When Are Variety Gains from Trade Important?
Comparative Advantage and the Cost of Protectionism*
Adina Ardelean**
Santa Clara University, Beacon Economics
Volodymyr Lugovskyy***
Georgia Institute of Technology
December 2007
Abstract:
We argue that domestic differentiated goods are substitutes to the foreign goods. As a
result, industries with comparative advantage experience lower welfare losses from trade barriers
because these industries are more efficient in substituting for the disappearing foreign varieties.
Empirically we confirm our conjecture by showing that the demand for imported varieties is more
elastic for industries with comparative advantage. For an average good facing a median barrier,
doubling the importer's comparative advantage decreases the number of imported varieties by 17%.
Our findings suggest that the damaging effect of high trade barriers is disproportionately placed on
countries with inefficient industries.
JEL Classification: F1, F12
Key Words: variety gains, trade, comparative advantage, product differentiation, monopolistic competition
*
We are especially grateful to David Hummels for excellent comments and encouragement throughout the
entire project. We thank to Jason Abrevaya, Sirsha Chatterjee, Thomas Hertel, Russ Hillberry, Ricardo
Lopez, Julian Emami Namini, Georg Schaur, Alexandre Skiba, Chong Xiang, participants of 2007 Spring
Midwest Trade Meetings, and seminars at Purdue U, U Memphis, and USIC for helpful comments. Will
Martin graciously provided us access to COMTRADE data. Lugovskyy thanks Fogelman College and Wang
CIBER for financial support.
**
Department of Economics, Santa Clara University, 500 El Camino Real, Santa Clara, CA 95053-0385,
[email protected]
***
Corresponding Author: Georgia Institute of Technology, School of Economics, Atlanta, GA 30332-0615;
[email protected].
I. Introduction
Even though the importance of gains from new imported varieties has been recognized
since Krugman (1979), the empirical literature on evaluating the variety gains from trade has
been emerging only recently. Largely, data availability and model tractability have been the
major challenges that hinder the researchers’ ability to estimate the variety gains. While trade
data at a highly disaggregated commodity level have become available for many countries,
disaggregated data on domestic production are still scarce. Thus, researchers have to make
simplifying assumptions regarding the interaction between foreign and domestic varieties. As
a result the importance of the domestic sector is often downplayed.
This paper highlights the importance of the domestic varieties in evaluating the variety
gains from trade. We demonstrate theoretically and confirm empirically that the efficiency in
producing domestic varieties has a significant impact on the demand for foreign varieties.
Subject to the same data constraints we shed light on the importance of domestic varieties in
calculating welfare gains from trade. And until better data become available, policymakers
should take caution in relying on current estimates in the literature.
The magnitude of the variety gains estimates varies depending on the underlying
assumptions and data used. In a calibrated model, Romer (1994) shows that the GDP losses
associated with the exit of foreign varieties can reach up to 20% as a result of only a 10%
tariff. Klenow and Rodriguez-Clare (1997) confirm Romer’s qualitative predictions on the
Costa Rican data, but find the size of the effect to be an order of magnitude lower. More
recently, Broda and Weinstein (2006) estimate that the US imported varieties have quadrupled
between 1972 and 2001, which has increased the US welfare by 3% of GDP.
1
These papers, however, assume away the competition between foreign and domestic
varieties. In Romer’s (1994) model the importer is a small open economy incapable of
producing its own varieties. Klenow and Rodriguez-Clare (1997) relax this assumption by
allowing only one domestic variety in each sector. While allowing for multiple domestic
varieties, Broda and Weinstein (2006) assume away the substitutability between foreign and
domestic varieties by using a Cobb-Douglas upper-level utility function.
We claim that the domestic productivity is a key factor in evaluating the variety gains
from trade when foreign and domestic varieties are substitutes. In our model we show that
countries with stronger domestic industries import fewer varieties and suffer smaller welfare
losses from trade barriers. To illustrate, we provide a numerical example where a 10%
increase in a trade barrier will cause a 7.5% welfare loss if the importer is unable to produce its
own varieties, a 3.75% welfare loss if the importer can produce its varieties at the same cost as
the exporter, and only a 0.83% welfare loss if the importer is twice as productive as the
exporter. However, independent of the importer’s relative productivity, a 10% increase in a
trade barrier generates a 3.75% welfare loss when domestic and foreign varieties are not
substitutes.
Empirically we provide indirect evidence on the effect of comparative advantage on
variety gains from trade since data availability restricts our ability to structurally estimate the
direct effect. In particular we explore the impact of comparative advantage on the demand for
imported varieties. Consistent with the previous literature, trade barriers have a negative effect
on the number of imported varieties. However, in our model this effect is magnified by the
importer-exporter productivity: the higher is the relative productivity, the more foreign
varieties exit the importer’s market because of a trade barrier. To test this hypothesis we
2
estimate the demand for imported varieties and show that the elasticity of the number of
imported varieties with respect to trade barriers co-varies positively with the importer’s relative
productivity in that sector. Under the alternative, if domestic and foreign goods are not
substitutes, the elasticity is independent of the comparative advantage.
Our dataset consists of a panel of bilateral trade data disaggregated at 6 digit
Harmonized System commodity level from UN’s COMTRADE that covers many country pairs
spanning over 1995-2003. We measure the number of imported varieties as the extensive
margin, which represents the cross-section equivalent of the variety growth measure derived by
Feenstra (1994), and the bilateral productivity ratio by the corresponding ratio of Relative
Export Performances (henceforth, REP). Since transport costs and tariffs data are sparse for a
large number of countries, we proxy trade costs with bilateral distance.
The results are consistent with the predictions of our model. As expected, the distance
decreases the demand for foreign varieties. More importantly, the magnitude of this effect
increases in the importer’s comparative advantage proxied by the bilateral (importer-exporter)
REP. In the pooled regression, doubling the bilateral REP yields a 17 percent decrease in the
number of imported varieties for a median trade barrier. The data reveals substantial variation
in the effect across sectors: 41% for ‘electronics’, 34% for ‘machinery and transportation’, and
0% for ‘petroleum and plastics’, ‘mining and basic metals’, and ‘wood and paper’. A direct
welfare implication is that laggard industries stand to lose more from higher trade barriers,
even though they lose fewer varieties. The results are robust to employing various model
specifications, datasets, and measures of trade barriers.
This paper relates, and contributes, to several lines of research. First, we contribute to a
rapidly growing literature evaluating the variety gains from trade by focusing on the interaction
3
between domestic and imported differentiated products. We show that ignoring the
substitutability between foreign and domestic varieties might overstate the welfare gains from
trade for the more productive importers and understate them for the less productive importers.
Second, we identify an additional factor – comparative advantage – determining the
demand for foreign varieties. Previously the number of imported varieties was shown to covary positively with the market size and GDP per capita (Hummels and Klenow – 2002, 2005)
and negatively with trade barriers (Klenow and Rodriguez-Clare -1997, Feenstra and Kee –
2004, 2007). We show theoretically and confirm empirically that in sectors with higher degree
of comparative advantage the number of imported varieties is smaller, and, more importantly,
the demand for imported varieties is more responsive to variation in trade barriers. The later is
consistent with the empirical evidence provided by Kehoe and Ruhl (2002) who show that the
untraded or least traded goods experience the highest trade growth after trade liberalization.
The rest of the paper is structured as follows: section II presents the model and solves
for the equilibrium; section III takes the predictions of the model to the data; section IV checks
the robustness of our estimates; and section V concludes.
II. Theoretical Framework
We want to explore how relative productivity affects the variety gains form trade when
domestic and imported varieties are substitutes. For this purpose we develop a two country
general equilibrium model of trade. The world consists of F+1 countries: a country named
Importer (indexed by i) and f = 1, 2,..., F other countries. Consumers’ preferences in each
country are defined over many differentiated varieties produced by s=1,2,…,S differentiated
sectors and a homogeneous good (indexed by 0). Within each sector, domestic and imported
4
varieties are substitutes, and varieties produced in the same country are closer substitutes. We
implement this idea by combining the Armington and Dixit-Stiglitz preferences: each
differentiated sector is modeled as an index of country-specific differentiated goods, where
each good is a CES composite of many varieties.1
On the production side, the mix of imported and domestic varieties is defined by the
fixed cost of entering the market, relative productivity, and trade barriers. By allowing the
productivity to vary across sectors, we are able to pinpoint the effect of the comparative
advantage on the welfare and the number of imported varieties.
A. Preferences
The preferences are symmetric in all countries. For brevity, we will set up the model
only from Importer’s perspective. Iporter’s representative consumer has a Cobb-Douglas
utility function across sectors:
S
S
(1)
∑µ
U i = qiµ00 ∏ Cisµs
s =1
s
= 1,
s=0
where qi 0 -- is Importer’s individual consumption of the numeraire;
µs > 0 -- is a constant representing the expenditure share of sector s;
Cis -- is a composite index of Importer’s individual consumption of differentiated goods
in sector s.
The quantity index Cis is a two-level CES subutility function. The upper-case
1
Alternatively, substitutability between foreign and domestic varieties can be achieved by employing the
Melitz-type heterogeneity in differentiated sectors, as, e.g., in Bernard et al. (2007) or Balistreri et al. (2007).
Recall, however, that the main goal of our paper is to discriminate between the cases of ‘substitutability’ and
‘no substitutability’ between foreign and domestic varieties. Our choice was thus dictated by a preference to
have a ‘no substitutability’ scenario as a special case of our model (see discussion in II.A), where the Melitztype approach assumes the same elasticity of substitution between any two varieties.
5
subutility aggregates over goods from different countries with ε being the constant elasticity
of substitution between these goods, while each good is a composite of many symmetric
varieties with σ being the elasticity of substitution between any two of them:
(2)

σ
  Niis σ −1  σ −1 
Cis =   ∑ qiisuσ  

 
  u =1

 
ε −1
ε
 Nifs σ −1 σσ−1 


+ ∑  ∑ qifsσν  


f =1  ν =1
 


F
ε −1
ε





ε
ε −1
σ > ε >1,
where N ifs -- is the set of varieties imported by Importer from country f in sector s;
N iis -- is the set of varieties produced and consumed by Importer in sector s;
qiisu -- is Importer’s individual consumption of a domestic variety u in sector s;
qifsv -- is Importer’s individual consumption of variety ν imported from f in sector s;
The upper level preference structure assumes that varieties from different countries are
substitutes. At one extreme, when (ε − 1) / ε → 1 , the consumer regards composites of varieties
from different countries as perfect substitutes. At the other extreme, when (ε − 1) / ε → 0 , the
consumer spends a constant share of her income on each composite of varieties as in the CobbDouglas utility function. If σ = ε , all varieties enter the utility function symmetrically and the
consumer perceives them as equally substitutable. We impose an assumption of σ > ε , in
which case any two varieties from the same country are better substitutes than any two
varieties from different countries. At the same time, given that ε > 1 , the consumer has a
decreasing marginal valuation for same country’s varieties.2
2
Ardelean (2007) estimates consumer’s love of variety and provides evidence that indeed consumers
perceive within-country varieties as better substitutes than varieties originating from different countries.
6
B. Production
Labor is the only factor of production. Importer’s endowment of labor is Li where
labor is assumed to move freely across sectors. Consequently, the wage wi , defined in terms of
the numeraire, is the same in all sectors. The numeraire sector is characterized by perfect
competition and constant returns to scale. In Importer, one unit of labor can produce β i units
of the numeraire. The numeraire is traded at zero cost. We assume that the numeraire sector is
large enough for both countries to have strictly positive output of the numeraire. The
introduction of the numeraire in the model simplifies the balance of trade calculation and ties
the wage to productivity in the numeraire sector.
In the differentiated sectors, monopolistically competitive firms produce the varieties of
the differentiated goods and are free to enter and exit. In Krugman’s (1980) framework the
fixed cost of production limits the number of varieties the country produces. At the same time,
all varieties produced are exported since there is no fixed trade cost. Alternatively Romer
(1994) introduces a fixed cost of entering the market for each foreign variety, which generates
more realistic predictions3: the number of imported varieties increases in market size and
decreases in trade costs. In our model we introduce a market-specific fixed cost for each
variety. As a result, we are able to endogenize both the number of domestic and imported
varieties.
The technologies are country and sector specific: Importer’s marginal cost in sector s is
1
Ais β h
and market-specific fixed cost is
α
units of labor, where Ais is Importer’s
Ais βi
technological parameter in sector s – higher Ais corresponds to a higher productivity. In
3
See Hummels and Klenow (2002, 2005) for empirical evidence.
7
country f the technological parameter in sector s is defined as Afs = Ais / ϕifs , where ϕ s is the
technological gap between Importer and country f in sector s – the higher the ϕifs , the more
productive is Importer relative to country f in s. The marginal and fixed costs of country f in
sector s are then
1
(A
is
ϕ ifs ) β f
and
α
, respectively.
( Ais ϕifs ) β f
To summarize, there are several levels at which we allow for differences in
productivity. First, countries differ in productivity in the numeraire sector ( β i and β f ); second,
productivity varies across sectors ( Ahs ); and, third, there is a country pair-sector-specific
productivity gap ( ϕifs ).
The trade costs are ad-valorem in nature: if we look at exports from f to i in sector s, the
exporter will be receiving 1 / τ ifs dollars for each dollar of the delivered price, where τ ifs > 1 .
C. Equilibrium Number of Traded Varieties
We start the derivation of equilibrium by finding the link between the nominal wage
and productivity in the numeraire sector. Recall that the numeraire good is produced in both
countries and is tradable at zero cost. As a result, the wage in each country is equal to the labor
productivity in the numeraire sector:
(3)
wi = β i and w f = β f
since otherwise there is an opportunity for arbitrage. Note that β h , h ∈ {i,1, 2,..., F } , serves as a
productivity multiplier in all sectors: a 5% increase in β h yields a 5% increase in productivity
across all sectors of country h. That is, we can interpret the variation in β h as aggregate shocks
8
to labor productivity in country h. This will be important in our discussion of the model’s
predictions.
Next we turn our attention to an Importer’s differentiated sector s. Preferences across
varieties within a good are CES with σ being the elasticity of substitution between any two
varieties originating from the same country. Building on the Dixit-Stiglitz (1977) results, the
equilibrium prices are equal to the monopolistic markup multiplied by the marginal cost:
(4)
piis =
σ
1
σ − 1 Ais
pifs =
τ ifs
.
σ − 1 Ais ϕ ifs
σ
Due to the free entry condition, monopolistically competitive firms earn zero profit, which
allows us to solve for the quantity per variety:
(5)
Qiis = Qifs = α (σ − 1) .
Now let us derive the number of domestic and imported varieties consumed by Home
in sector s. In equilibrium, the marginal rate of substitution between any imported and
domestic varieties is equal to the ratio of their prices:
 N iis 


 N ifs 
−
σ −ε
ε (σ −1)
 qiis 


 qifs 
−
1
ε
=
piis
.
pifs
By substituting for the equilibrium prices and quantities with (4) and (5), we derive the relative
number of varieties:
(6)
N ifs
N iis
= (ϕ ifsτ ifs )
−
ε (σ −1)
σ −ε
.
To derive the number of domestic varieties, N iis , we use the fact that that the uppercase utility function is Cobb-Douglas, and thus consumers allocate a constant expenditure
share to sector s:
9
ε
−1
ε −1
ε −1 ε −1
− ( ε −1)
− (ε −1) ε −1

 
σ
σ
1
1

ε
ε
−
−







F
F 
 N σ −1Q

σ −1
σ −1
σ −1


+
N
Q
N
p
+
N
p
∑ f =1  ifs ifs    iis iis 
∑ f =1  ifs ifs   = µs Li wi ,
 iis iis 

  






where the first multiplier in square brackets is the quantity index and the second multiplier is
the price index of sector s. Next we replace wi with (3), piis and pifs with (4), Qiis and Qifs
with (5), and
N ifs
N iis
with (6), to get
σ (ε −1)

−
 1  F
σ −ε + 1 = µ L β ,
ϕ
τ
(
)

  ∑ f =1 ifs ifs
s i i
 Ais  

ασ N iis 
from which the number of varieties produced and sold domestically is:
−1
(7)
σ (ε −1)
 F

−
µ Lβ
N iis = s i i Ais  ∑ f =1 (ϕifsτ ifs ) σ −ε + 1 .
σα


By combining the result in (7) with the relative number of varieties (6), we find the
number of varieties imported from country i in sector s:
−1
(8)
ε (σ −1)
σ (ε −1)
 F

−
−
µ s Li β i
σ
ε
−
ϕifsτ ifs )
N ifs =
Ais  ∑ f =1 (ϕifsτ ifs ) σ −ε + 1 .
(
σα


D. Variety Gains from Trade
Now we are ready to show that the larger is the relative importer-exporter productivity
in sector s the less vulnerable is an importer’s welfare to trade barriers. By plugging the
equilibrium numbers of domestic and imported varieties given by (7) and (8) and quantity per
variety (5) into utility function (1), we obtain the indirect utility:
10
µs
σ −ε
σ


σ (ε −1)
ε
−
(
σ
−
1
 1)(σ −1) 
−

 µs Li β i   F
µ0
*
σ
ε
−
+ 1
U h = qi 0 ∏ (σ − 1) 
Ais   ∑ f =1 (ϕifsτ ifs )
 .
1/σ
 σα
 
s =1 




S
For simplicity let us consider a two country case (F=1). It is easy to show that the indirect
utility is decreasing in trade barriers:
−1
(9)
ρU
*
i ,τ ifs
σ (ε −1)

∂ ln U i*
σ 
ϕ
τ
=
= − µs
( ifs ifs ) σ −ε + 1 < 0 .
σ −1 
∂ ln τ ifs

Note that the magnitude of the effect is weaker the higher is the relative productivity in s:
(10)
∂ ln ρU * ,τ
i
∂ ln ϕ ifs
−1
ifs
σ (ε −1)

−
σ (ε − 1) 
ϕ
τ
=−
( ifs ifs ) σ −ε + 1 < 0 .
σ −ε 

Let us provide a numerical example. Assume that the varieties of s consumed by
Importer consist of both domestically produced varieties and imported varieties. Furthermore
let us assume the parameters of the model are µs =0.5, σ =3, ε = 2, and τ ifs = 1 . If, as in
Romer (1994), the domestic sector were infinitely less competitive than the foreign sector
(which is a special case of our model when ϕifs → 0 ), the value of the elasticity evaluated at
this point is
∂ ln U i*
= −0.75 . That is, a 10% increase in a trade barrier ( τ ifs = 1.1 ) would
∂ ln τ ifs
decrease the welfare by 7.5%. If Home’s productivity were as high as the Foreign’s ( ϕ ifs = 1 ),
a 10% increase in a trade barrier would increase the welfare by only a half of the previous
magnitude (3.75%). Finally, if Home were twice as productive as Foreign in sector s
( ϕifs = 2 ), a 10% increase in a trade barrier would decrease Home’s welfare by only 0.83%.
Note that, independent of the importer’s relative productivity, a 10% increase in a trade barrier
generates a 3.75% welfare loss if we assume away the substitutability between domestic and
11
foreign varieties ( ε → 1 ). That is, ignoring substitutability when it is, in fact, present, would
yield a 3.75/0.83=4.5 times larger welfare losses for a more productive importer, and a
7.5/3.75=2 times smaller welfare losses for a less productive importer.
E. Comparative Statics
Since the data on the number of domestic varieties are unavailable, we will restrict our
attention to formulating testable hypotheses with respect to the number of imported varieties.
In our model the productivity in the numeraire sector is equal to wage, β i = wi , and
thus changes in β i have no effect on costs in the differentiated sector since they are completely
absorbed by changes in wage, wi . As a result, β i has no effect on the equilibrium quantities
and prices of the differentiated varieties. At the same time an increase in β i increases
(proportionally) the aggregate income wi Li and, consequently, the number of imported
varieties in all sectors:
(11)
∂ ln N ijs
∂ ln β i
> 0.
An increase in Home’s productivity in sector s, Ais , while keeping the relative
productivity ϕijs constant, is equivalent to a simultaneous increase in sector s productivity both
in Importer and in country j. Therefore, the number of imported varieties is increasing in Ais :
(12)
∂ ln N ijs
∂ ln Ais
> 0.
Finally, an increase in the relative productivity in sector s, ϕhis , and an increase in the
trade barrier τ his will decrease the number of imported varieties in the same fashion:
12
(13)
ηijs =
∂ ln N ijs
∂ ln τ ijs
=
∂ ln N ijs
∂ ln τ ijs
1+
=−
σ (ε −1)

−
σ (ε − 1) 
ϕ
τ
 ∑ f ≠ j ( ifs ifs ) σ −ε +1
σ −ε 

σ (ε −1)
 F

−
σ −ε +1
ϕ
τ
(
)
 ∑ f =1 ifs ifs



−1
< 0.
Note that the magnitude of the elasticity ηijs is increases in the relative productivity in sector s:
(14)
∂ ln N ijs
∂ ln
∂ ln τ ijs
∂ ln ϕijs
−1
2
σ (ε −1)

−
 σ (ε − 1)  
F
σ −ε
1
+
ϕ
τ
(
)
∑


ifs
ifs
 σ −ε 
f =1

 

=
> 0.
−1
σ ( ε −1)
σ (ε −1)


−
−
1
ε
σ
(
)
σ −ε +1
(ϕijsτ ijs ) σ −ε
 ∑ f ≠ j (ϕ ifsτ ifs )
 +
−
σ
ε


That is, the higher the importer-exporter relative productivity, the stronger the number of
imported varieties reacts to changes in trade barriers. Alternatively, if domestic and foreign
varieties are not substitutes ( ε = 1 ), ηijs is constant and independent of the relative productivity.
We take this prediction to the data by testing whether an importer with a higher relative
productivity has a more sensitive demand for foreign varieties.
III. Empirics
In this section we relate the number of imported varieties to trade barriers, relative
productivity, and other controls to test the qualitative predictions of our model. Our main
focus is to confirm empirically that the elasticity of the number of imported varieties with
respect to trade cost co-varies positively with importer’s relative productivity. Alternatively, if
domestic and imported varieties are not substitutes, the elasticity is constant and independent
of importer’s relative productivity.
13
A. Data and Variable Construction
We use a panel of bilateral trade data disaggregated at 6 digit Harmonized System level
(more than 5,000 product categories) from the UN’s COMTRADE, obtained through
UNCTAD/World Bank WITS data system. Our sample covers 108 importers and 219
exporters spanning the years between 1995 and 2003.4 In the trade data, we measure the
bilateral number of imported varieties and construct a proxy for importer’s relative
productivity (discussed below). We also use data on GDP per capita and population size for all
the importers in our sample from World Development Indicators (1995-2003) and bilateral
great circle distance data between capital cities of trading partners from Head and Mayer
(2002).
Extensive margin – a measure of imported varieties
Empirical studies define a product variety either as a commodity category or a marketcommodity category at different levels of commodity disaggregation imposed by data
availability. We measure imported varieties as a weighted count of the number of categories or
market-categories, known as extensive margin, where the weights are the world trade (or rest
of the world) in each category or market-category. The extensive margin is the cross-section
equivalent of the product varieties measure derived by Feenstra (1994). The weighted count
measure is more appropriate than the simple count because it allows varieties to be traded in
unequal prices and quantities.
To formalize, following Feenstra and Kee (2004), the bilateral extensive margin in
sector s can be defined as:
4
In our efforts to construct a balanced panel, we restrict our data sample to 9 years in which the number of
reporting importers is roughly constant.
14
∑M
(15)
EM ijs =
ν ∈I ijs
M wjs
s
wjν
,
where M wjs is the world’s import from j in sector s, and the nominator is the world’s import
from j only in those categories in which j exports to i (within sector s). The extensive margin
represents the weighted number of varieties ν imported by country i from exporter j relative to
the weighted number of varieties j sells worldwide.
Note, that the bilateral extensive margin weighs each category using the j’s world
exports (including importer i), M wjs ν .5. EM ijs measures the share of the weighted number of
varieties imported by i from j in the total number of varieties exported by j. Note that, if the
world imports from j are equal across all varieties, then EM ijs represents the share of the simple
count of varieties that i imports from j in the total number of varieties the world imports from j.
Empirically, we follow Hummels and Klenow (2005) and define a variety ν as a 6
digit HS category and a product/sector s as a 2 digit HS category6. We construct the bilateral
extensive margin EM ijs for each year and for each 2 digit HS. Table 1 shows that the number
of varieties an importer buys within a sector varies considerably across exporters.
Relative Export Performance - Productivity Proxy
Ideally, in order to estimate sectoral productivity, we would need disaggregated
production data for many countries. Unfortunately the data are not readily available. We
refrain from using GDP per capita as a proxy for sector-specific productivities, since it is sector
5
We could also weigh each variety using the j’s exports to the rest of the world (the world exports excluding
i). However both measures are highly correlated and our results remain robust to this alternative measure of
the number of imported varieties.
6
For example, “Microwave ovens” (HS 851650), “Electric toasters” (HS 851672), “Telephone answering
machines” (HS 852020), and “Video monitors, color” (HS 852821) are varieties of ‘Electronics’ (HS 85).
15
invariant.7. Instead we use the GDP per capita a proxy for productivity in the numeraire sector,
and export performance as a proxy for the sector-specific productivity. The underlying
assumption is that export performance is highly correlated with domestic productivity: a
country with a productive industry participates in the world market, and the more productive
the industry, the more it exports worldwide (Melitz - 2003). At the micro level, the assumption
is supported by the empirical literature on firm level heterogeneity (Bernard et al. – 2003).
We measure the export performance of country i using the REP (Relative Export
Performance) index, also known as export-based Revealed Comparative Advantage
(Richardson and Zhang - 1999) or Balassa Index (Balassa - 1965):
M wis
(16)
REPi s =
M
s
ww
M wi
,
M ww
where M wis -- is the world import of product s from country i;
M wi -- is the world imports of all products from country i;
s
M ww
-- is the world total import of product s;
M ww -- is the world total trade.
The REP index represents country i’s share of worldwide exports of product s relative
to the world counterpart. The index also measures country i’s export performance. By using
the share of product s’s exports in country i’s total exports, the REP index controls for country
i having a higher REP because it exports more of all products. Furthermore, constructing it
relative to the world share of product s, we measure the importance of country i’s exports of
7
The correlation between relative export performance and GDP per capita across all sectors and for our
sample of countries in 1999 is very small and equal to 0.19. Moreover, sector-specific correlations are
oftentimes negative.
16
product s relative to the world counterpart. Consequently, in a cross-country comparison, the
REP index gives an appropriate ranking of countries’ sectoral export performance.
Consistent with the construction of the extensive margin, we define a sector as a 2 digit
HS category and we construct the relative export performance for each sector, country and year
using UN’s COMTRADE data. Next we construct the bilateral REP index, which measures
the export performance of country i relative to j:
M wis
(17)
REPijs =
M wi
M wjs
=
REPi s s
Ait .
REPjs
M wj
In the data we observe a large dispersion of the bilateral REP index for a given importer, year,
and sector (see Table 1). This gives us substantial variation to assess whether the elasticity of
extensive margin with respect to trade costs is increasing in importer’s relative productivity.
Distance as a proxy for trade barriers
Detailed data on trade costs such as transport costs and tariffs are sparse for a large
number of countries. While detailed data on transport cost are available only for a handful of
countries, MFN tariff data have better coverage. Still, the variation of tariffs that importers
levy on their trading partners in a 2 digit HS category is very small (the coefficient of variation
is 0.018), which limits the usefulness of these data for our purposes. Thus we employ data on
the bilateral great circle distance which many papers in the literature use as a crude proxy for
trade costs. Following Hanson and Xiang (2004), we model trade costs as a function of
8
For each importer-year and HS 2 category, we calculate the coefficient of variation and then we report the
median of the coefficient over all importer-year-HS 2. The data source is UNCTAD TRAINS obtained
through World Bank’s WITS data system. TRAINS data provides information on MFN tariffs.
17
distance, ln τ ijk = δ k ln d ij , where dij is the bilateral great circle distance between the capitals of
trading partners. As a robustness check, we employ the U.S. Imports of Merchandise data
which provide detailed information on “duties paid at the border” (discussed in section IV).
B. Specifications and Results
Based on the qualitative predictions of the model (11)-(14), we estimate the demand for
imported varieties:
(18)
log EM ijts = α s + δ log X it + γ 1 log d ij + γ 2 log REPits + γ 3 log REPijts + γ 4 log d ij *log REPijts + ε ijts ,
where EM ijts is the extensive margin of importer i from exporter j , for product s, at time t;
X it are the controls for importer i’s market size, at time t such as population and GDP per
capita; REPits is the export performance of importer i for product s, at time t; REPijts is the
corresponding export performance of importer i relative to exporter j; and dij is the distance
between i and j.
The importer’s population and GDP per capita capture the impact of the market size
and average productivity. REPits serves as a proxy for the importer’s sector-specific
productivity; REPijts – as a proxy for the importer-exporter relative productivity in sector s ϕijts ;
and distance dij – as a proxy for trade barrier τ ijt
Recall that the extensive margin represents the share of the weighted number of
varieties imported by i from j in the total number of varieties exported by j. Thus, by
construction, we difference out exporter-year fixed effects, such as exporter’s market size,
average productivity, and production structure.
18
Our main hypothesis is that an increase in bilateral distance decreases the extensive
margin and the magnitude of the effect co-varies positively with the importer – exporter
Relative Export Performance. That is, to test whether domestic and foreign varieties are
substitutes, we seek to confirm that the interaction coefficient between distance and importerexporter REP is negative ( γ 4 < 0 ). Under the alternative hypothesis γ 4 = 0 , which means that
domestic and foreign product varieties are not substitutes.
Additional predictions from our model are:
i) the total effect of distance on the extensive margin is negative ( γ 1 + γ 4 log REPijts < 0 );
ii) the total effect of the relative importer-exporter REP on the extensive margin is
negative ( γ 3 + γ 4 log τ ij < 0 );
iii) the impact of importer’s REP on the extensive margin is positive ( γ 2 > 0 ).
Initially, we pool across all 2-digit HS categories. This is equivalent to assuming that
the effect of trade costs on the bilateral extensive margin is identical across all products9:
log EM ijts = α s + 0.14 log Popit + 0.22 log CGDPit − 0.29 log d ij +
(0.01)
(19)
(0.01)
(0.01)
+ 0.17 log REP − 0.02 log REP − 0.02 log d ij * log REPijts + ε ijts
(0.01)
s
it
(0.03)
s
ijt
(0.00)
R 2 =0.13, N. obs.=3,022,129
The bilateral extensive margin is decreasing in distance and the magnitude of the effect is
increasing in bilateral REP. In particular, doubling sector-specific productivity gap decreases
the number of imported varieties by 17% for a median trade barrier, or, alternatively, if we
rank importers according to their REP, distance has a 62% stronger (negative) effect on the
number of varieties for the 90th percentile importer compared with the 10th percentile importer.
9
Our results are based on the panel estimation, but they remain robust if we run the estimation in crosssection or if we include importer-exporter-HS 2 fixed effects.
19
That is, we can clearly reject the alternative hypothesis that domestic and foreign varieties are
not substitutes ( γ 4 = 0 ).
All other signs also match our theory. The extensive margin is increasing in importer’s
market size, average and sectoral productivities, and decreasing in relative importer-exporter
REP.
Following Feenstra and Kee (2004) we then classify the HS 2 categories into seven
industries: ‘agriculture,’ ‘textiles and garments,’ ‘wood and paper,’ ‘petroleum and plastics,’
‘mining and basic metals,’ ‘machinery and transportation’, and ‘electronics’.10 We first pool
across all 2 digit HS categories within the manufacturing industries (i.e., ‘machinery and
transportation’ and ‘electronics’):
log EM ijts = α s + 0.15log Popit + 0.20 log CGDPit − 0.27 log d ij +
(0.01)
(20)
(0.01)
(0.02)
+ 0.39 log REP + 0.09 log REP − 0.06 log d ij * log REPijts + ε ijts
(0.02)
s
it
(0.06)
s
ijt
(0.01)
R 2 =0.18, N. obs.=434,542
For these industries we can clearly see that the effect of distance on the extensive margin as
well as the variation of this effect across importers are higher. If we rank importers according
to their REP as we did for the previous specification, distance has a roughly eightfold stronger
(negative) effect on the number of varieties for the 90th percentile importer compared with the
10th percentile importer. Furthermore doubling sector-specific bilateral relative export
performance decreases the number of imported varieties by 53% for a median distance. The
fact that the magnitudes of the observed effects are larger for the manufactures is consistent
with our model. Indeed, the model is built on the assumption of product differentiation and
10
We pool across HS 2 categories within an industry instead of estimating the effect for each HS 2 category
because we have sufficiently few observations within a HS 2 that our estimates lose precision.
20
thus should work better for differentiated industries, such as ‘electronics’ and ‘machinery and
transportation’.
Next we run the regression for each industry and report the estimates in Tables 2 and 3.
The estimates for ‘agriculture,’ ‘textiles and garments,’ ‘electronics’, and ‘machinery and
transportation’ provide statistically significant support for the importance of domestic varieties
and their substitutability to foreign varieties. The effects differ across industries with
industries such as ‘electronics’ and ‘machinery and transportation’ featuring the strongest
effects. At the same time the estimate of γ 4 is statistically insignificant in ‘petroleum and
plastics,’ ‘wood and paper’ and ‘mining and basic metals.’ That is, for these industries, which
are less likely to have differentiated product varieties, the substitutability between foreign and
domestic varieties is negligible and the welfare calculations as applied by Broda and Weinstein
(2006) are justified.
IV. Robustness Checks
In this section, we explore the robustness of our estimates through three checks. The
first exercise concerns the potential simultaneity in our specification; the second set of
exercises checks whether our estimates are robust to the sample of goods, years, and countries;
and the third set of checks employs an alternative measure of trade barriers – duties paid –
using the U.S. Imports of Merchandise dataset.
A. Addressing the Potential Simultaneity Bias
The domestic productivity may not be exogenous as we assume in our model. The
number of imported varieties may increase an importer’s productivity, thus affecting export
performance through two channels that are outside of our model. First, if we consider
21
imported varieties as inputs in production, more input varieties increase an importer’s
productivity under the assumption of gains from specialization (Romer - 1990, Feenstra and
Kee -2004). Second, tougher competition from foreign varieties leads to intra-sector
reallocations towards the most efficient firms and higher average productivity (Melitz - 2003).
Note, that if one or both sources of endogeneity are present, the bias in our estimates works
against us and controlling for it will only strengthen the impact of the relative export
performance on the number of imported varieties.
To address the potential endogeneity we instrument for the REP with the factor
endowments in two manufacturing sectors11: ‘electronics’ and ‘machinery and transportation’.
The data on employment and capital endowments is available only for two years, 1995 and
2000, and for a sub-sample of countries.12 Thus we have to restrict our sample to these two
years and 63 importers.
log EM ijts = α s + 0.10log Popit + 0.11log CGDPit − 0.46log d ij +
(0.04)
(21)
(0.08)
(0.06)
+ 1.02 log REP − 0.24 log REP − 0.06 log d ij * log REPijts + ε ijts
(0.19)
s
it
(0.24)
s
ijt
(0.03)
R 2 =0.09, N. obs.=33,109
Despite the fact that we have a smaller sample of countries and years, the IV
estimates13 provide further support for our main hypothesis: the negative effect of trade costs
on the extensive margin is magnified by the relative REP. When compared to the OLS
estimates on the same sample of years, sectors, and countries14, the IV estimates are
significantly different in a statistical sense but the economic significance of the magnitudes for
the impact of distance on the imported number of varieties is quite similar. As expected, the
11
We focus on the manufacturing sectors since the key factors employed in the other sectors are more likely
to be a country’s endowment of land for agriculture, minerals, climate, geography, etc., and, thus,
employment and capital are likely to be very weak instruments.
12
We used the dataset constructed by Choi (2006).
13
The first stage regressions for the IV estimates show that the instruments are strongly partially correlated
with relative export performance.
14
See last column of Table 4
22
effect of the importer’s relative export performance on the extensive margin has increased
relative to the OLS estimates but the potential simultaneity does not seem to affect the
magnitude of the interaction term estimate.
B. Robustness with respect to Sample Selection
To test whether the predictions of the model work for intermediate goods, we restrict
the sample to 6 digit HS categories classified as intermediate goods and construct the extensive
margin and relative export performance on the restricted sample.
log EM ijts = α s + 0.05log Popit + 0.11log CGDPit − 0.11log d ij +
(0.01)
(0.01)
(0.01)
+ 0.06 log REP + 0.04 log REP − 0.01log d ij * log REPijts + ε ijts
(22)
(0.01)
s
it
(0.05)
s
ijt
(0.00)
R 2 =0.08, N. obs.=50,928
Even though the magnitudes of the coefficients have decreased compared to the full sample of
goods, the qualitative predictions of our model are confirmed. Despite the sample being
severely restricted, the effect of distance on the extensive margin varies considerably: the
(negative) effect for the 90th percentile importer is twice as large as for the 10th percentile
importer.
Table 4 shows additional sets of estimates when we estimate (18) in cross-section (only
for 2000) or as pooled cross-sections (including sector – year fixed effects). The results are
similar in statistical and economic significance to the estimates in (19).
C. Tariff as a Trade Barrier
Finally, to address the limitations of using distance as a proxy for trade barriers, we run
the regression using the U.S. Imports data with detailed information on duties paid at the
border.
23
There are a few changes in the construction of variables and the regression
specification. First, for a given year and 2 digit HS category, the extensive margin is a
weighted count of the varieties imported by the U.S. from exporter j relative to the weighted
count of varieties imported by the U.S. from all exporters. The weights are the total dollar
values of varieties imported by the US. Since the U.S. reports trade flows for more than
13,300 highly detailed 10 digit-HS categories, we are able to define a variety at a more
disaggregated level. This gives us an opportunity to test whether the restrictive definition of
variety as a 6 digit HS category for the large panel of countries affects our results. If anything,
the extensive margin constructed by defining a variety at 10 digit HS level should strengthen
our estimates.
Second, we employ the relative export performance for the U.S. as for any exporter j
constructed using COMTRADE data as in section III. Since U.S. data has detailed information
on duties paid, we use ad-valorem tariffs (one plus the ratio of duties paid and total dollar
imports value) as a proxy for trade costs. We aggregate tariff to the 2 digit HS level in two
ways: by taking the simple mean of the duties paid on the 10 digit HS categories within an HS
2 and also by calculating the weighted mean where weights are the value of trade for each HS
10 category. We run regressions for both tariff measures.
There are three differences between the U.S. specification and (19). First, since the U.S.
is the only importer, we replace all i indices with ‘US’. Second, the tariff varies across sectors
and years for a given U.S. trade partner. Third, by construction of the extensive margin, we
difference out any importer-year specific effects faced by all exporters that determines the size
of extensive margin. For example, these determinants might be the U.S. market size and
average productivity.
24
As in the specification with distance, all the signs match our theory and all estimates
are statistically significant (see Table 5). For both simple mean and weighted mean tariff
specifications, doubling the sector-specific U.S.-exporter REP decreases the number of
imported varieties by 36% for the median tariff. If we rank exporters to the U.S. according to
their REP, the simple tariff has a 334% stronger (negative) effect on the number of varieties for
the 90th percentile compared with the 10th percentile U.S.-exporter relative export performance.
The effect is halved when estimated using the weighted tariff. The U.S. estimates provide
further support for the importance of the domestic varieties and, as expected, using distance as
a crude proxy for trade costs introduces a downward bias in our estimates since the elasticity of
transport costs with respect to distance is less than one (Hummels - 2001).
V. Conclusions
Romer (1994) shows that including the fixed costs of importing in a trade model with
differentiated goods magnifies the importance of trade costs in the calculation of the welfare
losses. A higher trade cost not only decreases the volumes of imported goods as in traditional
models, but also shrinks the set of imported differentiated goods. The reduction in the number
of imported varieties results in first-order welfare losses. However, while evaluating the
variety gains from trade, Romer and subsequent literature assume that imported varieties
cannot be substituted with the importer’s own varieties, which can potentially bias the impact
of protectionism on welfare if the data reveal that domestic and foreign varieties are indeed
substitutes.
This paper shows that the production of domestic varieties adjusts to changes in the
number of imported varieties. And the substitutability between domestic and foreign varieties
has important implications for evaluating the product variety gains from international trade. A
25
country with higher differentiated sector productivity depends less on foreign varieties and its
welfare losses because of trade barriers are smaller compared to a country whose production of
domestic varieties is less efficient. For instance, a country with comparative disadvantage in
electronics reaps higher benefits from trade liberalization than a country that is a world leader
in electronics. The evidence provided in this paper strengthens the arguments for trade
liberalization of a country’s inefficient sectors irrespective of its aggregate level of
development.
26
References:
Ardelean, Adina (2007). “How Strong is the Love of Variety?” mimeo.
Balassa, Bela. 1965. "Trade Liberalization and Revealed Comparative Advantage." Manchester
School: 33: 99-12
Balistreri, Edward J., Russell H. Hillberry, and Thomas F. Rutherford (2007). "Structural
Estimation and Solution of International Trade Models with Heterogeneous Firms", mimeo
Bernard, Andrew, Jonathan Eaton, J. Bradford Jensen, and Samuel Kortum (2003), “Plants and
Productivity in International Trade”, American Economic Review 93 (September), 12681290.
Bernard, Andrew, Stephen Redding, and Peter Schott (2007) "Comparative Advantage and
Heterogeneous Firms," Review of Economic Studies 2007 vol 74 (1)
Broda, Christian and Weinstein, David E. (2006), “Globalization and the Gains from Variety”,
Quarterly Journal of Economics, 121(2)
Choi, Yo Chul (2006) “Essays on Product Quality Differentiation and International Trade”,
Purdue University, Ph.D. Dissertation.
Feenstra, Robert C. (1994), “New Product Varieties and the Measurement of International
Prices”, American Economic Review, Vol. 84, No. 1
Feenstra, Robert and Kee, Hiau Looi (2004), “Export Variety and Country Productivity”, NBER
Working Paper #10830
Feenstra, Robert and Kee, Hiau Looi (2007) “Trade Liberalisation and Export Variety: A
Comparison of Mexico and China” The World Economy Volume 30 Issue 1 Page 5-21, January
2007
Head, Keith & Thierry Mayer, 2002. "Illusory Border Effects: Distance Mismeasurement
Inflates Estimates of Home Bias in Trade," Working Papers 2002-01, CEPII research center
Hanson H. G. and C. Xiang (2004) "The Home Market Effect and Bilateral Trade Patterns,"
American Economic Review, September 2004, 94(4), pp 1108 - 1129.
Hummels, David (2001) “Toward a Geography of Trade Costs”, Mimeo
Hummels, David and Klenow, Peter J. (2002),“The Variety and Quality of a Nation’s Trade”,
NBER Working Paper #8712
Hummels, David and Klenow, Peter J. (2005), “The Variety and Quality of a Nation’s Exports”,
American Economic Review 95, p704-723
27
Kehoe, Timothy J. and Kim J. Ruhl (2002) "How Important is the New Goods Margin in
International Trade?", Mimeo.
Klenow P. and A. Rodriguez (1997), “Quantifying Variety Gains from Trade Liberalization”,
Mimeo.
Krugman, P. (1980), “Scale Economies, Product Differentiation, and the Pattern of Trade”,
American Economic Review, 70(5), 950-959.
Melitz, M. (2003), “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry
Productivity”, Econometrica 71 (6).
Richardson, David J., and Chi Zhang (1999) "Revealing Comparative Advantage: Chaotic or
Coherent Patterns across Time and Sector and U.S. Trading Partners," NBER Working Paper
7212.
Romer, P. (1994), “New Goods, Old Theory, and the Welfare Costs of Trade Restrictions”,
Journal of Development Economics, 43, 5-38
28
Table 1: Variation in Extensive Margin, Relative Export Performance and Distance
Coefficient of Variation
EMijts
REPijts
Distanceij
All industries
0.78
2.07
0.70
Agriculture
0.77
1.94
0.71
Petroleum & Plastics
0.75
2.26
0.69
Wood & Paper
0.73
2.10
0.70
Textiles & Garments
0.77
2.08
0.70
Mining & Basic Metals
0.85
2.01
0.68
Electronics
0.89
2.67
0.64
Machinery & Transportation
0.82
2.21
0.66
Note: For each importer(i) – year(t) – HS 2(s), calculate coefficient of variation
CoV ( xijts ) = stdev ( xijts ) mean( xijts ) . The table reports the median value of the coefficient
of variation over all its for each industry.
Data Source: United Nations’ Comtrade dataset (1995-2003)
29
Table 2: Dependent variable - Extensive margin
Industry
Agriculture
Distanceij
REPits
REPijts
-0.23
0.07
0.01
Distanceij *
REPijts
-0.01
(0.01)
(0.01)
(0.03)
(0.00)
Popit
CGDPit
0.11
0.21
(0.01)
(0.01)
Petroleum &
Plastics
-0.30
0.24
-0.19
-0.01
0.14
0.15
(0.02)
(0.01)
(0.05)
(0.01)
(0.01)
(0.01)
Wood & Paper
-0.32
0.11
-0.19
0.01
0.13
0.19
(0.01)
(0.01)
(0.04)
(0.01)
(0.01)
(0.01)
Textiles &
Garments
-0.29
0.18
0.05
-0.02
0.15
0.29
(0.02)
(0.01)
(0.04)
(0.00)
(0.01)
(0.01)
Mining & Basic
Metals
-0.33
0.16
-0.09
-0.01
0.18
0.25
(0.01)
(0.01)
(0.04)
(0.00)
(0.01)
(0.02)
Electronics
-0.36
0.52
-0.11
-0.05
0.20
0.25
(0.02)
(0.03)
(0.09)
(0.01)
(0.02)
(0.02)
-0.24
0.34
0.12
-0.05
0.13
0.17
(0.02)
(0.02)
(0.05)
(0.01)
(0.01)
(0.01)
Machinery &
Transportation
Notes:
1. i- importer, j-exporter, t-year, s-HS2 category
2. All regressions include HS 2 fixed effects
3. All variables are in logs and robust standard errors are in parentheses
4. Observations are clustered by importers.
30
Number of
obs.
767,470
0.10
489,817
0.14
191,261
0.15
647,491
0.14
491,548
0.13
129,546
0.23
304,996
0.16
R2
Table 3: The total effect of distance and bilateral REP on the extensive margin
Distance1
Doubling relative
REP2
Agriculture
42%
-10%
Petroleum & Plastics
0%
-19%
Wood & Paper
0%
-19%
Textiles & Garments
72%
-20%
Mining & Basic Metals
0%
-9%
Electronics
170%
-41%
Machinery & Transportation
817%
-34%
Industry
Notes:
1. The total effect of distance is calculated for the 90th percentile importer relative to the
10th percentile importer.
2. The total effect of bilateral REP is evaluated for the median distance.
31
Table 4: Various OLS Specifications
Dependent variable: Extensive Margin
Distanceij
REPits
REPijts
Distanceij
* REPijts
Popit
CGDPit
No. obs.
R2
Panel I
Panel II
Pooled
crosssections
-0.29
-0.33
-0.29
-0.29
-0.27
-0.38
(0.01)
(0.01)
(0.01)
(0.01)
(0.02)
(0.04)
0.17
0.11
0.17
0.18
0.39
0.42
(0.01)
(0.01)
(0.01)
(0.01)
(0.02)
(0.02)
-0.02
-0.01
-0.02
-0.02
0.09
Notes:
1.
2.
3.
4.
Manufactures,
all years,
all countries
Manufactures,
restricted
sample
0.12
(0.03)
(0.03)
(0.03)
(0.03)
(0.06)
(0.09)
-0.02
-0.02
-0.02
-0.02
-0.06
-0.06
(0.00)
(0.00)
(0.00)
(0.00)
(0.01)
(0.01)
0.14
0.16
0.14
0.13
0.15
0.15
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.02)
0.22
0.24
0.22
0.21
0.20
0.25
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.02)
0.13
3,022,129
0.23
0.13
348,018
0.13
434,542
0.18
33,109
0.22
Years
Industries
No. of
Importers
Fixed
effects
Crosssection
1995-2003
2000
1995-2003 1995 & 2000
Only 6 & 7
All
All
All
All
108
108
108
108
HS 2
HS 2 *
Imp*Exp
HS 2 *
Year
HS 2
108
63
HS 2
i - importer, j-exporter, t-year, s- HS 2 category.
All variables are in logs and robust standard errors are in parentheses.
Observations are clustered by importers in all specifications.
The restricted sample is based on the availability of factor endowments data across
years and countries.
32
Table 5: Robustness Check (U.S. Tariff)
Dependent variable: Extensive Margin
Simple Mean of
Tariff
Tariff USjts
-1.38
-1.61
-2.33
-2.17
(0.33)
(0.25)
(0.37)
(0.28)
REP USts
REP USjts
Tariff USjts * REP USjts
Pop jt
CGDP jt
No. of obs.
R2
Notes:
1.
2.
3.
4.
Weighted Mean of
Tariff
0.20
0.20
(0.06)
(0.05)
-0.31
-0.31
(0.00)
(0.00)
-0.42
-0.41
(0.04)
(0.05)
0.47
0.44
0.47
0.44
(0.00)
(0.01)
(0.00)
(0.01)
0.55
0.52
0.55
0.52
(0.00)
(0.00)
(0.00)
(0.00)
58,983
58,983
58,983
58,983
0.39
0.49
0.39
0.49
j-exporter, t-year, s- HS 2 category.
All variables are in logs and robust standard errors are in parentheses.
Observations are clustered by years in all specifications.
All regressions include HS 2
Data Source: U.S. Bureau of Census (1996-2003), “U.S. Imports of Merchandise” CD-ROM
33