ASIA-PACIFIC INTEGRATION WITH CHINA
VERSUS THE U NITED S TATES : E XAMINING
TRADE PATTERNS UNDER HETEROGENEOUS
AGRICULTURAL SECTORS
KARI E. R. HEERMAN, SHAWN ARITA, AND MUNISAMY GOPINATH
This article examines bilateral trade patterns in the Asia-Pacific using a new model in which
comparative advantage within the agricultural sector is linked to agro-ecological characteristics,
and trade costs are product-specific. Bilateral market share is a function of productivity and trade
costs. However, countries with similar land and climate characteristics systematically have high
productivity in similar products making them disproportionately sensitive to changes in each
other’s trade costs. We use a random coefficients logit model to estimate a parametric distribution
of comparative advantage and trade costs across products and calculate regional trade liberalization elasticities for each exporter in each import market. Unlike most existing models, the value
of the elasticity depends on the degree to which liberalization includes competitors with similar
comparative advantage within the agricultural sector. We find disproportionately larger trade
elasticities under China-led liberalization relative to U.S.-led liberalization among close U.S. competitors compared to countries whose agricultural products are unlikely to compete head-to-head
with U.S. exports. For the United States, we find that the “lost opportunity” cost of exclusion
from regional liberalization is increasing in the extent to which its close competitors gain new
access.
Key words: agricultural trade, Asia-Pacific integration, free trade agreements, trade liberalization.
JEL codes: Q17, Q18, F13, F14, F15.
The Asia-Pacific region has witnessed an
active and ambitious free trade agenda in
recent years. The United States is leading
discussions on the Trans-Pacific Partnership
(TPP), which includes eleven other countries
but excludes China. China is in discussions
toward a Regional Comprehensive Economic Partnership (RCEP) with fifteen other
Asia-Pacific countries, which excludes the
Kari E. R. Heerman and Shawn Arita are agricultural
economists and Munisamy Gopinath is Director, Market
and Trade Economics Division with the U.S. Department
of Agriculture, Economic Research Service. The authors would
like to thank AJAE editor James Vercammen, as well as two
anonymous reviewers for their helpful comments on previous versions of this manuscript. The views expressed here
are those of the author(s), and may not be attributed to
the Economic Research Service or the U.S. Department of
Agriculture.
This article was subjected to an expedited peer-review
process that encourages contributions that frame emerging and
priority issues for the profession, as well as methodological
and theoretical contributions.
United States, but includes seven countries
that are also part of the TPP negotiations.
Transcending these active negotiations is the
idea of a Free Trade Area of the Asia Pacific
(FTAAP), a proposed trade bloc encompassing the United States, China, and nineteen
other Pacific-Rim countries, which has been
periodically discussed in the context of
Asia-Pacific Economic Cooperation (APEC).
In this article we study how the trade promoting forces of comparative advantage
and the trade barriers that obscure them
determine bilateral agricultural trade patterns in the region. The countries we are
interested in have very different land and
climate characteristics and occupy distinct
geographical regions straddling the Pacific
Ocean. China and the United States are
the largest markets, but their agricultural
trade flows contrast in both size and nature.
First, the United States is a technologically
advanced agricultural producer and major
Amer. J. Agr. Econ. 97(5): 1324–1344; doi: 10.1093/ajae/aav038
Published online July 21, 2015
Published by Oxford University Press on behalf of the Agricultural and Applied Economics Association 2015.
This work is written by (a) US Government employee(s) and is in the public domain in the US.
Heerman, Arita, and Gopinath
Asia-Pacific Integration with China vs the United States
global exporter, whereas China is a low-cost
producer and large net importer. Second, differences in the characteristics of Chinese and
U.S. resources give each of them comparative advantage in distinct sets of agricultural
products.
We present a model that relates bilateral
trade flows to the sources of trade-promoting
comparative advantage within the agricultural sector and the distribution of trade
barriers across agricultural products. Existing
quantitative models of trade patterns largely
abstract from these forces within sectors.
Our point of departure is the gravity-like
relationship derived in the multicountry
model of Eaton and Kortum (2002) in which
producers have access to technology with
heterogeneous productivity. As in Eaton
and Kortum, the model delivers a structural
relationship between the probability a country has comparative advantage in a given
export market for an individual agricultural
product and the bilateral costs of producing
and exporting the product. Unlike Eaton and
Kortum, the distribution of productivity is
linked to country-specific land and climate
characteristics, and trade costs vary across
products within the sector.
A novel feature of this modelling approach
is that countries that systematically specialize in similar agricultural products are
more sensitive to changes in each other’s
trade costs, whereas in standard models,
including Eaton and Kortum, the elasticity of
each exporter’s trade flows with respect to a
given competitor’s trade costs is constant and
directly proportional to the exporter’s market share.1 Our methodology thus produces
a rich picture of the structure of competition in the region, revealing which countries
are most likely to compete head-to-head in
the same agricultural products and thus be
most sensitive to changes in each other’s
trade costs.
We use our structural model to estimate a
system of trade elasticities. We find that the
market shares of close U.S. competitors are
disproportionately more elastic to China-led
liberalization than U.S.-led liberalization
1
Arkolakis, Costinot, and Rodríguez-Clare (2012) define this
set of elasticities as characteristics of a “CES import demand
system” and note that it is a feature of models built on the Eaton
and Kortum framework as well as many of those built on Melitz
(2003) and on the Armington assumption as in Anderson and
van Wincoop (2003).
1325
relative to countries that are less likely to
compete head-to-head with U.S. exports.
Similarly, we find that the “lost opportunity”
cost to the United States of exclusion from
regional liberalization is increasing in the
extent to which its closest competitors gain
new access.
In the next section we briefly motivate
our approach with context from the general
equilibrium trade modeling literature. In the
third section we present a “systematic heterogeneity” model of bilateral agricultural trade.
In the fourth section we use an econometric
technique inspired by Berry, Levinsohn, and
Pakes (1995) to estimate a model of bilateral
trade that allows comparative advantage and
trade costs to vary within the sector. Our
approach has the significant advantage of
relying on very little data beyond what is
required for a gravity model of agricultural
sector trade. Finally we use the model to
examine how production and trade patterns
in the Asia-Pacific region are shaped by comparative advantage within agriculture and
trade costs using the system of elasticities
produced by our model.
Background: The Independence
of Irrelevant Exporters
Eaton and Kortum (2002) present a quantitative general equilibrium model in which
comparative advantage within the manufacturing sector is determined by the
realization of a productivity-augmenting
random variable that is independently distributed across products. The independence
assumption implies that the set of products
within the sector in which a country has
comparative advantage is determined randomly: Every country is equally likely to
specialize in any given product, and therefore no two countries are more likely to
compete head-to-head in the same products
than any other. This purely “random heterogeneity” assumption is convenient because
it delivers an analytical solution to the relationship between bilateral trade flows, trade
costs, and absolute advantage that is substantially equivalent to a log-linear gravity
model (Anderson 2011). However, it is not
without loss of generality because it imposes
strong restrictions on the response of trade
flows to changes in bilateral trade costs.
In a random heterogeneity model, indeed
in any model in which log-linear gravity is
1326
October 2015
the underlying model of bilateral trade flows,
the elasticity of each exporter’s trade flows
with respect to a given competitor’s trade
costs is constant and directly proportional
to the exporter’s market share, regardless
of whether they are likely to compete headto-head. We refer to this restrictive pattern
of trade elasticities as the independence of
irrelevant exporters (IIE) property. In models with the IIE property, changes to a third
country’s trade costs are “irrelevant” to the
ratio of any other two competitors’ market share in a given import market.2 If the
IIE property does not hold in the data, this
assumption results in at best imprecise and at
worst misleading predictions for the effects
of changes in trade costs on bilateral trade
and production patterns in comparative statics exercises. Notably, Arkolakis, Costinot,
and Rodríguez-Clare (2012) demonstrate
that many of the most common quantitative
general equilibrium trade models generate
a gravity-like relationship between bilateral
trade flows and variable trade costs. Their
results imply that, like Eaton and Kortum
(2002), many models implicitly impose the
IIE property, including those built on Melitz
(2003) and the Armington assumption as in
Anderson and van Wincoop (2003).3
Multisector extensions of the Eaton and
Kortum model, including Burstein and Vogel
(2010), Chor (2010), Costinot, Donaldson,
and Komunjer (2011), Shikher (2011, 2012),
Tombe (2011), and Kerr (2013) implicitly
recognize this limitation. These models allow
absolute advantage and average trade costs
to vary across a discrete number of sectors or
industries. In these models, the total response
of bilateral trade to a change in trade costs
depends on the distribution of absolute
advantage, adjusted for sector-average trade
costs, across sectors. Other extensions of
the Eaton and Kortum framework, notably
Bolatto (2013) and Caliendo and Parro
(2015), allow the trade elasticity’s constant
of proportionality to vary across sectors, but
it remains constant within each sector. In all
2
This property is equivalent to an assumption familiar in
the discrete choice demand literature as the independence of
irrelevant alternatives (IIA) property.
3
Extending the work of Novy (2013), Tan (2012) weakens
the assumption that delivers the log-linear form in a structural
gravity equation derived from an Armington model by introducing
translog preferences. Like our approach, this generates nonuniform bilateral trade elasticities. Unlike our model, the relative
magnitude of these elasticities is not linked to the underlying
forces that influence the likelihood that two countries will compete
head-to-head.
Amer. J. Agr. Econ.
of these models, the magnitude and distribution of the predicted trade response will
be sensitive to the number and definition of
sub-sectors chosen by the researcher.
Moreover, these models maintain the
assumption of random heterogeneity within
each sector or subsector, which implies that
the IIE property holds at that level. However, the IIE property is unlikely to hold in
a sector like agriculture where the characteristics of natural endowments are important
nonrandom drivers of comparative advantage within the sector.4 For example, the IIE
property implies that if Australia obtained
free access to the Japanese market, buyers in
Japan would substitute towards Australian
products and away from each of its other
trading partners in a constant and direct
proportion to their initial market share. Australia and the United States have similar land
and climate characteristics such that they systematically specialize in similar agricultural
products and thus compete head-to-head in
Japan and elsewhere. In contrast, Malaysia’s
tropical climate and densely-populated island
land area leads it to specialize in a very different set of agricultural products. Therefore,
in contrast to the shifts imposed by the IIE
property, we expect U.S. market share to
have a disproportionately larger decline in
response to lower Australian trade costs
relative to Malaysia.
While the IIE assumption is unlikely to
hold at the agricultural sector level, it will
hold for some appropriately-defined subset
of products. A reasonable approach might
therefore be to break up the agricultural
sector into subsectors of like products within
which the IIE property holds.5 However,
there are significant practical and theoretical disadvantages to this approach. First,
while some of the boundaries between subsectors might be obvious—coffee and cacao
should clearly be in a different sub-sector
than wheat and barley—for others, like many
animal products, it is less clear. Moreover,
large and systematic differences in trade
costs could bring about a violation of the IIE
property even among like products. Furthermore, testing for the IIE property is not a
4
In fact, Eaton and Kortum (2002) caution that their Ricardian
model may not be appropriate for analysis of sectors for which
natural resources are important.
5
Reimer and Li (2010) present an extension of the Eaton
and Kortum (2002) model in which they focus only on the crop
agriculture sector.
Heerman, Arita, and Gopinath
Asia-Pacific Integration with China vs the United States
trivial exercise in a model that includes many
exporters. Our approach avoids the need
to categorize like products ex ante. This is
a distinct advantage if the focus of analysis
encompasses the entirety of the agricultural
sector.
Even if a set of subsectors within each of
which IIE holds can be easily identified, our
approach has a second practical advantage.
Estimating a system of Eaton and Kortum–
style gravity equations requires gathering
data on bilateral market share for each subsector. Our empirical methodology only
requires this information at the agricultural
sector level. This is a significant advantage
because reliable market share data is more
difficult to obtain at a finer level of disaggregation, and may be very thin or not available
at all for the aggregation at which IIE holds.
At the same time, we will show that our
approach can take advantage of the information contained in more detailed level bilateral
trade flow data, which is more reliable and
widely available.
Finally, our approach ensures that the
econometric model we estimate is consistent
with the underlying theory that generates the
gravity-like structural relationship delivered
by the Eaton and Kortum model. This relationship relies on the assumption that each
tradable sector is comprised of a continuum
of products or varieties. If ensuring the IIE is
not violated in the empirical implementation
requires a very fine definition of a “sector,”
this assumption becomes less plausible.
Model
We propose a model of agricultural trade
with systematic heterogeneity across products within the sector. This model does not
impose the IIE assumption. As in Eaton
and Kortum (2002), within sector specialization is driven by productivity and trade
costs. However, product-specific productivity is not entirely random. Instead, it is a
stochastic function of the coincidence of a
product’s land and climate requirements and
exporter land and climate characteristics.
Thus countries with similar land and climate
characteristics systematically specialize in
similar agricultural products. Such countries
are more likely to compete head-to-head in
any given import market. This likelihood is
strengthened when countries also face similar
trade costs. In a second departure from the
1327
Eaton and Kortum approach, trade costs are
allowed to vary within the sector. This allows
for variation in the extent to which comparative advantage arising from productivity
differences is revealed across agricultural
products.
The model environment includes I countries engaged in bilateral trade. Importers are
indexed by n and exporters by i. The agricultural sector is comprised of a continuum
of products indexed by j ∈ [0, 1]. To produce
quantity qi ( j ) of product j requires labor
(Ni ), land (Li ), and intermediate inputs Qi
combined according to the function:
(1)
αi
β
i
qi ( j ) = zi ( j ) Ni i (ai ( j )Li )1−βi Q1−α
i
where zi ( j ) represents product j-specific
technological productivity. Technological
productivity is modeled as an independently
distributed Frechet random variable with
mean parameter Ti and dispersion parameter
θ as in Eaton and Kortum (2002). Exporters
with high values of Ti have a greater probability of a high realization of zi ( j ) for any
given agricultural product.
Building on the Eaton and Kortum production function, we add a second, systematic
source of productivity. Product-specific
land productivity, ai ( j ), reflects the overall
suitability of exporter i’s environment for
product j. We assume ai ( j ) follows a parametric density that is a deterministic function
of exporter i’s agro-ecological characteristics
and product j’s production requirements.
For example, countries with volcanic soil
and tropical climate will tend to have higher
values of ai ( j ) for pineapple.
Markets are perfectly competitive. Therefore, the price offered by exporter i for
product j in market n is equal to the unit
cost of producing in country i and marketing in country n. Exporters face additional
costs, τni ( j ) > 1 to sell product j in import
market n. Trade costs are assumed to
take the iceberg form, with τnn ( j ) = 1 and
τni ( j ) ≥ τnl ( j )τli ( j ). We assume τni ( j )
follows a parametric density across products that is a deterministic function of
product-specific policies and other marketing
requirements. Productivity and trade cost
distributions are assumed to be independent
of each other.
Trade occurs as buyers in each import market seek out the lowest price offer for each
product. Exporters specialize endogenously
1328
October 2015
Amer. J. Agr. Econ.
in the set of products for which they have
the highest probability of offering the lowest
price in markets around the world. Heerman
(2013) shows that exporter i’s total share
of market n agricultural expenditure is the
unconditional probability it offers the lowest
price for an agricultural product:
(2)
πni = πni ( j )dFãn (ã)dFτn (τn )
≡
Ti (ãi ( j )ci τni ( j ))−θ
l
−θ
l=1 Tl (ãl ( j )cl τnl ( j ))
× dFãn (ã)dFτn (τn )
where ãi ( j ) = ai ( j )−αi (1−βi ) , ci is the cost
of an input bundle, and dFãn (ã)dFτn (τ)
is the joint density of ã = [ã1 , . . . , ãI ] and
τn = [τn1 , . . . , τnI ] over all agricultural products consumed in import market n. Like the
gravity equation at the heart of the Eaton
and Kortum model, equation (2) relates
market share to exporter competitiveness
and bilateral trade costs. It is a weighted
sum of the product-specific probability of
having comparative advantage, πni ( j ), where
the weights reflect the importance of each
product in market n consumption.
In a random heterogeneity model, the set
of products in which an exporter has comparative advantage is solely determined by
realizations of the independently distributed
random variable zi ( j ), so πni ( j ) = πni (k) =
πni . In contrast, in our systematic heterogeneity model, the set of products in which an
exporter has comparative advantage is nonrandomly influenced by the characteristics of
its agricultural endowment through ai ( j ) and
the degree to which prices for these products are obscured by τni ( j ). This difference
fundamentally alters the model’s predictions
for how bilateral trade patterns respond to
changes in trade costs. To see this, consider
the elasticity of πni with respect to competitor l’s trade costs. This “trade elasticity” can
be written as follows:
(3)
bilateral trade patterns to changes in trade
costs. The relative magnitude of the elasticity
reflects the degree to which the number of
products exported to market n by exporter i
changes when τnl falls, everything else equal.
The model essentially picks up the likelihood exporter i has the second lowest price,
given the domestic or foreign competitor we
observe with the lowest price. In a random
heterogeneity model, this likelihood is fully
described by absolute advantage adjusted for
average trade costs. In our model, it depends
on the nature of comparative advantage
within the sector and the distribution of trade
costs.
First consider the elasticity with respect to
competitor country l = i’s trade costs. This
elasticity is increasing in the covariance of
product-specific comparative advantage,
cov(πni ( j ), πnl ( j )), which is driven by covariance in ai ( j ) and τni ( j ). This means that
country i’s market share is more likely to
contract in response to a fall in competitor l’s
trade costs if both countries have high land
productivity in the same products and low
costs to deliver the same products to market
n. Changes in trade costs thus have a greater
impact among countries whose agricultural
production environments are more likely to
produce agricultural products that are close
substitutes for each other.
When we consider the elasticity with
respect to exporter i’s own trade costs, since
πni is well below 0.5 for all country pairs,6
own trade elasticity is increasing in market
share. It is also decreasing in the variance of
the probability exporter i offers the lowest
price over agricultural products. Countries
with high var(πni ( j )) tend to be globally
competitive in a few agricultural products
but generally have low-productivity agricultural sectors. This reflects the fact that there
are relatively fewer products in which these
countries are likely to have the second-lowest
price.
In the random heterogeneity model cov
(πni ( j ), πnl ( j )) = var(πni ( j )) = 0. Elasticity is
⎧
⎪ θ
∂πni τnl ⎨ π (cov(πni ( j ), πnl ( j )) + πni × πnl )
ni
=
∂τni πni ⎪
⎩−θ(1 − π )π − var(π ( j ))
nl
ni
ni
These elasticities are the very elements of
the model that characterize the response of
if l = i
otherwise.
6
The maximum value is the 27% U.S. market share in Costa
Rica.
Heerman, Arita, and Gopinath
Asia-Pacific Integration with China vs the United States
thus driven entirely by absolute advantage, average trade costs, and the dispersion
parameter of the technological productivity
distribution (θ). The magnitude of the elasticity in a random heterogeneity model is thus
almost entirely determined by θ, the value of
which is a subject of its own literature (see
Simonovska and Waugh 2014).7 Bilateral
market share is extremely small for most
exporters in most import markets—nearly
95% of all bilateral market shares are less
than 1% and the median value is 0.02%.
Therefore, a random heterogeneity model
generates very little variation in both own
country and competitor country trade elasticities across exporters and import markets.
Specification and Results
We estimate parameters for the productivity
and trade cost distributions by specifying
equation (2) as a random coefficients logit
model. We begin as in Eaton and Kortum
(2002) by defining Si = ln(Ti ) − θln(ci ). This
is exporter i’s average agricultural sector
technological productivity adjusted for unit
production costs.
Land Productivity Distribution
We specify ai ( j ) as a parametric function of
exporter agro-ecological characteristics and
product agro-ecological requirements:
(4)
ln(ai ( j )) = X i δ( j ) = X i δ + X i (E( j ))
+ X i (νE ( j ) E )
where Xi is a 1 × k vector of variables
describing country i’s agro-ecological characteristics; δ is a k × 1 vector of coefficients;
E( j ) is a 1 × m vector of product j-specific
agro-ecological production requirements that
can be observed and quantified; is an m ×
k matrix of coefficients that describe how
the relationship between elements of Xi and
land productivity varies across products with
E(j); and νE ( j ) is a 1 × k vector that captures
the effect of unobservable product j-specific
requirements with scaling matrix E .
Variables in Xi play two roles. First,
they define a relationship between the
characteristics of exporter i’s natural
endowment and its absolute advantage in
7
Arkolakis, Costinot, and Rodríguez-Clare (2012) explain that
the elasticity of substitution plays the role of θ in other models.
1329
agriculture through the term Xi δ. More
importantly in the context of this model,
Xi also describes each exporter along the
dimensions that systematically influence the
set of products in which it has comparative
advantage within the sector.
We specify three types of characteristics in
Xi : climate, elevation, and agricultural land
availability: Xi = [ali elvi trpi tmpi bori ]
where ali is the log of arable land per capita,
which proxies for agricultural land abundance, elvi is the share of rural land between
800 and 3000 meters above sea level, and
the remaining elements are the shares of
total land area in tropical, temperate, and
boreal climate zones. We argue that the more
exporters differ along these dimensions, the
more likely they are to specialize in distinct
sets of agricultural products, and not compete
head-to-head in global markets, everything
else equal.
The vector j = [E( j ) νE ( j )] defines
products in terms of their suitability for
production under the conditions defined by
Xi . Just as we define individual exporters’
production environments, we endeavor to
define individual products in terms of their
land and climate requirements. While we
cannot directly observe the land and climate
production requirements for each agricultural
product, we do observe the conditions under
which different agricultural products are
produced and exported around the world.
Therefore, we use this information to
construct values for elements of the “observable” product requirements matrix E(j)
as export-weighted averages of country
characteristics. This is valid under two
assumptions: First, E(j) is distributed
across products following the empirical
distribution of requirements for agricultural products defined at the “item” level
by the Food and Agriculture Organization of the United Nations (FAO). Second,
exporting is positively correlated with high
natural productivity. We calculate observable requirements for each of the J = 134
items for which the FAO publishes both production and trade data. We define: E( j ) =
[alw( j ) elv( j ) trp( j ) tmp( j ) bor( j )]
where alw(j) is the log of the product j
export-weighted average arable land per agricultural worker, elv(j) is the export-weighted
average share of land at high elevation, and
the remaining variables similarly describe
climate requirements. Notice that we use
land per agricultural worker to define the
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October 2015
Amer. J. Agr. Econ.
land intensity of product j rather than agricultural land per capita, as we used in Xi .
While the elements of Xi are intended to
capture the structural factors that influence
exporter i’s potential comparative advantage,
elements of E(j) are intended to capture the
ideal conditions under which product j is produced. Therefore, products are represented
by their observed production conditions, but
countries are represented in terms of their
potential production conditions.
where dF̂En (E)dF̂νn (ν) is the empirical
density of products imported by market n
defined jointly by their land and climate
characteristics, unobserved agro-ecological
requirements, and trade costs. We estimate equation (6) using a simulated method
of moments approach similar to that in
Berry, Levinsohn, and Pakes (1995), which
is detailed in Nevo (2000) and Train (2009).
To evaluate the integral, we use the “smooth
simulator” suggested by Nevo (2000):
1 exp{S̃i + θαi (1 − βi )Xi δ( j ) − θtni β( j )}
l
ns
l=1 exp{S̃l + θαl (1 − βl )Xl δ( j ) − θtnl β( j )}
j=1
ns
(7)
πni =
Trade Cost Distribution
We specify product-j trade costs as:
(5)
ln(τni ( j )) = tni β( j ) = tni β
+ exi + tni (νtn ( j ) t ) + ξni
where tni is a 1 × m vector describing the
relationship between exporter i and import
market n; β is an m × 1 vector of parameters;
exi is an exporter-specific trade cost captured
by a fixed effect; νtn ( j ) is a 1 × m vector that
captures the effect of unobservable product
j-specific trade costs with scaling matrix t ;
and ξni captures unobservable or unquantifiable bilateral trade costs that are common
across products and orthogonal to the regressors. We define: tni = [bni lni rtani dni ]
where bni , lni , and rtani equal one if the two
countries share a common border or language or are members of a common regional
free trade agreement. The 1 × 6 vector dni
assigns each country pair to one of six distance categories as defined in Eaton and
Kortum (see table 1).
Random Coefficients Logit Model:
Computation and Data
Using our definitions of ai ( j ) and τni ( j ) in
equation (2), we obtain a random coefficients
logit model of agricultural market share:
(6)
πni =
where S̃i = Si + θαi (1 − βi )Xi δ is a country
fixed effect. The ns = 100 products used
to evaluate equation (7) for each importer
and its trading partners are drawn from
dF̂En (E)dF̂νn (ν). We construct this distribution in two steps. First, we use FAO item
level import data to estimate dF̂En (E), the
empirical distribution of E(j) across products
imported by each market. To do this, we
compile a list of 1000 items imported by each
market, defined by the vector E(j).
Unique values of E(j) are represented in
the list in proportion to their associated FAO
item’s share in total imports. That is, if 15%
of importer n’s total agricultural imports are
of the FAO item “wheat,” then E(wheat)
makes up 150 entries on dF̂En (E). We draw
ns = 100 values of E(j) at random from
each country’s distribution. The distribution
dF̂En (E)dF̂νn (ν) is completed by associating
each product with νn ( j ) = [νE( j ) νtn ( j )]
drawn from a standard multivariate normal distribution, effectively generating a
“data set” of 100 unique products imported
by each market and their unobservable
product-specific trade costs. Finally, we use
the minimum distance procedure suggested
ˆ
by Nevo (2000) to obtain Ŝi and δ from S̃i .
We finish by calibrating the values for ξ̂ni as
exp{Si + θαi (1 − βi )Xi δ( j ) − θtni β( j )}
dF̂En (E)dF̂vn (ν)
l
l=1 exp{Sl + θαl (1 − βl )Xl δ( j ) − θtnl β( j )}
Heerman, Arita, and Gopinath
Asia-Pacific Integration with China vs the United States
Table 1. Definition of Distance Variables
Population-weighted
Average Distance between
Largest Cities, miles
Variable
Distance
Distance
Distance
Distance
Distance
Distance
1
2
3
4
5
6
[0, 375)
[375, 750)
[750, 1500)
[1500, 3000)
[3000, 6000)
[6000, maximum]
Note: Distance defined as in Eaton and Kortum (2002).
the value that sets equation (6) equal to the
observed market share.
In our data set, bilateral market shares
are calculated using 2006 production and
trade data on the 134 agricultural items
for which data on both bilateral trade and
the gross value of production in U.S. dollars are available (FAO 2013). Data on
arable land per capita and land per agricultural worker come from World Bank
(2012). Climate information comes from the
Global Trade Analysis Project Land Use
Database (Monfreda, Ramankutty, and Hertel 2009). Elevation data come from Center
for International Earth Science Information
Network (2010). Elements of tni are obtained
from the Centre d’Études Prospectives et
d’Informations Internationales gravity data
set (Head, Mayer, and Ries 2010).
Results: Land Productivity Distribution
Table 2 contains estimates for the land productivity distribution parameters δ, , and
E . The total effect of each exporter characteristic on the probability of comparative
advantage in a given product is the sum of
the mean effect in column 2 and the productspecific effects in the columns that follow.
Thus, for each characteristic in Xi , we obtain
a distribution of effects around the mean.
Coefficients on all climate variables are
normalized to sum to zero. As such, coefficients on exporter climate characteristics
are interpreted with respect to the average
climate; and the effects of product-specific
climate requirements are interpreted with
respect to the average production requirement.8 The mean effect, δ̂trp = 1.50, implies
that market share is increasing in the extent
8
The average climate is 27% tropical, 58% temperate, and
15% boreal. The average traded product is 26% tropical, 61%
temperate, and 13% boreal.
1331
to which a country has a larger than average
share of land in a tropical climate zone. The
negative and statistically significant value of
λ̂trp,alw = −0.41 indicates that this advantage
is decreasing for land-intensive products
while λ̂trp,trp = 2.50 implies it is increasing for
tropical products. In contrast, δ̂tmp = −1.63
implies that market share is decreasing in the
extent to which a country has a larger-thanaverage share of land in a temperate climate
zone. However, λ̂alw = 0.29 implies that this
disadvantage is diminished significantly for
land-intensive products. Economically and
statistically significant values of σtrp , σtmp ,
and σbor suggest that variation in the value
of each climate zone varies across products
for additional reasons beyond the agroecological requirements in our specification.
Figures 1 and 2 are frequency plots of the
total effects of tmpi and trpi across all 5,800
traded products in our constructed data set.
These figures illustrate that having a larger
than average share of tropical or temperate
land increases the probability of comparative
advantage in some products and decreases it
for others.
Estimates for S̃i are listed in table 3. These
values are normalized to sum to zero and
are interpreted as average agricultural sector
productivity relative to the average country in the average product. Recall that S̃i is
increasing in average technological and land
productivity but decreasing in costs of production ci . Therefore, a country with high
average productivity may nevertheless have
a small S̃i if it has, for example, very high
wages or land rental rates.
We can use the parameters in tables 2
and 3 to calculate product-specific expected
competitiveness, E[ζi ( j )] = Ŝi + Xi δ̂( j ), for
each product and each exporter. Figures 3
and 4 depict the expected competitiveness
distributions of the United States, Canada,
China, and New Zealand. The lines plot the
percent deviation of E[ζi ( j )] from the cross
country average for each of the j = [1 : 128]
items in the FAO data that are traded among
the countries in our data set. Products are
sorted in terms of decreasing U.S. expected
competitiveness. These distributions suggest
that the U.S. and Canada are “natural competitors,” as are China and New Zealand.
The similarity between China and New
Zealand is perhaps surprising. However,
like New Zealand, China is a net exporter of
fresh fruits such as apples and, meaningfully,
1332
October 2015
Amer. J. Agr. Econ.
Table 2. Land Productivity Distribution Parameter Estimates
Exporter
Characteristics
Mean
Effect (δ)
ln Arable land
per Ag worker
High elevation
Tropical climate
share
Temp. climate
share
Boreal climate
share
−0.12∗∗
Unobserved
Reqs ( E )
0.04
Agro-Ecological Requirements ()
elv( j )
−7.58∗∗∗
2.50∗∗∗
1.50∗∗∗
−0.01
0.90∗∗∗
0.00
6.19∗∗∗
−1.63∗∗∗
−0.23∗∗∗
0.00
0.12
−0.67∗∗∗
−6.19∗∗∗
alw( j )
trp( j )
tmp( j )
bor( j )
−0.02
1.32∗∗∗
1.46∗∗∗
−2.78
2.41∗∗∗
−0.41∗∗∗
9.31∗∗∗
2.50∗∗∗
−3.51∗∗∗
0.00
−5.80
−2.51
0.29∗∗∗
−1.54∗∗∗
0.13
1.41
0.12
−0.96∗∗∗
−0.13
1.09
Note: Parameters are Simulated Method of Moments (SMM) estimates based on 3481 observations with ns = 100 product draws. Dependent variable
is exporter i’s total share of market n agricultural expenditure. Coefficients on all climate variables are normalized to sum to zero. Values in this
table are inclusive of the term θαi (1 − βi ). Single asterisk (∗ ) denotes significance at the 10% level, double asterisk (∗∗ ) denotes significance at the
5% level, and triple asterisk (∗∗∗ ) denotes significance at the 1% level.
Figure 1. Frequency plot temperate land share effect
kiwi fruit. Figure 3 suggests that in the
absence of trade costs, New Zealand and
China are likely to compete head to head in
many of the same products.
We examine the United States and China
more closely in figure 4, where we have resorted the products along the x-axis in terms
of decreasing land intensity. First, notice that
the distribution of U.S. competitiveness is
virtually a reflection of China’s distribution.
This suggests that in the absence of trade
costs, the United States and China would
specialize in different sets of products. Second, the products for which China’s natural
Heerman, Arita, and Gopinath
Asia-Pacific Integration with China vs the United States
1333
Figure 2. Frequency plot of tropical land share effect
competitiveness is greatest tend to be the
least land-intensive.
Both the United States and China have
predominantly temperate climates. However,
they differ starkly in terms of arable land per
capita. In fact, the size of land holdings is a
critical challenge to China’s agricultural sector and an asset to the United States. China’s
agricultural economy is dominated by 200
million small family farms that operate on
less than 0.5 hectares based on 2006 data
(Gao, Huang, and Rozelle 2012). As a result
of limited land resources and incomplete
reform of land tenure practices, aggregation
of production in China is costly, and the
resulting atomistic land structure leads to
higher cost and land inefficiencies (Lohmar
et al. 2009). In contrast, the average U.S.
farm size is 176 hectares, and production is
heavily concentrated in large farms (NASS
2012).
Observed U.S. and Chinese agricultural
trade patterns are consistent with our estimated distribution of competitiveness. The
United States tends to export land-intensive
commodities such as grains, oilseeds, and
livestock, while China exports labor intensive
horticultural products.9 In third-country markets, the United States and China compete
in very few products. Both countries export
fresh fruit and vegetables such as apples, carrots, and turnips but generally supply these
products to different markets.
Results: Trade Cost Distribution
Table 4 contains estimates for the trade cost
distribution parameters β and t . Negative
mean coefficient values imply higher trade
costs, but lower expected market share. Elements of t capture the heterogeneity in the
effect of each element of tni across products
and can thus be interpreted like a standard
error around the mean effect.
A positive mean effect implies that sharing a common language and common RTA
membership increase market share on average, while negative coefficients on increasing
9
China does obtain a significant share of its consumption of
the land-intensive grains rice, wheat, and corn domestically, but
producers benefit from government support policies designed to
maintain self-sufficiency [?], a factor that is not directly addressed
by our model.
1334
October 2015
Amer. J. Agr. Econ.
Table 3. Country Fixed Effects Estimates
Country
Argentina
Austria
Bulgaria
Chile
Colombia
Cote d’Ivoire
Denmark
Estonia
Finland
Germany
Greece
Hungary
India
Ireland
Italy
Kazakhstan
South Korea
Malaysia
Morocco
New Zealand
Peru
Portugal
Slovakia
South Africa
Sri Lanka
Switzerland
Tunisia
United Kingdom
Vietnam
ˆ
S̃i
4.69∗∗∗
−2.93∗∗∗
−2.13∗∗∗
−4.58∗∗∗
0.84∗
4.00∗∗∗
−4.86∗∗∗
1.09∗∗
3.73∗∗∗
−3.58∗∗∗
−0.65
−0.85∗
5.19∗∗∗
1.37∗∗
−2.63∗∗∗
2.78∗∗∗
1.37∗∗∗
1.82∗∗∗
0.50∗
4.85∗∗∗
−2.97∗∗∗
−2.54∗∗∗
2.64∗∗∗
−1.24∗∗
4.34∗∗∗
−1.05∗∗
−0.32
−4.79∗∗∗
4.23∗∗∗
êxi
Country
ˆ
S̃i
êxi
0.29∗
Australia
Brazil
Canada
China
Costa Rica
Czech Republic
Ecuador
Ethiopia
France
Ghana
Honduras
Iceland
Indonesia
Israel
Japan
Kenya
Lithuania
Mexico
Netherlands
Norway
Poland
Russian Federation
Slovenia
Spain
Sweden
Thailand
Turkey
Ukraine
USA
0.36
3.58∗∗∗
−5.85∗∗∗
3.70∗∗∗
−8.36∗∗∗
−2.68∗∗∗
−0.42
1.94∗∗∗
−3.41∗∗∗
6.12∗∗∗
1.02∗∗
−0.37
0.96∗
−5.30∗∗∗
−1.59∗∗∗
4.12∗∗∗
0.00
−2.55∗∗∗
0.37
5.43∗∗∗
−1.46∗∗∗
0.80∗
−1.38∗∗∗
−5.38∗∗∗
−0.05
2.64∗∗∗
1.46∗∗∗
0.86∗
−2.87∗∗∗
1.09∗∗∗
0.80∗∗∗
3.10∗∗∗
0.31∗∗
1.75∗∗∗
−0.07
0.49∗∗∗
−1.11∗∗∗
1.79∗∗∗
−1.83∗∗∗
−1.08∗∗∗
−2.03∗∗∗
1.27∗∗∗
1.18∗∗∗
−0.24∗
−1.24∗∗∗
−1.49∗∗∗
1.25∗∗∗
0.64∗∗∗
−3.36∗∗∗
0.21∗∗∗
−0.26∗
−2.03∗∗∗
2.01∗∗∗
−0.35∗∗∗
0.31∗
0.08
−0.21
2.55∗∗∗
−0.04
0.30∗∗
2.25∗∗∗
0.35∗∗
−0.80∗∗∗
1.24∗∗∗
−2.26∗∗∗
−1.85∗∗∗
1.76∗∗∗
0.13
0.06
0.05
−1.31∗∗∗
1.45∗∗∗
−1.87∗∗∗
−1.03∗∗∗
0.79∗∗∗
−0.84∗∗∗
−0.69∗∗∗
1.24∗∗∗
−0.29∗∗∗
−2.41∗∗∗
0.19
−0.39∗∗
−0.69∗∗∗
−0.62∗∗∗
1.80∗∗∗
−0.32∗∗
Note: Parameters are SMM estimates based on 3,481 observations with ns = 100 product draws. Dependent variable is exporter i’s total share of
ˆ
market n agricultural expenditure. All values are normalized to sum to zero. S̃i is interpreted as exporter i’s average agricultural sector productivity
relative to the average country in the average product. exi captures exporter i’s country-specific cost of exporting relative to the average country.
Positive numbers imply the exporter faces lower costs of exporting than the average country. Single asterisk (∗ ) denotes significance at the 10%
level, double asterisk (∗∗ ) denotes significance at the 5% level, and triple asterisk (∗∗∗ ) denotes significance at the 1% level.
distance tends to decrease it. It may appear
surprising that the mean effect of sharing a
border is negative and that of being in the
nearest distance category is positive. However, the larger magnitude values of σ̂b = 2.89
and σ̂D1 = 1.98 relative to the corresponding
mean effects imply that these factors increase
the probability of comparative advantage
for some products and decrease it for others.
This is illustrated in figures 5 and 6, which
are frequency plots of the effects of these
two variables on market share across all
5,800 agricultural products in our constructed
data set.
The wide variation in the effects of these
variables, both of which generally capture
proximity, is sensible in agriculture. For
example, one can imagine increasing benefits of proximity with the degree of product
perishability. On the other hand, there are
many reasons why proximity may be a detriment for some products. Countries that are
geographically near are likely to have similar agro-ecological characteristics and thus
specialize in the same products. As such,
the benefit of proximity for an exporter may
be diminished for some products if they
are more likely to compete head-to-head
with domestic producers in these products. An alternative explanation is that
this result is capturing politically sensitive
import-competing products that are shielded
from liberalization by tariff and nontariff
barriers.
Values of êxi are reported in table 3. The
values are normalized to sum to zero, so positive (negative) values imply that exporter i is
a higher (lower)-than-average-cost exporter.
Our results suggest that the United States
and Canada are the lowest-cost exporters.
Heerman, Arita, and Gopinath
Asia-Pacific Integration with China vs the United States
Figure 3. Distribution of competitiveness: close competitors
Figure 4. Distribution of competitiveness across agricultural products
1335
1336
October 2015
Amer. J. Agr. Econ.
Table 4. Trade Cost Distribution Parameters
Country Pair
Characteristics
Common Border
Common Language
Common RTA
Distance 1
Distance 2
Distance 3
Distance 4
Distance 5
Distance 6
Mean Effect (β)
Unobserved
Heterogeneity ( t )
−1.97∗∗∗
1.26∗∗∗
0.14
0.17
−4.10∗∗∗
−3.76∗∗∗
−5.89∗∗∗
−7.64∗∗∗
−8.88∗∗∗
2.89∗∗∗
−1.03∗∗∗
0.82∗∗
1.98∗∗∗
−2.45∗∗∗
0.41
−1.12∗∗∗
0.17
0.45
Note: Parameters are SMM estimates based on 3,481 observations with ns = 100 product draws. Dependent variable is exporter i’s total share of
market n agricultural expenditure. Values in this table are inclusive of the term θ. Single asterisk (∗ ) denotes significance at the 10% level, double
asterisk (∗∗ ) denotes significance at the 5% level, and triple asterisk (∗∗∗ ) denotes significance at the 1% level.
Figure 5. Distribution of shared border effect
The parameter estimates presented above
describe a distribution of comparative advantage across the continuum of agricultural
products for each exporter in each import
market. Land productivity distributions
uncover which countries are likely to systematically specialize in the same products
and thus be close natural competitors. The
trade cost distribution describes the extent to
which each exporter’s natural advantage is
obscured in each import market.
Figure 3 suggests that, in the absence of
trade costs, Chinese and New Zealand producers are likely to compete head-to-head
in many of the same products. However,
transportation costs, tariffs, and other policy
and marketing costs may be as important
to comparative advantage in a given import
market as technological and natural productivity differences. Regardless of whether they
arise from government policy or a country’s
geographical location relative to its trading
Heerman, Arita, and Gopinath
Asia-Pacific Integration with China vs the United States
1337
Figure 6. Distribution of D1 effect
Note: D1 refers to a distance interval less than 375 miles.
partners, these costs diminish the degree to
which natural competitiveness drives trade
patterns. Therefore, even countries that are
close natural competitors may not compete
head-to-head in the same products in a given
import market.
Figure 7 plots the product-specific probability of comparative advantage πIDN,i ( j ),
i = CHL, CHN for each of the 100 products imported by Indonesia in our data set.
Products are sorted along the x-axis in order
of descending probability of Chinese comparative advantage, πIDN,CHN ( j ). Where
the two distributions are close together,
the two countries compete head-to-head to
export the associated products. While figure 3
(8)
dπni,L = θ
l∈L
that trade costs make them less intense
competitors in the Indonesian market.
Cross country substitution patterns and
regional trade liberalization
Regional trade agreements lower trade costs
among a group of countries more-or-less
simultaneously. We explore how the forces
of comparative advantage within the agricultural sector affect the response of bilateral
trade patterns to a symmetric 1% change in
average trade costs among a group of AsiaPacific countries using the total differential of
πni with respect to trade costs of competitors
L ∈ I:
dτnl
dτnl cov(πni ( j ), πnl ( j ))
+
×πnl
τnl
τnl
suggests that China and New Zealand are
close natural competitors, figure 7 reveals
l∈L
dτni
− ((1 − πni )πni − var(πni ( j )))
.
τni
We refer to equation (8) as exporter i’s
regional trade liberalization elasticity in
1338
October 2015
Amer. J. Agr. Econ.
Figure 7. China and New Zealand: comparative advantage in Indonesian market
Note: Distribution of πIDN,i ( j ) across products.
market n. This elasticity is a measure of the
change in bilateral market share holding
all prices in the economy constant. We will
interpret it as a measure of the sensitivity of
exporter i’s market share in import market n
to regional trade liberalization.
Equation (8) has two components. The
term in the first parentheses captures the
effect of falling competitor trade costs.
Regional trade liberalization elasticity is
decreasing in the extent to which members of
L: (1) have a high probability of comparative
advantage in the same products as country
i; and (2) have a large existing share of the
country n market. The second term captures
the effect of the decline in country i’s own
trade costs. Regional trade liberalization
elasticity is generally increasing in market
share and decreasing in var(πni ( j )).
Recall that in a model without systematic differentiation, cov(πni ( j ), πnl ( j )) =
var(πni ( j )) = 0, and the magnitude of the
elasticity is entirely driven by market share,
πni . Remember that πni is equivalent to the
unconditional probability country i offers
the lowest price in market n. Market share is
therefore a measure of exporter i’s absolute
advantage in delivering agricultural products
to market n. It is thus clear that in a random
heterogeneity model, sensitivity to a change
in trade costs only depends on the absolute
advantage of the participants. If liberalization
includes the most competitive agricultural
producers, there will be fewer additional
products for which lowering trade costs gives
exporter i the lowest price. This effect is
moderated in a systematic heterogeneity
model to the extent that the competitors in L
specialize in exporting, for example, tropical
fruits to market n, while exporter i specializes
in temperate grains. Conversely, the effect is
intensified to the extent that competitors in L
specialize in the same products as exporter i.
We explore how lower trade costs in
the Asia-Pacific region affect the structure of comparative advantage in import
markets. Since the countries in this region
include both highly competitive agricultural
exporters and net importers, and since they
are located in geographically and culturally
nl
ni
0.96
0.73
0.94
1.00
0.90
0.99
0.91
0.55
0.95
0.94
0.88
0.92
0.72
0.79
0.83
0.87
0.75
0.95
0.98
0.96
0.97
0.95
0.98
0.77
0.83
0.90
0.91
0.87
0.88
0.87
1.00
0.88
1.00
0.96
0.85
0.97
0.89
0.99
0.90
1.00
0.92
0.91
0.98
1.02
1.01
1.02
0.99
0.98
0.89
1.02
1.02
0.91
0.86
0.91
1.00
0.99
0.98
1.02
0.98
0.98
0.91
0.97
0.97
0.98
0.93
1.05
0.89
1.04
1.06
1.12
1.06
1.12
0.62
1.06
1.04
1.13
1.11
0.55
0.98
0.85
0.83
0.64
0.43
0.83
0.59
1.00
0.86
1.05
0.56
1.14
0.88
0.59
0.50
0.89
0.92
0.71
0.62
0.74
0.78
0.94
0.76
0.61
0.74
0.71
0.96
0.79
0.85
0.84
0.88
0.91
0.98
0.94
0.90
0.96
1.00
0.99
0.87
0.90
0.93
1.00
0.88
0.84
1.02
0.85
0.95
1.00
0.93
0.83
0.99
0.97
0.91
0.91
0.89
1.01
0.86
1.02
0.87
0.93
1.01
0.99
0.85
0.89
0.93
1.10
0.53
0.99
0.99
0.89
0.93
0.87
0.95
0.95
0.94
0.96
0.89
0.44
0.83
0.88
0.87
0.90
0.98
0.92
0.97
0.96
0.73
0.97
0.99
0.97
0.83
Vietnam
Thailand
Peru
New Zealand
Mexico
Import Markets
Malaysia
Japan
Indonesia
China
Chile
Canada
Australia
1339
Note: For exporters, values greater than one imply the random heterogeneity model understates regional liberalization elasticity. For importers, it implies the random heterogeneity model overstates the elasticity.
11
Australia
Canada
Chile
China
Indonesia
Japan
Malaysia
Mexico
New Zealand
Peru
Thailand
Vietnam
USA
We set θ = 4.12 as in Simonovska and Waugh (2014).
L = Australia, Canada, Chile, China, Indonesia, Japan,
Malaysia, Mexico, New Zealand, Peru, Thailand, and Vietnam.
12
In
a
random
heterogeneity model,
dπni,L =
dτ
dτnl
− ((1 − πni )πni ) τ ni .
θ
l∈L πni × πnl τ
10
Exporters
distinct regions straddling the Pacific Ocean,
both absolute and comparative advantage
effects have an important influence on the
response of bilateral trade and production
patterns to trade liberalization.
We calculate regional trade liberalization elasticities10 with respect to the United
States and a group of twelve countries listed
in table 5.11 This hypothetical liberalization
is not intended to represent any free trade
negotiations in progress; as such, our results
should not be interpreted as predictions of
how bilateral market share would respond
to Asia-Pacific liberalization. Instead, they
should be interpreted as a description of
how the forces of absolute and comparative
advantage contribute to determine trade
patterns in the region.
First, to demonstrate our parameterized
model’s ability to capture variation in trade
elasticities driven by within-sector comparative advantage, we compare the elasticities
produced by our systematic heterogeneity
model to those implied by a random heterogeneity model.12 Table 5 is a matrix of ratios
of each country’s systematic heterogeneity
trade liberalization elasticity to the random
heterogeneity elasticity in each import market. For exporters, values greater than one
imply that the random heterogeneity model
understates the sensitivity of market share
to regional liberalization. For example, the
value 0.73 for Canada in the U.S. market
implies that the random heterogeneity model
understates the sensitivity of Canada’s U.S.
market share to Asia-Pacific liberalization by
almost 25%. For domestic producers, values
greater than one imply that the random heterogeneity model overstates the sensitivity of
market share.
The results in table 5 imply that absolute
advantage does not sufficiently describe
market share sensitivity. The random heterogeneity model often overpredicts market
share elasticity in neighboring countries and
under-predicts it in more distant markets.
Perhaps partially because of this, the random
heterogeneity model systematically underpredicts Mexico’s market share elasticity outside
of North America. In contrast, we find that
USA
Asia-Pacific Integration with China vs the United States
Table 5. Systematic Heterogeneity Model Offers a Richer Pattern of Regional Trade Liberalization Elasticities
Ratio of regional trade elasticity under systematic heterogeneity model to elasticity under random heterogeneity model
Heerman, Arita, and Gopinath
9.65
3.29
0.48
3.93
0.36
0.03
0.73
0.16
1.00
2.30
8.35
–
3.83
3.16
0.21
0.06
0.13
0.05
1.96
2.24
0.69
1.00
–
2.18
Note: Values greater than one imply the exporter’s market share is more elastic under U.S.-led liberalization than the median exporter to the import market.
1.32
1.00
0.20
0.02
0.02
0.03
2.43
2.60
2.55
–
1.93
0.02
66.70
5.83
0.98
0.18
0.10
0.82
6.76
1.00
–
1.25
40.92
0.94
2.58
1.12
0.00
0.03
0.00
0.02
4.65
–
3.77
1.00
3.66
0.31
72.44
17.48
0.59
0.74
0.14
0.94
–
1.00
12.02
2.28
72.39
0.54
14.21
2.24
0.50
0.21
0.05
–
2.34
0.85
2.57
0.20
1.87
1.00
39.94
60.14
6.56
15.58
–
0.00
2.72
0.69
1.00
0.69
0.01
0.01
41.46
14.00
0.54
–
2.41
0.00
6.68
0.42
1.00
3.02
0.01
0.09
98.81
21.34
–
7.58
0.22
0.00
2.45
0.84
0.38
0.09
1.43
1.00
Australia
Vietnam
Thailand
Malaysia
Indonesia
Japan
Peru
Chile
Mexico
Canada
18.10
–
5.27
3.03
0.80
0.07
2.36
0.71
1.00
0.35
0.92
1.02
USA
Canada
Mexico
Chile
Peru
Japan
Indonesia
Malaysia
Thailand
Vietnam
Australia
New Zealand
dπni,USA /medianl (dπnl,USA )
Exporters
13
Import Markets
it consistently over-predicts Japanese market share elasticity. Equation (8) suggests
this may be explained by a high value of
var(πn,Japan ( j )). The random heterogeneity model also understates the sensitivity of
domestic market share for every country: the
random heterogeneity model suggests that
the Canadian domestic market share is only
half as elastic as the systematic heterogeneity
model implies.
Next we compare two different hypothetical Asia-Pacific liberalizations: one that
includes the United States but excludes
China and another that includes China
but excludes the United States. China and
the United States are by far the largest
economies in the Asia-Pacific region and are
thus expected to dominate any trade blocs
in which they participate, but their impacts
on agricultural trade are likely to contrast
sharply in both nature and magnitude. First,
the United States is a highly competitive
global exporter with a median market share
of about 2% in Asia-Pacific markets, whereas
China is a large net importer with a median
market share of about 0.2% in the region.
Second, as we showed in figure 4, differences
in the characteristics of Chinese and U.S.
natural resource endowments make them
unlikely head-to-head competitors in most
import markets as trade costs fall. We denote
regional trade liberalization elasticities when
the United States leads the liberalization
dπni,USA , and we denote elasticities when
China leads the liberalization, dπni,CHN .
Table 6 is a matrix of each exporter’s market share elasticity relative to the median
elasticity in each import market under U.S.led liberalization.13 The highlighted portions
of the table divide the Asia-Pacific countries
into geographically distinct regions. Values
greater than one imply that the exporter’s
market share is more elastic under U.S.-led
liberalization than the median country in
the import market. For example, U.S. market share is more than 14 times as sensitive
to Asia-Pacific liberalization as the median
country in the Japanese market. That is,
holding prices constant, we would expect a
14 times larger U.S. market share expansion
than the median country.
The results in table 6 suggest that trade
costs are very high in the agricultural sector.
This is reflected in regional trade elasticities
Amer. J. Agr. Econ.
New Zealand
October 2015
Table 6. The Participation Effect
Market share elasticity relative to the median under U.S.-led liberalization
1340
Heerman, Arita, and Gopinath
Asia-Pacific Integration with China vs the United States
which are below the median for almost every
exporter outside of its own sub-region. Thus,
holding prices equal, we would expect AsiaPacific liberalization to offer most countries
an opportunity to export a few new products
outside of their sub-region, but to see more
expansion within their region. The exceptions
to this rule are the most highly competitive
agricultural exporters: the United States,
Canada, Indonesia, and to a lesser extent
Australia, New Zealand, and Thailand.
Liberalization offers an opportunity for significant global market share expansion for
these exporters.
The large magnitude U.S. trade liberalization elasticities relative to the median
reported in table 6 suggest that U.S. market
shares in the Asia-Pacific region are very
elastic with respect to regional liberalization. Likewise, regional liberalization that
excludes the United States implies a deterioration of its comparative advantage in these
markets as competitors’ relative trade costs
decline. Table 7 presents the ratio of the U.S.
trade liberalization elasticity to the median
elasticity across all other countries under
China-led liberalization for each import
market.14 Larger magnitude elasticities in
table 7 imply a larger erosion in the United
States’ advantage. The results demonstrate
the consequences of exclusion from regional
liberalization for the United States. The ratio
is always negative since exclusion implies
higher relative trade costs and thus a shrinking market share. Where the magnitude of
this ratio is greater than one, it implies that
U.S. market share would decline more than
the median country’s market share would
increase, holding all prices fixed. This is the
case in seven of the twelve import markets
we consider.
Next we compare the implications of U.S.
vs. China-led liberalization for competitors. Table 8 contains the ratios of dπni,USA
to dπni,CHN in the Canadian, Chilean, and
Malaysian import markets for each exporter.
A ratio less than one implies that market
share is more elastic under China-led liberalization than U.S.-led liberalization. That
is, holding all prices constant, market share
would expand more under China-led liberalization than U.S.-led liberalization. In
Canada, Chile, and Japan, where the United
States has a larger market share, regional
14
dπnUSA,CHN /medianl=USA (dπnl,CHN )
1341
Table 7. The Lost Opportunity Effect
U.S. market share elasticity relative to the
median under China-led liberalization
Import Market
Australia
Canada
Chile
China
Indonesia
Japan
Malaysia
Mexico
New Zealand
Peru
Thailand
Vietnam
Relative Elasticity
−0.56
−2.53
−1.32
−0.63
−8.15
−3.71
−0.95
−4.26
−0.63
−3.98
−5.01
−0.17
Note: Values greater than one imply U.S. market share is more elastic
than the median exporter in the import market.
trade liberalization elasticities are smaller
under U.S.-led liberalization than China-led
liberalization, while domestic producers’
market shares are more elastic. The reverse
holds for the most part in the Malaysian
market, where China has a larger market
share.
The disproportionate relative effects on
close competitors are particularly interesting. In Japan, Australia and Canada have
proportionately much larger elasticity under
China-led liberalization relative to U.S.led liberalization. This implies that these
countries are more likely to compete headto-head with the United States than other
exporters in Japan. In the Canadian market,
Australia, Peru, Mexico, and New Zealand
have proportionately larger elasticity under
China-led liberalization. Canada and Mexico
are likewise disproportionately sensitive in
the Chilean market.
In Malaysia, the median country’s market
share is 1% more elastic under U.S.-led liberalization relative to China-led liberalization.
Indonesia, Chile, Japan, and New Zealand
are disproportionately more sensitive under
China-led liberalization, which suggests they
compete head-to-head with China more than
other countries in the Malaysian market. In
contrast, Thailand and Vietnam are disproportionately less sensitive under China-led
liberalization, which suggests that lower
trade costs in Malaysia would reveal U.S.
comparative advantage in a few more products currently exported from Thailand and
Vietnam than other exporters.
1342
October 2015
Amer. J. Agr. Econ.
Table 8. U.S.-led Liberalization vs. China-led Liberalization
Ratio of dπni,USA to dπni,CHN in selected import markets
Import Markets
Exporter
Australia
Canada
Chile
Indonesia
Japan
Malaysia
Mexico
New Zealand
Peru
Thailand
Vietnam
Canada
Chile
Malaysia
0.87
2.13
0.88
0.88
0.88
0.88
0.87
0.84
0.87
0.88
0.88
0.97
0.95
2.47
0.97
0.97
0.97
0.91
0.97
0.97
0.97
0.97
0.98
0.95
1.08
1.04
1.30
0.96
1.01
1.13
1.01
0.99
1.00
Japan
0.73
0.61
0.88
0.88
1.77
0.89
0.86
0.91
0.87
0.86
0.90
Note: Ratios less than one imply market share is more elastic under China-led liberalization.
Conclusion
The opportunity offered by regional trade
liberalization in agriculture depends on
the nature of the participants’ comparative advantage within the sector and the
distribution of trade barriers across products. Agricultural trade negotiators are well
aware of this, which is why commitments are
typically exchanged across products rather
than one-for-one in the same products. The
random heterogeneity model of Eaton and
Kortum (2002), as well as other models of
bilateral trade based on a log-linear gravity
relationship, only differentiate countries
based on sector-level absolute advantage and
only measure sector average trade costs. Our
systematic heterogeneity model recognizes
the diversity in comparative advantage within
agriculture, as well as the variation in the
degree to which it is revealed and obscured
by low and high trade costs. Unlike the
random heterogeneity model of Eaton and
Kortum, our approach generates elasticities
that are functions of exporters’ productspecific competitiveness, which may vary
across import markets due to differences in
trade costs. It thus reveals a complex set of
forces that determine the degree to which
bilateral trade flows respond to lower trade
barriers.
Our results confirm high agricultural trade
costs in the Asia-Pacific region, particularly
between countries in distant subregions.
We find that lower trade barriers provide a
greater force for expanded market share for
some countries more than others, but that
this force is not necessarily in proportion to
existing market share: Lowering trade barriers increasingly reveal productivity-driven
comparative advantage and thus intensifies
competition among countries whose land
and climate characteristics drive them to specialize in the same products proportionately
more than among countries that specialize in
different products.
We see this in the comparison of the
effects of Asia-Pacific regional trade liberalization led by the United States to
liberalization led by China. Our results
suggest that close competitors of the United
States, such as Canada and Australia, have
a relatively greater opportunity to increase
market share when the United States is
excluded from negotiations than countries
that are not close U.S. competitors. This
relative difference across competitors is
not captured by a random heterogeneity
model, nor is it accounted for in any of the
many other standard quantitative models of
bilateral trade based on a log-linear gravity
relationship.
Our results have important implications
for the U.S. regional trade liberalization
agenda in the Asia-Pacific. Our estimated
elasticities suggest that the United States has
a relatively large opportunity to expand agricultural exports in the Asia-Pacific region.
Conversely, standing back from regional
liberalization while competitors pursue free
trade pacts has a significant cost for U.S. agricultural exporters. While U.S. market access
may not change, not engaging in regional
liberalization while it proceeds among competitors is far from equivalent to maintaining
the status quo. A free trade agreement not
Heerman, Arita, and Gopinath
Asia-Pacific Integration with China vs the United States
only provides new access to its participants,
it also increases the relative cost of access for
nonparticipants. Unique among models of
bilateral trade flows, in the systematic heterogeneity model this “lost opportunity” effect
is increasing in the extent to which exporters
pursuing liberalization are countries like
Canada and Australia, which frequently compete head-to-head in the same products or in
close substitutes for U.S. agricultural exports
in many import markets.
While it is beyond the scope of this article, the systematic heterogeneity model of
trade flows can be derived theoretically in a
multisector quantitative general equilibrium
model as in Heerman (2013). We have shown
that the systematic heterogeneity model of
bilateral trade flows can provide valuable
information on the structure of comparative advantage in international markets on
its own. However, the parameter estimates
obtained from this model have additional
value as parameters of a general equilibrium model in which true comparative statics
exercises can be conducted.
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