Mean
St. dev.
Top 10 trading
partners only
Mean St. dev.
Antidumping (AD) tariff imposed
0.0017
0.0418
0.0046
0.0675
--
--
AD or safeguard tariff imposed
0.0032
0.0562
0.0060
0.0770
0.0323
0.1768
--
--
0.0030
0.0547
--
--
89.7
94.4
116.7
104.0
161.5
99.4
Growth of imports_ikt-1†
0.102
0.947
0.084
0.567
--
--
Change in US market share_ikt-1^
0.000
0.004
0.001
0.006
0.005
0.012
-1.991
1.517
-1.995
1.526
-1.982
1.523
0.241
0.170
--
--
--
--
Standard deviation of import growth_ik^
0.723
0.660
0.378
0.435
0.393
0.425
Percent change in real exchange rate_it-1‡
0.007
0.116
0.001
0.087
0.017
0.015
3.468
0.608
--
--
--
--
10.377
1.029
--
--
--
--
Value-added/Shipments_kt-1*‡
0.513
0.118
--
--
--
--
Inventories/Shipments_kt-1*‡
0.129
0.063
--
--
--
--
Indicator for industry k is steel*
0.013
0.113
--
--
--
--
Indicator for industry k is chemicals*
0.021
0.144
--
--
--
--
China only
Mean St. dev.
Dependent Variables
ln(1+AD tariff)
AD tariff conditional on a positive value
Explanatory Variables
ln 1/
1/
f
x
f
x
f
m
f
m
_k ‡
_k ^
Domestic industry variables
ln(Four firm concentration ratio)_k*‡
ln(Employment)_kt-1*‡
Observations
82,341
20,775
2,075
-4
*These variables are based on only 81,943 observations. † Rescaled by a factor of 10 for estimation.
-2
-3
^ Rescaled by a factor of 10 for estimation. ‡ Rescaled by a factor of 10 for estimation.
21
Table 2: US Antidumping Tariff Imposition: Marginal Effects from a Binary
Model using Import Growth
Baseline
specification
(1)
Substitute
alternative
elasticity
measures
(2)
Remove
elasticity
outliers
(3)
Top 10
trading
partners
only
(4)
Logit
model with
multiway
clustering
(5)
4.44***
( 1.55)
4.86***
(1.75)
5.66***
(1.63)
28.93***
(8.59)
27.58***
(9.69)
0.58***
(0.14)
--
0.86***
(0.20)
1.36***
(0.39)
1.31*
(0.75)
--
0.36***
(0.05)
--
--
--
-0.16***
(0.02)
-0.18***
(0.02)
-0.18***
(0.03)
-0.54***
(0.16)
-0.54
(0.45)
1.09**
(0.55)
1.15**
(0.58)
1.07*
(0.59)
13.91***
(2.91)
12.05*
(7.13)
82,341
-1002.19
82,341
-998.17
67,262
-857.30
20.775
-582.18
20,775
-582.23
...at means
0.17
0.17
0.19
0.46
0.46
…for one standard deviation increase
to growth of imports
0.23
0.24
0.27
0.69
0.71
…for one standard deviation increase
to elasticities
0.32
0.26
0.35
0.73
0.74
…for one standard deviation increase
to standard deviation of import
growth
0.04
0.04
0.04
0.22
0.22
…for one standard deviation increase
to real exchange rate
0.20
0.20
0.21
0.61
0.60
Growth of imports_ikt-1
ln 1/
1/
f
x
f
x
f
m
f
m
_k
_k
Standard deviation of import growth_ik
Percent change in real exchange rate_it-1
Observations
Log-likelihood
Predicted probability of antidumping
tariff, expressed in percent,† ...
Notes: Dependent variable is a binary indicator that a US antidumping tariff was imposed on exporting country i in industry k
after an investigation initiated in year t. Probit model used to estimate all specifications except for the logit model used to
estimate specification (5). Huber-White robust standard errors in parentheses, except for specification (5) which implements
Cameron, Gelbach and Miller (2011) multiway clustering on industry and trading partner. ***, **,* indicate statistical
significance of marginal effects at the 1%, 5% and 10% levels, respectively. †Predicted probabilities expressed in percent
terms; e.g., 0.17 is a predicted probability of seventeen hundredths of one percent, or 0.0017.
22
Table 3: US Antidumping and Safeguard Tariff Imposition: Marginal Effects
from a Binary Model using Change in Market Share
Substitute
change in US
market share
for import
growth
(1)
Top 10
trading
partners
only
(2)
AD and
safeguard
tariff
policies‡
(3)
China
only‡
(4)
Tobit model
with
dependent
variable as
ln(1+AD tariff)
(5)
5.48***
( 0.87)
14.41***
(2.80)
15.82***
(3.13)
18.28
(22.67)
2800.09***
(553.22)
0.58***
(0.14)
1.35***
(0.42)
1.86***
(0.48)
6.76**
(2.95)
244.10***
(77.78)
-0.15***
(0.02)
-0.38***
(0.12)
-0.60***
(0.15)
-3.26***
(1.06)
-73.25***
(24.64)
Percent change in
real exchange rate_it-1
1.20**
(0.61)
14.82***
(3.08)
22.50***
(3.56)
582.99***
(217.19)
2777.37***
(518.14)
Observations
Log-likelihood
82,341
-995.40
20,775
-579.51
20,775
-716.28
2,075
-285.03
20,775
-634.95
...at means
0.17
0.46
0.60
3.23
--
…for one std. dev. increase to
change in US market share
0.20
0.56
0.72
3.44
--
…for one std. dev. increase to
elasticities
0.32
0.72
0.99
4.38
--
…for one std. dev. increase to std.
dev. of import growth
0.05
0.27
0.29
1.79
--
…for one std. dev. increase to
percent change in real exchange
rate
0.20
0.61
0.85
4.19
--
Change in US market share_ikt-1
ln 1/
f
x
f
m
_k
Standard deviation of
import growth_ik
Predicted probability of antidumping
(or safeguard)‡ tariff, expressed in
percent,†
Notes: Dependent variable for specifications (1) and (2) is a binary indicator that a US antidumping tariff was imposed
on exporting country i in industry k after an investigation initiated in year t. ‡Antidumping or safeguard tariff
indicator used as dependent variable in specifications (3) and (4). Probit model used to estimate all specifications
except for the Tobit model (censored at zero) used to estimate specification (5). Huber-White robust standard errors
in parentheses. ***, **,* indicate statistical significance of marginal effects at the 1%, 5% and 10% levels,
respectively. †Predicted probabilities expressed in percent terms; e.g., 0.17 is a predicted probability of seventeen
hundredths of one percent, or 0.0017.
23
Table 4: US Antidumping and Safeguard Tariff Imposition: Import Growth
and Industry Effects
Antidumping
and
safeguard
tariff policies
(1)
Add
politicaleconomy
covariates
(2)
Add steel
and
chemical
indicators
(3)
6.11***
(1.71)
3.44***
(1.14)
3.34**
(1.37)
0.39
0.38
0.39
1.19***
(0.19)
0.71***
(0.12)
0.24***
(0.06)
0.66
0.63
0.39
Standard deviation of
import growth_ik
-0.25***
(0.03)
-0.14***
(0.02)
-0.16***
(0.02)
0.08
0.10
0.09
Percent change in
real exchange rate_it-1
4.91***
(0.65)
2.70***
(0.48)
2.75***
(0.43)
0.42
0.40
0.40
ln(Four firm conc. ratio)_k
--
0.25***
(0.10)
0.13
(0.11)
--
0.38
0.35
ln(Employment)_kt-1
--
1.04***
(0.14)
0.70***
(0.10)
--
0.62
0.47
Value-added/Shipments_kt-1
--
-3.58***
(0.64)
-1.08**
(0.54)
--
0.20
0.28
Inventories/Shipments_kt-1
--
6.82***
(0.98)
4.98***
(0.75)
--
0.47
0.41
Indicator for industry k is
steel
--
--
0.04***
(0.01)
--
--
0.33
Indicator for industry k is
chemicals
--
--
0.01***
(0.00)
--
--
0.39
0.32
0.32
0.32
Growth of imports_ikt-1
ln 1/
f
x
f
m
_k
Predicted probability of antidumping or
safeguard tariff for one standard
deviation increase in each explanatory
variable, expressed in percent†
(1)
(2)
(3)
Domestic industry variables
Predicted probability of antidumping or safeguard tariff,
expressed in percent,† at means
Observations
Log-likelihood
81,943
-1631.52
81,943
-1512.05
81,943
-1346.50
Notes: Dependent variable is a binary indicator that a US antidumping tariff or safeguard was imposed on exporting country i
in industry k after an investigation initiated in year t. Probit model used to estimate all specifications. Huber-White robust
standard errors in parentheses. ***, **,* indicate statistical significance of marginal effects at the 1%, 5% and 10% levels,
respectively. †Predicted probabilities expressed in percent terms; e.g., 0.32 is a predicted probability of thirty-two hundredths
of one percent, or 0.0032.
24
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Activity: Microeconomics pp. 51–118.
26
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6
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