The determinants of bilateral trade
Huiwen Lai SAS Institute Inc.
Susan Chun Zhu Department of Economics, Michigan State
University
Abstract. In this paper we present a monopolistic competition model that incorporates
asymmetric trade barriers and international differences in production costs. The model
implies a highly non-linear bilateral trade equation. Estimation of this equation yields
parameters for the elasticity of substitution and trade costs that are more reasonable
than those found in previous studies. A simulation indicates that trade liberalization
will shift trade from rich countries to poor countries and from within continental
trading partners with preferential trade agreements to intercontinental trading partners.
JEL Classification: F1
Les de´terminants du commerce bilate´ral. Ce mémoire présente un modèle de concurrence
monopolistique qui postule des barrières commerciales asymétriques et des différences
internationales dans les coûts de production. Le modèle suggère une équation de
commerce bilatéral fortement non linéaire. La calibration de cette équation suggère
des paramètres pour l’élasticité de substitution et les coûts de commerce qui sont plus
raisonnables que ceux proposés dans des études antérieures. Une simulation indique que
la libéralisation du commerce va déplacer le commerce des pays riches vers les pays
pauvres, et des partenaires commerciaux intracontinentaux qui jouissent d’accords
préférentiels vers les partenaires commerciaux intercontinentaux.
1. Introduction
New trade theory, as represented by the monopolistic competition model, has
been developed to explain intra-industry trade among OECD countries
We are indebted to Dan Trefler for his encouragement and helpful conversations. We have also
benefited from comments of Hungyi Li, Peter Pauly, Nadia Soboleva, and other members of
the University of Toronto International Trade Workshop. We are particularly grateful to the
anonymous referees for their helpful comments and suggestions. Email: zhuc@msu.edu
Canadian Journal of Economics / Revue canadienne d’Economique, Vol. 37, No. 2
May / mai 2004. Printed in Canada / Imprimé au Canada
0008-4085 / 04 / 459–483 /
#
Canadian Economics Association
460
H. Lai and S.C. Zhu
(Krugman 1979; Helpman 1981; Helpman and Krugman 1985). A large portion
of trade among these countries involves differentiated products (Helpman
1999; Evenett and Keller 2002). Monopolistic competition provides a coherent
theory for product differentiation.
Since it is straightforward to incorporate trade costs into the standard
monopolistic competition model, the theory has also motivated empirical
investigations of the relationship between trade costs and bilateral trade.
Some empirical studies have found that large variations in trade are explained
by tariffs and transport costs (e.g., Harrigan 1993; Hummels 1999), while
others have modelled trade costs indirectly as unexplained econometric fixed
effects (e.g., Hummels and Levinsohn 1995; Harrigan 1996). Besides the monopolistic competition model, a variety of empirical studies estimating the gravity
equation have found that trade barriers explain a large portion of trade (e.g.,
Bergstrand 1985; Anderson and van Wincoop 2003).
Trade costs operate primarily via prices. In the context of the monopolistic
competition model, the difficulty is created by the complexity of the constant
elasticity of substitution (CES) price index in the presence of asymmetric trade
costs. To resolve this difficulty, three approaches have been taken: (i) GDP price
indexes are used to capture the price effects in the gravity equation, as in Bergstrand
(1985, 1989) and Baier and Bergstrand (2001); (ii) estimated border effects are used
to measure the price effects, as in Anderson and van Wincoop (2003) and Balistreri
and Hillberry (2001); and (iii) fixed effects are used to account for the price effects,
as in Harrigan (1996), Hummels (1999), Redding and Venables (2002), and others.
In this paper we add to the literature by estimating a monopolistic competition model that incorporates a richer set of international asymmetries. These
include asymmetric trade barriers and international differences in production
costs. Production costs, tariffs, and distance-related barriers enter firms’ pricing
and output decisions. These decisions, when set against the backdrop of CES
preferences, yield a precise bilateral trade estimating equation. Indeed, the theory
even predicts the functional form for the dependence of bilateral trade on tariffs,
distance, and production costs. In contrast to the existing literature, our estimation relies on the exact functional form as predicted by the theory. In particular,
our estimation takes account of the non-linear CES price term explicitly.
We draw from a large database on trade, endowments, and wages described in
Antweiler and Trefler (2002). In addition, we make great efforts to build a unique
and comprehensive time series on bilateral tariffs by industry and year (see the
appendix). With this rich database, we estimate the highly non-linear model. A
panel data approach is applied to address the importance of the country-pair
fixed effects. Except Lai and Trefler (2002), no other study has incorporated both
tariffs and a full set of country-pair fixed effects. This is because no other study
has had access to a detailed tariff panel. Further, the highly non-linear functional
form allows us to identify the distance effect in the fixed-effects model.
Estimation of the non-linear model reveals a number of interesting results.
After taking account of the CES price index and a full set of country-pair fixed
The determinants of bilateral trade
461
effects, we obtain estimates of the elasticity of substitution across varieties and the
impact of trade costs that are smaller than usually reported. In particular, our
fixed-effects estimate of the elasticity of substitution is 3.99, which is smaller than
most of the earlier estimates; for example, in Harrigan (1993) the median value of
industry-level elasticities of substitution is 8.63. In a world of already low tariffs
and distances among major trading partners (OECD, European Union, and
NAFTA), the smaller estimates meet the prior expectations for the parameters
while still accounting for the large missing trade between actual and predicted
data. We also show that when the CES price index and/or country-pair fixed
effects are excluded, elasticity of substitution and the distance effect have to be
unreasonably large in order to reconcile the large missing trade observed in the
data and low tariffs and distances among major trading partners.
The estimates imply that the elimination of tariffs would create more trade
for poor countries (7.9%) than for richer countries (2.5%). They also imply
that tariff elimination would divert trade away from continental preferential
trading areas (e.g., European Union and NAFTA) and towards intercontinental trading partners. That is, tariff liberalization would shift trade from the rich
to the poor and from the local to the global.
This paper is organized as follows. In section 2 we develop the theoretical
framework. In section 3 we derive the estimating equation of bilateral trade.
In section 4 we describe data sources. In section 5 we present the estimates of the
bilateral trade equation and the results of sensitivity analysis. In section 6 we
simulate the effect of trade liberalization on trade flows. Section 7 concludes.
2. Theory
In this section we will present a monopolistic competition model that incorporates asymmetric trade barriers and international differences in production
costs. Our set-up is similar to Krugman’s (1980) model with transportation
costs. However, we cast our model in a multi-country setting.
Consumers have identical Cobb-Douglas preferences over goods and CES
preferences over varieties. With Cobb-Douglas preferences we can look at one
good at a time: fix the good and suppress the goods index. Let k index
varieties. Let i and j index user countries and producer countries, respectively.
The total number of countries is N. In the first stage a representative consumer
in country i allocates Yi to the good in question. In the second stage the
representative consumer maximizes the CES subutility function subject to the
expenditure constraint:
X X
1
1
N
k max Ui ¼
qij
j¼1
k2
j
fqkij g
XN X
s:t:
pk qk ¼ Yi ,
k2
ij ij
j¼1
j
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H. Lai and S.C. Zhu
where j is the set of varieties produced in country j, qkij is country i’s demand
for variety k produced in country j, pkij is the consumer price associated with qkij ,
and is the elasticity of substitution between varieties ( > 1). When the
number of varieties is large, in equilibrium is also the elasticity of demand
for varieties. It is straightforward to derive country i’s demand for variety k
produced in country j as
pkij
qkij ¼ P P
1 Yi :
k0
0
0
j
k 2
j0 pij 0
(1)
Next, we will add a rich set of international asymmetries to the standard
monopolistic competition model. These asymmetries help us to identify the
structural parameters in the model, for example, the elasticity of substitution
(). First, firms located in different countries incur different costs of production. We take firms’ location choices as given. Once a firm is set up, the firm’s
fixed costs are irrelevant to price and quantity decisions. That is, these decisions will be affected only by marginal costs. Let cj be the marginal cost for
firms in country j. In addition, we allow for tariffs and distance-related
transportation costs. Let ij be 1 plus the ad valorem tariff rate imposed by
country i on goods imported from country j. Let d(Dij) be 1 plus the per unit
transportation cost that is a function of the bilateral distance Dij between
countries i and j. It means that for each unit of output reaching country i,
d(Dij) 1 units are lost in the transaction process.1 Note that ij 1 with ij ¼ 1
when i ¼ j. Also, d() is an increasing function of bilateral distance, with
d(0) ¼ 1 when i ¼ j.
Trade barriers drive a wedge between the price paid by consumers and the price
received by producers. Let pkj be the price charged by the producers of variety k in
country j. Given that the elasticity of demand for varieties is , the producer price is
set as pkj ¼ cj =( 1). Then consumers in country i will face the price
pkij ¼ d(Dij )ij pkj ¼
d(Dij )ij cj :
1
(2)
Substituting this price into equation (1) gives the quantity demanded:
qkij
d(Dij )ij cj
1
Yi :
¼P P
1
j0
k0 2
j0 d(Dij 0 )ij 0 cj 0
(3)
1 For many products, physical shipping costs may be small, but the cost associated with
gathering information about the demand conditions in distant markets or dealing with remote
merchants may be significant (Hanson 2001).
The determinants of bilateral trade
463
We are interested in bilateral trade flows. Using the price equation (2) and
the quantity-demanded equation (3), the value of country i’s imports from
country j is derived as the sum of country i’s demand for varieties produced in
country j:
Mij ¼
X
k2
j
pkij qkij
1
nj d(Dij )ij cj
¼P
1 Yi ;
j 0 nj 0 d(Dij 0 )ij 0 cj 0
(4)
where nj is the number of firms (or varieties) in country j.
With factor price equalization and no trade barriers, equation (4) becomes
Mij ¼ (nj =j 0 nj 0 )Yi . That is, country i’s imports from country j are proportional
to country i’s industrial expenditure, with the proportion being country j’s
share of firms (or varieties) in the world as a whole. This prediction is very
similar to the symmetric model of Helpman and Krugman (1985). Although
central to the model, does not show up in the symmetric version of the trade
equation and thus is not identified without international asymmetries.
In equation (4), trade barriers and marginal costs for all countries affect
imports for each country. The underlying parameters, including , can be
estimated by relating the variation in trade patterns to variation in tariffs,
transportation costs, and marginal costs. Specifically, we have
(i)
@Mij
@Mij
@Mij
< 0, and (iii)
< 0,(ii)
< 0:
@ij
@d(Dij )
@cj
(i) and (ii) capture the negative effects of trade barriers; (iii) captures the role of
production costs. These derivatives have the expected signs. Variables related
to countries other than the trading partners also enter the bilateral trade
equation, but their effects are less straightforward.
3. Bilateral trade estimating equation
In this section we derive the estimating equation of bilateral trade from
equation (4). All estimation is at the level of aggregate manufacturing.
Lai and Trefler (2002) estimate based on both aggregate and more disaggregate data. Their results are consistent with the interpretation that the for
aggregate manufacturing is an aggregate of the from individual manufacturing
industries.2 This means that the analysis of aggregate manufacturing industry
is sufficient to explore the key economic interpretations of our model. Furthermore,
2 Lai and Trefler (2002) also find interesting variation in the across industries. Their results
largely support the view that the monopolistic competition model works well for industries that
exhibit product differentiation and increasing returns to scale, for example, industrial chemicals
and electric and electronic equipment.
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H. Lai and S.C. Zhu
estimation at the aggregate level can better illustrate the different roles of tariffs,
distance, and production costs in trade determination.
Following Brainard (1997), we set d(Dij) ¼ eDij, which has the required
properties d(0) ¼ 1 and d0 > 0. The parameter is interpreted as the trade
cost per unit of distance. To measure marginal costs, we assume cj ¼ (w*j )
,
where w*j denotes adjusted wages. In order to have an international comparable
measure, we adjust wages in two ways. One way is to divide wages by labour
productivity, which is measured by PPP-adjusted GDP per worker. The alternative adjustment uses total factor productivity (TFP), which is measured as
the multi-lateral TFP index.3
Substituting d(Dij) and cj into equation (4) then taking logarithms and
adding time subscripts and an error term yield the estimating equation of
bilateral trade:
log Mijt ¼ log njt þ log Yit þ (1 )
log wjt * þ (1 )Dij þ (1 ) log ijt
X
h
i1 N
Dij0
(5)
log
n0 0 e
w*j0 t
þ "ijt :
j 0 ¼1 j t ij t
The first two terms of equation (5) capture the effects of country size: the
industrial supply of the exporter and the industrial demand of the importer.
The third term captures international differences in production costs. Because
the production cost differences can also be viewed as consequences of relative
endowment differences among countries, the third term can also be interpreted
as capturing the role of endowments for trade determination. The fourth and
fifth terms capture the effects of bilateral distance barriers and tariffs. The final
term is the log summation of some highly non-linear terms related to variables
of all countries. This final term comes from the denominator of the CES price
index. It collects factors that influence firms’ pricing decisions and consumer
prices such as production costs, tariffs, and trade costs.
Equation (5) is a generalization of two types of specifications used in the
empirical literature. The first specification explains trade by country size variables and attributes all other trade determinants to the residual (Helpman
1987; Hummels and Levinsohn 1995; Jensen 2000). The second specification
adds bilateral trade barriers (e.g., Harrigan 1993; Hummels 1999). Equation
(5) further adds the marginal cost of the exporting country and a highly
3 Following Caves, Christensen, and Diewert (1982a, 1982b), and Keller (2002), we construct the
multi-lateral TFP index as follows. Let Vit denote value added in country i and year t, Lit labour
inputs, Kit capital stocks, it labour cost shares, and N the number of countries in the sample.
Define ln Vit i ln Vit =N, ln Lit i ln Lit =N, ln Kit i ln Kit =N, and eit 12 (it þ i it /N).
Then the multi-lateral TFP index is calculated as
ln (TFPit ) ( ln Vit ln Vit ) eit ( ln Lit ln Lit ) (1 eit )( ln Kit ln Kit ):
The determinants of bilateral trade
465
non-linear pricing term. The notable feature of equation (5) is that it is directly
derived from the theory of monopolistic competition.
This paper has a close relationship with Lai and Trefler (2002). However,
there are also several major differences between them. First, Lai and Trefler
(2002) focus on tariffs while ignoring the effect of production costs and
distance on trade flows. This is because they want to explore in more depth
the country-pair sample variation in tariffs. In contrast, our framework incorporates tariffs, distance, and production costs. We want to examine their
different effects on bilateral trade flows. Second, Lai and Trefler (2002) also
assess the CES monopolistic competition model for evidence of econometric
mis-specification. They find that the monopolistic competition model and
other models in the literature are likely to be misspecified. In contrast, model
selection is not the focus of our paper. Our use of the monopolistic competition
model is due to its theoretical importance in the trade literature as well as its
clear-cut empirical predictions about the relationship between trade costs and
bilateral trade flows. Finally, Lai and Trefler (2002) do estimation based on
both aggregate and more disaggregate data. Their industry-level estimates
reveal interesting variation in the elasticities of substitution across industries.
In contrast, we present estimates at only the level of aggregate manufacturing.
This is because we want to highlight the different effects of tariffs, distance and
production costs on bilateral trade flows.
We will first estimate parameters 1 ¼ (1 )
, 2 ¼ (1 ), and
3 ¼ 1 . Then we will infer the structural parameters ¼ 1 3, ¼ 2/ 3,
and ¼ 1/3. In no previous paper have all three parameters (, , and )
been estimated. Gravity-type regressions are normally used to estimate a
parameter related to distance, which is similar to our 2. In those regressions,
and cannot be disentangled to distinguish between the trade cost effect and
the substitution effect. By introducing marginal costs and distance-related
barriers in addition to tariffs, our specification allows for the estimation of and as well as .
4. Data
Data on trade flows are from the World Trade Database (Feenstra, Lipsey,
and Bowen 1997). Data on the number of establishments are taken from the
Yearbook of Industrial Statistics and the International Yearbook of Industrial
Statistics. Distance between countries is taken from Antweiler (1996). The
expenditures on manufacturing goods (Yit) are calculated as manufacturing
output plus net imports. Gross industrial output is from the 1999 UNIDO
industrial statistics database (3-digit ISIC). This database also contains value
added, gross fixed capital formation, employment, and wages. The gross fixed
capital series are used to construct capital stocks using a 15-year double
declining balance method (Leamer 1984, 230–4). The initial year is 1965. The
data are deflated using domestic investment price indexes with PPP-adjustments
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H. Lai and S.C. Zhu
from the Penn World Table 5.6 (Summers and Heston 1991). Value added,
capital stocks, employment, and wages are then used to construct the
multi-lateral TFP indexes (see fn. 3 for the formula). PPP-adjusted GDP per
worker from the Penn World Table 5.6 is used to measure labour productivity.
The data on trade, endowments, and wages are also described in Antweiler and
Trefler (2002).
Bilateral tariff data by industry and year have been carefully compiled from
different sources and took eight months of full-time work to construct. See the
appendix for details of the data. The database represents a major improvement
on the types of tariff data previously used and allows us to work with a panel.4
The years for which the trade equation was estimated include 1980, 1984, 1988,
and 1992. One reason for not using every year of data is that one would then
need to carefully model serial correlation. Lai and Trefler (2002) also exploit
this panel of bilateral tariffs. They show that serial correlation is not a problem
with longer-spaced intervals such as ours, provided one uses fixed effects.
Limitations on data for bilateral tariffs and the number of manufacturing
establishments restrict our sample to 34 countries. The countries include most
of the OECD, many of the developing countries intensively engaged in trade,
and some developing countries that are not heavy traders. See appendix table A1
for a list of countries and summary statistics on key variables.
5. Estimation
We now estimate equation (5) to examine the effects of tariffs, distance-related
barriers, and production costs on bilateral trade flows.
5.1. Preliminary estimates
We start with simple linear regressions. For all regressions we restrict the
coefficients on log (njt) and log (Yit) to be 1. This allows us to infer the three
key parameters of the model (, , and ). This also makes the preliminary
estimates comparable with the baseline estimates of the non-linear model.
The top panel of table 1 lists estimates of 1, 2, and 3 and their standard
errors. The first two parameters capture the effects of cost disadvantage on
trade; they are a combination of cost factors (
, ) and substitutability (). The
third parameter, 3 ¼ 1 , is directly related to the elasticity of substitution.
4 Baier and Bergstrand (2001) also work with a tariff panel. It is worth pointing out the major
differences between their data and ours. First, since Baier and Bergstrand focus on trade among
OECD countries, their dataset does not contain any non-OECD countries. Their data come
from Prewo (1978) and Deardorff and Stern (1986, 1990). In contrast, our data set involves 15
non-OECD countries and 19 OECD countries, 15 of which are also in Baier and Bergstrand’s
data set. Second, Baier and Bergstrand’s tariff data cover the two periods, 1958–60 and 1986–88,
while our data are for the period 1972–92. Third, Baier and Bergstrand’s tariff data are
aggregated at the national level. In contrast, our data are at the industry level (3-digit ISIC),
which makes it possible to explore the industry-level variation in tariffs.
The determinants of bilateral trade
467
TABLE 1
Preliminary estimates
Distance Average Time-varying Labour-productivityadjusted wages
tariffsa tariffs
TFP-adjusted
wages
(1)
(6)
(2)
(3)
(4)
(5)
12.18
(0.27)
2.26
(0.08)
0.30
(0.03)
1.31
(0.02)
0.89
(0.02)
0.20
(0.01)
0.19
(0.01)
14.71 14.58
(0.68)
(0.69)
8.70
(0.58)
2.28
(0.40)
included
2.07
(0.43)
included
1.40
(0.10)
0.99
(0.15)
0.14
(0.03)
1 ¼ (1 )
2 ¼ (1 )
1.51
(0.01)
3 ¼ 1 1.30
(0.02)
dit
Production-cost
effect: Distance effect: 0.09
(0.01)
0.09
(0.01)
0.10
(0.01)
0.09
(0.03)
0.09
(0.02)
Elasticity of
substitution: 15.71
(0.68)
15.58
(0.69)
9.70
(0.58)
3.28
(0.40)
3.07
(0.43)
0.54
0.54
0.40
0.83
0.81
corr (log Mijt, log M̂ijt)
0.51
NOTES: This table reports OLS estimates of equation (5) with the CES price term replaced by
interactions between importers and years (dit); that is, log (Mijt =njt Yit ) ¼ (1 )
log w*jt þ (1 )
Dij þ (1 ) log ijt þ dit þ ijt . There are 4,164 observations involving 34 countries and 4 years
(1980, 1984, 1988, and 1992). Since all specifications do not include intercepts, R2s are not
reported. Standard errors are in parentheses. The standard errors of and are calculated using
the delta method. See Greene (1997, 278–79).
a The average tariffs are bilateral tariff rates ( ijt) averaged over the 4 years, 1980, 1984, 1988 and 1992.
In column 1 we include only distance. As expected, the coefficient on distance
is significantly negative. Furthermore, as shown at the bottom of column 1, the
b ijt ) is high (0.51).
correlation between log (Mijt) and its prediction log (M
We then add tariffs. In our sample, tariffs vary across country pairs and years.
In column 2 we first add bilateral tariffs averaged over time. In this case, the tariff
effect is identified from country-pair variation in tariffs. In column 3 we use timevarying bilateral tariffs instead. The two sets of estimates are almost identical,
indicating that the variation in tariffs across country pairs largely dominates the
variation over time. In addition, the results show that tariffs reduce bilateral trade
significantly, which implies a large elasticity of substitution b ¼ 15:58 (see column
3). However, as displayed in column 4, when we include wages (adjusted by
labour productivity), the estimate of drops to 9.70.
So far we have ignored the non-linear CES price term. A more rigorous
treatment of this term will be given in the next section. In the light of the fact
that the CES price term varies only across i and t, we now simply replace it
with interactions between importers and years (dit). This approach has two
advantages. First, tariffs, distance, and wages may not fully capture all sources
468
H. Lai and S.C. Zhu
of price variation. As shown in Balistreri and Hillberry (2001), missing some
variables in equation (5) or using proxies like distance instead of true transportation costs can lead to unreasonable predications for price index variation.
However, using interactions between importers and years can control for some
unmeasured factors that affect prices. Second, including the interaction term
allows us to use the simple OLS technique. As shown at the bottom of column
5, after including the interaction term dit, the correlation between log (Mijt) and
b ijt ) rises to 0.83. The estimate of turns positive. Notably, the magnilog (M
tude of the tariff effect is significantly reduced. The estimate of drops to just
3.28. In columns 6 we include TFP-adjusted wages instead. The estimate of is
3.07, which is very similar to the estimate when labour-productivity-adjusted
wages are used. Therefore, taking account of the price term yields a much
smaller estimate of .
5.2. The basic model
Now we use the maximum likelihood method to estimate equation (5). Results
are shown in table 2. In columns 1–4 labour-productivity-adjusted wages are
included. We first estimate equation (5) without imposing any parameter
restrictions. This baseline estimate is given in column 1. Estimates of 1, 2,
and 3 have the right signs. The estimated b is 6.42, which is remarkably close
to the point estimate of 6.43 in Baier and Bergstrand (2001). It is also close
to those in Hummels (1999). His average estimates are 5.79, 6.23, and 7.04 for
1-digit, 2-digit, and 3-digit manufacturing industries, respectively. Notably,
our basic model estimate of is considerably lower than a b of 9.70 when
the CES price term is omitted (see column 4 of table 1). This result confirms
the point stressed by Anderson and van Wincoop (2003) that exclusion of the
price term induces omitted variable bias.5
The distant effect is significant. With distance scaled to 1,000 miles per unit,
b of 0.06 implies that 6% of a product’s value is lost per thousand miles of
a
shipping. The effect of production costs is also statistically significant. Further,
b ijt ) is 0.78, which is
the correlation between log (Mijt) and its prediction log (M
much higher than the correlation of 0.40 when the CES price term is
excluded.
In column 2 we omit wages by assuming ¼ 0. The estimate of decreases
to 4.52, while the estimate of increases to 0.10. The correlation between log
b ijt ) drops slightly to 0.75. In column 3 we exclude distance by
(Mijt) and log (M
setting ¼ 0. The estimate of rises substantially to 11.55. The correlation
b ijt ) drops to 0.69. It is obvious that omitting
between log (Mijt) and log (M
distance has a larger impact on other estimates than omitting wages. In column
4 we set ¼ 1. In this case we cannot disentangle the effects of tariffs and
wages. Column 4 shows that the estimate of drops to 3.75. However, as
5 Anderson and van Wincoop (2003) focus on the border effects in a gravity model. Differing
from the approach in our paper, they do not estimate .
The determinants of bilateral trade
469
TABLE 2
The basic model
Labour-productivity-adjusted wages
Baseline
¼0
¼0
¼1
TFP-adjusted
wages
(1)
(2)
(3)
(4)
(5)
1 ¼ (1 )
2.51
(0.10)
2 ¼ (1 )
0.33
(0.00)
0.34
(0.01)
3 ¼ 1 5.42
(0.30)
3.52
(0.24)
0
Production-cost
effect: 0.46
(0.02)
Distance effect: 0.06
(0.00)
0.10
(0.01)
Elasticity of
substitution: 6.42
(0.30)
4.52
(0.24)
Log likelihood
corr (log Mijt, log M̂ijt)
Likelihood ratio
test (2-statistics)a
4391.76
0.78
0
4663.54
0.75
543.56
3.49
(0.17)
2.75
(0.12)
0.22
(0.03)
0
0.34
(0.01)
0.33
(0.01)
10.55
(0.31)
2.75
(0.69)
3.66
(0.21)
0.33
(0.01)
0
11.55
(0.31)
5584.39
0.69
2385.26
1
0.06
(0.01)
0.12
(0.03)
0.09
(0.01)
3.75
(0.69)
4.66
(0.21)
4454.18
0.78
124.84
4641.39
0.75
NOTES: The table reports ML estimates of equation (5). There are 4,164 observations involving
34 countries and 4 years (1980, 1984, 1988, and 1992). Standard errors are in parentheses.
a The hypotheses for the likelihood ratio tests in columns 2–4 are ¼ 0, ¼ 0 and ¼ 1,
respectively. The 5% critical value is 3.84.
reported in the last row of table 2, the likelihood ratio test statistics for the
above three restrictions are 543.56, 2385.26, and 124.84, respectively. Thus, all
restrictions are rejected at the 5% significance level, which indicates that tariffs,
distance, and production costs have separate effects on bilateral trade flows.
In column 5 we include TFP-adjusted wages instead. The signs of b2 and b3
are as predicted. Although b1 has a wrong sign, the predicted effect of production cost is close to 0 (b
¼ 0:06). Thus, it is not surprising that the b of 4.66 is
remarkably close to the estimate of 4.52 in column 2 when wages are excluded.
But the b of 4.66 is much smaller than the estimate of 6.42 in column 1 when
labour-productivity-adjusted wages are used. In the next section we will show
that when country-pair fixed effects are controlled for, our estimates are more
robust to the way that wages are adjusted.
To summarize, we find that excluding the CES price term can lead to a large
omitted variable bias. In addition, tariffs, distance-related barriers and production costs are important determinants of trade flows, though the effect of
production costs is less robust.
470
H. Lai and S.C. Zhu
5.3. The fixed-effects model
Many studies have emphasized the importance of country-pair fixed effects for
capturing unmeasured explanatory variables. Hummels and Levinsohn (1995)
provide one of the most prominent examples in the trade literature. Although
the specification in equation (5) has rigorously incorporated tariffs and distance in a general equilibrium framework, a number of potential explanatory
variables are still excluded. Examples include non-tariff barriers (NTBs), language and adjacency dummies, and c.i.f-f.o.b factors. To take account of
country-pair fixed effects, we make the following assumption about the error
term: "ijt ¼ ij þ vijt and vijt N(0, 2).6 Results are given in table 3, which has
the same format as table 2.
Column 1 of table 3 displays the baseline estimate for the fixed-effects
model. The likelihood is 206.71, which is much larger than that of 4391.76
for the basic model. This result implies a likelihood ratio test statistic of 8370.1
for the hypothesis of zero fixed effects. This hypothesis can be rejected at the
5% significance level, which indicates that including the fixed effects is crucial
for understanding the sample variation.
The fixed-effects estimate of is just 3.99, which is much smaller than the
basic model estimate of 6.42. But this fixed-effects estimate is fairly close to the
estimate of 3.28 when the price term is proxied by interactions between
importers and years (see column 5 of table 1). The smaller estimate of is
well in line with the estimates obtained from micro studies of demand for
differentiated products (e.g., Feenstra 1994). It is also close to the elasticities
that are employed in the CGE literature; for example, in Deardorff and Stern
(1990), 17 out of 21 non-agricultural traded goods industries have elasticities of
substitution that are less than 3.1.
At the same time, the distance effect becomes slightly smaller. However, the
estimates of differ significantly across the two models: although both have
the expected sign, the basic model estimate of is 0.46 with a t-statistic of 23,
whereas the fixed-effects estimate of is insignificantly different from 0.
Therefore, the estimates of , , and become smaller when fixed effects are
included. The smaller parameters estimated for the fixed-effects model have an
interesting explanation. The actual world trade volume is far less than predicted by the simple monopolistic competition trade model (Helpman and
Krugman 1985) and the gravity-type equation (McCallum 1995; Wei 1996).
Given the already low tariffs among major trading countries and the short
bilateral distances within major preferential trading areas (EU and NAFTA),
this gap can be explained by either a large distance effect (big ), a large
marginal cost effect (big ), a large substitution effect (big ), and/or large
unmeasured effects (fixed effects). Without considering the fixed effects, a very
6 We assumed ij ¼ 0 in the basic model.
The determinants of bilateral trade
471
TABLE 3
The fixed-effects model
Labour-productivity-adjusted wages
Baseline
¼0
¼0
¼1
TFP-adjusted
wages
(1)
(2)
(3)
(4)
(5)
1 ¼ (1 )
0.06
(0.12)
2 ¼ (1 )
0.14
(0.05)
0.14
(0.01)
3 ¼ 1 2.99
(0.38)
2.97
(0.31)
0
Production-cost
effect: 0.02
(0.04)
Distance effect: 0.05
(0.02)
0.05
(0.01)
Elasticity of
substitution: 3.99
(0.38)
3.97
(0.31)
Log likelihood
corr (log Mijt, log M̂ijt)
Likelihood ratio test
(2-statistics)a
206.71
0.75
0
206.84
0.75
0.26
0.11
(0.09)
0
3.81
(0.17)
0.03
(0.03)
0
4.81
(0.17)
210.63
0.67
7.84
0.31
(0.12)
0.01
(0.02)
0.27
(0.05)
0.13
(0.06)
0.31
(0.14)
3.02
(0.40)
1
0.00
(0.01)
0.88
(0.46)
0.04
(0.02)
1.31
(0.14)
4.02
(0.40)
235.65
0.77
57.88
206.70
0.75
NOTES: The table reports ML estimates of equation (5) with country-pair fixed effects. There are
4,164 observations involving 34 countries and 4 years (1980, 1984, 1988, and 1992). Standard errors
are in parentheses.
a The hypotheses for the likelihood ratio tests in columns 2–4 are ¼ 0, ¼ 0 and ¼ 1,
respectively. The 5% critical value is 3.84.
large , , and , or a combination of these three is needed to explain the large
missing trade.
Table 3 also illustrates that our fixed-effects estimates are more robust to
alternative specifications. As shown in column 2, excluding wages does not
have any significant impact on other estimates. The likelihood ratio test
(2 ¼ 0.26) suggests that the hypothesis of ¼ 0 cannot be rejected at the 5%
level. This insignificant wage effect further implies that the fixed-effects estimates should be robust to the way that wages are adjusted. This point is
supported by the result in column 5. In particular, when TFP-adjusted wages
are used, the estimate of becomes 4.02, which is remarkably close to the b of
3.99 in column 1 when labour-productivity-adjusted wages are included.
In column 3 we exclude distance. The estimate of increases slightly to 4.81.
Recall that omitting distance in the basic model induces a substantial increase
in b. This disparity suggests that distance in the basic model may also capture
other country-pair fixed effects rather than distance per se. Hence, excluding
distance generates a bigger impact on the basic model estimates than on the
fixed-effects estimates.
472
H. Lai and S.C. Zhu
30
Predicted log (Mijt)
25
20
15
10
5
5
10
15
20
25
30
log (Mijt)
FIGURE 1 Model fit of the bilateral trade equation
NOTES: The predicted value of log (Mijt) is calculated using the estimates of the fixed-effects
model in column 1 of table 3. The prediction excludes the fixed effects term.
Finally, the second last row of table 3 shows that the correlation between
b ijt ) is between 0.67 and 0.77. Figure 1 plots
log (Mijt) and its prediction log (M
log (Mijt) against its prediction, using the fixed-effects estimate in column 1.
(The prediction excludes the fixed effects term.) The high correlation of 0.75 is
reflected in the plot. Overall, the predicted log (Mijt) is slightly biased upwards
and more points lie above the 45-degree line than below. In particular, the
model fits best for observations with larger bilateral trade flows.
5.4. Influential observations
Table 4 presents the results of sensitivity analysis for the fixed-effects model
when labour-productivity-adjusted wages are used. For each specification, the
estimation of the three structural parameters and their standard errors are
reported in one row. The ‘Baseline’ row carries over the results from column 1
of table 3. The remaining rows omit all observations related to the indicated
country. In general, the estimation does not change significantly when any
single country is dropped. Two exceptions are Japan and the United Kingdom.
When Japan is excluded, shrinks to its minimum of essentially zero and rises to its maximum of up to 5.23. When the United Kingdom is omitted, rises to its maximum and falls to its minimum. By implication, is not stable
The determinants of bilateral trade
473
TABLE 4
Sensitivity results of the bilateral trade equation
Baseline
Omitting the indicated country
Japan
India
USA
Greece
Tunisia
Ecuador
Chile
Germany
Austria
Sweden
Netherlands
Korea
Norway
Belgium
Italy
Morocco
Canada
Australia
Portugal
Indonesia
Malaysia
Venezuela
Denmark
Thailand
Finland
Hong Kong
Ireland
Sri Lanka
Singapore
New Zealand
Brazil
Spain
Mexico
United Kingdom
Std. Err.
Std. Err.
Std. Err.
3.99
0.38
0.05
0.02
0.02
0.04
5.23
4.64
4.50
4.48
4.36
4.13
4.10
4.10
4.09
4.08
4.07
4.05
4.03
4.01
3.98
3.97
3.94
3.93
3.92
3.90
3.89
3.88
3.87
3.86
3.84
3.82
3.82
3.82
3.79
3.71
3.69
3.68
3.64
3.37
0.42
0.46
0.45
0.48
0.42
0.38
0.36
0.43
0.42
0.39
0.40
0.37
0.38
0.40
0.40
0.36
0.36
0.42
0.37
0.38
0.39
0.36
0.39
0.37
0.37
0.38
0.39
0.37
0.38
0.42
0.41
0.42
0.32
0.32
0.00
0.03
0.00
0.02
0.03
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.05
0.05
0.04
0.05
0.06
0.05
0.06
0.06
0.06
0.06
0.05
0.06
0.06
0.05
0.05
0.05
0.06
0.07
0.08
0.01
0.10
0.13
0.01
0.01
0.01
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.01
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.03
0.02
0.01
0.02
0.03
0.09
0.06
0.15
0.04
0.04
0.01
0.02
0.02
0.03
0.01
0.02
0.00
0.05
0.01
0.01
0.04
0.05
0.01
0.01
0.06
0.07
0.00
0.01
0.02
0.06
0.01
0.03
0.09
0.06
0.04
0.01
0.04
0.05
0.02
0.03
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.05
0.05
0.04
0.05
0.05
NOTES: ‘Baseline’ refers to the estimated results from the fixed-effects model in column 1 of table 3.
Each of the other rows refers to the case when observations related to the indicated country are
excluded. The countries are sorted by in a descending order.
across countries. Specifically, Japan has inelastic preferences ( small) and high
trade costs ( large). In contrast, the United Kingdom has elastic preferences
and low trade costs. This may be due to these countries’ important role in
international trade and the fact that Japan is located far away from its major
trading partners, while the distance between the United Kingdom and its
trading partners is relatively small. Deleting either of them throws out important sample variation relating trade to distance.
474
H. Lai and S.C. Zhu
TABLE 5
Cross-sectional analysis
Baseline
1980
1984
1988
1992
(1)
(2)
(3)
(4)
(5)
1 ¼ (1 )
2.51
(0.10)
2.74
(0.24)
2.46
(0.21)
2.55
(0.20)
2.63
(0.17)
2 ¼ (1 )
0.33
(0.00)
0.38
(0.01)
0.35
(0.01)
0.32
(0.01)
0.27
(0.01)
3 ¼ 1 5.42
(0.30)
4.86
(0.52)
5.34
(0.44)
4.80
(0.39)
7.13
(0.57)
Production-cost effects: 0.46
(0.02)
0.56
(0.07)
0.46
(0.04)
0.53
(0.05)
0.37
(0.03)
Distance effects: 0.06
(0.00)
0.08
(0.01)
0.07
(0.01)
0.07
(0.01)
0.04
(0.00)
Elasticity of substitution: 6.42
(0.30)
5.86
(0.52)
6.34
(0.44)
5.80
(0.39)
8.13
(0.57)
Log likelihood
corr (log Mijt, log M̂ijt)
4391.76
0.78
1197.04
0.77
1143.66
0.77
1024.11
0.80
943.20
0.81
NOTES: The table reports cross-sectional estimates of equation (5). Column 1 reports the baseline
estimate carried over from column 1 of table 2. Columns 2–5 report ML estimates, respectively, for
the 4 years, 1980, 1984, 1988, and 1992. Standard errors are in parentheses.
5.5. Cross-sectional analysis
Many studies that estimate variants of equation (5) find a larger elasticity of
substitution than our baseline estimates. As discussed above, this disparity may
arise from the way in which the CES price term is treated. When the non-linear
price term is ignored, the estimate of tends to be biased upward. We also find
that controlling for country-pair fixed effects can significantly reduce the
estimate of .
Another possible source for the disparate estimates may be that we exploit a
tariff panel while other studies use cross-sectional data. To examine this, we
turn our attention to the cross-sectional analysis. Note that country-pair fixed
effects cannot be included in this case. Results are displayed in table 5. For
comparison, columns 1 reports our basic model estimate carried over from
column 1 of table 2.
In general, the estimates of , , and are stable over time. The only
b and b decline. In contrast,
exception occurs between 1988 and 1992. Both b rises to 8.13, which implies that countries with lower tariffs had much higher
imports in 1992 than in previous years. On the other hand, it is apparent that
the cross-sectional estimates are largely consistent with the baseline estimate.
In particular, the cross-sectional estimates of are between 5.80 and 8.13,
which are fairly close to the baseline estimate of 6.42. This result is also
consistent with our observation that the variation in tariffs across country
The determinants of bilateral trade
475
pairs dominates the variation over time (see section 5.1). Therefore, we conclude that large estimates of in other studies may reflect omitted variable bias
due to exclusion of the CES price term or country-pair fixed effects.
b gets smaller over time. In
Table 5 also illustrates another interesting result: b falls by half from 0.08 to 0.04. This
particular, between 1980 and 1992 diminishing distance effect may reflect technical progress in transportation or
reduced information costs associated with distance.7
5.6. The CES price term
The monopolistic competition model provides us a rigorous framework to
examine the relationship between trade costs and bilateral trade. In this section
we examine to what extent our estimates rely on the specific functional form as
predicted by the theory. Given the essential role of the CES price index in our
estimation, we allow for a coefficient in front of the non-linear price term. We
need to emphasize that this parameter does not come from the theory. Thus, it
has no economic interpretation. Here we have two purposes. First, we want to
test whether this parameter would deviate significantly from 1 (as predicted by
the theory) or from 0 (i.e., the CES price term can be ignored). Second, and
more important, we want to see to what extent our estimates would be changed.
Results are displayed in Table 6. For comparison, column 1 reports the
baseline estimate carried over from column 1 of table 2. As shown in column 3,
estimates of 1, 2, and 3 have predicted signs. The estimated coefficient on
the CES price term is 1.09, which is close to 1, as implied by the monopolistic
competition model. However, the likelihood ratio test rejects the hypothesis
that the coefficient on the CES price term is 1 at the 5% significance level
(2 ¼ 1089.2). This echoes the point raised by Lai and Trefler (2002) that the
monopolistic competition model may be misspecified. On the other hand, we
should notice that the estimates of , , and change only slightly. Also, the
b ijt ) improves slightly
correlation between log (Mijt) and its prediction log (M
(from 0.78 to 0.79).
In contrast, exclusion of the CES price term induces serious omitted variable bias. Column 2 shows that b becomes unreasonably large when the price
b ijt ) drops
term is ignored. Also, the correlation between log (Mijt) and log (M
substantially to 0.40. Furthermore, the likelihood ratio test rejects the hypothesis of zero price effects at the 5% significance level (2 ¼ 11552.90).
To summarize, we find that (i) taking account of the CES price term and
country-pair fixed effects can yield a smaller and more reasonable estimate of
the elasticity of substitution across varieties; (ii) the fixed-effects estimates are
7 We also estimate equation (5) by allowing the distance effect to vary across years. The estimates
of for the four years are 0.07, 0.07, 0.06, and 0.05, respectively. Thus, the pooled estimates
also display a diminishing distance effect. Further, the result suggests that the baseline estimate
of (0.06) can be interpreted as an average distance effect across the four years.
476
H. Lai and S.C. Zhu
TABLE 6
The CES price term
Baseline
(1)
(2)
(3)
1 ¼ (1 )
2.51
(0.10)
12.18
(0.27)
2.05
(0.08)
2 ¼ (1 )
0.33
(0.00)
0.89
(0.01)
0.21
(0.01)
3 ¼ 1 5.42
(0.30)
8.70
(0.53)
4.17
(0.24)
CES price term
1
0
1.09
(0.00)
Production-cost effects: 0.46
(0.02)
1.40
(0.10)
0.49
(0.03)
Distance effects: 0.06
(0.00)
0.10
(0.01)
0.05
(0.00)
Elasticity of substitution: 6.42
(0.30)
9.70
(0.53)
5.17
(0.24)
Log likelihood
corr (log Mijt, log M̂ijt)
Likelihood ratio test (2-statistics)a
4391.76
0.78
1089.20
9623.61
0.40
11552.90
3847.16
0.79
NOTES: Column 1 in this table reports the baseline estimate carried over from column 1 of table
2. Column 2 displays the ML estimate of equation (5) by excluding the CES price term. Column 3
shows the estimate of equation (5) by allowing for a coefficient in front of the CES price term.
Standard errors are in parentheses.
a The hypothesis for the likelihood ratio test in column 1 is that the coefficient on the CES price
term is 1 (as predicted by the theory). The hypothesis for the likelihood ratio test in column 2 is
that the coefficient on the CES price term is 0 (i.e., zero price effects). The 5% critical value is 3.84.
more robust than the basic model estimates; and (iii) tariffs, distance-related
barriers and production costs are important factors affecting bilateral trade
flows, although the effect of production costs is less robust.
6. Economic implications
Focusing on the explainable part of the model, we can quantify the extent to
which trade barriers have restricted the volume of world trade. In the nonlinear setting, the percentage change due to the hypothetical elimination of
existing tariffs in any time period t is calculated as
X X Tariff effect ¼
E Mijt jijt ¼ 0
i
j
X X E Mijt jijt > 0 =
E
M
j
>
0
:
ijt
ijt
i
j
The determinants of bilateral trade
477
TABLE 7
The estimated tariff effect grouped by OECD and non-OECD countries, 1992
Exporters
Importers
OECD
Non-OECD
Tariff effect
OECD
Non-OECD
2.5%
12.0%
2.4%
7.9%
Trade volume
OECD
Non-OECD
1603.2
333.1
274.7
98.2
NOTE: The tariff effect is calculated using the estimates of the fixed-effects model in column 1 of
table 3. The trade volume refers to the value of 1992 exports in billions of U.S. dollars.
The distance effect is defined similarly. We calculate the tariff and distance
effects for the year 1992, using the estimates of the fixed-effects model in
column 1 of table 3.
When we do this calculation, we need to keep one caveat in mind: since
wages are exogenous in our model, our estimates may not fully capture general
equilibrium effects via changes in wages. The general equilibrium effects via
endogenous wages have been carefully examined in Redding and Venables
(2002). On the other hand, since the wage effect is not significantly different
from 0 (b
¼ 0:02 with a t-statistic of 0.53), our calculation should be considered as a reasonable approximation to the general equilibrium effects of trade
liberalization.
The estimated tariff effect is 3.7%, which means that the 1992 level of tariffs
reduced bilateral trade by 3.7%. The estimated distance effect is 23.3%, meaning that if all distance between countries were eliminated bilateral trade would
rise by 23.3%. Note that the distance effect captures the disadvantage of being
distant from destination markets. Such disadvantages can include shipping
costs, linguistic or cultural differences, and slow responsiveness to changing
consumer demands. These numbers show that distance-related barriers are a
greater impediment to trade than are tariffs. As the distance-related barriers
are unlikely to be eliminated in the short run, these numbers imply serious
limits on the impact of trade policy on world trade.
World-wide numbers disguise large regional variations in the effect of
trade policy. It is well known that most existing trade is among OECD
countries and the highest tariffs are among non-OECD countries. As a
result, the impact of tariffs on trade within OECD countries is likely less
than 3.7% whereas the impact of tariffs on trade among non-OECD
countries likely exceeds 3.7%. This is born out by table 7. The estimated
tariff effect is only 2.5% for trade within OECD countries and a much
bigger 7.9% for trade among non-OECD countries. However, this does not
478
H. Lai and S.C. Zhu
TABLE 8
The estimated tariff effect grouped by regions, 1992
Exporters
Importers
Europe
North America
Europe
North America
Asia
Others
5.5%
6.8%
9.4%
15.0%
6.6%
3.3%
5.1%
11.7%
Europe
North America
Asia
Others
828.9
122.4
79.9
27.7
127.6
266.2
125.8
38.0
Asia
Others
Tariff effect
5.4%
7.4%
8.1%
12.1%
4.0%
3.1%
9.7%
0.5%
Trade volume
141.4
240.9
224.4
23.0
16.9
18.0
20.9
7.2
NOTE: The tariff effect is calculated using the estimates of the fixed-effects model in column 1 of
table 3. The trade volume refers to the value of 1992 exports in billions of U.S. dollars. See table A1
for a list of the countries.
mean that non-OECD countries should focus on lowering trade barriers
among themselves while ignoring trade negotiations with OECD countries.
In fact, as shown in the bottom panel of table 7, exports from non-OECD
to OECD countries are worth $274.7 billion, while exports within nonOECD countries are only $98.2 billion. Thus, in terms of trade volume, the
OECD country markets are more important for non-OECD countries than
the southern markets of other non-OECD countries.
Table 8 investigates further. Trade volumes within the EU block and within
the NAFTA block actually decrease with tariff liberalization. All other trade
volumes rise. They even rise between Europe and North America, despite the
low level of 1992 tariffs. In summary, although the effect of trade liberalization
on overall world trade is not large, its distribution is skewed: it shifts trade
from rich countries to poor countries and from local preferential trading areas
to global intercontinental trading partners.
7. Conclusions
In this paper, we developed a monopolistic competition model that takes into
account asymmetric trade barriers and international differences in production
costs. The model implies a highly non-linear bilateral trade equation. This
equation allowed us to estimate the underlying parameters, for example, the
elasticity of substitution and trade costs. This is a significant contribution to
the literature: previous studies placed zero restrictions on at least some of these
parameters. Taking account of country-pair fixed effects leads to smaller
estimates of parameters related to trade costs and the elasticity of substitution.
The determinants of bilateral trade
479
These smaller and more reasonable parameters, combined with country-pair
fixed effects, are consistent with large missing trade in a world of low tariffs
and small distances among major trading partners.
Estimates of the structural parameters allowed us to assess the impact of the
1992 worldwide tariff structure. Elimination of these tariffs would raise world
trade by 3.7%. Further, the effect is skewed. Trade liberalization would shift
trade from rich countries to poor countries and from local preferential trading
areas to intercontinental trading partners.
Appendix: Panel data on tariffs
A.1. Data sources
A.
UNCTAD TRade Analysis and Information System (TRAINS). This is
the most comprehensive inventory of bilateral tariffs available. It includes
bilateral tariffs and trade at 6-digit to 10-digit Harmonized Tariff System
(HS) levels for many developed and developing countries. For 1992 it
covers the 36 countries used in this paper.
B. GATT Tariff Study for the 14 OECD Countries. The GATT Tariff Study
data are available for 1979, 1983, and 1987. The data are from the same
source used in Trefler (1993). The classification system is HS.
C. Indicators of Tariff and Non-tariff Trade Barriers. Data from this OECD
CD-ROM include 1988 and 1992 average tariffs for six OECD countries
by importer and 3-digit ISIC. These six countries are Sweden, Austria,
Spain, Portugal, Australia, and New Zealand.
D. The Michigan Model of World Production and Trade: Theory and Applications (Deardorff and Stern 1986). This book contains 1972 and 1979
average tariff data for 17 OECD countries. It includes all those countries
in (B) except Greece. It also includes Sweden, Austria, Australia, and
New Zealand. It is organized by importer and 3-digit ISIC (Revision 2)
industry. These data are originally from an early version of the GATT
tariff study.
E. UNCTAD Directory of Import Regimes (DIR). This UNCTAD publication includes average tariff rates for 16 non-OECD countries.8 The data
are available for various years between 1980 and 1993, depending on the
country. It is organized by importer and an industry classification system
that is close to 3-digit ISIC (Revision 2).
F. Government Finance Statistics Yearbook (GFS) and International Finance
Statistics Yearbook (IFS). These publications contain data on import
duties and total imports by country from 1972 to 1992, for all 36
8 The 16 countries are Argentina, Brazil, Chile, Ecuador, Hong Kong, India, Indonesia, Korea,
Malaysia, Mexico, Morocco, Singapore, Sri Lanka, Thailand, Tunisia, and Venezuela.
480
H. Lai and S.C. Zhu
TABLE A1
Countries and summary statistics on key variables
w*i;92
Countries
Mi,92
Yi,92
ni,92
i,92
Asia
Hong Kong
India
Indonesia
Japan
Malaysia
Singapore
South Korea
Sri Lanka
Thailand
69.6
14.2
23.9
132.0
37.2
71.9
63.8
2.1
36.3
23.2
83.5
59.4
1288.5
22.5
30.1
156.4
2.3
109.3
41.7
109.2
17.8
415.1
7.5
3.9
74.7
2.1
18.4
0%
91%
18%
4%
16%
1%
16%
36%
31%
5.5
7.5
8.4
7.8
6.6
6.6
5.9
6.6
6.6
0.4
1.6
0.6
0.7
0.5
0.4
0.6
0.4
0.9
Europe
Austria
Belgium
Denmark
Finland
Germany
Greece
Ireland
Italy
Netherlands
Norway
Portugal
Spain
Sweden
United Kingdom
47.2
93.8
30.1
17.0
291.0
17.4
22.4
124.1
120.4
26.4
24.4
73.2
43.9
183.6
82.0
73.3
46.2
34.4
980.8
43.2
14.3
312.3
164.5
40.8
67.0
256.1
83.7
401.3
8.6
26.5
6.8
5.9
44.2
8.1
2.6
25.2
6.7
4.1
16.8
138.7
8.7
139.3
2%
1%
1%
1%
1%
1%
1%
1%
1%
1%
6%
6%
1%
2%
1.9
1.8
2.0
2.8
3.0
3.1
2.5
2.7
2.4
2.6
2.5
3.1
2.5
3.6
0.6
0.5
0.7
0.6
0.7
0.6
0.7
0.4
0.6
0.6
0.6
0.5
0.5
0.7
North America
Canada
Mexico
U.S.A.
131.0
62.6
453.8
204.3
147.9
2051.7
34.5
2.9
488.3
3%
22%
3%
3.2
4.5
7.7
0.7
0.7
0.6
Other
Australia
Brazil
Chile
Ecuador
Morocco
New Zealand
Tunisia
Venezuela
37.5
16.6
8.5
2.7
3.9
8.8
4.1
13.9
128.3
188.0
19.4
7.3
13.0
15.1
8.2
36.7
41.5
172.9
1.8
1.8
6.1
19.7
2.1
10.4
8%
27%
19%
8%
18%
8%
29%
19%
11.0
9.3
10.4
7.1
3.6
10.3
2.4
6.1
0.7
0.7
0.7
0.8
1.1
0.7
0.7
0.7
Di
NOTES: The table reports summary statistics for the year 1992. Mi,92 is country i’s total imports
from other countries in the sample. Yi,92 is country i’s manufacturing output plus net imports. Both
Mi,92 and Yi,92 are in billions of U.S. dollars. ni,92 (in 1,000) is country i’s total number of
establishments in the manufacturing sector. i,92 is country i’s weighted average tariff rate with
import shares as weights. Di (in 1,000 miles) is country i’s weighted average distance from its
trading partners with import shares as weights. w*i;92 represents country i’s wage rate divided by
labour productivity, which is measured by PPP-adjusted GDP per worker.
countries. We calculated tariff rates as the ratio of import duties to total
imports. There is no industry dimension.
The determinants of bilateral trade
A.2.
1.
2.
Four steps in the construction of the tariff panel data set
Construct 1992 bilateral tariff rates by importer, exporter, and 3-digit
ISIC (Revision 2) industries for all 36 countries. Using converters from
Statistic Canada, we converted the TRAINS data from 6-digit HS to 4digit ISIC (Revision 3) and then to 3-digit ISIC (Revision 2). We used
import shares as weights to aggregate the tariff data.
Construct the panel data set for the 14 OECD countries. We first converted
data in (B) to 3-digit ISIC in the same manner as in (1). Then combining with
data in (1), we obtained bilateral tariffs in 1979, 1983, 1987, and 1992 for
these 14 importers. With 1979 bilateral tariffs, we extrapolated 1972 tariffs
for 13 countries (excluding Greece) using the formula
DS
DS
gij;72 ¼ gij;92 gi;72
=gi;79
;
3.
4.
5.
481
(A1)
where g indexes industries, i indexes importers, j indexes exporters, and
DS represents tariff rates from Deardorff and Stern (1986). For Greece,
we used GFS and IFS data between 1972 and 1979 to form the ratio of
1972 tariff rate relative to 1979. Data for 1980, 1984, and 1988 were then
linearly interpolated. Since these three years are close to 1979, 1983, and
1987, the interpolation can be considered to be accurate.
Construct the panel data set for Sweden, Austria, Spain, Portugal, Australia, and
New Zealand as importers. For each country and industry, we first used data in
(C) to compute a time series variable representing a ratio of tariff data of different
years relative to 1992. For earlier years we also used data in (D) and (F) to
calculate the time series variable. We then interpolated the data using the formula
DS
DS
in equation (A1) but with gi;72
=gi;79
replaced by the time series variable.
Construct the panel data set for the 16 poorer countries. For each country
and industry, we first used data in (E) to compute a time series variable
representing a ratio of tariff data of different years relative to 1992. For
earlier years we also used data in (F) to calculate the time series variable.
We then interpolated the data using the formula in equation (A1) but with
DS
DS
gi;72
=gi;79
replaced by the time series variable.
Combining (2), (3), and (4), we obtain the panel data set on tariffs by year,
ISIC, importer, and exporter.
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