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Does Tariff Liberalization Kick the Good Apples Out?
Theory and Evidence
Alan C. Spearot∗
University of California - Santa Cruz
February 24, 2011
Abstract
This paper examines the liberalization of a common tariff when imported varieties vary
in quality and cost. Varieties of higher quality and/or lower cost (a) are imported at lower
absolute demand elasticities and (b) earn higher revenues. By virtue of larger demand elas-
ticities, low revenue varieties benefit the most from tariff liberalization. Further, if varieties
are substitutable, low revenue varieties may benefit at the expense of high revenue varieties.
These predictions are confirmed using a case study of US Uruguay Round tariff cuts, where
within narrowly defined products, low revenue exporters experienced large gains, and high rev-
enue exporters experienced negligible gains. Further, I find evidence suggesting that product
quality played a role in governing the effects of tariffs on trade flows.
JEL Classifications: F12, F13, F14
Keywords: Firm Heterogeneity, Trade Liberalization, Quality, Non-discrimination
∗Email: [email protected]. Address: Economics Department, 1156 High Street, Santa Cruz, CA, 95064.I thank Pol Antras, David Hummels, Wolfgang Keller, Dirk Krueger, Volodymyr Lugovskyy, Phillip McCalman,Peter Morrow, Thanh Xuan Nguyen, Anson Soderbery, Robert Staiger and seminar participants at the 2009 OtagoWorkshop on International Trade, the London School of Economics, the 2009 Asia Pacific Economics AssociationAnnual Conference, the 2009 Empirical Investigations in Trade and Geography conference, the University of Colorado,the Stanford Institute for Theoretical Economics, and the University of Michigan for very helpful comments. I alsothank James Allen from the New Zealand Ministry of Foreign Affairs and Trade for HS8 trade data used for anearlier working paper. All remaining errors are my own.
1
1 Introduction
If one point has been made abundantly clear by the theory of international trade, it is that trade
is often driven by heterogeneity. This heterogeneity can result from classical Ricardian differences
in relative autarky prices, to the modern treatment of heterogeneity at the firm level. Regardless
of the source, heterogeneity almost always reflects in prices and/or market shares. And, as many
authors have documented (Schott 2004, in particular), trade data exhibits substantial variation in
unit values and trade volumes within precise product categories.1
However, despite the massive amount of heterogeneity in varieties across trading partners within
narrowly defined products, the rules of the GATT/WTO, on a basic level, seem designed for a more
homogenous environment. With regard to tariffs, this point is particularly salient, as outside of spe-
cial safeguards, retaliatory measures, and all-or-nothing regional agreements, members have very
little latitude regarding tariffs applied to different varieties of the same product. One particu-
lar guiding principle, “non-discrimination”, imposes that all GATT/WTO members receive equal
treatment, usually via a common, or “most favored nation” (MFN), tariff. This applies within any
product, across all export sources without preferential status, and (obviously) does not discrimi-
nate by quality or other characteristics. Further, this equal treatment rule extends to the process
of liberalization, and allows for no consideration of which exporters stand to gain more or less from
lower tariffs.
Overall, there is a clear friction between the precise intent of WTO rules regarding tariffs, and
the natural differentiation which occurs in trade flows. Critically, even though the WTO prefers
(and arguably promotes) the multilateral process over other preferential or regional schemes, it
remains unclear how the effects of MFN liberalization accrue within products that exhibit significant
within-product differentiation. In what way should MFN tariff reductions influence bilateral trade
flows? Are certain countries more likely to gain from MFN liberalization based on the fundamental
characteristics of the products they sell? Overall, how are the benefits of additional import market
access distributed across competing and differentiated exporters?
This paper answers these questions. Using a simple theoretical model based on Melitz and
Ottaviano (2008), I show that the liberalization of a common ad-valorem tariff need not increase
bilateral imports of all varieties. In particular, if varieties are differentiated by quality and/or
1As an example, consider the import of “Men’s or Boys’ Shirts, of Cotton” (HS6 code 620520) by New Zealand in1999. Overall, New Zealand imported varieties within this product category from 50 different countries. While theaverage pre-tariff free-on-board unit value was $20 exporter-specific unit values varied substantially. On one end ofthe spectrum, Belgium exported varieties at an average of $75 per unit. On the other end, Indonesia exported at anaverage of $3.60 per unit. Clearly, imports from Belgium and imports from Indonesia are in fact very different. Yet,the WTO mandates that these products are treated equally in setting tariffs, and when liberalizing tariffs.
2
production cost, I show that liberalization of a common tariff may increase imports of low revenue
varieties at the expense of high revenue varieties. In analyzing the effects of US Uruguay Round
tariff cuts, I find robust support for the model. Specifically, I find that the benefits of import
tariff liberalization are large and statistically significant for low revenue varieties and smaller and
statistically insignificant for high revenue varieties. Further, I find that this relationship breaks
down when characterizing varieties by their relative price within products, which as detailed below,
suggests that quality plays a role in governing the effects of tariffs on trade flows.
The key to the model is the degree to which demand elasticities vary across varieties. Specifically,
varieties will be sold at a lower (absolute) demand elasticity if they are of higher quality and/or
produced at a lower cost. In both cases, varieties earn larger revenues. Since demand is fairly
inelastic for these varieties, changes to tariffs have a fairly small direct effect on the value of bilateral
imports. This is in stark contrast with varieties of low quality and/or produced at a high cost. In
these cases, revenues are fairly small, and equilibrium demand elasticities are relatively large. Thus,
for these varieties, changes to tariffs have a relatively large effect on the value of bilateral imports.
In equilibrium, the effects of changes to a common tariff are aggregated across all varieties, and
competition is generally tougher when tariffs fall. That is, when tariffs fall, so does the residual
demand for each variety, all else equal. It is the resolution of the tension between this aggregate
effect and the effects related to demand elasticities which determine the varieties that benefit from
tariff liberalization and to what extent. In particular, I show that the aggregate effect may in
fact be larger than the direct effect for some varieties. As detailed above, these are the varieties
that earn relatively large revenues before tariffs are cut. Thus, as a novel result, I show that tariff
liberalization may in fact decrease imports of varieties that earn large revenues before the tariff
cut. In contrast, I show that varieties earning low revenues always benefit from tariff liberalization
by virtue of the high demand elasticity at which these varieties are sold. Overall, I show that the
traditional negative effects of tariffs are amplified for low revenue varieties, and smaller and/or of
opposite sign for high revenue varieties.
Empirically, the model is evaluated using a case study of tariff reductions by the United States
resulting from Uruguay Round GATT negotiations. Despite being a result of bargaining within
the GATT, this case study is sensible on a number of levels. For one, data are available at the
HS10 level, which provides a useful amount of detail within narrowly defined products.2 Second,
reductions to MFN tariffs were relatively quick, most of which occurring over the period 1995-
1999. Third, the evidence suggests that import growth rates before the Uruguay Round agreements
2For example, “Grand Pianos” is an HS8 product, and HS10 provides detail such as “Containing a case measuringless than 152.40 cm in length”.
3
were drafted were independent from whether or not an HS8 product received a tariff cut after the
Uruguay Round was completed. Finally, the US HS8 tariff data provides information on whether
non-tariff barriers were present. As these policy instruments are outside the scope of the model,
such information is crucial, and not available in other datasets such as TRAINS.
Using a number of different specifications, I find evidence which is broadly supportive of the
model. Regressing bilateral import growth rates on tariffs and an interaction with pre-Uruguay
Round import values, I find that within-products, bilateral import elasticities (with respect to
tariffs) were large, significant, and negative for the export sources with the smallest pre-Uruguay
Round import values. In contrast, estimated elasticities are small and, with few exceptions, insignif-
icantly different from zero for export sources with the largest pre-Uruguay Round import values.
Further, I find that this result is sharpened when focusing on products with below-median levels of
exporter concentration, and in products with a relatively large number of varieties. These results
are also fairly robust to a large set of supplemental regressions which control for an exporter-specific
response to US tariffs and potential outlier exporters.
Finally, to address a possible role for quality rather than only cost-heterogeneity, I expand on
the main theoretical result by showing that if quality plays a role, variety-specific revenue is the
only measure that captures the characteristics relevant for assessing the effects of tariffs. That is,
without precise assumptions over the relationship between quality and costs, there will not exist a
monotone relationship between other measures (prices and quantities) and demand elasticity when
quality is present. Empirically, this is confirmed in the data, where I show that ordering varieties
by their relative price within products rather than revenues results in a sizeable drop in power in
predicting the effects of tariff cuts. Further, I find that low price varieties benefited the most from
tariff cuts, suggesting that many low price varieties are also low quality varieties. Hence, the data
suggests that quality is playing a role in governing the effects of tariffs on bilateral trade flows.
Related literature
This paper adds to a number of different areas related to trade, firm heterogeneity, and trade policy.
Most notably, it is related to the burgeoning literature on the role quality and cost heterogeneity play
in trade flows. The results are reminiscent of Hummels and Skiba (2004), who evaluate the effect
of trade costs and tariffs on the average price of US exports. The allowance for both quality and
cost dimensions is similar to Baldwin and Harrigan (2011), and Johnson (2009). However, unlike
both papers, I do not assume a specific relationship between quality and costs, rather showing that
variety-specific revenue captures the characteristics necessary to evaluate a variety-specific effect
4
of tariffs. Indeed, the use of revenues as measure that captures characteristics of both costs and
quality is similar to recent work by Hallak and Sivadassan (2009).
The rather robust response of low revenue varieties is similar to Kehoe and Ruhl (2009). Indeed,
motivated in-part by the results in Kehoe and Ruhl (2009), Arkolakis (2009) presents a framework
based on endogenous marketing costs that generates a larger response to trade liberalization by low
revenue varieties. While similar to the results in my paper, the results are driven by a completely
different mechanism. Further, there is one crucial qualitative difference in equilibrium predictions,
where the Arkolakis framework guarantees that all firms gain from liberalization, which is not the
case in my paper.3 Indeed, in the forthcoming empirical work, the top-20% of exporters (within
HS8 products) rarely increase trade after tariff liberalization occurs.
There is a relatively recent literature examining the design of the WTO within the context of
heterogeneous countries and products. Saggi (2004) compares optimal tariffs set on an MFN basis
with those set via unconstrained discrimination in a n-country oligopoly model with heterogeneous
suppliers. Generally, Saggi’s model suggests that MFN tariff-setting benefits low cost suppliers
since the optimal tariffs applied to their exports would be lower. The model does not examine the
removal of tariffs on an MFN basis, and the corresponding effects on trade, which is the focus of
my work.4
On broader level, my paper is related to the work of Rose (2004) and Subramanian and Wei
(2007), who estimate the effects of GATT/WTO membership on bilateral trade flows using a stan-
dard gravity model. Critically, my model suggests that this particular class of empirical studies is
misspecified by failing to allow for differential effects of GATT/WTO membership as a function of
pre-GATT/WTO market penetration within products. In this way, the results detailed below may
also motivate modifications of the standard Anderson and Van Wincoop (2003) gravity model.
In terms of the US case study employed in this paper, a number of papers are relevant. Romalis
(2006) shows that MFN liberalization by the United States increased both the degree of openness
and the growth rates of developing countries. Ludema and Mayda (2009) examine the relationship
between exporter concentration and observed tariff concessions by the US. In particular, they show
that the US offered larger tariff concessions in products with a more concentrated group of export
sources. Finally, very recent work by Feenstra and Weinstein (2010) adapts the methods from
3Via a CES demand assumption, all firms in the Arkolakis (2009) framework receive the same percentage demandshock resulting from tariffs. Thus, all firms gain from a reduction in a common iceberg transport cost, but differ intheir supply response based on the marginal cost of reaching a larger fraction of consumers.
4In later work, Saggi and Sara (2007) uses a two country model to examine the National Treatment clause whenproducts may differ in quality. A similar result is reached as in Saggi (2004), where a national treatment clause tendsto help those exporters selling the most competitive goods (highest quality goods).
5
Feenstra (1994) to estimate the gains for trade using a non-CES (translog) demand system. Their
estimates imply pricing/mark-up behavior which is consistent with Melitz and Ottaviano (2008) -
the model at the heart of my paper.
The rest of the paper is organized as follows. Section two presents a simple theoretical model,
with an extension provided in the appendix. Section three details the dataset, and tests the pre-
dictions from section two. In section four, I briefly conclude.
2 Theory
Consumers
The key to the model is the way in which consumers value product variety and quality. Similar to
Melitz and Ottaviano (2008), I assume that consumer preferences are quasi-linear, non-homothetic,
and exhibit love-of-variety within a differentiated sector. However, I depart from Melitz and Otta-
viano by assuming that within the differentiated sector consumers earn utility based on “quality-
adjusted consumption” of each variety. Preferences of this type can be defined as follows:
U = x0 + θ
∫i∈Ω
λiqidi−1
2η
(∫i∈Ω
λiqidi
)2
− 1
2γ
∫i∈Ω
(λiqi)2 di (1)
Here, Ω is the measure of available varieties, qi is the consumption of variety i, and λi is its
associated quality. Thus, quality-adjusted consumption of variety i is defined as λiqi. The precise
role of quality will be discussed shortly. Further, x0 is the numeraire good, where θ (> 0) and η
(> 0) determine the substitution pattern between the differentiated industry and the numeraire.
Finally, γ (> 0) represents the degree to which consumers value product variety.
The budget constraint faced by consumers is written as,
x0 +
∫i∈Ω
pciqidi ≤ I
where I is income of the representative consumer, and pi is the price of variety i. Solving the
maximization problem of the representative consumer yields the following inverse demand function
for each variety,
pci = λi (A− γλiqi) (2)
where,
A = θ − η∫i∈Ω
λiqidi
6
Here, pci is the price at which consumers purchase varieties. Also, note that the demand intercept,
A, embodies the competitiveness of the differentiated market. All else equal, the market is more
competitive if the quality-adjusted consumption of all varieties,∫i∈Ω
λiqidi, is larger.
Before moving to the firm’s problem, it is instructive to examine the effects of variety-specific
quality, λi, on the demand for each variety. First, note that by differentiating (2), the willingness
to pay for a variety is increasing in quality if:
∂pci∂λi
= A− 2γλiq > 0
This will be satisfied in equilibrium, as A− 2γλiq > 0 when marginal revenue is positive. Second,
note the equation for demand elasticity as a function of variety specific quality and price:
εD,i =∂qiqi
pci∂pci
= − pciAλi − pci
Two results are apparent in the equation for εD,i. First, conditional on quality, varieties sold at
lower prices will be sold at lower absolute demand elasticities. Further, conditional on price, higher
quality varieties (higher λi) will be sold at lower absolute demand elasticities. Given that consumers
will tend to spend higher shares of their income on varieties with a lower quality-adjusted price,
in equilibrium, there will be a negative relationship between the absolute elasticity of demand and
variety-specific revenues. The relationship between revenues and elasticities is crucial for the main
theoretical results of the paper, and the link to the empirics.
Firms
For this particular model, each supplier will produce one variety, and thus suppliers are also indexed
by i. Further, supplier i will produce variety i at a constant marginal cost, ci. I will not assume
a specific relationship between the marginal cost of production ci and the quality of each variety,
λi (different from Baldwin and Harrigan, 2011, and Johnson, 2009). It will soon be clear that
the relationship between marginal costs and quality has no bearing on the results outside of their
relationship to the observed distribution of revenues within products.
For simplicity, I assume away the extensive margin of trade, focusing instead on a fixed measure
N of imported foreign varieties. An extended version of the model with an active extensive margin
is presented and discussed in the appendix. I also assume that there exists no domestic sector.5 To
5The results of the model go through with a domestic sector. The only difference is that a domestic sector tempersthe effect of tariffs on the aggregate measure, A. However, without reliable data that reports the size of the domestic
7
sell a variety in the import market, the supplier must pay an ad-valorem tariff τ for each unit sold.
Hence, the relationship between the consumer price detailed above and the price that producers
receive is pci = (1 + τ)psi . This yields the following inverse demand function that foreign suppliers
use to optimally set production for the import market.
psi =λit
(A− γλiqi)
Here, t = (1 + τ). Suppliers choose quantities to maximize profits:
π (λi, ci) = maxqi
λit
(A− γλiqi) · qi − ciqi
Optimal production of a given variety i, q (λi, ci), is written as,
q (λi, ci) =A− ci
λit
2γλi, (3)
where the price received for this variety is,
p (λi, ci) =λit
(A+ ci
λit
2
), (4)
and finally, the pre-tariff value of imports of variety i is written as:
v
(ciλi
)=
A2 −(ciλi
)2
t2
4γt
. (5)
As trade data reports the value of trade, and not the profits of each supplier, equation (5) is the
object of interest. The value of trade will be affected by tariffs through two channels. The first is
directly via the negative effect of tariffs on variety-specific revenue. This can be seen by the negative
impact of tariffs on the numerator of (5), and the positive impact of tariffs on the denominator of
(5). However, there are also indirect effects of tariffs through the competitiveness term, A:
A = θ − ηQ
= θ − ηNq
sector at the HS8 level, I cannot test this aspect of the model. Hence, I choose to develop the model without adomestic sector.
8
Here, q is the average quantity of imported varieties. Defining(cλ
)as average quality-adjusted costs
of imported varieties, and solving for A, we have,
A =
(2γ
2γ + ηN
)θ +
(ηN
2γ + ηN
)( cλ
)t (6)
In (6), higher tariffs increase A. Intuitively, higher tariffs decrease quality-adjusted production for
the import market, thus making the market less competitive. This yields a higher residual demand
for each variety, A. A useful way to characterize this result will be the elasticity of A with respect
to t:
εA ≡∂A
∂t
t
A≡
η(cλ
)t
2γθN
+ η(cλ
)t∈ (0, 1) (7)
While A is clearly increasing in t, on a percentage basis, A increases slower than t. Further, εA is
increasing in N and η. Respectively, these imply that in more competitive markets, in which there
is a larger degree of substitution between the differentiated sector and the numeraire, the response
of A is stronger relative to a change in t.
Trade Liberalization
As stated in the introduction, the goal of this paper is to evaluate how the effects of trade liber-
alization are distributed across export suppliers that may not sell varieties of the same quality, or
varieties produced at the same marginal cost. By taking logs and then fully differentiating (5) with
respect to all endogenous variables and t, I can write the following:
∂vivi
=2A∂A− 2t
(ciλi
)2
∂t
A2 −(ciλi
)2
t2− ∂t
t
To simplify this expression, I will exploit the monotone link between(ciλi
)2
and v(ciλi
). Solving
for(ciλi
)2
in (5) and substituting into the above equation yields a simple relationship between the
marginal effect of tariffs and the value of imports prior to the change in tariffs.
∂vi =A2
<0︷ ︸︸ ︷(εA − 1)
2γt
∂t
t+ vi
∂t
t(8)
9
Defined as a total elasticity of import value with respect to tariffs, I can write:
εv,i ≡∂vi∂t
t
vi=A2
<0︷ ︸︸ ︷(εA − 1)
2γtvi+ 1 (9)
In (8), the marginal effect of tariffs is a function of two terms. The first is a negative effect which
is common across all suppliers. The second is specific to the value of imports prior to the change
in tariffs, where the negative effect of tariffs is mitigated for higher value imports. This result is
summarized in the first proposition of the paper.
Proposition 1 The elasticity of trade value with respect to tariffs for variety i is increasing in
revenues earned by variety i.
Proof. Immediate from (8)
The result in Proposition 1 simply establishes that there is a disparate effect of tariffs across
imports within products, where specifically, these effects are governed by the elasticity at which each
variety is imported. In this case, high-quality and/or low-cost varieties are consumed at a lower
absolute elasticity of demand, and hence, experience a smaller effect from a percentage supply shock
(in this case a tariff cut).
There exist other papers, Arkolakis (2009) in particular, that yield a similar result to Proposition
1 but are not due to differences in demand elasticity. I will discuss this and other papers later in the
manuscript. However, in order to set-up a comparison of the existing literature with the demand-
side effects which are the focus of this paper, I will first go deeper into the quality-cost framework,
and in particular, the way in which tariffs induce shifts in demand for each variety.
Crucially, when noting that the tariff changes in this model are pervasive across all imported
varieties (MFN tariff cuts), competition is more fierce after the tariff cut. This is embodied in the
residual demand level, A, which falls as tariffs fall. In equilibrium, the fall in A may be larger or
smaller than the direct effects summarized in Proposition 1. I now examine when, and for which
varieties, changes to A are large enough to outweigh these direct effects. Precisely, lower tariffs
increase imports of variety i only if the value of imports prior to lower tariffs is sufficiently small.
That is, given ∂tt< 0, ∂vi > 0 only if:
vi <A2 (1− εA)
2γt
10
In contrast, when vi is sufficiently high, it is possible that lower tariffs decrease trade. To see this,
using (5), the highest possible value of trade is written as:
vmax = v (0) =A2
4γt
Substituting vmax into ∂vi, we have:
∂vmaxi =
A2 (2εA − 1)
4γt
∂t
t
Thus,∂vmaxi
∂t> 0 if εA > 1
2. In other words, the percentage change in residual demand A must be
sufficiently large. Using (7), as a function of model parameters, this occurs if:( cλ
)t >
2γθ
ηN(10)
This condition is summarized in the following proposition:
Proposition 2 If γθηN
is relatively small, then tariff liberalization increases imports only if pre-
tariff-cut revenues are sufficiently small.
As detailed in Proposition 2, within this adjusted Melitz-Ottaviano framework, it is possible for
the value imports of a given variety to fall when tariffs are cut. This is more likely to occur when love
of variety γ is small, η is large or N is large. Hence, in the forthcoming empirical work, along with
the differential effect of tariffs described above, I will focus on the absolute tariff-response of high
revenue varieties in order to examine whether variety-specific demand elasticities are empirically
relevant in the way the models suggests. Further, as N can be approximated in the empirical
framework, I will evaluate how these effects change when looking at products with relatively large
values of N .
The Role of Quality
A second way to distinguish this particular theory from alternate frameworks is to focus on the role
of quality, and in particular, the role quality plays in determining revenues, prices, and demand
elasticities.
When demand is of constant elasticity and consumers make decisions on quality-adjusted quan-
tity (utility defined over λiqi), within a CES demand system, quality and cost have no effect on
equilibrium demand elasticities. In contrast, within the quadratic preferences described above,
11
quality and cost have a very clear effect. Precisely, equilibrium absolute demand elasticities are
written as:
εD,i =A+ ci
λi
A− ciλi
(11)
In (11), equilibrium demand elasticities are increasing in costs and decreasing in quality, which
implies a monotone relationship between quality-adjusted costs and absolute demand elasticity.
In deriving the elasticity of trade value with respect to tariffs, this relationship is crucial, since
there is also a monotone, in this case negative, relationship between vi and ciλi
. Hence, initial trade
value captures exactly the variety-specific characteristic (demand elasticity) required for the main
propositions in the paper.
In contrast, when quality plays a role, variety-specific quantity and price do not necessarily
capture the necessary characteristics related to demand elasticities. To see this, first focus on
variety-specific price, p (λi, ci) = λit
(A+
ciλit
2
). Here, p (λi, ci) is increasing in both variety-specific
quality and cost. However, since absolute demand elasticities are falling in quality and rising in
cost, this implies that unless there is a specific relationship between quality and cost, there does
not exist a monotone relationship between prices and demand elasticities. Hence, prices do not
necessarily capture the variety-specific characteristics relevant to evaluate the effects of tariffs on
bilateral trade flows.
Next, consider variety-specific quantity, q (λi, ci) =A− ci
λit
2γλi. Clearly, q (λi, ci) is decreasing in ci.
The question is whether q (λi, ci) is increasing in λi. To see when this is the case, I can write the
elasticity of quantity with respect to λi as follows:
εD,λi =ciλit− 1
A− ciλit
(12)
In (12), quantity is increasing in quality only if ciλit > 1. Hence, if this condition is satisfied for all
i, then like revenues, quantity will have a monotone relationship with demand elasticities. If not,
then quantity does not have a monotone relationship with demand elasticities, and cannot be used
to evaluate any disparate effect of tariffs across varieties.
The discussion regarding demand elasticities is summarized in the following proposition.
Proposition 3 Without a quality dimension, variety-specific revenues, quantities, and prices all
have a monotone relationship with demand elasticities. With a quality dimension, only variety-
specific revenues are guaranteed to have a monotone relationship with demand elasticities.
The upshot of this discussion and the result in Proposition 3 is that when evaluating the effects
12
of tariffs, if quality plays a role at all, the only unconditional proxy for quality-adjusted cost is
revenue. Hence, as suggested by equation (8), when testing the model, I will construct measures
of within-product market penetration based on revenues, where the main predictions are that (1)
tariff liberalization disproportionately benefits the lowest-revenue varieties and (2) it is not necessary
that high revenue varieties gain at all from tariff liberalization. Further, to evaluate a potential
role for quality, I will also construct within-product rankings of quantities and prices. If the latter
two measures deliver results that are qualitatively similar to revenue-based rankings, then I will
conclude that support for the model is primarily driven by cost-heterogeneity, or a trivial link
between cost heterogeneity in quality heterogeneity. In contrast, if using rankings based on quantity,
and especially price, yield different results when compared with those based on revenues, then I will
conclude that quality is likely playing a role in demand elasticities, and hence, the effects of tariffs
on trade flows.
Discussion
The results detailed above are not the first to look at the relationship between product characteristics
and bilateral trade flows. For example, recent work by Baldwin and Harrigan (2011) and Johnson
(2009) have allowed for a precise impact of quality, via prices, on trade flows. Further, Hummels and
Skiba (2004) examine how trade costs and tariffs affect the price-composition of exports. However,
to my knowledge, the above framework is the first to demonstrate that the impact of import
liberalization may in fact be negative for some measure of imported varieties.
This main theoretical result is closely related to the discussion of tariffs in Hummels and Skiba
(2004). Along with per-unit trade costs, Hummels and Skiba allow for an ad-valorem component,
such as tariffs. Theoretically, they show that higher tariffs reduce the relative demand for high
quality (price) goods, thus lowering the average FOB price of exports. The opposite occurs with
transportation costs. Relative to Hummels and Skiba (2004), my model extends the literature in
a unique way, where I allow for elasticity differences that are arbitrarily correlated with prices. To
be more specific, consider an example in which all imported varieties are produced at the same
marginal cost, but yet differ in quality. In this case, higher quality varieties earn higher revenues,
and are sold at lower equilibrium demand elasticities. If raising tariffs, high quality varieties will
suffer less than low quality varieties, by virtue of the equilibrium demand elasticity at which each
are sold. Thus, higher tariffs increase the relative demand for high quality goods - the opposite of
what Hummels and Skiba (2004) predict. The critical difference is that the quality component in
my model may shift/skew the demand curve such that higher price goods are sold at lower demand
elasticities. In contrast, the model in Hummels and Skiba (2004) is consistent with a restricted
13
version of my model where quality and costs and correlated in such a way to ensure that demand
elasticities are always larger with higher prices. Indeed, in later empirical results, I will present
evidence that suggests that some low prices goods are actually subject to relatively high demand
elasticities.
As mentioned earlier, Arkolakis (2009) presents a model that delivers a differential response to
tariffs similar to Proposition 1, but that derives from features unrelated to demand elasticities. In
particular, Arkolakis details a model in which firms reach consumers by investing in marketing,
itself subject to a convex cost function. Via a CES demand assumption, all firms in the Arkolakis
framework receive the same percentage demand shock resulting from tariffs. Thus, all firms gain
from a reduction in a common iceberg transport cost, but differ in their supply response based on
the marginal cost of reaching a larger fraction of consumers. Indeed, small/less-productive firms
that charge higher prices, who are not as far up the marketing cost curve, experience a larger total
elasticity of import value with respect to tariffs. The difference in predictions between Arkolakis
(2009) and my work are subtle, though where they exist, they highlight the demand-side issues
at the heart of my model. Specifically, Proposition 2 highlights the possibility that high revenue
varieties do not increase imports after tariff liberalization. Indeed, in the forthcoming empirical
work, I show that the top-20% of exporters (within-products) rarely increase trade after tariff
liberalization occurs. Further, Proposition 4 implies that high-price varieties benefit the most from
liberalization only if quality has no role in demand elasticities. I will also show that if anything,
low-price varieties benefit the most, suggesting a role of quality that affects demand elasticities, and
hence, the effects of tariff liberalization on bilateral trade flows.
Finally, before moving to the empirics, a note about welfare. From a trade policy perspective,
the results in Proposition 1 seem in direct conflict with the intent of the central tenet of the WTO:
the principle of non-discrimination. That is, while non-discrimination requires that countries apply
and liberalize tariffs equally across MFN exporters, the benefits of that tariff reduction do indeed
discriminate along natural margins. The question then becomes, is non-discrimination a sensible
policy?6 Or should countries be allowed to discriminate along some margin?7
6As detailed in Jackson (1997), there are three traditional arguments which support the use of MFN. One is thatMFN prevents policies which distort bilateral patterns of comparative advantage. Further, some argue that MFNresults in more liberalization than under preferential systems. Finally, others argue that allowing for discriminatorytariffs would increase the costs of rule formation, where many tariff lines would be more costly to impose and enforcewhen compared to a single tariff line applied to all exporters.
7This debate has a very long history. Indeed, Irwin, Mavroidis, and Sykes (2007) describe how Keynes’, duringthe framing of the GATT, wished to keep open the option of discriminatory tariffs. While this was mainly arguedwithin the context of Great Britain’s imperial relationships, Keynes seems to express a notion that the post-wartrading system ought not to be constrained by MFN.
14
While I leave an in-depth analysis of welfare within a multi-country political economy model
for a follow-up paper, I can comment on how consumers in the importing country view non-
discrimination, and what would happen if they were allowed to discriminate. Specifically, I can
show that, similar to a Ramsey-type tax rule, the importing country would exacerbate the disad-
vantage to high revenue suppliers under discrimination.8 This is similar to the result in Saggi (2004)
that was discussed in the introduction. Hence, it seems that non-discrimination, while not neces-
sarily helpful to high revenue varieties, likely is preventing optimal discrimination that would make
their situation worse. Crucially, as this argument is based on different elasticities within products,
it begs the question whether there is evidence of variable elasticities in the data. To examine this
possibility, I now move to the empirical section of the paper.
3 Tariffs and bilateral trade: The US and the Uruguay
Round
In this section, I use detailed import and tariff data for the United States, over the period 1989 to
2004, to estimate the degree to which the effects of MFN tariffs differ within products. To do this,
I use HS10 bilateral import data from the UC Davis Center for International Data, as described in
Feenstra, Romalis, and Schott (2001). HS8 tariff data is obtained from the US International Trade
Commission and from Romalis (2004).9 I restrict attention to continuing HS10 products over the
entire sample, which abstracts from new and/or dying HS10 codes.10 More information regarding
the construction of the sample will be provided shortly.
The level of refinement in the import data will be the HS10-Exporter level. I will henceforth
refer to HS10-Exporter pairs as “varieties”. Since MFN tariffs in the US are set at the HS8 level,
I will refer to HS8 classifications as “products”. “Industries” will be defined either at the HS2 or
HS4 levels of aggregation.
The theory described in section two focused on the effects of a reduction in a common ad valorem
tariff that is pervasive across varieties. As such, I will restrict the sample to focus on the effects of
8I can prove this result in two separate ways, both of which are available upon request. First, starting from freetrade, I examine whether an importing country with no domestic sector can change tariffs in a discriminatory mannersuch that zero revenue is earned but consumer surplus increases. In this case, the optimal policy is to increase thetariff on high revenue varieties and subsidize imports of low revenue varieties. In the second example, also startingfrom free trade but allowing for revenues to change, I evaluate the direction of an optimal policy on a single variety.Precisely, I show that there exists a threshold of quality-adjusted costs above which import subsidies are optimal,and below which import tariffs are optimal.
9The Romalis data is also obtained from the UC Davis Center for International Data.10See Pierce and Schott (2009) for a discussion and concordance for new and dying product codes.
15
Figure 1: MFN Tariffs: 1989-2004
1990 1995 2000
02
46
810
Mean and Median MFN Tariffs
Year
MFN
Tar
iff
Mean TariffMedian Tariff
0 20 40 60 80 100
010
2030
40
MFN Tariff Distribution - 1992 and 2004
Percentile
MFN
Tar
iff
19922004
ad-valorem MFN tariffs on MFN imports. The issue, however, is that not all imports are subject
to MFN tariffs, and not all tariffs are ad-valorem. To deal with this, I will adopt the following
restrictions on the observations included in my dataset.
R1: HS10-Exporter pairs must always be imported under MFN over the entire sample period.
R2: HS8 products must not list any non-tariff barriers to trade over the entire sample period.
The first condition removes non-MFN trade from the dataset, and in particular, varieties for
which preferential access is an (relevant) option. This is particularly important for NAFTA trade,
and to test this restriction, I will later drop NAFTA countries from the sample to examine how the
results may change. In the second restriction, I remove products for which at some point in the
sample, non-tariff barriers, or specific tariffs, are listed in the HS8 tariff files.
Before presenting the precise regression framework (which will further restrict the sample), I first
detail the reductions in US import tariffs which resulted from the Uruguay Round. Two particular
characteristics of this round of tariffs cuts are presented in Figure 1. In the left panel, I have plotted
16
mean and median tariffs across HS8 products for each year, 1989-2004. The last year prior to the
enactment of the Uruguay Round (1994) is denoted by the vertical line. Notably, there is very
little movement in tariffs prior to the enactment of the Uruguay Round. Directly after, tariffs fell
steadily, flattening out between 2000 and 2004. Overall, mean and median tariffs fell by roughly
50%. Further, in the right-panel of Figure 1, I plot the empirical distribution of MFN tariffs in 1992
and 2004 (which will be the years over which I evaluate the effects of tariffs). Of note, US MFN
reductions in the Uruguay Round doubled the number of MFN tariffs that were zero.
Next, to give a broad picture of the effects of tariff reductions, I will impose two additional
restrictions on the sample:
R3: Tariffs must be stable over the period 1989-1994
R4: Tariffs must not rise over the period 1994-2004.
Together, the two restrictions remove 2.2% of HS8 products from the dataset, which removes
only 0.29% of the total number of HS10-Exporter pairs. The first restriction focuses the sample to
products with a stable “pre-period” during which initial market penetration can be measured. The
second restricts products to those where tariffs were cut or stayed constant after the formation of
the WTO. Since non-tariff barriers were allowed to be “tariffied” into ad-valorem rates upon their
removal, getting rid of the few products with increasing ad-valorem tariffs provides a secondary
check that these products are removed from the database.
In Figure 2, I provide some illustrative evidence on the effect of MFN tariff reductions, and a
differential effect of tariff reductions by market penetration before the tariff cut. To begin, focus on
the left panel of Figure 2, where I have plotted relative trade growth for products that received a
tariff cut and products that did not. To calculate relative trade growth, I first calculate the log of
the ratio of imports in year t to imports in 1992 for each HS10-Exporter pair. Then, for each year,
I de-mean these growth rates across Exporter-HS4 pairs. Thus, in each year, average trade growth
relative to 1992 is equal to zero. In the left panel of Figure 2, I have decomposed this “zero” into
HS8 products that received a tariff cut, and HS8 products that did not. Further, I have denoted the
first draft of the final act of the Uruguay round, which occurred at the end of 1991, and the year in
which the final act was signed, 1994, by vertical black lines. Clearly, products that received a tariff
cut experienced positive trade growth relative to those products that did not. More notably, trade
growth rates for each product group were very close to one another prior to 1994. In the appendix,
I present another chart showing that trade growth rates were similar all the way back to 1980.
This suggests that after controlling for factors unrelated to tariff cuts (the Exporter-HS4-Year fixed
17
Figure 2: Bilateral Trade Growth - 1989-2004
1990 1995 2000
-0.4
-0.2
0.0
0.2
0.4
Trade Growth and Tariff Cuts
Year
Trad
e G
row
th (1
992
Bas
e)
First Draft ->
HS8 Tariff CutNo HS8 Tariff Cut
<- Final Act Signed
1990 1995 2000
-0.4
-0.2
0.0
0.2
0.4
Trade growth by 1989/1990 Import Penetration (calculated within HS8 products)
Year
Trad
e G
row
th (1
992
Bas
e)
First Draft -> <- Final Act Signed
Bottom 80% + Zeros - HS8 Tariff CutBottom 80% + Zeros - No HS8 Tariff CutTop 20% - HS8 Tariff CutTop 20% - No HS8 Tariff Cut
effects), products which received a tariff cut were not growing differently than those that did not
receive a tariff cut.11 Given that good instruments for tariffs at the HS8 level do not exist for this
analysis (to the author’s knowledge), the fact that trade growth pre-WTO appears to be invariant
to future tariffs cuts is particularly helpful.
In the right panel of Figure 1, I have further decomposed these “zeros” by the relative posi-
tion of each HS10-Exporter observation within their respective HS8 product category. Precisely, I
decompose the averages in the left-panel by whether or not, within each HS8 product, an HS10-
Exporter was in the top 20% of positive trade values over the period 1989-1990. Clearly, the group
11This is a statistically robust statement. Regressing the log change in imports of each HS8 product between1989 and 1992 on a dummy variable identifying a tariff cut and HS4 fixed effects yields an insignificant relationshipbetween pre-Uruguay round trade growth rates and future tariff cuts. Precisely,
log(vi,j,92vi,j,89
) = −0.0647[0.054]
· I(ti,04ti,94
< 0) + HS4
where i represents HS10 varieties and j represents exporters. Standard errors are robust and clustered by Exporterand HS2 industry.
18
that benefited the most from a tariff cut relative to their no-tariff cut control group were those
HS10-Exporter pairs that either did not trade (“zeros”) or were in the bottom 80% of their given
product category. In contrast, it is unclear whether varieties in the top 20% of their given HS8 cat-
egory benefited from tariff cuts relative to similar varieties in the no-tariff cut HS8 control groups.
Overall, the evidence in Figure 2 details a differential impact of MFN tariffs which is consistent
with the theory developed in section two. I now test the robustness of these features using a broad
set of regressions.
Tariffs and Relative Market Penetration: Baseline regressions
Given that tariffs only changed once and over a relatively short period, the empirical strategy will
be similar to Trefler (2004), using long differences to evaluate the effects of tariffs on bilateral trade.
The object of interest will be the growth rate in trade over the period 1992-2004. This growth rate
will be labeled log(vi,j,04/vi,j,92), where vi,j,s measures the value of imports in nominal US dollars of
HS10 variety i from exporter j in year s. Hence, this growth rate requires the following (and final)
restriction on the sample used for the majority of the analysis.
R5: HS10-Exporter pairs must report imports in both 1992 and 2004.
The growth rate will be regressed on a number of factors within a number of different specifica-
tions, though the common regressor in each will be the log change in MFN tariffs over the period
1994-2004. Precisely, I will measure tariffs as in section two, where ti,s = 1 + τi,s. Here, τi,s is the
percentage point MFN tariff for HS10 variety i in year s (though understanding that tariffs are set
at the HS8 level). To calculate a measure of import elasticity, I will measure the change in tariffs
using log differences, log(ti,04ti,94
), similar to the way in which I measure the change in import value
for each HS10-Exporter. Finally, note that I measure the change in tariffs starting in 1994 because
tariffs were essentially frozen during WTO negotiations. Via (R3), those few products with tariffs
that move in the pre-period are dropped from the dataset.
Restriction (R3) is crucial in measuring pre-tariff-cut market penetration, as doing so requires
that policy measures were not moving. Initial market penetration will defined by six sub-groups,
five of which based on quintiles of positive imports over the period 1989-1990 (the earliest periods
such that HS10 classifications were used). After dropping observations of varieties that did not trade
in both 1992 and 2004, within each HS8 product, I first collect all varieties (HS10-Exporter pairs)
that did not report trade in both 1989 and 1990, and call this “group zero”. This group is identified
by a dummy variable D0i,j. Next, HS10-Exporter observations for which imports occurred during
1989-1990 are grouped into quintiles within each HS8 product. These quintiles will be identified in
19
Table 1: Mean and Median Import Value by within-product quintile
Group: 0 1 2 3 4 5Mean ($’s): 0 89,143 355,891 1,038,760 3,336,006 21,426,412
Median ($’s): 0 31,880 144,084 444,908 1,409,428 6,151,874Notes: This table presents mean and median import values over HS10-Exporter pairs broken downby import penetration group.
order by dummy variables D1i,j, D
2i,j, D
3i,j, D
4i,j, and D5
i,j, with the lowest 20% of positive imports
within each HS8 product identified by D1i,j, and the highest 20% of positive imports within each
HS8 product identified by D5i,j.
Table 1 presents the mean and median import values over 1989-1990 for each of these groups,
after dropping HS8 products without five HS10-Exporter observations with positive trade (i.e.
dropping HS8 products where quintiles are not defined). The point to be taken from Table 1 is that
the top 20% of HS10-Exporter observations are, on average, roughly 7X larger than those in the
60-80th percentile. A similar magnitude difference is evident at the median. Overall, the largest
20% of exporters are, in fact, quite large, which is consistent with similar results presented at the
firm level in Bernard, Jensen, Redding, and Schott (2007). Thus, given the theory in section two, I
hypothesize that the effects of MFN liberalization on this group are modest relative to groups 0-4.
I now seek evidence in support of this hypothesis.
To test for any differences across quintiles, I start by estimating the following equation.
log(vi,j,04
vi,j,92
) =(βUS D0D
0i,j + βUS + βUS D2D
2i,j + · · ·+ βUS D5D
5i,j
)log(
ti,04
ti,94
) (13)
+βD0D1i,j + · · ·+ βD5D
5i,j + Fixed+ εi,j
In (13), I allow for a differential impact of tariffs across groups, where in particular, I measure the
effects of group membership and a differential effect of tariff cuts relative to group one. In (13),
βUS represents the long-run elasticity of import value with respect to tariffs for the group of HS10-
Exporters that reported trade in 1989 and 1990, but were in the bottom 20% of reported import
values within their respective HS8 product. Thus, βUS Dk represents the difference in elasticity,
relative to group one, for group k. To be clear, βUS D5 represents the difference in import elasticity
of the largest exporters relative to the smallest exporters.
20
An alternate way of estimating the model in (13) is by directly estimating elasticities for each
group, and not differences relative to a base group. Equation (14) does exactly this:
log(vi,j,04
vi,j,92
) =(βUS D0D
0i,j + · · ·+ βUS D5D
5i,j
)log(
ti,04
ti,94
) (14)
+βD1D1i,j + · · ·+ βD5D
5i,j + Fixed+ εi,j
In both (13) and (14), I will include a robust set of fixed effects at the Exporter-Industry level
to control for trends unrelated to changes in MFN tariffs. For example, some exporter-industry
pairs may be declining or growing due to development or a long-run adjustment toward different
industries. This is particularly important in light of the Kehoe and Ruhl (2009) explanation for
extensive margin growth, which is that price-shocks in countries like China are one factor driving the
growth of the extensive margin. If price-shocks play a critical role, these Exporter-Industry trends
should help control for them. Further, these Exporter-Industry fixed effects may help control for
export sources which engage in liberalization of their own in certain industries, and hence, there
are additional issues of reallocation that may be absorbed in the fixed effects.
I will occasionally include two additional controls, dV Ti,t and V Growi,t. In the first, I measure
the log of total trade growth between 1992 and 2004 for products in variety i′s industry (defined
either by HS2 or HS4 industries) but not variety i′s HS8 product. This is especially important in
light of tariff cuts in other HS8 products that are in the same HS2 or HS4 industry. I hypothesize that
the coefficient on this variable will be less than zero, where higher demand outside an HS8 industry
will force down demand within that particular HS8 industry.12 The second control, V Growi,t,
measures the log growth in imports for each HS10-Exporter variety over the period 1989-1990. I
will only be able to use this control when excluding group zero from the sample. The motivation
for adding in this control is to at least partially rule out a mean-reversion story. If there is mean
reversion, high import growth in 1989-1990 might suggest low import growth in later periods.
The results from estimating (13) and (14) are presented in Table 3. Within each quadrant in
Table 3, I present estimates for a restricted version of the model without tariff interactions in the
first two columns. In the second two columns, I add in tariff interactions from (14). Finally, in the
last two columns, I present the results for (13), which evaluates differential elasticities.
Table 3 reports strong support for the model developed in section two. To begin, I will discuss
the results for the full sample, which are in the top panels of Table 3. First, note columns 1
12For example, suppose that tariffs fall 3% in a given HS8 product, but that tariffs in their particular HS2 industryfall on average 10%. Thus, while the within-HS8 tariff cuts are the focus of the paper, disproportionally large outsidetariff cuts may shift down demand for all varieties in a given HS8 industry.
21
and 2, which report that the average elasticity of trade value with respect to tariffs is negative
and significantly different from zero. Depending on the assumptions over fixed effects, the point
estimates of the average elasticity of import value with respect to tariffs ranges from -3 to -6. As
foreshadowed in Figure 1, the tariff cuts which were implemented during the Uruguay Round had
a significant effect on imports into the US.
In terms of the predictions from section two, focusing on columns 4 and 10 in the top panel of
Table 3, it is clear that for varieties in group one, the smallest 20% of active varieties in 1989-90 as
measured by revenues, tariffs had a negative, large, and statistically significant effect on the growth
rate of imports between 1992 and 2004. As we move down rows from group one toward group
five, we see that the interaction with tariffs becomes less negative with each successive group. This
culminates with varieties in group five, where tariffs did not have a significant negative impact on
import values. Further, in columns 6 and 12, it is clear that when measuring a differential elasticity
between groups 1 and 5, the difference is positive and significantly different from zero. Focusing
on the last row in the top panel of Table 3, we see dV Ti,t has a negative and significant impact on
bilateral imports.
Next, in the bottom panels of Table 3, I estimate the model but while dropping varieties for
which trade was not reported in 1989 and 1990. Here, we find that the results are robust within
this smaller sample. Within this sample, we are also able to estimate the effect of both dV Ti,t
and V Growi,j,t. The former again has a significant and negative effect on trade. For the latter,
high trade growth in the pre-period tends to be associated with low growth in later periods. This
suggests that some sort of mean reversion is present in the data, though not enough such that the
effects of tariffs by groups are changed in a large way.
One last issue to discuss is the estimates for the group dummy variables that are not interacted
with tariffs. Here, we find that higher revenue varieties tend to have lower growth rates. This would
be supported by the model if I also included a cost shifter across varieties that was not related to
tariffs. For example, this might include a common rate of productivity growth, independent of
whether or not a product received a tariff cut.
Tariffs and Relative Market Penetration: Exporter Interactions
Next, I estimate a number of specifications which include interactions between US MFN tariffs and
exporter characteristics which are more general than variety-specific market penetration. First, I
estimate (13) allowing for a differential impact of tariffs as a function of exporter GDP per capita
(data taken from the Penn World Tables). The coefficient on this interaction is labeled βUS GDP .
This interaction is sensible on a number of levels. First, less-developed countries may have poor
22
institutions, and thus may respond slowly to changes in tariffs relative to more developed exporters.
In this case, βUS GDP would be negative. Or, perhaps developed countries are more likely to employ
unionized labor, which may yield a laggard response to price shocks. In this case, βUS GDP would
be positive. Another explanation for positive βUS GDP would be that developed countries are more
likely to sell high quality products, and that the effect of high quality products may be picked up
to some extent by this coefficient. Indeed, in the top panels of Table 4, though insignificant at
conventional levels in all regressions, this is exactly what I find, where there is an imprecise but
positive relationship between the effect of tariffs and GDP per capita of the exporting country.
However, despite this positive interaction between tariffs and exporter GDP per capita, we still find
that there is a positive and statistically significant difference in estimated elasticities between group
one and group five, as the theory predicts.
Moving beyond exporter development, I now allow for a full interaction between exporter dummy
variables and the log change in US MFN tariffs. Precisely, I estimate the following:
log(vi,j,04
vi,j,92
) =(βUS D0D
0i,j + βUS D2D
2i,j + · · ·+ βUS D5D
5i,j
)log(
ti,04
ti,94
) (15)
+ (βUS Exp · Exp) log(ti,04
ti,94
) + βD1D1i,j + · · ·+ βD5D
5i,j + Fixed+ εi,j
In (15), Exp is a vector of exporter dummies, and βUS Exp represents a vector of exporter-specific
import elasticities for group zero. The motivation for this interaction is as follows. In Appendix
A, when including a flexible extensive margin of trade, the relationship between quality-adjusted
costs (as defined by the Pareto shape parameter) and import value is negative only if the measure
of potential exporters is unresponsive to the Pareto shape parameter, and the pool of potential
exporting firms does not vary across exporters. Indeed, the interaction between exporter dummies
and US MFN tariff changes may account for both a larger difference in the pool of potential
exporters, or some differential effect of tariffs on this pool. Also, if financing issues vary substantially
across countries (as in Manova, 2008), then the exporter-tariff interaction is warranted.
The results from estimating (15) are presented in the bottom panel of Table 4. Again, βUS D0−βUS D5 measure the differential effect of MFN tariffs by quintile relative to an exporter-specific tariff
elasticity for group one. Relative to group one, higher revenue varieties fared worse with lower
tariffs than lower revenue varieties. Indeed, this effect is most pronounced in group five. Overall,
despite a huge number of additional interactions with the tariff variable, the results in Table 4 are
supportive of the theory from section two.
Next, I return to the specifications in (13) and (14), evaluating the effects of potential outlier
23
Table 2: Mean and Median Import Value by within-product quintile
Group: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%HHI: 0.009 0.103 0.156 0.198 0.231 0.266 0.307 0.362 0.441 0.563 0.984
N : 5 9 11 14 17 21 27 37 55 97 429N (no D0): 5 6 8 10 13 16 20 26 36 70 345Notes: This table presents the empirical distribution for a number of HS8 product measures. The first, “HHI”, presents decilesof a share-based Herfindahl Index of Exporter concentration within HS8 products. The last two rows present the number ofHS10-Exporter pairs in each HS8 industry, with and without group zero.
exporters on the results presented in Table 3. In particular, I run regressions excluding NAFTA
countries, and also regressions excluding China. The motivation for the former is that while it is
possible that much trade from NAFTA countries may be accounted as MFN even if preferences
are available (since rules-of-origin can be onerous), it seems odd that either country (in particular
Canada) would enter the database significantly. Regarding the latter, China is obviously a large
and fast-growing export market, which enters the WTO during the sample and also engages in a
substantial amount of vertical trade with the US. Thus, I remove both NAFTA countries and China
separately to evaluate whether the results are driven by large regional trading partners and/or a
massive growing exporter. The results from excluding these countries are presented in Table 5.
Overall, high revenue varieties experience a muted effect of liberalization, and low revenue varieties
experience an amplified effect. Further, relative to group one, high revenue varieties fared worse
with lower tariffs than low revenue varieties, though when China is excluded, this difference is not
significant at conventional levels.
Tariffs and Relative Market Penetration: HS8 Characteristics
The key to the model in section two is that quality-cost heterogeneity yields heterogeneous demand
elasticities across varieties. To rank elasticities, import values are used, since they have a negative,
monotone relationship with absolute demand elasticities. While the results thus far tend to support
theory, I will now dig deeper to evaluate other explanations for the results, along with identifying
HS8 product “types” that are more likely to support the theory.
First, I will examine how the level of exporter concentration within a given product influences
the differential effect of tariffs within the same product. The effects of exporter concentration on
bargained tariff cuts have been examined by Ludema and Mayda (2009), where they show that
import markets with a higher concentration of export sources are forced to engage in larger tariff
concessions. This is confirmed empirically using a US case study over a similar time-period to
24
that in this paper. The intuition is that since tariff cuts in the GATT/WTO are determined via
extensive bargaining, agents in concentrated export sectors are less likely to free-ride off of tariff
concessions negotiated by other exporters (since the other exporters are small in comparison). Thus,
when exporter concentration is relatively large, free-riding is minimized and bargained tariff cuts
in the import market are larger. In the specifications above, the Exporter-Industry fixed effects
should control for mean differences exporter concentration. However, if exporter concentration has
unmodeled and unobserved effects on the differential response to tariffs, the above estimates may
be contaminated.
To examine these issues, within an HS8 product, I first aggregate trade from the HS10-Exporter
level to the Exporter level over the period 1989-1990. Then, I construct a Hirschman-Herfindahl
index (HHI) for each HS8 product based on the market share of exporters. The distribution of HHI
is presented in the first row of Table 2. After identifying the median value of HHI across the entire
sample, I group each product into “Low” and “High” measures of HHI. To examine the effects
of exporter concentration, I interact dummy variables identifying low and high HHI products with
all non-fixed effects in (14). The results are presented in the left panels of Table 6. Here, we
find distinct differences in import elasticities for products that likely have very different industry
structures, where only those products with below median HHI (low concentration) yield estimates
which are consistent with the theory. In particular, for low HHI products, the estimated effect of
tariffs is smallest for the top 20% of export sources in all regressions. In contrast, for products with
high HHI, there is no systematic difference in elasticities across groups. As high HHI products do
not seem to support the model developed in section two, this suggests that high HHI products may
have different underlying demand characteristics, or that supply conditions are more favorable for
suppliers which are highly successful relative to their peers.
Finally, recall that Proposition 2 details a condition on the relative values of γ, η, and N such
that the differential effect of tariff liberalization is strong enough to “kick out” high revenue varieties.
While γ and η are hard to back-out of the data, N can be approximated by the relative number of
varieties within an HS8 product category. In Table 2, the last rows detail the distribution of the
number of HS10-Exporter observations within each HS8 product. Remarkably, there is significant
variation in the number of varieties within HS8 products across the sample. The minimum is 5
(by construction since quintiles require at least 5 observations). The maximum is over 400. I also
report the number of varieties when restricting the sample to varieties with positive trade in 1989
and 1990.
Similar to HHI, I estimate the model with dummy variables identifying below median and above
median values of N , and an interaction of these dummy variables with all non-fixed effects in the
25
model. The estimates, presented in the right panels of Table 6, are supportive of the result in
Proposition 2. Here, when an HS8 product has a relatively large number of varieties, there is never
a significant effect of tariffs on high revenue varieties, and always a significant effect of tariffs on
low revenue varieties. In contrast, for HS8 products with a relatively low value of N , the results
are not supportive of the model.
Tariffs and Relative Market Penetration: Does Quality play a role?
As a final test of the model, I will address a potential role of variety-specific quality by constructing
alternate within-product rankings based on quantity and price, to be evaluated alongside the value
bins that have been used in the paper thus far. As discussed in the simple model in section 2,
pre-tariff-cut revenues map in a monotonic fashion with quality-adjusted costs, and hence, demand
elasticities. In contrast, it is not necessary that quantity or price map to quality-adjusted costs in a
monotonic fashion. Hence, it makes sense to define bins by quantity and price, and to see whether
the qualitative results are similar to value-based bins. If so, then it is likely that quality does not
matter, or that costs and quality are related in a systematic fashion. If not, then it is sufficient to
conclude that non-trivial correlations in quality and cost exist, both of which influencing demand
elasticities.
To define quantity and price bins, I must restrict the sample to products for which units are
consistent during the pre-period. Precisely, I restrict the sample to those HS8 products that use
only one quantity over 1989 and 1990. After restricting the sample in this fashion, I define quantity
and price bins according to a hypothetical ranking if quality played a minimal role. Precisely, when
constructing quantity bins, varieties that did not trade in 1989 or 1990 will be placed in group
Q0. For varieties that were imported in both 1989 and 1990, groups Q1 to Q5 are ranked as above
according to increasing quintiles of quantity within each HS8 product. For prices, P0 will represent
varieties that were not traded in 1989 or 1990. For groups P1 to P5, I assign varieties according
to decreasing unit-value quintiles within HS8 products. To be clear, the highest 20% of reported
prices will be in group P1, and the lowest 20% of reported prices will be in group P5.
To evaluate a potential role of quality, I run the same regression on the same sample, with the
exception that varieties may be placed in different bins depending on the ranking scheme that is
used. If quality plays a role, I hypothesize that value-based bins will capture more variation in
trade growth than quantity and price bins. Further, if quality plays a role, the response to tariffs
when bins are defined by price may be very different than with value bins. The results from these
regressions are presented in Table 7. Whether using the full sample, or the restricted sample,
the results when using value bins are similar to above, where specifically, low revenue varieties
26
benefit strongly from liberalization, and high revenue varieties do not benefit on any significant
level. This is again suggestive of elasticities that are diminishing in revenues. When using quantity
bins, the results are somewhat similar, though the ranking of coefficient estimates is not exactly
consistent with those from regressions using value-based bins. Further, the value of adjusted R2,
which measures the residual variation explained by the model after accounting for fixed effects, is
consistently one percentage point less than when using value bins. Since quality is the only factor
that might remove the positive relationship between quantity and revenues, this suggests a role for
quality in the model.
This point is even more stark when evaluating the differences in regression results when defining
bins according to HS10-Exporter prices. Here, lower price varieties are assigned to higher groups,
which adopts a convention that if quality does not play a role, lower price varieties should be
imported at lower absolute demand elasticities. When looking at these results, first, we see that the
amount of variation captured by the model is less than half of the variation captured when using
value bins. Thus, price bins do not seem to capture the necessary characteristics that govern the
effects of tariffs. Further, we see that of any group, the lowest price varieties (groups 4 and 5 when
defined by price bins), which would have low elasticities with no quality dimension, are the most
sensitive to tariffs. This suggests that there is a relevant role of quality in determining demand
elasticities, and hence, a role of quality in governing the effects of tariffs.
4 Conclusion
This paper has presented a simple model of import market liberalization and bilateral trade. Theo-
retically, I show that when quality/cost heterogeneity yields differences in demand elasticities, trade
liberalization necessarily increases imports only of varieties that are of high cost or low quality -
both of which sold at relatively high demand elasticities and earning relatively low revenues. In
evaluating the model using a case study of US tariff reductions resulting from the Uruguay Round,
I find robust support for the model.
As for future work, the most promising extension is a policy-equipped framework to evaluate
whether, in the presence of quality-cost heterogeneity, the current rules of the WTO are sufficient
to guarantee efficiency in trade negotiations. I plan to address this issue in a follow-up paper.
27
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[6] Cameron, Colin, Jonah B. Gelbach, and Douglas L. Miller (2006), “Robust Inference withMulti-way Clustering”, mimeo UC Davis.
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[8] Feenstra, Robert C. and David Weinstein (2010), “Globalization, Mark-ups, and the US PriceLevel”, NBER Working Paper No. 15749.
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[14] Irwin, Douglas A., Petros Mavroidis and Alan O. Sykes (2007), “The Genesis of the GATT”,mimeo Stanford University
[15] Jackson, John H., The World Trading System, MIT Press, 1997
28
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29
A Extensive Margin
An aspect absent from the above model is a treatment of the extensive margin. As bilateral trade
values, the object of interest in the empirical analysis, are recorded at the country level and not
the variety level, it is crucial to address the extent to which aggregation and entry of new varieties
matters for the above results. To examine these issues, I now derive an extended model in which L
exporters, indexed by l, supply to a common import market. Each exporter l is identical with the
exception of the distribution of quality-adjusted marginal costs, and the pool of potential exporting
firms, Nl. Precisely, I assume that, for exporter l, quality-adjusted costs are distributed from 0 to
cmax with Pareto shape parameter kl (> 1). The upper-bound on costs, cmax, is assumed to be non-
binding, and thus larger than the demand intercept, A. Thus, the distribution of quality-adjusted
costs for each country l is written as:
Gl
( cλ
)=
(cλ
)kl(cmax)kl
Here, higher values of kl yield a higher quality-adjusted cost, on average.
Focusing on an arbitrary exporter l, I now will derive the relationship between quality-adjusted
costs and import values. In (3), firms cannot sell unless their post-tariff price is less than or equal
to A. This will occur if(ciλi
)∈[0, A
t
]. Thus, assuming that quality-adjusted costs are distributed
according to Gl
(cλ
), the import value from country l is written as:
Vl =Nl
cklm
A2+kl
2γt1+kl(kl + 2)(16)
Similarly, total quality-adjusted quantity imported from importer l is written as:
Ql =Nl
cklm
A1+kl
2γtkl(kl + 1)(17)
Next, I will establish that Vl is decreasing in kl and then derive the effect of tariffs on import value
from exporter l. Taking logs of Vl and differentiating with respect to kl :
∂ log(Vl)
∂kl= log
(A
cmt
)− 1
kl + 2< 0
30
The inequality follows from the assumption that cm is non-binding.13 Holding the level of potential
exporters, Nl, constant, the value of imports from exporter l is decreasing in kl. Thus, unless
the number of potential exported varieties does not rise strongly with kl, those exporters with the
highest quality-adjusted cost earn the lowest revenues.14
With this relationship in mind, I now show that the results from the basic model described
above remain despite including a flexible response at the extensive margin. Precisely, taking logs
of Vl and differentiating with respect to t, the elasticity of import value with respect to tariffs for
country l is written as follows::
εv,l = kl(εA − 1)︸ ︷︷ ︸<0
+ (2εA − 1)︸ ︷︷ ︸?
(18)
where,
εA =1
1 + θη∑k∈LQlkl
∈ (0, 1)
Again εA > 0, as higher tariffs decrease competitiveness and increase the residual consumer demand
for each variety.
In (18), higher values of kl increase the negative impact of tariffs. Put differently, higher values
of kl yield a larger range of εA such that the impact of trade liberalization is negative. Precisely,
rearranging (18), εv,l < 0 if the following condition is satisfied:
εA <kl + 1
kl + 2(19)
As the right-hand side of (19) is increasing in kl, higher values of kl yield a larger range of εA such
that liberalization increasing imports from exporter l. In the limit, as kl approaches infinity, (19)
is always satisfied.
In summary, countries with a higher average quality-adjusted cost, who under reasonable con-
ditions earn lower revenues, benefit the most from the liberalization of MFN tariffs.
13cmt > cm > A implies that log(
Acmt
)< 0
14Allowing Nl to change with kl, there is still a negative relationship between kl and Vl if ∂Nlkl
∂klNl<
kl
(1
kl + 2− log
(A
cmt
))︸ ︷︷ ︸
>0
. This seems like a sensible assumption to make, since if firms need capital to enter the
foreign market, it is unlikely that more capital would be available in less efficient markets.
31
B Trade Growth Pre-1989
In Figure 2, I provided evidence suggesting that tariffs had a meaningful effect on trade, and that
trade growth before tariffs were cut was unrelated to future tariffs cuts. In Figure 3, I extend
the illustration of trade growth back to 1980 using TSUSA data from the UC Davis for Interna-
tional Data. The issue with using TSUSA is that there exists no nested concordance with the HS
classification system.
To begin, I calculate whether a TSUSA product receives a tariff cut during the Uruguay Round
according to the following procedure. Precisely, I adopt the convention that a TSUSA product
receives a tariff cut if at least one of the HS8 products that links to it receives a tariff cut. Once
I have identified which TSUSA products received a tariff cut, I merge this information with the
Exporter-TSUSA bilateral import data over the period 1980-1988.
To measure relative trade growth for observations occurring in 1989 or later, I first calculate
the log of the ratio of imports in year t to imports in 1992 for each HS10-Exporter pair. Then,
for each year, I de-mean these growth rates across Exporter-SITC4 pairs. SITC4 is used because
it has a nested link to both HS8 and TSUSA. Thus, in each year after 1989, average trade growth
relative to 1992 is equal to zero. For observations occurring before 1989, I first calculate the log of
the ratio of imports in year t to imports in 1988 for each TSUSA-Exporter pair. Then, for each
year, I de-mean these growth rates across Exporter-SITC4 pairs. Thus, in each year before 1988,
average trade growth relative to 1988 is equal to zero.
In Figure 3, I have decomposed this “zero” into HS8 products that received a tariff cut, and HS8
products that did not. Similar to Figure 2, we see that there is no obvious difference in pre-Uruguay
Round import growth rates between products that received a tariff cut over 1994-2004 and those
that didn’t.
32
Figure 3: Bilateral Trade Growth - 1980-2004
1980 1985 1990 1995 2000
-0.10
-0.05
0.00
0.05
0.10
Year
Trad
e G
row
th (1
992
Bas
e fo
r HS
, 198
8 B
ase
for T
SU
SA
)
TSUSA Data <<<>>> HS Data
HS8 Tariff Cut in 1994-2004No HS8 Tariff Cut in 1994-2004
<- Final Act Signed
33
Table 3: Effects of MFN tariffs by within-product quintile
Full Sample1 2 3 4 5 6 7 8 9 10 11 12
βD1 -0.017 -0.027 -0.017 -0.028 -0.017 -0.028 0.006 0.011 -0.024 -0.015 -0.024 -0.015
[0.058] [0.059] [0.069] [0.069] [0.069] [0.069] [0.062] [0.063] [0.079] [0.079] [0.079] [0.079]βD2 -0.336*** -0.349*** -0.314*** -0.33*** -0.314*** -0.33*** -0.275*** -0.27*** -0.275*** -0.265*** -0.275*** -0.265***
[0.064] [0.066] [0.074] [0.076] [0.074] [0.076] [0.074] [0.075] [0.081] [0.083] [0.081] [0.083]βD3 -0.612*** -0.626*** -0.594*** -0.61*** -0.594*** -0.61*** -0.558*** -0.553*** -0.541*** -0.527*** -0.541*** -0.527***
[0.07] [0.072] [0.075] [0.076] [0.075] [0.076] [0.079] [0.077] [0.078] [0.076] [0.078] [0.076]βD4 -0.925*** -0.939*** -0.884*** -0.901*** -0.884*** -0.901*** -0.852*** -0.848*** -0.828*** -0.817*** -0.828*** -0.817***
[0.095] [0.097] [0.093] [0.095] [0.093] [0.095] [0.093] [0.091] [0.09] [0.089] [0.09] [0.089]βD5 -1.288*** -1.303*** -1.223*** -1.24*** -1.223*** -1.24*** -1.243*** -1.242*** -1.207*** -1.195*** -1.207*** -1.195***
[0.104] [0.106] [0.109] [0.111] [0.109] [0.111] [0.102] [0.103] [0.123] [0.123] [0.123] [0.123]βUS -3.898** -3.294** -5.03** -4.399* -5.457*** -4.325*** -7.441*** -6.377***
[1.689] [1.637] [2.292] [2.272] [1.265] [1.17] [1.652] [1.465]βUS_D0 -5.092** -4.401** -0.062 -0.002 -5.887*** -5.065*** 1.555 1.313
[2.128] [2.111] [1.943] [1.945] [1.983] [1.952] [2.02] [1.977]βUS_D1 -5.03** -4.399* -7.441*** -6.377***
[2.292] [2.272] [1.652] [1.465]βUS_D2 -3.881** -3.387* 1.15 1.012 -5.874*** -4.777*** 1.568 1.601
[1.835] [1.73] [1.533] [1.532] [1.744] [1.743] [1.53] [1.514]βUS_D3 -4.11** -3.502** 0.921 0.897 -4.996*** -3.682** 2.446 2.695
[1.777] [1.715] [1.954] [1.972] [1.807] [1.739] [2.078] [2.047]βUS_D4 -2.911 -2.358 2.119 2.041 -4.592** -3.391 2.849 2.986
[2.04] [2.021] [1.728] [1.769] [2.114] [2.088] [2.269] [2.262]βUS_D5 -1.66 -1.054 3.37** 3.346** -3.962* -2.528 3.479 3.849*
[1.676] [1.609] [1.465] [1.475] [2.073] [1.933] [2.129] [2.108]βdVT -‐3.751*** -3.75*** -3.75*** -1.153*** -1.154*** -1.154***
[0.771] [0.77] [0.77] [0.122] [0.122] [0.122]
Adj R2 0.038 0.044 0.038 0.044 0.038 0.044 0.036 0.05 0.036 0.051 0.036 0.051
N
Fixed
# Fixed
Clusters
Restricted Sample: Positive Trade in 1989 and 1990
1 2 3 4 5 6 7 8 9 10 11 12βD2 -0.333*** -0.334*** -0.313*** -0.32*** -0.313*** -0.32*** -0.293*** -0.296*** -0.262*** -0.268*** -0.262*** -0.268***
[0.033] [0.033] [0.046] [0.047] [0.046] [0.047] [0.046] [0.047] [0.047] [0.05] [0.047] [0.05]βD3 -0.615*** -0.615*** -0.598*** -0.602*** -0.598*** -0.602*** -0.589*** -0.589*** -0.552*** -0.55*** -0.552*** -0.55***
[0.034] [0.032] [0.055] [0.055] [0.055] [0.055] [0.054] [0.052] [0.063] [0.063] [0.063] [0.063]βD4 -0.925*** -0.92*** -0.886*** -0.884*** -0.886*** -0.884*** -0.878*** -0.872*** -0.837*** -0.832*** -0.837*** -0.832***
[0.06] [0.059] [0.058] [0.059] [0.058] [0.059] [0.068] [0.065] [0.069] [0.069] [0.069] [0.069]βD5 -1.293*** -1.286*** -1.234*** -1.228*** -1.234*** -1.228*** -1.275*** -1.266*** -1.217*** -1.202*** -1.217*** -1.202***
[0.077] [0.075] [0.08] [0.079] [0.08] [0.079] [0.083] [0.081] [0.098] [0.097] [0.098] [0.097]βUS -3.914** -3.254** -5.31** -4.49* -5.791*** -4.607*** -7.574*** -6.443***
[1.698] [1.63] [2.331] [2.318] [1.326] [1.152] [1.763] [1.608]βUS_D1 -5.31** -4.49* -7.574*** -6.443***
[2.331] [2.318] [1.763] [1.608]βUS_D2 -4.267** -3.756** 1.043 0.734 -5.92*** -4.972*** 1.654 1.471
[1.898] [1.797] [1.449] [1.446] [1.901] [1.855] [1.092] [1.177]βUS_D3 -4.484*** -3.85** 0.826 0.639 -5.628*** -4.373** 1.946 2.07
[1.738] [1.659] [1.879] [1.931] [1.888] [1.753] [2.088] [2.086]βUS_D4 -3.281 -2.649 2.029 1.84 -5.478*** -4.371** 2.096 2.072
[2.024] [1.984] [1.755] [1.784] [1.905] [1.776] [2.222] [2.198]βUS_D5 -2.2 -1.474 3.11** 3.015** -4.539** -3.047* 3.035 3.396*
[1.743] [1.638] [1.443] [1.44] [1.928] [1.715] [2.085] [2.046]βGrow -0.105*** -0.105*** -0.105*** -0.114*** -0.114*** -0.114***
[0.011] [0.011] [0.011] [0.014] [0.014] [0.014]βdVT -3.93*** -3.932*** -3.932*** -1.215*** -1.217*** -1.217***
[0.829] [0.828] [0.828] [0.141] [0.141] [0.141]
Adj R2 0.039 0.051 0.039 0.051 0.039 0.051 0.04 0.063 0.04 0.063 0.04 0.063
N
Fixed
# Fixed
ClustersNotes: The dependent variable is the log ratio of import value in 2004 to import value in 1992 for exporter j in HS10 industry i. The excluded group is D0, which is thedummy variable identifying whether exporter j in HS10 product i was not imported to the US during the years 1989 or 1990. The "Full Sample" includes all observations,including those of varieties that were not imported in 1989 or 1990. The "Restricted Sample" excludes these varieties. Fixed effects are estimated using a withinprocedure according to the groups listed in the column "Fixed". Standard errors are heteroskedasticity robust and clustered by Exporter and Fixed effect industry(Exp_HS4 fixed effects implies Exporter and HS4 clusters used for standard errors) according to the multi-level procedure in Cameron, Gelbach, and Miller (2006). Thelabels *, **, and *** identify estimates which are significantly different from zero at the 10%, 5%, and 1% levels, respectively. "Adj R2" reports adjusted R2 values forthe regression after the data has been demeaned by the fixed effects.
13970
Exporter,HS4
TARIFFS
TARIFFS
48600
Exporter_HS2
3242
Exporter,HS2
48600
Exporter_HS4
36634
Exporter_HS2
2540
Exporter,HS2
36634
Exporter_HS4
11056
Exporter,HS4
34
Table 4: Effects of MFN tariffs by within-product quintile: Exporter Characteristics
GDP-Tariff Interaction
βD1 -0.022 -0.033 -0.03 -0.02[0.069] [0.069] [0.079] [0.079]
βD2 -0.319*** -0.335*** -0.28*** -0.27*** -0.313*** -0.32*** -0.262*** -0.268***[0.074] [0.076] [0.081] [0.083] [0.046] [0.047] [0.047] [0.05]
βD3 -0.6*** -0.615*** -0.546*** -0.533*** -0.599*** -0.604*** -0.551*** -0.549***[0.076] [0.077] [0.077] [0.076] [0.055] [0.054] [0.063] [0.063]
βD4 -0.89*** -0.906*** -0.834*** -0.823*** -0.887*** -0.884*** -0.838*** -0.833***[0.094] [0.096] [0.09] [0.09] [0.058] [0.059] [0.069] [0.07]
βD5 -1.227*** -1.244*** -1.209*** -1.198*** -1.233*** -1.228*** -1.214*** -1.199***[0.109] [0.111] [0.122] [0.123] [0.08] [0.079] [0.098] [0.096]
βUS -18.127* -16.689 -18.733 -17.762 -12.918 -11.081 -16.043* -15.628*[10.268] [10.47] [12.161] [12.072] [8.205] [7.949] [8.656] [7.982]
βUS_D0 0.211 0.254 1.778 1.543[1.902] [1.9] [2.032] [1.991]
βUS_D2 1.205 1.061 1.615 1.647 1.065 0.745 1.706 1.526[1.508] [1.508] [1.516] [1.499] [1.42] [1.42] [1.086] [1.166]
βUS_D3 0.952 0.924 2.503 2.752 0.834 0.637 2.005 2.134[1.923] [1.943] [2.061] [2.03] [1.855] [1.909] [2.07] [2.07]
βUS_D4 2.147 2.064 2.863 3 2.037 1.841 2.106 2.087[1.732] [1.772] [2.273] [2.267] [1.752] [1.781] [2.232] [2.205]
βUS_D5 3.407** 3.375** 3.543* 3.905* 3.137** 3.032** 3.107 3.466*[1.45] [1.464] [2.114] [2.096] [1.431] [1.433] [2.079] [2.042]
βUS_GDPPC 1.37 1.286 1.188 1.198 0.795 0.69 0.887 0.962[1.114] [1.13] [1.243] [1.25] [0.924] [0.895] [0.875] [0.826]
βGrow -0.106*** -0.114***[0.011] [0.014]
βdVT -3.761*** -1.155*** -3.947*** -1.217***[0.77] [0.123] [0.829] [0.141]
Adj R2 0.038 0.045 0.037 0.051 0.039 0.051 0.04 0.063N
Fixed# FixedClusters
Exporter-Tariff Interaction
βD1 -0.018 -0.029 -0.032 -0.022[0.069] [0.069] [0.08] [0.08]
βD2 -0.315*** -0.33*** -0.28*** -0.269*** -0.314*** -0.32*** -0.263*** -0.269***[0.075] [0.076] [0.082] [0.084] [0.047] [0.047] [0.048] [0.05]
βD3 -0.597*** -0.61*** -0.548*** -0.533*** -0.607*** -0.609*** -0.554*** -0.552***[0.078] [0.078] [0.079] [0.077] [0.055] [0.055] [0.064] [0.063]
βD4 -0.886*** -0.899*** -0.833*** -0.821*** -0.891*** -0.886*** -0.838*** -0.832***[0.095] [0.097] [0.091] [0.091] [0.057] [0.058] [0.07] [0.07]
βD5 -1.224*** -1.237*** -1.214*** -1.201*** -1.238*** -1.228*** -1.217*** -1.201***[0.109] [0.111] [0.124] [0.125] [0.077] [0.076] [0.097] [0.095]
βUS_D0 0.143 0.166 2.081 1.854[1.931] [1.921] [2.053] [1.996]
βUS_D2 1.292 1.201 1.818 1.848 0.915 0.695 1.766 1.588[1.609] [1.597] [1.552] [1.531] [1.498] [1.475] [1.209] [1.26]
βUS_D3 1.029 1.082 2.743 3.036 0.436 0.387 2 2.148[1.936] [1.95] [2.11] [2.083] [1.828] [1.875] [2.105] [2.094]
βUS_D4 2.26 2.277 3.056 3.241 1.72 1.702 2.142 2.14[1.825] [1.875] [2.362] [2.364] [1.876] [1.901] [2.309] [2.291]
βUS_D5 3.46** 3.58** 3.553 4.007* 2.645 2.81* 2.956 3.384[1.715] [1.743] [2.208] [2.186] [1.622] [1.648] [2.126] [2.082]
βGrow -0.106*** -0.114***[0.011] [0.014]
βdVT -3.745*** -1.159*** -3.938*** -1.22***[0.772] [0.123] [0.836] [0.142]
Adj R2 0.041 0.047 0.04 0.054 0.042 0.053 0.042 0.066N
Fixed# FixedClusters
TARIFFS
TARIFFS
Full Sample
Full Sample Restricted Sample: Positive Trade in 1989 and 1990
Restricted Sample: Positive Trade in 1989 and 1990
Exporter_HS2 Exporter_HS4 Exporter_HS2 Exporter_HS4
Exporter,HS4 Exporter,HS2 Exporter,HS4
48449 48449
138673172 2519
36584
13970 2540 11056
36584
11023
48600 48600 36634 36634
Exporter,HS2
Notes: The dependent variable is the log ratio of import value in 2004 to import value in 1992 for exporter j in HS10 industry i. The excluded group is D0, which is thedummy variable identifying whether exporter j in HS10 product i was not imported to the US during the years 1989 or 1990. The "Full Sample" includes all observations,including those of varieties that were not imported in 1989 or 1990. The "Restricted Sample" excludes these varieties. Fixed effects are estimated using a within procedureaccording to the groups listed in the column "Fixed". Standard errors are heteroskedasticity robust and clustered by Exporter and Fixed effect industry (Exp_HS4 fixedeffects implies Exporter and HS4 clusters used for standard errors) according to the multi-level procedure in Cameron, Gelbach, and Miller (2006). The labels *, **, and*** identify estimates which are significantly different from zero at the 10%, 5%, and 1% levels, respectively. "Adj R2" reports adjusted R2 values for the regression afterthe data has been demeaned by the fixed effects.
Exporter_HS2 Exporter_HS4 Exporter_HS2 Exporter_HS4
Exporter,HS2 Exporter,HS4 Exporter,HS2 Exporter,HS43242
35
Tab
le5:
Eff
ects
ofM
FN
tari
ffs
by
wit
hin
-pro
duct
quin
tile
:N
oN
AF
TA
,C
HIN
A
Full S
am
ple
β D1
-0.0
15-0
.015
-0.0
06-0
.006
-0.0
36-0
.036
-0.0
09-0
.009
-0.0
25-0
.025
-0.0
03-0
.003
[0.0
73]
[0.0
73]
[0.0
85]
[0.0
85]
[0.0
72]
[0.0
72]
[0.0
83]
[0.0
83]
[0.0
77]
[0.0
77]
[0.0
89]
[0.0
89]
β D2
-0.2
87**
*-0
.287
***
-0.2
36**
*-0
.236
***
-0.3
37**
*-0
.337
***
-0.2
58**
*-0
.258
***
-0.2
95**
*-0
.295
***
-0.2
25**
-0.2
25**
[0.0
76]
[0.0
76]
[0.0
88]
[0.0
88]
[0.0
79]
[0.0
79]
[0.0
86]
[0.0
86]
[0.0
79]
[0.0
79]
[0.0
91]
[0.0
91]
β D3
-0.5
94**
*-0
.594
***
-0.5
08**
*-0
.508
***
-0.6
14**
*-0
.614
***
-0.5
05**
*-0
.505
***
-0.6
02**
*-0
.602
***
-0.4
96**
*-0
.496
***
[0.0
78]
[0.0
78]
[0.0
85]
[0.0
85]
[0.0
8][0
.08]
[0.0
83]
[0.0
83]
[0.0
84]
[0.0
84]
[0.0
89]
[0.0
89]
β D4
-0.8
43**
*-0
.843
***
-0.7
62**
*-0
.762
***
-0.9
13**
*-0
.913
***
-0.8
19**
*-0
.819
***
-0.8
58**
*-0
.858
***
-0.7
65**
*-0
.765
***
[0.0
93]
[0.0
93]
[0.0
95]
[0.0
95]
[0.1
][0
.1]
[0.0
92]
[0.0
92]
[0.1
02]
[0.1
02]
[0.1
01]
[0.1
01]
β D5
-1.2
11**
*-1
.211
***
-1.1
53**
*-1
.153
***
-1.2
71**
*-1
.271
***
-1.2
03**
*-1
.203
***
-1.2
55**
*-1
.255
***
-1.1
87**
*-1
.187
***
[0.1
04]
[0.1
04]
[0.1
22]
[0.1
22]
[0.1
14]
[0.1
14]
[0.1
23]
[0.1
23]
[0.1
07]
[0.1
07]
[0.1
2][0
.12]
β US
-3.8
09-6
.796
***
-4.2
65*
-6.3
02**
*-3
.747
-6.7
66**
*[2
.7]
[1.9
91]
[2.3
2][1
.523
][2
.717
][2
.089
]β U
S_D0
-3.9
79**
-0.1
7-5
.565
***
1.23
1-4
.145
**0.
119
-5.1
94**
*1.
108
-3.7
69*
-0.0
22-5
.679
***
1.08
7[1
.993
][2
.243
][1
.965
][2
.045
][2
.098
][1
.95]
[1.8
94]
[1.9
91]
[1.9
93]
[2.2
91]
[1.9
08]
[2.1
57]
β US_D1
-3.8
09-6
.796
***
-4.2
65*
-6.3
02**
*-3
.747
-6.7
66**
*[2
.7]
[1.9
91]
[2.3
2][1
.523
][2
.717
][2
.089
]β U
S_D2
-3.1
25*
0.68
5-5
.711
***
1.08
4-3
.178
*1.
087
-4.6
94**
*1.
608
-2.8
87*
0.86
-5.3
3***
1.43
6[1
.705
][1
.924
][1
.949
][1
.669
][1
.682
][1
.532
][1
.639
][1
.445
][1
.735
][1
.893
][1
.908
][1
.635
]β U
S_D3
-3.9
3**
-0.1
21-4
.728
***
2.06
8-3
.253
*1.
012
-3.2
54*
3.04
8-3
.684
**0.
063
-4.3
39**
2.42
7[1
.657
][2
.245
][1
.631
][2
.464
][1
.719
][1
.991
][1
.815
][2
.179
][1
.705
][2
.316
][1
.805
][2
.703
]β U
S_D4
-2.0
191.
79-3
.492
3.30
4-2
.245
2.02
-3.4
81*
2.82
1-1
.814
1.93
3-3
.298
3.46
8[2
.065
][2
.27]
[2.3
47]
[2.6
95]
[2.0
15]
[1.8
3][2
.071
][2
.239
][2
.166
][2
.39]
[2.4
1][2
.723
]β U
S_D5
-1.9
731.
836
-3.5
863.
21-1
.363
2.90
1*-2
.751
3.55
1*-2
.397
1.35
-3.9
48*
2.81
8[1
.908
][1
.831
][2
.186
][2
.505
][1
.51]
[1.5
][1
.858
][2
.06]
[1.8
54]
[1.9
23]
[2.1
53]
[2.5
15]
β dVT
-3.6
14**
*-3
.614
***
-1.1
17**
*-1
.117
***
-3.8
21**
*-3
.821
***
-1.1
71**
*-1
.171
***
-3.6
67**
*-3
.667
***
-1.1
35**
*-1
.135
***
[0.7
59]
[0.7
59]
[0.1
21]
[0.1
21]
[0.7
82]
[0.7
82]
[0.1
22]
[0.1
22]
[0.7
66]
[0.7
66]
[0.1
21]
[0.1
21]
Ad
j R
20.
044
0.04
40.
052
0.05
20.
046
0.04
60.
051
0.05
10.
042
0.04
20.
049
0.04
9N
Fixed
# F
ixed
Clu
sters
Rest
rict
ed
Sam
ple
: P
osi
tive
Tra
de in
19
89
an
d 1
99
0
β D2
-0.2
91**
*-0
.291
***
-0.2
44**
*-0
.244
***
-0.3
16**
*-0
.316
***
-0.2
62**
*-0
.262
***
-0.2
88**
*-0
.288
***
-0.2
28**
*-0
.228
***
[0.0
64]
[0.0
64]
[0.0
62]
[0.0
62]
[0.0
45]
[0.0
45]
[0.0
5][0
.05]
[0.0
64]
[0.0
64]
[0.0
67]
[0.0
67]
β D3
-0.6
03**
*-0
.603
***
-0.5
52**
*-0
.552
***
-0.5
89**
*-0
.589
***
-0.5
14**
*-0
.514
***
-0.5
95**
*-0
.595
***
-0.5
24**
*-0
.524
***
[0.0
58]
[0.0
58]
[0.0
7][0
.07]
[0.0
57]
[0.0
57]
[0.0
71]
[0.0
71]
[0.0
6][0
.06]
[0.0
76]
[0.0
76]
β D4
-0.8
48**
*-0
.848
***
-0.8
09**
*-0
.809
***
-0.8
84**
*-0
.884
***
-0.8
28**
*-0
.828
***
-0.8
44**
*-0
.844
***
-0.7
93**
*-0
.793
***
[0.0
66]
[0.0
66]
[0.0
71]
[0.0
71]
[0.0
68]
[0.0
68]
[0.0
73]
[0.0
73]
[0.0
77]
[0.0
77]
[0.0
77]
[0.0
77]
β D5
-1.2
24**
*-1
.224
***
-1.1
97**
*-1
.197
***
-1.2
38**
*-1
.238
***
-1.2
02**
*-1
.202
***
-1.2
44**
*-1
.244
***
-1.2
08**
*-1
.208
***
[0.0
88]
[0.0
88]
[0.1
06]
[0.1
06]
[0.0
86]
[0.0
86]
[0.0
97]
[0.0
97]
[0.0
98]
[0.0
98]
[0.1
1][0
.11]
β US
-3.9
14-6
.604
***
-4.3
83*
-6.5
64**
*-3
.776
-6.7
55**
*[2
.763
][2
.192
][2
.371
][1
.682
][2
.791
][2
.251
]β U
S_D1
-3.9
14-6
.604
***
-4.3
83*
-6.5
64**
*-3
.776
-6.7
55**
*[2
.763
][2
.192
][2
.371
][1
.682
][2
.791
][2
.251
]β U
S_D2
-3.4
24*
0.49
-5.2
12**
*1.
392
-3.5
52**
0.83
1-4
.96*
**1.
604
-3.2
06*
0.57
-4.9
6**
1.79
5[1
.778
][1
.903
][2
][1
.541
][1
.762
][1
.411
][1
.811
][1
.092
][1
.802
][1
.89]
[1.9
75]
[1.5
33]
β US_D3
-4.3
04**
*-0
.39
-5.0
84**
*1.
521
-3.4
53**
0.93
-3.7
02**
2.86
2-3
.968
**-0
.192
-4.6
06**
2.14
9[1
.663
][2
.227
][1
.745
][2
.547
][1
.666
][1
.94]
[1.7
95]
[2.2
35]
[1.6
55]
[2.2
9][1
.847
][2
.75]
β US_D4
-2.2
921.
621
-4.2
14**
2.39
-2.4
961.
888
-4.4
11**
2.15
3-2
.044
1.73
3-4
.064
*2.
69[2
.045
][2
.308
][2
.075
][2
.706
][1
.98]
[1.8
87]
[1.7
62]
[2.2
08]
[2.1
04]
[2.4
28]
[2.0
78]
[2.7
43]
β US_D5
-2.4
211.
493
-3.9
83**
2.62
1-1
.573
2.81
1*-3
.119
*3.
445*
-2.7
221.
054
-4.3
22**
2.43
3[1
.967
][1
.8]
[1.9
84]
[2.4
92]
[1.5
34]
[1.5
][1
.693
][2
.054
][1
.886
][1
.917
][1
.975
][2
.52]
β Grow
-0.1
04**
*-0
.104
***
-0.1
13**
*-0
.113
***
-0.1
02**
*-0
.102
***
-0.1
11**
*-0
.111
***
-0.1
***
-0.1
***
-0.1
1***
-0.1
1***
[0.0
11]
[0.0
11]
[0.0
15]
[0.0
15]
[0.0
11]
[0.0
11]
[0.0
14]
[0.0
14]
[0.0
12]
[0.0
12]
[0.0
16]
[0.0
16]
β dVT
-3.7
92**
*-3
.792
***
-1.1
83**
*-1
.183
***
-3.9
81**
*-3
.981
***
-1.2
36**
*-1
.236
***
-3.8
21**
*-3
.821
***
-1.2
02**
*-1
.202
***
[0.8
25]
[0.8
25]
[0.1
39]
[0.1
39]
[0.8
34]
[0.8
34]
[0.1
4][0
.14]
[0.8
31]
[0.8
31]
[0.1
38]
[0.1
38]
Ad
j R
20.
048
0.04
80.
061
0.06
10.
051
0.05
10.
064
0.06
40.
048
0.04
80.
062
0.06
2N
Fixed
# F
ixed
Clu
sters
T A R I F F S T A R I F F S
4528
8
Expo
rter
,HS2
Expo
rter
,HS4
Expo
rter
,HS4
4528
847
081
4708
143
760
4376
0
3079
2978
Expo
rter
_HS2
Expo
rter
_HS4
Expo
rter
,HS2
Expo
rter
,HS4
Expo
rter
,HS2
Expo
rter
,HS4
2454
Expo
rter
,HS4
3153
1323
2
1046
424
0710
575
Expo
rter
_HS2
Expo
rter
_HS4
3425
134
251
3554
6
Expo
rter
_HS4
Expo
rter
,HS2
Expo
rter
_HS4
Expo
rter
_HS2
Expo
rter
,HS2
Expo
rter
,HS4
Expo
rter
,HS2
Expo
rter
_HS2
3554
633
157
1334
9
2312
9979
3315
7Ex
port
er_H
S2
Expo
rter
_HS4
No
tes:
The
depe
nden
tva
riab
leis
the
log
ratio
ofim
port
valu
ein
2004
toim
port
valu
ein
1992
for
expo
rter
jin
HS10
indu
stry
i.Th
eex
clud
edgr
oup
isD
0,w
hich
isth
edu
mm
yva
riab
leid
entify
ing
whe
ther
expo
rter
jin
HS10
prod
uct
iwas
not
impo
rted
toth
eU
Sdu
ring
the
year
s19
89or
1990
.Th
e"F
ullS
ampl
e"in
clud
esal
lobs
erva
tion
s,in
clud
ing
thos
eof
variet
ies
that
wer
eno
tim
port
edin
1989
or19
90.
The
"Res
tric
ted
Sam
ple"
excl
udes
thes
eva
riet
ies.
Fixe
def
fect
sar
ees
tim
ated
usin
ga
withi
npr
oced
ure
acco
rdin
gto
the
grou
pslis
ted
inth
eco
lum
n"F
ixed
".Sta
ndar
der
rors
are
hete
rosk
edas
tici
tyro
bust
and
clus
tere
dby
Expo
rter
and
Fixe
def
fect
indu
stry
(Exp
_HS4
fixed
effe
cts
impl
ies
Expo
rter
and
HS4
clus
ters
used
for
stan
dard
erro
rs)
acco
rdin
gto
the
mul
ti-l
evel
proc
edur
ein
Cam
eron
,G
elba
ch,
and
Mill
er(2
006)
.Th
ela
bels
*,**
,an
d**
*id
entify
estim
ates
whi
char
esi
gnifi
cant
lydi
ffer
ent
from
zero
atth
e10
%,
5%,
and
1%le
vels
,re
spec
tive
ly.
"Adj
R2"
repo
rts
adju
sted
R2
valu
es for
the
reg
ress
ion
afte
r th
e da
ta h
as b
een
dem
eane
d by
the
fix
ed e
ffec
ts.
No
Ch
ina
No
NA
FTA
No
Ch
ina,
NA
FTA
No
Ch
ina
No
NA
FTA
No
Ch
ina,
NA
FTA
1259
9Ex
port
er_H
S2
Expo
rter
_HS4
36
Table 6: Effects of MFN tariffs by within-product quintile: Exporter HHI, and Number of Varieties
HS8 Exporter Concentration HS8 Variety
Low HHI High HHI Low HHI High HHI Low N High N Low N High NβLow -0.087 -0.105 0.076 0.037
[0.083] [0.101] [0.093] [0.109]βD1 -0.018 -0.068 -0.011 -0.051 -0.233** 0.028 -0.291* 0.05
[0.093] [0.092] [0.087] [0.122] [0.113] [0.087] [0.166] [0.087]βD2 -0.281*** -0.449*** -0.217** -0.404*** -0.594*** -0.248*** -0.546*** -0.194**
[0.083] [0.101] [0.094] [0.112] [0.115] [0.079] [0.112] [0.093]βD3 -0.621*** -0.604*** -0.529*** -0.553*** -0.724*** -0.583*** -0.671*** -0.5***
[0.091] [0.093] [0.085] [0.11] [0.111] [0.087] [0.114] [0.086]βD4 -0.846*** -1.039*** -0.778*** -0.956*** -1.132*** -0.836*** -1.076*** -0.763***
[0.106] [0.119] [0.093] [0.123] [0.138] [0.102] [0.155] [0.093]βD5 -1.144*** -1.488*** -1.097*** -1.506*** -1.363*** -1.222*** -1.422*** -1.156***
[0.126] [0.11] [0.137] [0.13] [0.115] [0.12] [0.153] [0.133]βUS_D0 -4.684** -3.631 -5.077** -4.499* -2.715 -5.127** -3.017 -5.876**
[2.379] [2.763] [2.449] [2.649] [2.505] [2.485] [2.581] [2.785]βUS_D1 -4.866* -3.536 -6.905*** -5.09** -3.744 -5.182* -5.222* -7.436***
[2.712] [2.704] [2.086] [2.292] [2.903] [2.78] [3.049] [2.213]βUS_D2 -2.193 -5.488** -3.285 -7.558*** -7.131*** -1.89 -7.904*** -3.737
[2.157] [2.769] [2.698] [2.794] [2.337] [2.148] [2.917] [2.632]βUS_D3 -4.523*** -1.603 -4.088*** -2.498 -3.729 -3.553* -5.021* -3.097
[1.645] [2.375] [1.566] [3.053] [2.627] [1.813] [2.742] [2.319]βUS_D4 -1.807 -3.441 -3.312 -3.421 -5.024** -1.342 -5.546** -2.666
[2.355] [2.266] [2.22] [2.791] [2.492] [1.968] [2.686] [2.488]βUS_D5 0.125 -3.549 -1.006 -5.826** -2.014 -0.734 -5.903*** -1.199
[2.399] [2.31] [3.063] [2.388] [2.425] [2.315] [2.209] [2.988]βdVT -3.741*** -3.769*** -1.153*** -1.163*** -3.842*** -3.739*** -1.195*** -1.142***
[0.771] [0.789] [0.126] [0.124] [0.774] [0.763] [0.128] [0.126]Adj R2
NFixed
# FixedClusters
Low HHI High HHI Low HHI High HHI Low N High N Low N High NβLow -0.006 -0.08 -0.112 -0.283
[0.111] [0.119] [0.134] [0.178]βD2 -0.283*** -0.393*** -0.216*** -0.386*** -0.383*** -0.295*** -0.251* -0.269***
[0.065] [0.078] [0.064] [0.101] [0.103] [0.062] [0.13] [0.065]βD3 -0.613*** -0.578*** -0.542*** -0.572*** -0.523*** -0.632*** -0.426** -0.589***
[0.056] [0.091] [0.066] [0.11] [0.135] [0.065] [0.178] [0.076]βD4 -0.834*** -0.99*** -0.793*** -0.936*** -0.93*** -0.872*** -0.825*** -0.844***
[0.072] [0.081] [0.085] [0.101] [0.105] [0.079] [0.154] [0.095]βD5 -1.136*** -1.447*** -1.102*** -1.483*** -1.16*** -1.266*** -1.148*** -1.234***
[0.098] [0.108] [0.123] [0.13] [0.138] [0.089] [0.145] [0.12]βUS_D1 -5.377* -2.914 -7.428*** -4.319 -3.395 -5.444* -5.03 -7.599***
[2.778] [2.768] [2.19] [2.65] [2.637] [2.962] [3.189] [2.59]βUS_D2 -3.052 -5.02* -3.814 -7.165** -6.814*** -2.568 -7.269** -4.224
[2.319] [2.772] [2.702] [3.065] [2.248] [2.318] [3.023] [2.692]βUS_D3 -4.916*** -1.939 -5.068*** -2.794 -3.526 -4.162** -5.006 -4.101*
[1.591] [2.299] [1.643] [3.091] [2.6] [1.779] [3.154] [2.331]βUS_D4 -2.187 -3.615 -4.33** -4.351* -5.496** -1.494 -6.843*** -3.455
[2.376] [2.213] [1.941] [2.637] [2.41] [1.951] [2.617] [2.143]βUS_D5 -0.544 -3.564 -1.805 -5.815** -2.19 -1.23 -6.043** -1.787
[2.474] [2.331] [2.878] [2.524] [2.457] [2.429] [2.394] [2.747]βGrow -0.102*** -0.114*** -0.109*** -0.13*** -0.105*** -0.107*** -0.127*** -0.111***
[0.011] [0.023] [0.017] [0.028] [0.03] [0.01] [0.027] [0.016]βdVT -3.941*** -3.91*** -1.219*** -1.215*** -4.088*** -3.912*** -1.284*** -1.196***
[0.829] [0.846] [0.145] [0.141] [0.834] [0.822] [0.141] [0.148]Adj R2
NFixed
# FixedClusters
0.046 0.053
0.053 0.066
13970Exporter,HS4
Restricted Sample
0.051
Exporter,HS2 Exporter,HS4
48600Exporter_HS2
3242Exporter,HS2
48600Exporter_HS4
2540 11056
Full Sample
0.051
TARIFFS
36634 36634Exporter_HS2 Exporter_HS4
Exporter,HS2
36634
0.045
Exporter_HS4366340.064
Exporter_HS2
11056
TARIFFS
48600
Exporter_HS22540
Notes: The dependent variable is the log ratio of import value in 2004 to import value in 1992 for exporter j in HS10 industry i. The excluded groups are D0,which is the dummy variable identifying whether exporter j in HS10 product i was not imported to the US during the years 1989 or 1990, and the dummyvariable identifying above median HHI or N. The "Full Sample" includes all observations, including those of varieties that were not imported in 1989 or 1990.The "Restricted Sample" excludes these varieties. Fixed effects are estimated using a within procedure according to the groups listed in the column "Fixed".Standard errors are heteroskedasticity robust and clustered by Exporter and Fixed effect industry (Exp_HS4 fixed effects implies Exporter and HS4 clusters usedfor standard errors) according to the multi-level procedure in Cameron, Gelbach, and Miller (2006). The labels *, **, and *** identify estimates which aresignificantly different from zero at the 10%, 5%, and 1% levels, respectively. "Adj R2" reports adjusted R2 values for the regression after the data has beendemeaned by the fixed effects.
Full Sample
Restricted SampleExporter,HS2 Exporter,HS4
48600Exporter_HS4
3242 13970
Exporter,HS4
37
Tab
le7:
Com
par
ison
ofV
alue,
Quan
tity
,an
dP
rice
bin
s
Full S
am
ple
Bin
Defi
nit
ion
:β D
1-0
.09
-0.0
76-0
.051
-0.0
23-0
.426
***
-0.3
93**
*-0
.083
-0.0
64-0
.008
0.00
4-0
.368
**-0
.348
**[0
.133
][0
.135
][0
.132
][0
.135
][0
.144
][0
.147
][0
.132
][0
.132
][0
.133
][0
.131
][0
.153
][0
.151
]β D
2-0
.393
***
-0.3
83**
*-0
.348
***
-0.3
23**
-0.5
11**
*-0
.486
***
-0.3
31**
-0.3
09**
-0.2
92**
-0.2
76**
-0.4
07**
*-0
.387
***
[0.1
36]
[0.1
37]
[0.1
25]
[0.1
27]
[0.1
28]
[0.1
31]
[0.1
35]
[0.1
36]
[0.1
29]
[0.1
27]
[0.1
35]
[0.1
31]
β D3
-0.6
64**
*-0
.653
***
-0.5
18**
*-0
.493
***
-0.5
95**
*-0
.571
***
-0.5
89**
*-0
.566
***
-0.4
63**
*-0
.448
***
-0.5
22**
*-0
.509
***
[0.1
49]
[0.1
5][0
.119
][0
.121
][0
.143
][0
.145
][0
.147
][0
.145
][0
.123
][0
.121
][0
.151
][0
.148
]β D
4-0
.977
***
-0.9
65**
*-0
.862
***
-0.8
37**
*-0
.561
***
-0.5
37**
*-0
.922
***
-0.8
99**
*-0
.775
***
-0.7
61**
*-0
.507
***
-0.4
95**
*[0
.16]
[0.1
62]
[0.1
33]
[0.1
36]
[0.1
43]
[0.1
46]
[0.1
51]
[0.1
5][0
.137
][0
.134
][0
.142
][0
.139
]β D
5-1
.345
***
-1.3
31**
*-1
.178
***
-1.1
52**
*-0
.566
***
-0.5
43**
*-1
.329
***
-1.3
04**
*-1
.173
***
-1.1
58**
*-0
.532
***
-0.5
24**
*[0
.172
][0
.173
][0
.168
][0
.172
][0
.139
][0
.142
][0
.183
][0
.183
][0
.163
][0
.16]
[0.1
31]
[0.1
3]β U
S_D0
-3.1
84-2
.994
-2.9
87-2
.89
-2.9
02-2
.805
-5.1
42-4
.051
-5.5
95-4
.275
-5.4
23-4
.101
[2.3
16]
[2.3
57]
[2.6
56]
[2.6
22]
[2.6
91]
[2.6
62]
[3.5
11]
[3.4
36]
[3.4
49]
[3.3
69]
[3.5
44]
[3.4
57]
β US_D1
-3.7
87-3
.135
-2.6
2-1
.945
-3.2
12*
-2.4
53-7
.192
***
-6.2
34**
*-5
.746
***
-4.7
22**
*-4
.934
*-3
.599
[2.3
24]
[2.3
12]
[2.2
12]
[2.1
86]
[1.9
43]
[1.8
93]
[2.0
13]
[1.8
23]
[1.9
81]
[1.8
31]
[2.8
44]
[2.6
52]
β US_D2
-2.8
45-2
.336
-2.5
67-2
.003
-1.6
27-1
.054
-4.7
5**
-3.5
64-4
.541
-3.3
25-3
.425
*-2
.217
[2.1
59]
[2.0
71]
[2.1
78]
[2.1
1][2
.242
][2
.205
][2
.396
][2
.363
][2
.878
][2
.675
][2
.056
][1
.917
]β U
S_D3
-3.0
3*-2
.4-2
.946
*-2
.311
-1.2
71-0
.681
-4.0
43*
-2.6
52-5
.493
***
-4.2
32**
-3.7
11*
-2.4
95[1
.783
][1
.743
][1
.772
][1
.689
][1
.719
][1
.666
][2
.212
][2
.035
][2
.01]
[1.8
68]
[2.1
76]
[2.0
18]
β US_D4
-2.0
03-1
.394
-3.4
44*
-2.8
59-2
.573
-1.9
63-4
.194
*-2
.878
-4.4
89*
-3.2
27-5
.666
**-4
.402
**[2
.073
][2
.058
][2
.024
][2
.009
][1
.705
][1
.677
][2
.391
][2
.321
][2
.437
][2
.349
][2
.318
][2
.233
]β U
S_D5
-1.1
86-0
.482
-1.4
4-0
.748
-3.4
69-2
.864
-4.0
38*
-2.5
05-4
.212
*-2
.683
-4.8
61*
-3.6
36[1
.558
][1
.447
][1
.751
][1
.67]
[2.2
5][2
.259
][2
.412
][2
.2]
[2.2
11]
[2.0
1][2
.556
][2
.489
]β d
VT
-3.7
83**
*-3
.717
***
-3.7
07**
*-1
.265
***
-1.2
69**
*-1
.259
***
[0.8
31]
[0.8
31]
[0.8
18]
[0.1
57]
[0.1
59]
[0.1
61]
Ad
j R
20.
040.
046
0.03
0.03
60.
008
0.01
40.
040.
055
0.03
10.
046
0.00
70.
021
NFi
xed
# F
ixed
Clu
sters
Rest
rict
ed
Sam
ple
: P
osi
tive
Tra
de in
19
89
an
d 1
99
0B
in D
efi
nit
ion
:β D
2-0
.309
***
-0.3
13**
*-0
.243
***
-0.2
4***
-0.0
7-0
.071
-0.2
48**
*-0
.251
***
-0.2
49**
*-0
.253
***
-0.0
26-0
.03
[0.0
51]
[0.0
53]
[0.0
3][0
.037
][0
.053
][0
.053
][0
.05]
[0.0
55]
[0.0
75]
[0.0
74]
[0.0
7][0
.071
]β D
3-0
.578
***
-0.5
82**
*-0
.417
***
-0.4
11**
*-0
.149
***
-0.1
53**
*-0
.517
***
-0.5
17**
*-0
.425
***
-0.4
25**
*-0
.14*
*-0
.154
**[0
.064
][0
.065
][0
.06]
[0.0
63]
[0.0
55]
[0.0
56]
[0.0
67]
[0.0
7][0
.075
][0
.075
][0
.066
][0
.068
]β D
4-0
.887
***
-0.8
84**
*-0
.752
***
-0.7
43**
*-0
.099
-0.1
09-0
.83*
**-0
.821
***
-0.7
28**
*-0
.722
***
-0.1
11-0
.136
*[0
.069
][0
.071
][0
.075
][0
.078
][0
.065
][0
.067
][0
.083
][0
.084
][0
.088
][0
.087
][0
.074
][0
.076
]β D
5-1
.254
***
-1.2
44**
*-1
.065
***
-1.0
51**
*-0
.1-0
.11
-1.2
26**
*-1
.208
***
-1.1
06**
*-1
.095
***
-0.1
57-0
.181
*[0
.086
][0
.084
][0
.103
][0
.105
][0
.09]
[0.0
93]
[0.1
13]
[0.1
13]
[0.1
1][0
.11]
[0.0
99]
[0.1
02]
β US_D1
-4.0
31*
-3.2
78-3
.939
*-3
.497
-3.2
4*-2
.727
-7.5
02**
*-6
.434
***
-6.9
17**
*-5
.911
***
-4.8
24-3
.537
[2.3
95]
[2.3
85]
[2.2
05]
[2.1
96]
[1.9
53]
[1.9
1][2
.021
][1
.857
][2
.215
][2
.002
][2
.95]
[2.6
59]
β US_D2
-2.9
27-2
.457
-2.6
33-2
.07
-1.5
21-0
.931
-4.7
64*
-3.7
56-4
.349
-3.2
98-2
.876
-1.7
72[2
.148
][2
.037
][2
.206
][2
.105
][2
.285
][2
.249
][2
.487
][2
.41]
[3.0
53]
[2.7
89]
[2.0
01]
[1.8
43]
β US_D3
-3.1
53*
-2.6
21-3
.225
*-2
.733
-1.3
91-0
.852
-4.6
21**
-3.4
08*
-6.0
41**
*-5
.03*
**-3
.641
*-2
.57
[1.8
29]
[1.7
64]
[1.7
51]
[1.6
66]
[1.6
33]
[1.5
87]
[2.2
62]
[2.0
46]
[1.9
19]
[1.7
19]
[2.0
44]
[1.8
66]
β US_D4
-2.1
86-1
.633
-3.5
76*
-3.0
22-2
.451
-1.9
77-4
.535
**-3
.306
-4.8
18**
-3.6
49-5
.566
**-4
.603
**[2
.007
][1
.978
][1
.985
][1
.966
][1
.678
][1
.637
][2
.258
][2
.102
][2
.397
][2
.291
][2
.382
][2
.242
]β U
S_D5
-1.2
32-0
.497
-1.5
24-0
.834
-3.8
95*
-3.3
06-4
.111
*-2
.524
-4.4
67**
-2.9
66-5
.696
**-4
.576
*[1
.583
][1
.458
][1
.741
][1
.631
][2
.233
][2
.185
][2
.454
][2
.19]
[2.1
74]
[1.8
83]
[2.7
][2
.612
]β G
row
-3.7
83**
*-3
.713
***
-3.7
64**
*-1
.275
***
-1.2
75**
*-1
.26*
**[0
.888
][0
.889
][0
.874
][0
.163
][0
.162
][0
.163
]β d
VT
-0.1
05**
*-0
.11*
**-0
.113
***
-0.1
17**
*-0
.122
***
-0.1
29**
*[0
.011
][0
.011
][0
.011
][0
.015
][0
.015
][0
.017
]A
dj
R2
0.03
80.
050.
027
0.03
80.
001
0.01
30.
040.
062
0.03
0.05
20.
002
0.02
5N
Fixed
# F
ixed
Clu
sters
Quantity
3341
1Ex
port
er_H
S2
T A R I F F S T A R I F F S
Not
es:
The
depe
nden
tva
riab
leis
the
log
ratio
ofim
port
valu
ein
2004
toim
port
valu
ein
1992
for
expo
rter
jin
HS10
indu
stry
i.Th
eex
clud
edgr
oup
isD
0,w
hich
isth
edu
mm
yva
riab
leid
entify
ing
whe
ther
expo
rter
jin
HS10
prod
uct
iwas
not
impo
rted
toth
eU
Sdu
ring
the
year
s19
89or
1990
.Th
e"F
ullSam
ple"
incl
udes
allob
serv
atio
ns,
incl
udin
gth
ose
ofva
riet
ies
that
wer
eno
tim
port
edin
1989
or19
90.
The
"Res
tric
ted
Sam
ple"
excl
udes
thes
eva
riet
ies.
Fixe
def
fect
sar
ees
tim
ated
usin
ga
withi
npr
oced
ure
acco
rdin
gto
the
grou
pslis
ted
inth
eco
lum
n"F
ixed
".Sta
ndar
der
rors
are
hete
rosk
edas
tici
tyro
bust
and
clus
tere
dby
Expo
rter
and
Fixe
def
fect
indu
stry
(Exp
_HS4
fixed
effe
cts
impl
ies
Expo
rter
and
HS4
clus
ters
used
for
stan
dard
erro
rs)
acco
rdin
gto
the
mul
ti-l
evel
proc
edur
ein
Cam
eron
,G
elba
ch,
and
Mill
er(2
006)
.Th
ela
bels
*,**
,an
d**
*id
entify
estim
ates
whi
char
esi
gnifi
cant
lydi
ffer
ent
from
zero
atth
e10
%,
5%,
and
1%le
vels
,re
spec
tive
ly.
"Adj
R2"
rep
orts
adj
uste
d R2
valu
es for
the
reg
ress
ion
afte
r th
e da
ta h
as b
een
dem
eane
d by
the
fix
ed e
ffec
ts.
Value
Quantity
Price
Expo
rter
,HS2
3341
1Ex
port
er_H
S4
1028
9Ex
port
er,H
S4
2739
Value
2904
9Ex
port
er_H
S2
2298
Expo
rter
_HS2
8844
Expo
rter
_HS4
Value
Quantity
Price
Value
Quantity
Price
Price
2904
9
Expo
rter
_HS4
38