Post on 02-Apr-2018
Market Access and Regional Specialization in a Ricardian World
A. Kerem CosarU. of Chicago Booth
Pablo D. FajgelbaumUCLA
October 2012
Abstract
When trade is costly within countries, international trade leads to concentration of eco-
nomic activity in locations with good access to foreign markets. Costly trade within countries
also makes it harder for remote locations to gain from international trade. We investigate the
role of these forces in shaping industry location, employment concentration and the gains from
international trade. We develop a model that features Ricardian comparative advantages be-
tween countries, coupled with di¤erences in proximity to international markets across locations
within a country. In the model, international trade creates a partition between a coastal and
an interior region that di¤er in population density and specialization patterns. We assess the
model prediction for industry location across U.S. counties. In tune with the theory, we �nd
that U.S. export-oriented industries are more likely to locate and to employ more workers closer
to international ports. We use the model to measure the importance of international trade
in concentrating economic activity, and of domestic trade costs in hampering the gains from
international trade.
1 Introduction
International trade is an important determinant of the geographic distribution of economic
activity within countries. In China, the export growth of the past few decades occurred jointly
with movements of rural workers and export oriented industries toward coastal regions (World
Bank, 2009). After the U.S.-Vietnamese trade agreement, employment expanded in Vietnamese
comparative-advantage industries located closer to major seaports (McCaig and Pavnick, 2012).
In Mexico, increased trade with the U.S. led to larger manufacturing employment near the U.S.
border (Hanson, 1996). It is also well known that the distribution of employment is skewed towards
coasts: half of the world population lives within 100 kilometers of coastlines or navigable rivers,
while 19 out of the 25 largest cities in the world are coastal.1 While the appeal of coastal sites
derives in part from their resources and amenities, a reason for their primacy is that they are well
suited for trading with other countries.
These examples showcase the impact of international trade on the regional distribution of eco-
nomic outcomes. At the same time, they highlight the relevance of costly trade within countries.
The larger the domestic trade costs, the stronger the incentives for export oriented industries to
concentrate in places with good access to international markets, and the harder for remote locations
to gain from trade. In this paper, we investigate the role of these forces in shaping the location
of industries, the concentration of employment, and the gains from international trade. For that,
we develop a theory of international trade with costly trade within countries, and we assess its key
empirical and quantitative implications.
Our approach features Ricardian comparative advantages across countries coupled with di¤er-
ences in international market access across locations within a country. We �rst lay out a baseline
model, and we use it to characterize how international and domestic trade costs determine industry
location, employment concentration and the gains from trade. The model is highly tractable, and
it o¤ers analytic solutions for all our key outcome variables. Then, we apply the theory to the
U.S. economy. In tune with the theory, we �nd that U.S. export-oriented industries are more likely
to locate and employ more workers closer to international gates such as ports or land crossings.
Finally, we use the model to establish the quantitative relevance of international trade for the
concentration of economic activity, and to measure by how much domestic trade costs hamper the
gains from international trade.
In the theory, we furnish the canonical model of international trade driven by Ricardian com-
parative advantages with a geography within countries. Locations within a country are arbitrarily
arranged in a map and di¤er in distance to an international gate such as a seaport, an airport
or a land crossing into another country. Within the country trade is costly, and international
shipments must cross through the international gate. To produce, each location uses a perfectly
mobile resource (workers) and an immobile resource (land). Congestion forces are represented by
decreasing returns to labor, so that it is not optimal to concentrate production in a single location.
1Authors�calculation from Harvard CID. The reported number is for 1995.
2
Relative productivity across industries is the same in all locations within a country, but di¤ers
between countries. International di¤erences in institutions, recently emphasized as an important
determinant of trade and specialization, exemplify a source of comparative advantage that does not
vary systematically across locations within countries.
We use the model to study how international and domestic trade costs interact to determine
regional patterns of production and employment. We �nd that whenever an open economy is not
fully specialized in what it exports, two distinct regions necessarily emerge. The equilibrium fea-
tures a region with high population density near the international gate that specializes in producing
export-oriented goods, followed by a region with low population density that is incompletely spe-
cialized and does not trade with the rest of the world. Thus, even though trade costs are uniform
across space, international trade generates a partition between a "coastal" and an "interior" re-
gion. The boundary between these regions, as well as their shares in employment and output, is
endogenous and depends on international and domestic trade costs.
We analyze how changes in trade costs interact with factor movements within the country and
with the gains from trade. Reductions in international or domestic trade costs lead to migration of
mobile factors toward the locations with good international market access. Lower trade costs also
cause the boundary between the coastal and the interior regions to move inland, so that marginal
locations switch from autarky to trading with the rest of the world, and the geographic extension of
the integrated region increases. As a result, employment density and the share in national income
rises in the coastal region relative to the interior region.
We conclude the theoretical analysis by investigating the impact of domestic trade costs on
the gains from international trade. Aggregate welfare and real income can be decomposed into a
familiar term that captures the gains from trade without domestic geography, and a new term that
captures the e¤ect of domestic trade frictions. The �rst component depends on the terms of trade,
as in a standard Ricardian model, and on congestion forces. The second component depends on
domestic trade costs, and on the size of the trading region in terms of employment and land use.
Since larger domestic frictions cause the trading region to shrink, the gains from international trade
decrease with domestic trade costs.
We apply our theory to the U.S. economy. Since in the U.S. domestic trade costs are relatively
small, applying the theory in this context represents a useful benchmark. We start by assessing
the implications of the model for the location of industries. The key ingredients from the theory,
di¤erences in international market access and Ricardian comparative advantages, cause industries
to arrange in space based on their export orientation at the national level. This suggests a way
of detecting an e¤ect of international market access on regional specialization: export-oriented
industries should be more likely than import-competing ones to locate, and to employ more work-
ers, closer to international gates such as seaports, international airports or land crossings. This
explanation for industry location complements common explanations given the literature, such as
natural advantages or agglomeration.2
2See Holmes and Stevens (2004) for a summary assessment of the forces determining industry location in the U.S.
3
We use U.S. data on specialization at the county and national levels to investigate this pre-
diction. We classify industries as either export-oriented or import-competing at the national level,
and we also rank these industries by the strength of their revealed comparative advantages. Then,
we use county-level employment data to investigate how employment varies within each industry
across districts based on the industry export orientation and on the county distance to interna-
tional gates. We �nd support for the prediction that export-oriented industries, or industries with
stronger revealed comparative advantages, are relatively more likely to locate closer to international
gates such as seaports, land-crossings or airports. Moving inland from a representative port for
400 miles, a value close to the 90th percentile of the distribution of county distances to the nearest
port, employment in export-oriented industries relative to import-competing industries shrinks by
between 18% and 44%, depending on the speci�cation.
Finally, we propose a simple quantitative methodology for measuring the e¤ects of the theory
on the concentration of employment and the gains from trade. According to Rappaport and Sachs
(2003), U.S. coastal counties collectively account for 13% of continental US land area and for 51% of
its 2000 population. In our model, international trade is the only source of dispersion in economic
activity across locations, and it generates an endogenous partition between coastal and interior
regions. Therefore, we ask, How important is international trade for the concentration of activity
in coastal areas?
Second, we measure the impact of domestic trade costs on the gains from international trade. A
recent literature emphasizes that the gains from international trade are relatively small.3 Needless
to say, understanding the forces that determine the gains from trade is important to inform both
theory and policy. Therefore, we ask, How important are domestic trade costs in hampering the
gains from international trade?
The answer to these questions depends on some key parameters of the model: domestic trade
costs, decreasing returns to scale, international comparative advantages, and sectorial consumption
shares. First, we obtain these parameters matching features of the data that are independent from
the geographic distribution of employment. As natural targets for the key parameters we use the
share of shipping costs in export-oriented shipments, the share of land in sectorial income, trade
intensity at the country level, and household expenditure shares. Then, we use the calibrated model
to predict the population density in the coastal relative to the interior region, and to measure the
gains from trade for di¤erent levels of domestic trade costs.
Depending on our de�nition of coastal districts in the data, we �nd that Ricardian comparative
advantages explain close to 1=3 of the concentration of activity in coastal districts in the U.S.
relative to interior districts. The part left unexplained by the model is naturally attributable to
forces that we do not consider, such as amenities, factor endowments or agglomeration. Finally,
we measure the gains from international trade in the U.S. to be in the order 0:4%. At the same
time, keeping all other parameters the same, if the magnitude of domestic trade costs in the model
3For example, Arkolakis et al. (2011) show that a certain class of trade models predicts that the share of realincome that the U.S. would loose if access to international trade were shut down is around 1%.
4
shrink by 50% these gains rise to 0:7%, while if domestic trade costs are suppressed they rise to
4:2%.
These results on population density and gains from trade hold even though, as pointed out by
others, domestic trade costs in the U.S. are quite small. In our calibration, they represent 1:5%
of the f.o.b price of exported products. Given that the baseline model does not include a number
of additional forces, the large e¤ect on the gains from trade is likely an upper bound for the e¤ect
of domestic frictions.4 Still, this large potential e¤ect of small domestic costs suggests that local
trade frictions might play an even more important role in poor countries, where infrastructure is
presumably underdeveloped relative to the U.S..
Relation to the Literature A vast literature is concerned with studying the concentration of
economic activity in contexts with agglomeration. However, few papers in that literature consider
an interaction between international and domestic trade costs. In a context with demand linkages,
Krugman and Livas-Elizondo (1996) and Behrens et al. (2006) present models where two regions
within a country trade with the rest of the world. Henderson (1982) and Rauch (1991) embed
system of cities models in open economy frameworks.5 Rossi-Hansberg (2004) studies the location
of industries that di¤er in relative productivity on a continuous space with externalities. All these
papers are based on an agglomeration force to induce concentration. In contrast, in our context,
concentration results exclusively from the interaction between heterogeneous market access within
countries and comparative advantages between countries. We focus on these forces in our empirical
and quantitative applications.
Closer to a neoclassical environment, Bond (1993) and Courant and Deardor¤ (1993) present
models with regional specialization where relative factor endowments may vary across discrete
regions within a country. These papers do not include heterogeneity in access to world markets.
Venables and Limao (2002) study geographic specialization across regions trading with a central
location but do not allow for factor mobility. More recently, Ramondo et al. (2011) study the gains
from trade and ideas di¤usion allowing for multiple regions within countries, but do not focus their
analysis on di¤erences in world market access across locations or on labor mobility within countries.
Redding (2012) extends the framework in Eaton and Kortum (2002) with labor mobility within a
country to study regional gains from trade. In his analysis, for each good there are independent
productivity draws across locations. We assume that locations share access to the same technologies
within a country, so that industries choose their location based on comparative advantages at the
national level and on distance to international gates.
4 In Section 5 of the paper, we lay out an extended model that adds additional forces not considered in the baselinecalibration. In the extended model, we consider industry-speci�c congestion forces and consumption of housing andservices. In ongoing work, we are developing a calibration of the extended model.
5We share with Rauch (1991) the presence of Ricardian comparative advantages with internal geography. Incontrast with that paper, we ask a di¤erent set of questions, we assess both empirical and quantitative implicationsof the theory, and we present a neoclassical model that is specially tractable to characterize the distribution ofpopulation and to illustrate the answer to our main questions.
5
Structure of the Paper We start in Section 2 by laying out the baseline model, characterizing
the general equilibrium and presenting the comparative statics of population density and welfare
with respect to domestic trade costs. In Section 3 we assess the model prediction for regional
specialization with U.S. data, and in section 4 we present the quantitative assessment. In Section
5 we develop an extended model.6 Section 6 concludes. Proofs are in the appendix.
2 Baseline Model
Geography and Trade Costs A country consists of a set of locations arbitrarily arranged on
a map. We index locations by `, and we assume that only location ` = 0 can trade with the rest
of the world. That is, a good can be shipped internationally from a location ` only if it crosses
through location ` = 0.
As it will be clear later on, given the nature of our model only the distance at which each
location ` lies from location 0 matters for the equilibrium. Therefore, we assume without loss of
generality that ` represents the distance separating locations ` and 0. We also let ` represent the
location at maximum distance from ` = 0.
There are two industries, i 2 fX;Mg. International and domestic trade costs are industryspeci�c. The international iceberg cost in industry i between ` = 0 and the rest of the world (RoW)
is e�i0 . Within the country, iceberg trade costs are constant per unit of distance. Therefore, the
cost of shipping a good for distance d in industry i equals e�i1d. This implies a cost of international
trade equal to e�i0+�
i1` in industry i from location `.
Given this geography, we can interpret ` = 0 as a port, the set locations surrounding it as a
coast, and the rest of the country as an interior. In the equilibrium of the model, there will be a
formal sense in which locations endogenously belong to a coastal or to an interior region.
More generally, we can think of ` = 0 as the point in space with the best access to international
markets. What is key is that not all locations have the same technology for trading with the RoW.
This will drive concentration near points with goods access. Internal geography vanishes when
� i1 = 0 for both industries.7
Endowments There are two factors of production, a perfectly mobile factor and a �xed factor.
We refer to the mobile factor as workers, and to the �xed factor as land. More generally, the �xed
factor could account for immobile workers, or for structures that take long to depreciate.
We choose units such that the national land endowment equals 1, and we let � (`) be the amount
of land available in each location `. Land is owned by immobile landlords who do not work and
who spend their rental income locally.
6We are currently working on the calibration of the extended model.7Our analysis equivalently applies to an arbitrary number of ports, as long as all ports face the same relative
prices in the rest of the world. In that case, we would let ` index the distance to the nearest port. If ports di¤ered inthe international prices that they face, the analysis would be heavier in notation but all the key results would carrythrough.
6
We let n be the total number of workers, equal to the labor to land ratio at the national level.
These workers are mobile across locations `. We let n (`) denote employment density at location `,
which is to be determined in equilibrium.
Preferences Workers and landlords consume in the same location as they live and have homo-
thetic preferences on the �nal goods X and M . Therefore, indirect utility of a worker who lives in
` is a monotone transformation of
u(`) =w (`)
E(`); (1)
where w(`) is the wage at ` and
E(`) = ET (PX(`); PM (`)) (2)
is the cost of living index de�ned on the price of tradeable goods.8 For landowners, income equals
rents r(`) per unit of land and utility is therefore increasing in r (`) =E(`). Landowners are immobile,
but workers decide where to live.
We let p(`) � PX(`)=PM (`) be the relative price of X in `. Since preferences are homothetic,
there exists an increasing and concave function e(p(`)) that depends on the relative price of X,
e (p (`)) � ET (`)
PM (`).
Technology The production of good i requires one unit of land to operate a technology with
decreasing returns to scale in labor. We let ni(`) be employment used per unit of land in industry
i = X;M at location `. Pro�ts per unit of land in industry i at ` are
�i (`) = maxni(`)
fPi(`)qi (ni(`))� w(`)ni(`)� r (`)g . (3)
The production technology is
qi (ni(`)) = �ini(`)
�i
ai, (4)
where �i � ���ii (1� �i)�(1��i) is a normalization constant that helps to save notation later on.The coe¢ cient ai in (4) measures industry-speci�c production costs at the country level and
determines the Ricardian comparative advantages. We let
a � aXaM
be the relative cost of production in sector X.
Decreasing returns to scale �i act as congestion force and ensure that all land is populated
in equilibrium. To streamline the exposition throughout this section, we assume that decreasing
8 In the extended model of Section 5, we also allow for consumption of housing and services. The key qualitativeresults of the model carry on in that case.
7
returns are the same in both industries: �X = �M = � .9
The local pattern of specialization at ` is captured by the amount of land �i(`) � �(`) used inby each industry i = X;M .
2.1 Local Equilibrium
We de�ne can characterize a local equilibrium at each location ` that takes prices fPX (`) ; PM (`)gand the real wage u� as given.
De�nition 1 (Local Equilibrium) A local equilibrium at ` consists of employment density n (`),labor demands fni (`)gi=X;M , specialization patterns f�i (`)gi=X;M , and factor prices fw (`) ; r(`)gsuch that
1. workers maximize utility,w (`)
E (`)� u�; = if n (`) > 0; (5)
2. pro�ts are maximized,
�i (`) � 0; = if �i (`) > 0, for i = X;M; (6)
where �i (`) is given by (3);
3. land and labor markets clear, Xi=X;M
�i(`) = �(`); (7)
Xi=X;M
�i(`)
�(`)ni(`) = n (`) ; and (8)
4. trade is balanced.
Conditions 2 to 4 resemble a small Ricardian economy that takes prices as given. In addition,
in each local economy ` the employment density n (`) is determined by (5).
We let pA be the autarky price in location `. By this, we mean the price prevailing in the
absence of trade with any other location or with the rest of the world, but when labor mobility is
allowed. Since in autarky both goods must be produced, condition (6) implies
pA = a. (9)
The specialization pattern can be readily characterized based on relative prices. Using (6),
location `must be fully specialized inX when p (`) > pA, and fully specialized inM when p (`) < pA.
9 In the extended model of Section 5 we allow for di¤erences in �i across industries.
8
The main implication is that a trading location is (generically) fully specialized. When a location
trades, with either the rest of the world or with other locations, it takes relative price p (`) as given.
Unless the relative price p (`) coincides with pA, the location will be fully specialized in one of the
two industries. When p (`) coincides with pA, the location might either export or stay in autarky.
This logic also implies that an incompletely specialized location is (generically) in autarky.10
The solution to the �rm�s problem yields labor demand per unit of land used by industry i,
ni(`) =�
1� �
�Pi(`)
aiw(`)
�1=(1��)(10)
for i = X;M and ` 2�0; `�. To solve for the wage w(`) we note that whenever a location is
populated, the local labor supply decision (5) must be binding:
w(`) = E(`) � u�. (11)
Expressions (10) and (11) convey the various forces that determine the location decision of
workers. Agents care about the e¤ect of prices on both their income and on their cost of living.
Our assumptions guarantee that agents employed in an industry-location pair (i; `) enjoy a higher
real income when the local relative price of industry i is higher in location `. That is, the income
e¤ect from a higher relative price necessarily o¤sets any cost-of-living e¤ect.11 At the same time
there are congestion forces, so that everything else equal agents prefer to avoid places with high
employment density.
Using (9) and (10) we see that if p (`) = pA then employment density is the same across sectors,
nX(`) = nM (`). Therefore, using the market clearing conditions (7) and (8), employment density
n(`) in location ` is
n(`) =
(nX(`)
nM (`)ifp (`) � pAp (`) < pA
. (12)
When p (`) 6= pA, locations are fully specialized and necessarily export. In this circumstance
n(`) increases with the relative price of the exported good. Also, regardless of whether a location
trades or is in autarky, an increase in the national real wage u� keeping relative prices constant
causes workers to emigrate from `.
We summarize the properties of local equilibrium as follows.
Proposition 1 (Local Equilibrium) Let pA be the autarky price in location `. Then: (i) location` is fully specialized in X when p (`) > pA, and fully specialized in M when p (`) < pA; (ii) if
p (`) 6= pA, population density n(`) is increasing in the relative price of the exported good; and (iii)population density n(`) is decreasing in the real wage u�.
10This also holds in the more general case of section 5 where �i di¤ers across sectors. In that case, pA must bedetermined endogenously, but the specialization pattern is still independent from � (`).
11For this note that PX(`)=E(`) = p (`) =e (p (`)) is increasing in the relative price of X, while PM (`)=E(`) =1=e (p (`)) is decreasing in p (`).
9
2.2 General Equilibrium
We have characterized the local equilibrium independently from a location�s geographic position.
We move on to study how market access matters for the employment density and the specialization
pattern in general equilibrium. We study a small economy that takes international prices fP �X ; P �Mgas given. We let
p� =P �XP �M
be the relative price at RoW, and we let
� j �1
2
Xi=X;M
� ij for j = 0; 1
be the average international and domestic iceberg cost across sectors.
No arbitrage implies that for any pair of locations prices satisfy
Pi(`0)=Pi(`) � e�
i1j`�`0j for i = X;M , `; `0 2
�0; `�. (13)
This condition binds if a good in industry i is shipped from ` to `0. A similar condition holds with
respect to RoW. In particular, since location ` = 0 can trade directly with RoW, (13) implies
e�2�0 � p(0)=p� � e2�0 . (14)
The �rst inequality is binding if the country exports X to RoW, while the second is if it imports
X. In turn, for any location ` we have
e�2�1` � p(`)=p(0) � e2�1`, (15)
where the �rst inequality binds if ` exports X to RoW, and second does if ` imports X.
We are ready to de�ne the general equilibrium of the economy.
De�nition 2 (General Equilibrium) An equilibrium in a small economy given international
prices fP �X ; P �Mg consists of a real wage u�, local outcomesnfni (`) ; �i (`)gi=X;M ; n (`) ; w (`) ; r(`)
oand goods prices fPi(`)gi=X;M such that
1. given fPi(`)gi=X;M and u�, the local outcomesnfni (`) ; �i (`)gi=X;M ; n (`) ; w (`) ; r(`)
ocon-
stitute a local equilibrium by De�nition 1 for all ` 2�0; `�;
2. the real wage u� adjusts such that the national labor market clears,
Z `
0n (`)� (`) d` = n; and (16)
3. relative prices p(`) satisfy the no-arbitrage conditions (14) and (15) for all ` 2�0; `�.
10
Since De�nition 1 of a local equilibrium includes trade balance for each location, trade must
also balance at the national level.
We show next that the no-arbitrage conditions restrict the set of trade �ows that can arise in
equilibrium. This feature of the equilibrium is important to characterize regional specialization
patterns.
Lemma 1 There are no bilateral trade �ows between any pair of locations within the country.Hence, the country is in international autarky if and only if all locations in the country are in
autarky and incompletely specialized.
This result is a type of spatial impossibility theorem, in the tradition of Starrett (1978). Since
all locations share the same relative unit costs, there are no gains from trade within the country.
With this in mind we can characterize the general equilibrium. We can partition locations into
the set of those that trade with RoW and those that stay in autarky. If the country exports X,
then all locations that trade with RoW must also export X. It follows that all locations ` such that
e�2(�0+�1`)p� < pA must stay in autarky, for if they specialized in X then, given the specialization
pattern from Proposition 1, the resulting price p (`) = e�2(�0+�1`)p� would induce specialization in
Y . In the same way, all locations ` such that e�2(�0+�1`)p� > pA must specialize in X, for if they
stayed in autarky, the equilibrium price p (`) = pA would violate the no-arbitrage condition (15).
We conclude that the distance to ` = 0 is the only fundamental di¤erence across locations. This
justi�es our previous statement that locations may be arbitrarily arranged on a map, as well as our
initial choice of indexing them by their distance to the port.
Hence, if the country is not in international autarky there is some boundary b 2�0; `�such
that all locations ` < b are fully specialized in the export industry. In turn, all locations ` > b do
not trade with the RoW and stay in autarky. Hence, the internal boundary b divides the country
between a trading "coastal region" comprising all locations ` 2 [0; b] close to the international gate,and an autarkic "interior region" comprising all locations ` 2 (b; `].
Since all locations ` 2 (b; `] are in autarky, they are incompletely specialized and their relativeprice is pA = a. Given this price in the autarkic region and the regional pattern of production, the
no-arbitrage conditions (14) and (15) give the price distribution:
p(`) =
8<:p�e�2(�0+�1min[`;b]) if the country is net exporter of X;p�e2(�0+�1min[`;b]) if the country is net exporter of M:(17)
Using this relative price function we can describe the distribution of employment across loca-
tions. From now on we assume that a < p�, so that the economy has comparative advantages in
sector X. As shown below this implies that, if the economy exports, it must export X. In this case
the employment density depends on location as follows:
n(`) =�
1� �
�p(`)=e(p(`))
aX � u�
�1=(1��)for ` 2
�0; `�. (18)
11
Employment density is governed by relative prices. When the country trades with RoW, the relative
price of the export industry decreases toward the interior, so that employment also decreases as we
move away from the port. Therefore, international and domestic trade costs a¤ect the distribution
of employment across locations through their impact on the relative price gradient in (17).
Using (18) in the aggregate labor-market clearing condition (16) we solve for the real wage as
function the boundary b:
u� =1
aX
��= (1� �)
n
�1�� Z b
0
�p(`)
e(p(`))
� 11��
� (`) d`+
�p(b)
e(p(b))
� 11��
Z `
b� (`) d`
!1��. (19)
In turn, continuity of the relative function determines the location of the boundary b,
p(b) � pA, = if b < `. (20)
We note that, when p(`) > pA, then b = ` and the interior region does not exist. The general
equilibrium is fully characterized by the pair fu�; bg that solves (19) and (20). From the second
equation we �nd a unique value for b, and using that value in (19) we determine the real wage. All
other variables easily follow from these two outcomes.
For future reference we now de�ne the average population density in the coastal region,
nC =
Z b
0n (`)
� (`)R b0 � (`) d`
d`, (21)
while nA � n (b) is average population density in the autarkic interior region.We summarize our �ndings so far as follows.
Proposition 2 (Population and Industry Location in General Equilibrium) Given the in-ternational relative price p�, there is a unique small-country equilibrium, where: (i) in international
autarky, the distribution of prices, wages and labor is uniform across locations; (ii) if the country
trades, employment density increases toward the coast; and (iii) if the country trades and is not
fully specialized, there exists an interior region (b; `] that is in autarky and incompletely specialized,
and a coastal region [0; b] with higher population density that trades with RoW and specializes in
the export-oriented industry.
These results demonstrate that international trade drives concentration of economic activity
and industry location. In the absence of international trade, there are no di¤erences in economic
outcomes across locations. In contrast, when the economy trades, population increases towards
international gates. Furthermore, when the economy trades but is not fully specialized, two dis-
crete regions emerge: a coastal region surrounding international gates that is densely populated,
connected to international markets and specialized in the export-oriented industry; and an interior
region that is lowly populated, disconnected from the rest of the world and incompletely specialized.
12
In our reasoning so far we have assumed a given trade pattern at the national level. Next, we
establish the conditions on the parameters that determine the national trade pattern and existence
of the interior region.
Proposition 3 (National Trade Pattern and Existence of Interior Region) (i) The coun-try exports X if pA=p� < e�2�0; in that case, the interior region exists if and only if e�2(�0+�1`) <
pA=p�; (ii) the country exports M if e2�0 < pA=p
�; in that case, the interior region exists if and
only if pA=p� < e2(�0+�1`); and (iii) the country is in international autarky if e�2�0 < pA=p� < e2�0.
The �rst implication of these results is that domestic trade costs��X1 ; �
M1
, while capable of
a¤ecting the gains and the volume of international trade, can not a¤ect the pattern or the existence
of it. In other words, the conditions that determine when international trade exists as well as the
direction of international trade �ows are the same as in an environment without domestic geography.
The second implication is that, when then country trades, there is an interior region when trade
cost f�1; �0g or the extension of land ` are su¢ ciently large, or when comparative advantages,captured by pA=p�, are not su¢ ciently strong.
2.3 Impact of Changes in International and Domestic Trade Costs
We use the model to characterize the impact of international and domestic trade costs on the
concentration of economic activity and the gains from trade. In the quantitative section we measure
the importance of these e¤ects.
Our motivating examples from the introduction show that international trade integration is
associated with shifts in economic concentration. In our model, population density varies across
locations based on the proximity to the international gate, and population density in the coastal
region relative to the interior region is endogenous. We summarize the impact of trade costs on
these outcomes as follows.
Proposition 4 (Internal Migration) Consider an initial equilibrium where the boundary is at
b 2�0; `�. Then, a reduction in international or in domestic trade costs causes b to move inland,
a net population increase in the coastal region [0; b], and an increase in the relative coastal density
nC=nA.
The direct impact of a reduction in trade costs is that the relative price of the exported industry
increases in the coastal region. In the case of a reduction in �0, the shift is uniform across locations,
while a lower �1 results in a �attening of the slope of relative prices toward the interior. In both
cases, the change in prices causes the relative price at b to be larger than the autarky price pA,
so that locations at the boundary now �nd it pro�table to specialize in export industries and the
boundary moves inland.
What are the internal migration patterns associated with these reductions in trade costs? As
we show below, a consequence of lower trade costs is an increase in the real wage u�. Since in the
13
interior relative prices remain constant, this causes labor demand to shrink. As a result, workers
migrate away from interior locations toward the coast, and relative population density increases in
the coastal region.
These results reproduce the cases that we highlight in the introduction: as trade costs decline,
employment migrates to coastal areas that host comparative-advantage industries. In the quantita-
tive section, we compare the model-generated and empirical values for the relative coastal density
nC=nA to measure the relevance international and domestic trade costs on employment concentra-
tion in the U.S.
We conclude with the impact of domestic trade costs �1 on the gains from international trade.
We �rst de�ne the real wage in the absence of domestic trade costs in an economy that faces relative
prices equal to p:
u (p) � p=e (p)aX
�1� ��
n
���1. (22)
As in a standard Ricardian model, the real wage is increasing in the terms of trade. In addition,
as long as � < 1, it decreases with the number of workers. In our economy with positive trade
costs, the real wage that would prevail in each local economy ` if the national economy was in
international autarky is ua � u (pA). Using the solution for the real wage u� from (19) together
with (22), we can express the gains from international trade when �1 > 0 as follows:12
u�
ua=
Z b
0
�u (p (`))
ua
�1=(1��)� (`) d`+
Z `
b� (`) d`
!1��.
The aggregate gains of moving from autarky to free trade, u�=ua, are a weighted average
of the gains from international trade faced by each local economy, u (p (`)) =ua. The weights
across locations are given their land shares, � (`). Since in interior locations ` 2 [0; b] we haveu (p (`)) =u (pA) = 1, the gains from trade are increasing with the equilibrium position of the
boundary b.
It follows that the larger the size of the export-oriented region, the more a country bene�ts
from openness. Since �1 causes the export oriented region to shrink, the lower the domestic trade
costs, the more we should expect the country to bene�t from openness. A lower �1 makes exporting
pro�table for locations further away from the port, allowing economic activity to spread out and
mitigate the congestion forces in dense coastal areas.
To formalize these results, we de�ne the elasticity of the consumer price index,
"(p) =de(p)=e (p)
dp=p.
12There are two factors of production in this model. For the perfectly mobile factor (labor), the real income u� isequalized across locations, while for the �xed factor (land), the real return r (`) =E (`) depends on location. In whatfollows, we focus our analysis on u. However, our production technology implies that the average real return to land,R `0(r (`) =E (`))� (`) d`, is proportional to u�. Therefore, our statements about the real wage also apply to aggregate
welfare and to aggregate income de�ated at local prices.
14
We also de�ne the share of location ` in total employment,
s (`) =n (`)� (`)
n.
Using these de�nitions, we have the following.
Proposition 5 (Gains from International Trade) The change in the real wage due to changesin p�, �0 or �1 is cu� = Z b
0[1� "(p(`))] s (`) dp(`)
p(`)d`: (23)
Therefore: (i) the change in the real wage caused by terms of trade improvement of bp is boundedabove by the employment share in export-oriented locations,
cu�bp <
Z b
0s (`) d`;
and (ii) domestic trade costs �1 reduce the gains from trade,
d(u�=ua)
d�1< 0.
Expression (23) describes the gains from a reduction in trade costs, either domestic or interna-
tional, as function of the relative price change faced by export-oriented locations, in turn weighted
by population shares s (`). Reductions in domestic or international trade costs cause the relative
price of the exported good to increase. This increase in relative prices has a positive e¤ect on
revenues and a negative e¤ect on the cost of living. The latter is captured by the price-index
elasticity "(p (`)), and mitigates the total gains. In this context, (i) shows that the gains from an
improvement in the terms of trade, caused by either lower �0 or larger p�, are bounded above by
the share of employment in export-oriented regions. In turn, (ii) captures our intuition that gains
from international trade are decreasing in domestic trade costs.
In the quantitative section, we measure by how much domestic trade costs hamper the gains
from international trade. A natural benchmark for measuring the impact of domestic trade costs
consists in considering the gains from trade when �1 = 0. In that case, the e¤ect of domestic
geography disappears and the real wage is u � u (p (0)). Therefore, the gains of moving from
autarky to trade can be decomposed as follows:
u�
ua= (�1; b) �
u
ua. (24)
where
(�1; b) � Z `
0
�u (p (`))
u
�1=(1��)� (`) d`
!1��.
The actual gains from trade, u�=ua, equal the gains from trade without domestic trade costs, u=ua,
15
adjusted by (�1; b). This function depends only on parameters and on international prices, it
is strictly below 1 as long as �1 > 0, and it equals 1 if �1 = 0. In the quantitative section 4 we
measure each component in (24).
3 Specialization Patterns across U.S. Regions
We now analyze regional patterns of specialization in the U.S. using our framework. The
model has two broad implications; one on the openness of regions and the other on the location of
industries. First, it predicts that regions with favorable access to world markets trade more with
the rest of the world. This is not a sharp prediction, in that it can also be generated in frameworks
without Ricardian comparative advantages. A more speci�c prediction resulting from comparative
advantages is that export-oriented industries at the national level are more likely to locate, and to
employ more workers, in places with better access to international markets.
For both predictions, the �rst challenge in mapping the model to the data is to build a location-
speci�c measure of market access. In what follows, we use data at the U.S. state or county level.
For each of 48 continental states and 3077 counties, we proxy market access by the distance to
international trade gateways. Including airports, seaports and land crossings, there are 288 inter-
national ports in the mainland for U.S. goods trade. We use data on trade volume by customs
district to identify the 49 largest ports that account for 90% of total U.S. trade. These ports are
located in 38 di¤erent counties.13 For each U.S. state and county, we then calculate the great-circle
distance between its population center and the population center of the nearest county where one
of these ports is located. Table 1 presents summary statistics of our distance measure. There is
considerable variation at both levels of geographical aggregation.14
Table 1: Summary Statistics of the Distance Measure (miles)
States CountiesMean 158 194Standard Deviation 114 121Median 140 175Maximum 500 (Nebraska) 571Minimum 15 (Maryland) 0
In the model, the coastal region trades a positive share of its total value added, while the interior
region does not trade. Therefore, the share of trade in regional GDP decreases with distance to
the international gates. Equivalently, the model predicts that the share of employment in export-
oriented activities declines with distance. These measures are readily available at the state level,
13See Figure 7 in the Data Appendix for a map with the port locations.14See the Data Appendix for further details of the data construction. We recognize the limitations of our market
access proxy due to the presence of rivers as transportation arteries and the endogeneity of airport locations, butwe consider it a useful starting point for our main hypothesis. We are currently exploring results for di¤erenttransportation modes.
16
Figure 1: Export Intensity and Distance to Ports Across U.S. States
AL
AZ
AR
CA
CO
CT
DE
FL
GA IDIL
IN
IA
KSKYLA
ME
MD
MA
MI
MN
MSMO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PARI
SC
SD
TN
TXUT
VT
VA
WA
WV
WI
WY
0.2
5.5
.75
11.
251.
5
EX
PO
RTS
/ G
DP
0 100 200 300 400 500
Distance (miles)
slope : 0.001266tvalue : 4.03
ALAZ
AR
CACO
CT
DE
FL
GA
ID
ILIN
IA
KS
KY
LAME
MD
MA
MI
MN
MS
MO
MTNE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WVWI
WY
0.0
5.1
.15
.2.2
5.3
.35
.4
% o
f m
anuf
actu
ring
empl
oym
ent
rela
ted
to e
xpor
ts0 100 200 300 400 500
Distance (miles)
slope : 0.000134tvalue : 1.91
Notes: Exports data for states on the left panel is from the rep ort on the Origin of Movement of U.S. Exports by State issued by the Census Bureau .GDP data by state is from the Statistical Abstract of the United States, a lso issued by the Census Bureau . Data for the vertica l ax is on the rightpanel com es from the Census Exports from Manufacturing Establishments rep ort. A ll data is for 2008. See the text for the de�nition of the d istancem easure in the horizontal ax is. The �tted lines uses states� p opulation as weights.
and we plot them against state distance in the two panels of Figure 1. Both export intensity and
employment in export oriented goods decline with distance. The correlation coe¢ cient between
each of these measures and distance is negative and statistically signi�cant. The left panel implies
that a reduction in distance to international gates from 400 miles to less 100 miles results in a
three-fold increase in the export to value added ratio at the state level.
To dig further into the mechanism, we consider whether this increase in export participation
indeed re�ects industry composition as our theory predicts. As a preliminary inspection, Figure
2 plots the export/output ratios for 85 manufacturing industries in the four-digit North American
Industry Classi�cation System (NAICS) against the average industry distance to ports. Industry
distance is a weighted average of county distance, where the weights correspond to the shares of
employment across counties within each industry.15 The �gure shows that, on average, industries
with higher export/output ratios at the national level locate closer to ports.
We move on to a more systematic analysis of these associations using data at various levels
of geographical and industry aggregation. For that, we estimate versions of the following generic
equation:
eid = �i + �d + � � ln(distd)� TradeOrienti + "id; (25)
15 I.e., letting sic denote the share of industry i�s employment located in county c, then industry distance isdi =
Pc sicdc, where dc is county c�s distance to the nearest port and
Pc sic = 1. Therefore, the larger is di, the
farther away industry i locates from ports.
17
Figure 2: Export/Output Ratio and Average Distance to Ports Across U.S. Industries
0.1
5.3
.45
.6.7
5
Indu
stry
Exp
ort/O
utpu
t
0 50 100 150 200
Distance (miles)
slope : 0.0012tvalue : 2.23
Notes: The vertica l ax is uses industry export data from the Census Bureau Foreign Trade D ivision together w ith output data from the NBER-CESdatabase. There are 85 manufacturing industries at four-d ig it NAICS classi�cation . A ll data is averages over 2001-2005. Table 8 in the dataapp endix rep orts the industries and their exp ort/output ratios. See the text for the de�nition of the d istance m easure in the horizontal ax is. The�tted line uses industries� employment as weights.
where eid is employment of industry i at district d, TradeOrienti is a measure of international trade
orientation by industry that takes positive or larger values for more export-oriented industries, and
distd is the measure of market access by district. The district d indexes either states or counties.
Our model predicts that � < 0. That is, industries with higher export orientation are more
likely to locate in districts situated closer to ports. In what follows, we consider this prediction for
alternative ways of capturing trade orientation TradeOrienti, and for di¤erent levels of industry
and geographic aggregation.
3.1 Revealed Comparative Advantage as a Measure of Trade Orientation
In the model, industry X grabs a positive share of world trade while industry M has no partic-
ipation in world trade. Hence, we can use the revealed comparative advantage (RCA) by industry
to measure TradeOrienti in (25). The RCA of industry i is
RCAi =XUSi =XWorld
i
XUS=XWorld:
The numerator is the share of U.S. exports in world exports in industry i; and the denominator
is the share of aggregate U.S. exports in aggregate world exports. A higher RCAi is interpreted
as stronger comparative advantages in industry i. With two industries, as in the model, we would
have that RCAX > RCAM . To build the RCA measure, we use U.S. and world exports data from
Feenstra et al. (2004), and we concord it from SITC product codes into four-digit SIC industries.16
16See the Data Appendix for details.
18
Table 2: Impact of Distance to Ports: Industry Revealed Comparative Advantage
Dependent variable: ln(emp)I II III IV
ln(dist)�RCA 0.0404 -0.0556� -0.0321�� -0.0563�
(0.0270) (0.0333) (0.0131) (0.0292)Zeros in the sample Yes Yes No NoUndisclosed observations Imputed Dropped Imputed DroppedIndustry �xed e¤ects Yes Yes Yes YesState �xed e¤ects Yes Yes Yes YesN 17040 12925 14229 8831Adjusted R2 0.486 0.663 0.452 0.478
Notes: In th is tab le, we rep ort the resu lts from a linear regression of log employm ent at industry-state level to state and industry �xed e¤ects, andthe interaction of state d istance to p orts and industry RCA index. The sample contains 48 continental states and 355 manufacturing industries atfour-d ig it SIC classi�cation . See the text and the data app endix for the exp lanation of variab les. E icker-Hub er-W hite robust standard errors inparentheses.��� Sign i�cant at 1 p ercent level.�� Sign i�cant at 5 p ercent level.� Sign i�cant at 10 p ercent level.
The dependent variable eid is the log of state-wide employment in industry i for 355 manufac-
turing industries, averaged over the years 1997� 2000. We stop for a moment to discuss a featureof our employment data. The Quarterly Census on Employment and Wages, published by the Bu-
reau of Labor Statistics (BLS), suppresses certain district-industry cells to protect the identity of
a single large employer in the area. These undisclosed cells can be distinguished from true zeros in
each industry-state. In our speci�cations, we explore alternative ways of treating these undisclosed
cells. We either drop them as missing observations, or we �ll in a uniform number imputed from
the di¤erence between total employment in our data and aggregate manufacturing employment at
the national level. Thus, the second method takes into account that an undisclosed cell conveys
information on industry presence in a location.17
Table 2 reports the results for four speci�cations. In addition to the treatment of undisclosed
cells, speci�cations di¤er in whether true zeros are included. True zeros are preserved in columns
I-II but dropped in columns III-IV. In all speci�cations except column I, the interaction term is
negative and signi�cant at 5% or 10% con�dence. To get a sense of magnitudes, the employment
impact of increasing RCAi and decreasing distd in an average industry by one standard deviation
leads to an employment increase of 4:5% to 7:8%, depending on the speci�cation.
These results rely on a relatively coarse measure of employment and distance by state, and on
a relatively small number of states. We move on to county-level data, and to exploring alternative
measures of export orientation.18
3.2 Trade Balance as a Measure of Trade Orientation
In the model, industry X has positive trade balance, while industry M has negative trade
balance. We now let TradeOrienti equal one if the average trade balance of industry i over
17At the state level, 5897 out of 13525 cells with positive employment are undisclosed. Total employment inreported cells (14.1 mn) accounts for 80% of U.S. manufacturing employment for the period (17.5 mn).
18Due to data availability, we are still not able to use the RCA measure at the county level. Instead, we usealternative variables of trade orientation below.
19
Table 3: Impact of Distance to Ports: Industry Trade Balance
Dependent variable: ln(emp)I II III IV
ln(dist)� TradeBalance -0.0216��� -0.0575��� -0.0185��� 0.00224(0.00732) (0.00892) (0.00578) (0.0145)
Zeros in the sample Yes Yes No NoUndisclosed observations Imputed Dropped Imputed DroppedIndustry �xed e¤ects Yes Yes Yes YesCounty �xed e¤ects Yes Yes Yes YesN 261545 183430 85684 22561Adjusted R2 0.456 0.425 0.291 0.413
Notes: In th is tab le, we rep ort the resu lts from a linear regression of log employm ent at industry-county level to county and industry �xede¤ects, and the interaction of county distance to p orts and a binary variab le that equals one if the industry exports exceed its imports. Thesample contains 3077 counties and 85 manufacturing industries at four-d ig it NAICS classi�cation . See the text and the data app endix for theexp lanation of variab les. E icker-Hub er-W hite robust standard errors in parentheses.��� Sign i�cant at 1 p ercent level.�� Sign i�cant at 5 p ercentlevel.� Sign i�cant at 10 p ercent level.
2001 � 2005 is positive, and zero otherwise. We also move to more disaggregated geographicalunits. Now, our dependent variable is the natural logarithm of employment at the county level
over the same time period. Employment and U.S. trade data are both available in the same
NAICS classi�cation system at the county level from the BLS and the Census Bureau Foreign
Trade Division, respectively.19
In using county-level employment data, we face tighter data disclosure limitations than with
states. Therefore, we conduct our analysis at a relatively more aggregate industry classi�cation
than with states. We consider employment in the 85 manufacturing industries at the 4-digit level of
the NAICS in 3077 counties. 27 of these industries have positive net exports and our data captures
close to 60 percent of U.S. manufacturing employment. As in the previous subsection, we estimate
(25) using two samples that treat undisclosed data di¤erently.
Table 3 reports the results. We include industry and county �xed e¤ects in all cases. We
�nd a signi�cant negative slope for the interaction term except if we drop both true zeros and
nondisclosed observations. To get a sense of magnitudes, moving toward the nearest port by one
standard deviation of the distance distribution increases employment in export oriented industries
by between 7:5% and 23% relative to import competing industries. If we consider the extremes
of the distance distribution, we have that moving inland from a representative port for 400 miles,
a value close to the 90th percentile of the distribution of distance, causes relative employment in
export-oriented industries to shrink by between 18% and 44%.
3.3 Export/Output Ratio as a Measure of Trade Orientation
In the model, the fraction of all shipments that are exported is larger in industry X than
in industry M . As an alternative measure of trade orientation we consider the exported share
19An advantage of using the trade balance for TradeOrienti, as well as the export-output ratio that we considerbelow, is that, in contrast with using RCAi, we only need U.S. data sources and we can bypass the concordancebetween trade and industry classi�cations.
20
Table 4: Impact of Distance to Ports: Industry Export Intensity
Dependent variable: ln(emp)I II III IV
ln(dist)� (EXP=Q) -0.0245 0.142��� -0.307��� -0.167���
(0.0266) (0.0325) (0.00514) (0.00437)Industries in the sample All All All AllZeros in the sample Yes Yes Yes YesUndisclosed observations Imputed Dropped Imputed DroppedIndustry �xed e¤ects Yes Yes No NoCounty �xed e¤ects Yes Yes Yes YesN 261545 183430 261545 183430R-squared 0.449 0.418 0.301 0.298
Notes: In th is tab le, we rep ort the resu lts from a linear regression of log employm ent at industry-county level to county and industry �xed e¤ects,and the interaction of county d istance to p orts and industry export/output ratio . The sample contains 3077 counties and 85 manufacturingindustries at four-d ig it NAICS classi�cation . See the text and the data app endix for the exp lanation of variab les. E icker-Hub er-W hite robuststandard errors in parentheses.��� Sign i�cant at 1 p ercent level.�� Sign i�cant at 5 p ercent level.� Sign i�cant at 10 p ercent level.
of output. Using export and industry output from the NBER-CES manufacturing dataset, we
compute the export/output ratios at the national level for the 85 industries in the sample.20 As
dependent variable we use, as before, the natural logarithm of county employment by industry.
In tables 4 and 5 we move across speci�cations that di¤er in whether we include industry �xed
e¤ects, in how we treat undisclosed observations, and in whether we break down the sample by the
industry trade balance. To avoid cluttering the presentation, we only include results with all the
zeros in the sample, and relegate results without zeros to the appendix.
Columns I and II of Table 4 report the baseline speci�cation in (25). The coe¢ cient of interest
is either not signi�cantly di¤erent from zero or has the wrong sign. In columns III-IV we repeat
the baseline speci�cation, but we do not include industry e¤ects �i. Without industry e¤ects, we
see again a signi�cant and negative gradient.
This discrepancy could arise due to several aspects of the data. One possibility is that the
export/output ratio is not a good measure of trade orientation at this level of industry aggregation.
For example, export/output ratios could vary across industries due to di¤erent levels of intra-
industry trade rather than to comparative advantages of the U.S.21 Alternatively, the distribution
of zeros and employment might be such that industry e¤ects pick up the concentration of export-
oriented industries near ports, while employment and export-output ratios vary with distance within
each type of industries due to forces not accounted for in the model.
Therefore, we repeat the speci�cations I and II of Table 4, including industry �xed e¤ects, within
subgroups of industries that di¤er in their trade balance. Table 5 reports the results for export
oriented industries in columns I-II, and for import competing industries in columns III-IV. Now,
the slopes have opposing signs for industries with di¤erent trade balance. For the export oriented
20The average export-output ratio across industries is 15%, with a standard deviation of 13%. See table 8 in theAppendix for a list of industries with their trade balance and export-output ratio.
21The correlation between export/output and import/output ratios across the 85 industries in the sample is 0.44.The mean Grubel�Lloyd index of intra-industry trade (= 1�jEXPi�IMPij=(EXPi+IMPi)), which varies between0-1 and is increasing in the extent of intra-industry trade, equals 0.69.
21
Table 5: Impact of Distance to Ports: Industry Export Intensity II
Dependent variable: ln(emp)I II III IV
ln(dist)� (EXP=Q) -0.195��� -0.530�10�5 0.0388 0.202���
(0.0557) (0.0689) (0.0300) (0.0363)Industries in the sample EXP > IMP EXP > IMP EXP < IMP EXP < IMPZeros in the sample Yes Yes Yes YesUndisclosed observations Imputed Dropped Imputed DroppedIndustry �xed e¤ects Yes Yes Yes YesCounty �xed e¤ects Yes Yes Yes YesN 83079 58266 178466 125164R-squared 0.431 0.436 0.460 0.409
Notes: In th is tab le, we run the regression describ ed in the prev ious tab le for two sub-samples of the data group ed by net exports. E icker-Hub er-W hite robust standard errors in parentheses.��� Sign i�cant at 1 p ercent level.�� Sign i�cant at 5 p ercent level.� Sign i�cant at 10 p ercentlevel.
industries � is negative, while for import competing industries it is positive. The coe¢ cient is
either statistically insigni�cant or positive, depending on how undisclosed data is treated. When
we include undisclosed cells, export oriented industries with higher export/output ratio reduce
their employment as we move further away from the nearest port (column I), while across import
competing industries there is no statistically signi�cant e¤ect (column III).
To sum up, using various measures of trade orientation and di¤erent levels of geographical
aggregation we �nd a negative correlation between industry proximity to ports and export status.
This suggests that domestic trade costs and international comparative advantages may play a role
in the location of industries. While other forces might cause this correlation, we see the result as
�rst-pass motivating evidence in support of the theory.
We move on to measuring the e¤ect of domestic trade costs on the concentration of economic
activity and the gains from trade using our model.
4 Quantitative Analysis using the Baseline Model
We propose a quantitative methodology to measure the impact of market access and compar-
ative advantages. We �rst discipline the parameters of the model matching features of the data
related to international trade. Then, we compare the model prediction for the concentration of
employment with its empirical counterpart, and we compute the counter-factual gains from trade
under alternative levels of domestic trade costs.
4.1 Calibration Strategy
For the calibration, we assume Cobb-Douglas preferences with share X in the exported good.
Since the distribution of land endowments � (`) does not a¤ect the outcome for n (`) we set it to be
uniform, � (`) = 1=`. By proper choice of units, we can normalize to one the labor to land ratio n
22
and the maximum distance to an international gate `.22 We can also set aY = 1 and write aX = a
without a¤ecting the equilibrium outcomes for the calibration or the counterfactual exercises.
Therefore we have 5 key parameters: returns to scale �, domestic trade costs �1, taste for
exported goods X , relative price at the port p(0), and relative cost in export oriented industries a.23
The �rst two parameters measure the e¤ects of domestic geography, while the last two parameters
measure the strength of comparative advantages.
The parameters X and � are chosen to match direct empirical counterparts. We set X equal
to �nal expenditures in industries where the U.S. is a net exporter as share of total expenditures
in manufacturing.24 Using 1997 US input-output tables, Herrendorf and Valeyinti (2008) impute
the cost share of land in manufacturing to be 0:03: In the model, the share of land costs in the
production of tradeables is equal to 1 � �; so we use � = 0:97 in the baseline calibration. After
fully calibrating the model, we check the robustness of results to variation around these values.
The last three key parameters fp(0); �1; ag are obtained by matching model outcomes withmoments from the data. The comparative-advantage parameter, a, is chosen to match the export
share in manufacturing output, equal to an average of 16:6% over the years 1989-2000. This is a
conventional target in the international trade literature. In turn, �1 and p (0) are chosen to match
relatively more novel targets related to the nature of our exercise.
The domestic trade cost �1 is set to match the share of domestic shipping costs in the value
of export shipments. The 1997 Commodity Flow Survey (CFS) reports the total f.o.b value for a
representative sample of export shipments. It also informs the number ton-miles for the domestic
segment of these shipments. To �nd the shipping cost associated with export-bound cargo, we use
the cost of shipping per ton-mile from U.S. Federal Highway Administration data. We compute
shipping costs to be 1:5% of the f.o.b shipment value and use this value as calibration target.25
This way of computing domestic trade costs as well as the magnitude that we �nd are similar to
Glaeser and Kollhase (2004).26
Finally, to determine p(0) we match the empirical counterpart of the domestic boundary b: In
the model, population density n(`) in (18) declines monotonically with distance between ` = 0
and b, and stays constant in�b; `�: To identify the value for b in the data, in Figure 3 we plot the
empirical counterpart of n(`) in the model. The �gure shows population density for U.S. counties
22Given our calibration strategy, choosing a di¤erent value for ` would only scale the calibrated �1 proportionallywithout a¤ecting model outcomes such as the land and population shares of the coastal region.
23We do not need to consider world prices p� or external trade costs �0 separately. What matters for modeloutcomes is the relative price at the port, p(0) = p�e��0 .
24We use U.S. manufacturing exports, imports and shipments data for six-digit NAICS. Total consumption isC = Y + IMP � EXP where Y is aggregate gross shipments, IMP is total imports and EXP is total exports.We then calculate CX as the sum of consumption in all industries that have positive trade balance. This yieldsCX=C = 0:454 for the period 1989-2000.
25The cost of a ton-mile shipment varies between 5 to 15 cents in 1997 dollars depending on the size and categoryof the truck. In the CFS data, $339 billion worth of export shipments by trucks traveled 51 billion ton-miles. At$0:1 per ton-mile, this makes shipping costs equal to 1:5% of the f.o.b shipment value. In the CFS sample morethan 60% of the value of all export bound cargo is carried to ports by trucks, so that we consider this measureme asrepresentative of domestic shipping costs.
26 In our baseline calibration of �1 = 0:38, domestic frictions are small but non-trivial: the ad-valorem equivalentvalue of shipping a good from the port is 3:9% to the boundary b and 46% to the innermost location in the territory.
23
Figure 3: Population and Density Across U.S. Counties
03
69
1215
1821
2427
3033
Den
sity
(nor
mal
ized
)
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
Distance (normalized)
against the measure of distance to the nearest port de�ned in Section 3, as well as its �tted spline.
Population density is normalized to the national average, and distance is expressed relative to the
most distant county. The �gure re�ects the well-known fact that population density in the U.S. is
biased toward coasts (Rappaport and Sachs, 2003).27
We see that in the data population density follows a similar pattern to the model. Population
declines fast as we move away from ports, but the slope �attens near counties separated from
their closest port by a distance equal to 10% the distance of the most distant counties.28 After
that, average density stays relatively constant as we keep moving inland. This provides a natural
mapping with the theory. Accordingly, we set b=` = 10%. Then, we use the equilibrium condition
(20), which determines the boundary, to pick p(0) such that p(b) = p(0)e�2�1b = a.29
Table 6 summarizes the parameters from our calibration. The �rst two parameters are matched
to their empirical counterparts without solving the model, while for the last three parameters we
solve the model and match the outcomes to the empirical moments.
4.2 Density in Coastal Areas
Using the calibrated model we compute the population density in the coastal region de�ned in
(21). We let bnC be the model-predicted value, and neC be the empirical value. To measure the27Since our distance measure is de�ned in relationship to the largest ports for U.S. trade, this �gure partly re�ects
population density close to some the U.S. largest airports, some of them situated in the interior of the country. Seethe de�nition of the distance measure in Section 3 and the map with the ports in the Appendix.
28The most distant location is at 570 miles, so that the change in slope happens near locations situated at about60 miles from the nearest port.
29From this expression, it is clear that only p (0) =a matters for the location of the boundary. Comparativeadvantages a then determine the trade share of GDP given the value of b.
24
Table 6: Calibrated Parameters
Parameter Calibrated Value Target Value X 0.454 Share of Export Industries in Manuf. Consumption 45.4 %� 0.97 Cost Share of Land in Manuf. 3 %a 0.161 Export Share of Manuf. Output 16.6 %p(0) 0.174 Empirical b 10 %�1 0.383 Shipping Costs as Share of Export Value 1.5 %
quantitative success of the model, we use the following metric of model success:
success =bnC � 1neC � 1
.
This measure would attain a minimum of zero if the model failed to generate any concentration in
the coastal region (i.e. bnC = n � 1). This is the case in international autarky or if b = `, so that alllocations export X. At the same time, a value for the statistic above 1 (bnC > neC) would put ourexercise into question. In that case, the model would not leave room for additional concentration
forces, such as agglomeration economies or amenities, which are surely at work but outside of our
model.
The calibrated model yields a coastal population share of 19:45% on a surface share of 10%,
implying that nC = 1:95. That is, population density in the coast is almost twice the country
average. Consistently with our calibration strategy for b, to compute the empirical counterpart
neC we classify counties as coastal if their distance to the nearest port is less than 60 miles. The
population share of these counties is 41:4%, implying that neC = 0:414=0:1 = 4:14: As an alternative
measure, Rappaport and Sachs (2003) inform us that U.S. coastal counties collectively account for
13% of its continental land area but 51% of its 2000 population, implying that neC = 3:92. In this
de�nition, all counties on the ocean are considered as coastal.
Table 7: Share of Coastal Density Predicted by the Model
De�nition of Coastal Areas Empirical Coastal Density Model SuccessCounties within 60 miles of a port 4.14 30%Coastal counties (Rappaport and Sachs, 2003) 3.92 32%
In Table 7, we report the measure of success using both empirical measures of coastal density.
Our baseline calibration, implies a success rate of 30%. If we use the Rappaport-Sachs statistic, the
rate is 32%. That is, international trade forces and domestic trade costs alone predicts somewhat
less than 1=3 of U.S. population concentration in counties that have favorable access to international
markets.
Even though the calibration features a modest level of domestic shipping cost, the model gen-
erates a non-trivial variation in density. Figure 4 plots the normalized empirical density-distance
pro�le against its model generated counterpart. The model line has a kink at 0:1 by construction
25
Figure 4: Population Density and Distance: Model vs Data
02
46
810
12
Pop
ulat
ion
Den
sity
(nor
mal
ized
)
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
Distance (normalized)
Data Model
and reaches about a third of the empirical intercept.
In the last section we perform various robustness checks and we show that the model is capable
of generating a reasonable level of concentration for an empirically relevant range of parameter
values around our calibrated values.
4.3 Domestic Trade Costs and Gains from Trade
We now use the calibrated model to study the e¤ect of domestic frictions on welfare. For
that, we vary �1 around a range of values at its calibrated value of 0:383, while keeping all other
parameters �xed. For each value of �1, we compute the coastal density, nC , and the percent gains
from trade u�=ua � 1. The results are shown in Figure 5. In each case, we highlight the calibratedvalue of �1. As we move to the right, domestic trade costs increase and the boundary b moves
towards ` = 0.
The left panel of Figure 5 shows that larger domestic trade costs lead to higher population
density in the coastal region. For a su¢ ciently low value of �1 = 0:038, b = ` and the economy fully
specializes in X. In this case population density in the coastal regions equals the country average.
The right panel presents the e¤ect on welfare. The theory tells us that domestic and interna-
tional trade costs are complementary: increasing �0 from actual values to the level that precludes
international trade has a stronger e¤ect on welfare when �1 is smaller. The gains from trade at the
calibrated value for the parameters are 0:33%, and as �1 grows they approximate zero. When �1 is
half of its calibrated value u�=ua increases to 0:63%, implying an elasticity of the gains from trade
with respect to domestic trade costs close to one near the calibrated parameters.
The elasticity increases as we reduce domestic trade costs further. In the frictionless world, with
26
Figure 5: Counterfactual Analysis of a Reduction in Domestic Trade Costs
0.0383 0.25 0.3833 0.5 0.75 11
1.25
1.5
1.75
1.9467
2.25
τ1
Coa
stal
den
sity
(n
c)
0.0383 0.25 0.3833 0.5 0.75 10
0.0033
0.01
0.02
0.0236
0.03
0.0427
τ1
Gai
ns f
rom
tra
de in
pct
. (u
* /ua 1
)
complete specialization
completespecialization
calibrated value
calibrated value
�1 close to zero, real income is about 4:2% higher than in its autarky level. This is the upper bound
for the gains from trade, u=ua, that we de�ned in (24). Therefore, in terms of that decomposition,
we obtain:u�
ua|{z}�0:33%
= (�1; b)| {z }�1=13
� u
ua|{z}�4:2%
We conclude that domestic trade costs, despite having a small magnitude, have a potentially
large impact on the gains from trade. Since the baseline model does not include a number of
additional forces, this large e¤ect is likely an upper bound.
4.4 Robustness
We evaluate the sensitivity of the results to variation in the exogenously �xed parameters and
in the moments used to match the remaining parameters. The results do not vary considerably
with the external trade costs �0, with the preferences parameter X or with the export/output
target, but they are quite sensitive to the returns-to-scale parameter � and the domestic shipping
cost target. Figure 6 plots the model-generated coastal density in four cases, together with the
baseline value (red dashed line) and with the empirical value (black dashdot line).
First, we recalibrate the model for a range of values for � in the empirically relevant range of
[0:96; 0:98] according to estimates in the literature.30 The result is in in the upper-left panel of
30Caselli and Coleman (2001) use a value of 0:06 based on national income accounting by Jorgenson and Gollop(1992). Albouy (2012) and Rappaport (2008) report lower numbers, ranging between 0:016 and 0:025.
27
Figure 6: Robustness of the Calibration
0.96 0.965 0.97 0.9751
1.5
2
2.5
3
3.5
4
4.5
5a) Returnstoscale α
Coa
stal
den
sity
(n
c)
0.42 0.43 0.44 0.45 0.46 0.47 0.481
1.5
2
2.5
3
3.5
4
4.5
5b) Preference parameter γx
Coa
stal
den
sity
(n
c)
1.013 1.014 1.015 1.016 1.017 1.018 1.019 1.021
1.5
2
2.5
3
3.5
4
4.5
5c) Domest ic Trade Cost Target
Coa
stal
den
sity
(n
c)
0.14 0.15 0.16 0.17 0.18 0.191
1.5
2
2.5
3
3.5
4
4.5
5d) Export/Output Target
Coa
stal
den
sity
(n
c)
Figure 6. The results are quite sensitive to variation in �: For � = 0:96, we have nC = 1:56 which
corresponds to a success rate of 18%. For � = 0:98; the model generates a coastal density of 3:53
close to its empirical counterpart. As � gets closer to one, land as a �xed factor matters less, which
induces more concentration. In the limiting case of � = 1; all activity is located at ` = 0. In
the upper-right panel, we let X vary in a band of �0:035 around its baseline value of 0:454 andrecalibrate (�1; a). In that case, nC varies between 1:8 and 2:08 .
As shown in the lower-left panel, when we target domestic shipping costs between 1:25% and
2% around its baseline value of 1:5%, nC varies between 1:68 and 2:84 (lower-left panel). Finally,
we vary the export/output target in a band of �0:03 around its baseline level 0:166 and recalibrate(�1; a). In that case, nC varies between 1:93 and 1:97 (lower-right panel).31
5 Extended Model
Our calibration excludes a few forces that could be important and a¤ect our answers. To
provide a more careful answer to our quantitative questions, we extend our baseline model in a few
dimensions.
31We also choose values for �0 such that the ad-valorem equivalent external trade costs vary between 50% and100%, rather than 75% as in our baseline value, and we recalibrate (�1; a) in each case. We �nd that nC varies little,between 1:91 and 1:94 around its baseline level of 1:92.
28
First, we allow for congestion forces to di¤er across export-oriented and import-competing
industries. As we show next, allowing for industry-speci�c �i implies that population density
may not be higher in coastal areas. If import-competing industries are su¢ ciently labor intensive,
population density might be higher in the interior. In turn, non-tradeables are important as share
of employment and consumption, and they create congestion forces.
Since our aim is to explain concentration of employment across locations, another natural
concern with our baseline model is that a considerable fraction of employment lies in the service
sector while in our baseline setup all employment correspond to tradeables. In addition, housing
represents an important share of household consumption, so that population pressure a¤ects land
rents and further acts as a congestion force. Therefore, we add a service and a housing sector.
As we show next, the extended model preserves the key prediction that industries arrange in
space based on export orientation, but the added ingredients might overturn some of our results. We
fully characterize the extended model in general equilibrium, and we derive a closed-form expression
for population density in each location.
Now, the price index at ` takes the form:
E (`) = EN (`)�N ET (`)
1��N (26)
As before, ET represents the price index of tradeable goods. But now each consumer spends a
share �N of income in non-tradeables. Non-tradeables include housing and services and have price
index EN with the Cobb-Douglas form. We let H be the share of expenditures in non-tradeables
that goes to housing.
On the supply side, we assume each unit of housing requires aH units of land, and that each
unit of services requires aS units of labor. Therefore, in every populated location the price of each
unit of housing at ` is aHr (`), the price of each unit of services is aSw (`), and the price index for
non-tradeables is
EN (`) = aNr (`) H w (`)1� H , (27)
where aN � a HH a1� HS is a productivity index in the non-tradeable sector of the economy.
To de�ne the local equilibrium we need to use consumption of non-tradeables in each location.
Using (26) and (27) we obtain demand of housing and services per unit of land in location `:
cH (`) = �N Hy (`)
aHr (`); (28)
cS (`) = �N (1� H)y (`)
aSw (`); (29)
where
y (`) � w (`)n (`) + r (`)
is income per unit of land in location `.
29
5.1 Local Equilibrium
Does the extended model preserve the key properties from the baseline setup? We can show
that, as in the baseline setup, the specialization pattern is independent from the land endowment
and depends only on each location�s distance to the port. For that, we de�ne a local equilibrium
in the extended model.
De�nition 3 (Local Equilibrium in the Extended Model) A local equilibrium at ` consists
of employment density n (`), labor demands fni (`)gi=X;M , specialization patterns f�i (`)gi=X;M ,consumption of non-tradeable goods fcS (`) ; cH (`)g, and factor prices fw (`) ; r(`)g such that
1. workers maximize utility,
w (`)
EN (`)�N ET (`)
1��N� u�; = if n (`) > 0; (30)
with demand of non-tradeables fcS (`) ; cH (`)g given by (28), (29);
2. pro�ts are maximized,
�i (`) � 0; = if �i (`) > 0, for i = X;M; (31)
where �i (`) is given by (3);
3. land and labor markets clear,
aHcH (`) +X
i=X;M
�i(`) = �(`); (32)
aScS (`) +X
i=X;M
�i(`)
�(`)ni(`) = n (`) ; and (33)
4. trade is balanced.
The de�nition of a local equilibrium is the same as in De�nition 1 except that now the land and
labor market clearing conditions (7) and (8) include resources used for housing and services. Since
�i is allowed to di¤er by sector, now conditions 2 to 4 resemble a small Hecksher-Ohlin economy
with a non-tradeable sector. As in the baseline model, specialization turns out to be independent
from land abundance. Labor mobility neutralizes factor-proportions e¤ects, and e¤ectively turns
each local economy into a Ricardian economy as in our baseline setup.
We can characterize the pattern of specialization in the local economy as follows. First, using
conditions (30) and (31), we de�ne the wage-rental ratio w (`) =r (`) that would prevail if the local
economy ` were to produce the tradeable good i:
!i (`) =
aia�N=(1��N )N
Pi (`) =ET (`)u�1=(1��N )
! 11��i+ H�N=(1��N )
if �i (`) > 0, for i = X;M . (34)
30
The wage-rental ratio decreases with the price of output Pi (`), since it increases the productivity
of land relative to other locations. A higher real wage u� naturally causes the wage-rental ratio to
increase by rising workers�outside options, as does a higher cost of living ET (`), which increases
the wage demanded by workers. A higher �i or �N increases the wage-rental rate by making the
techniques used in ` more labor intensive.
Next, we let ! (`) � w (`) =r (`) be the actual wage-rental ratio at location `. Then, (30) and(31) also imply that, in a local equilibrium,
! (`) = min f!X (`) ; !M (`)g ,
where if !X (`) < !M (`) then local economy fully specializes in X, while if !X (`) > !M (`) the
local economy fully specializes in M .
From this, we conclude that Proposition 1 also applies in the extended model. As before, the
autarky price is independent from the land endowment and therefore identical in all locations.
Hence, the pattern of specialization can be determined based on whether p (`) is above or below
pA. In contrast to the baseline model, pA is no longer equal to a but corresponds to the unique
value of p (`) for which !X (`) = !M (`). As such, it now depends on the real wage u�.
5.2 Population Density
We have just demonstrated that, as in our baseline model, the relationship between relative
prices, technology and preference parameters is su¢ cient to determine the trade pattern in each
location. As a consequence, distance to the international gate is the only fundamental characteristic
of a location that determines its specialization pattern. We conclude, as before, that whenever the
country is incompletely specialized it is partitioned into a coastal region ` 2 [0; b] and an interiorregion ` 2
�b; `�.
Before moving on to the quantitative exercise, we characterize how population density varies
based on the geographic position of each location when the country exports X to RoW. Solving for
the local equilibrium, we can show that
n (`) =
8<:�
1�N H+(1��N )(1��X)
� 1�
1!X(`)�
1�N H+(1��N )(1��A(b))
� 1�
1!X(b)
if` 2 [0; b]` 2
�b; `� . (35)
Note that for ` 2 [0; b], n (`) depends on the labor intensity in the export oriented industry, �X .In turn, for ` 2
�b; `�, intensity in land use only depends on demand conditions. In that case, we
have that �A is a weighted average of �X and �M :
�A (b) = sA (b)�X + (1� sA (b))�M
31
where the weights are given by
s (b) =a
a+ [!X (b)]�M��X � [cM=cX (p (b))]
.
and where cM=cX (p) is demand for M relative to X when the relative price of X is b implied by
the homothetic price index in the tradeable sector, ET .
As before, population in specialized regions increases towards the coast. However, the introduc-
tion of non-tradeables makes congestion forces stronger relative to a the benchmark. In addition,
when �X di¤ers from �M then n (`) is no longer continuous at the boundary b. For ` 2 [0; b], laborintensity equals �X , while for ` 2
�b; `�labor intensity depends on relative consumption in each
sector. As a consequence if �X > �M then �A < �X , and population density jumps down at the
boundary.
A general equilibrium of the extended model is then characterized by the values of fu�; bg suchthat the aggregate labor market condition (16) and condition (20) for the determination of b hold.
5.3 Quantitative Analysis with the Extended Model
[To be Completed]
6 Conclusion
We developed and quanti�ed a theory to characterize the interaction between international trade
and the geographic distribution of economic activity within countries. We presented a framework
that combines standard forces in international trade with a geographic dimension within countries.
Locations within countries di¤er in access to international markets and congestion forces deters
economic activity from concentrating in a single point.
We �nd that whenever an open economy is not fully specialized in what it exports, two dis-
tinct regions necessarily emerge: a specialized, high-population density region close to the port
that trades internationally; and a low-population density region in the interior that stays in au-
tarky. Even though space is continuous and trade costs are uniform, heterogeneous access to world
markets is su¢ cient to generate a dual economy. The model is consistent with three basic facts:
economic activity concentrates in areas with good access to international markets; international
trade integration is correlated with migration toward these areas; and comparative-advantage in-
dustries locate in these regions. I also implies that the gains from international trade are larger
when domestic trade costs are smaller.
We empirically assessed the main model prediction for industry location. Using U.S. data on
specialization at the county level, we found that U.S. export-oriented industries at the national
level are more likely to locate and to employ more workers in places situated at lower distance from
international gates such as seaports, airports or land crossings. Finally, we calibrated the model
to U.S. data to measure the importance of international trade in concentrating economic activity
32
in coastal areas, as well as the importance of domestic trade costs in hampering the gains from
trade. Our counterfactuals using the baseline model indicate that international trade can account
for approximately 1=3 of the concentration in coastal regions of the U.S., and that domestic trade
costs, despite being small, severely reduce the gains from trade.
33
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35
A Proofs
Proof of Proposition 1 (i) Pro�t maximization implies that �i (`) = (Pi(`)=ai)1=(1��i)w (`)��i=(1��i).
Therefore, (6) implies that �X (`) � �M (`) ! p (`) � a and �X (`) � �M (`) ! p (`) � a.
Since autarkic locations must consume, �i (`) > 0 for i = X;M and therefore (6) implies that
�X (`) = �M (`) ! p (`) = pA = a. (ii) and (iii) follow from (12).
Proof of Lemma 1 Suppose that there is bilateral trade between locations `X ; `M at distance
� > 0. If `X is the X-exporting location in the pair, the Proposition 1 implies that p (`M ) � pA �p (`X), so that the relative price of X is larger higher in the X-exporting location. At the same
time, domestic trade costs imply that the relative price of X is strictly larger in the X-importing
location, as implied by (13), a contradiction.
Proof of Proposition 2 (i) From Lemma 1, in international autarky there is no trade between
locations within the country. Therefore, �i (`) > 0 for i = X;M and p (`) = pA for all `. This
implies that n (`) = n and, normalizing the price ofM in autarkic locations to 1, w (`) = u� �e (a)EAin all locations. (ii) With trade, if the country is net exporter of X then n (`) is given by 18. Since
p0 (`) < 0 and p=e (p) is decreasing with p, n0 (`) < 0. If the economy is net exporter of M , then
p0 (`) > 0 but n (`) = (�=1� �) (aM � u�e(p(`)))�1=(1��) so that n0 (`) < 0. (iii) If the country
is net exporter of X, all locations that trade with RoW must produce X. (15) implies that
e�2(�0+�1`)p� � p(`). Therefore, all locations ` such that e�2(�0+�1`)p� > pA specialize in X. Sincethe country is not fully specialized, there must be autarkic locations. If e�2(�0+�1`)p� < pA then
specialization X is not feasible, and the location must stay in autarky. Therefore, there must exist
b < ` such e�2(�0+�1b)p� = pA. Higher population density in the coastal region nC=nA > 1 follows
from the fact that n (`) is continuous and strictly decreasing in ` as long as �1 > 0.
Proof of Proposition 3 (i) and (ii) If pA=p� < e�2�0 but the country is in international autarky
or exports M then the no-arbitrage conditions (14) and (15) are violated. In that case, equilibrium
condition (20) implies that b < ` ! pA=p� < e2(�0+�1`). Similar reasoning applies when the
country exportsM . If e�2�0 < pA=p� < e2�0 but the country exports X orM then the no-arbitrage
conditions (14) and (15) are violated.
Proof of Proposition 4 Average population density in the interior region is:
nA =�
1� �
�a=e(a)
aX � u�
�1=(1��):
From (19), we have that @u�=@�1 < 0, @u�=@�0 < 0, @u�=@p� > 0. These shocks causes nA to
increase. At the same time, condition (20) implies that @b@�1
< 0, @b@�0
< 0, @b@p� > 0. Finally, using
(21) and the labor market clearing condition (16), the ratio of population density between the
36
coastal and interior regions can be expressed as:
nCnA
=n
nA�R `b � (`) d`R b0 � (`) d`
.
Since nA decreases and b increases with a reduction in �0 or �1 or an increase in p�, nC=nA increases
with these shocks. Since population density decreases in coastal locations�b; `�, it means that this
region experiences a decrease in population and the coastal region experiences a population increase.
Proof of Proposition 5 From the labor market clearing condition (16) and the condition (20)
for the boundary, any change in prices that is not caused by changes in�`; � (`) ; n
implies:
Z `
0
dn (`)
n(`)s (`) d` = 0. (36)
When the economy is net exporter of X, we have from (18) that
dn(`)
n(`)=
1
1� �
�dp(`)
p(`)� de (p(`))e (p(`))
� du�
u�
�(37)
Using the de�nition of "(p) and (37) in (36) we have:
du�
u�=
Z b
0[1� "(p(`))] s (`) dp(`)
p(`)d`.
For (i), we have that an increase in the terms of trade imply dp(`)=p (`) = p� for all ` 2 [0; b]. Then,(23) gives: cu�bp =
Z b
0[1� "(p (`))]s (`) d` <
Z b
0s (`) d`:
whereR b0 s (`) d` is the share of employment in export-oriented locations. For (ii), we have that
d� i1 > 0 implies dp(`)=p (`) = �`d� i1 < 0 for all ` 2 [0; b] so d(u�=ua)=d�1 < 0.
37
B Data Appendix
B.1 Construction of the distance measure
[ To be Completed ]
Figure 7: Top U.S. Ports by Export Share
B.2 Concordance between trade and industry data
[ To be Completed ]
38
Table 8: List of NAICS Manufacturing Industries Used in Section 3NAICS code Industry Trade Balance EXP/Q
3364 AEROSPACE PRODUCTS AND PARTS 1 0.4323336 ENGINES, TURBINES, AND POWER TRANSM ISSION EQUIPMENT 1 0.3543331 AGRICULTURE AND CONSTRUCTION MACHINERY 1 0.3513339 OTHER GENERAL PURPOSE MACHINERY 1 0.3383345 NAVIGATIONAL, MEASURING , ELECTROMEDICAL ETC . INSTRUMENTS 1 0.2773359 ELECTRICAL EQUIPMENT AND COMPONENTS, NESOI 1 0.2403251 BASIC CHEMICALS 1 0.2373252 RESIN , SYNTHETIC RUBBER, & ARTIFIC IAL & SYNTHETIC FIBERS 1 0.2343253 PESTIC IDES, FERTILIZERS AND OTHER AGRICULTURAL CHEMICALS 1 0.2163391 MEDICAL EQUIPMENT AND SUPPLIES 1 0.1843334 VENTILATION, HEATING , AIR -CONDITIONING ETC. EQUIPMENT 1 0.1703259 OTHER CHEMICAL PRODUCTS AND PREPARATIONS 1 0.1513112 GRAIN AND OILSEED MILLING PRODUCTS 1 0.1243256 SOAPS, CLEANING COMPOUNDS, AND TOILET PREPARATIONS 1 0.0953122 TOBACCO PRODUCTS 1 0.0923366 SHIPS AND BOATS 1 0.0833324 BOILERS, TANKS, AND SHIPPING CONTAINERS 1 0.0813261 PLASTICS PRODUCTS 1 0.0803116 MEAT PRODUCTS AND MEAT PACKAGING PRODUCTS 1 0.0793255 PAINTS, COATINGS, AND ADHESIVES 1 0.0773222 CONVERTED PAPER PRODUCTS 1 0.0633119 FOODS, NESOI 1 0.0623362 MOTOR VEHICLE BODIES AND TRAILERS 1 0.0573111 ANIMAL FOODS 1 0.0543231 PRINTED MATTER AND RELATED PRODUCT, NESOI 1 0.0493133 FIN ISHED AND COATED TEXTILE FABRICS 1 0.0463321 CROWNS, CLOSURES, SEALS AND OTHER PACKING ACCESSORIES 1 0.0113343 AUDIO AND VIDEO EQUIPMENT 0 0.6263344 SEM ICONDUCTORS AND OTHER ELECTRONIC COMPONENTS 0 0.4533341 COMPUTER EQUIPMENT 0 0.4513161 LEATHER AND HIDE TANNING 0 0.4063314 NONFERROUS METAL (EXCEPT ALUMINUM) AND PROCESSING 0 0.3863159 APPAREL ACCESSORIES 0 0.3543333 COMMERCIAL AND SERVICE INDUSTRY MACHINERY 0 0.3023169 OTHER LEATHER PRODUCTS 0 0.2913332 INDUSTRIAL MACHINERY 0 0.2803346 MAGNETIC AND OPTICAL MEDIA 0 0.2523399 M ISCELLANEOUS MANUFACTURED COMMODITIES 0 0.2293353 ELECTRICAL EQUIPMENT 0 0.2273329 OTHER FABRICATED METAL PRODUCTS 0 0.2253363 MOTOR VEHICLE PARTS 0 0.2133335 METALWORKING MACHINERY 0 0.1923342 COMMUNICATIONS EQUIPMENT 0 0.1913132 FABRICS 0 0.1863272 GLASS AND GLASS PRODUCTS 0 0.1753325 HARDWARE 0 0.1723369 TRANSPORTATION EQUIPMENT, NESOI 0 0.1703352 HOUSEHOLD APPLIANCES AND MISCELLANEOUS MACHINES, NESOI 0 0.1573162 FOOTWEAR 0 0.1463262 RUBBER PRODUCTS 0 0.1413322 CUTLERY AND HANDTOOLS 0 0.1373271 CLAY AND REFRACTORY PRODUCTS 0 0.1363365 RAILROAD ROLLING STOCK 0 0.1363313 ALUM INA AND ALUMINUM AND PROCESSING 0 0.1333254 PHARMACEUTICALS AND MEDICINES 0 0.1293221 PULP, PAPER, AND PAPERBOARD MILL PRODUCTS 0 0.1273152 APPAREL 0 0.1263351 ELECTRIC LIGHTING EQUIPMENT 0 0.1243361 MOTOR VEHICLES 0 0.1043279 OTHER NONMETALLIC M INERAL PRODUCTS 0 0.0983211 SAWMILL AND WOOD PRODUCTS 0 0.0953311 IRON AND STEEL AND FERROALLOY 0 0.0953326 SPRINGS AND W IRE PRODUCTS 0 0.0933149 OTHER TEXTILE PRODUCTS 0 0.0853141 TEXTILE FURNISHINGS 0 0.0613212 VENEER, PLYWOOD, AND ENGINEERED WOOD PRODUCTS 0 0.0603114 FRUIT AND VEGETABLE PRESERVES AND SPECIALTY FOODS 0 0.0573131 FIBERS, YARNS, AND THREADS 0 0.0523151 KNIT APPAREL 0 0.0503372 OFFICE FURNITURE (INCLUDING FIXTURES) 0 0.0493117 SEAFOOD PRODUCTS PREPARED, CANNED AND PACKAGED 0 0.0443113 SUGAR AND CONFECTIONERY PRODUCTS 0 0.0413241 PETROLEUM AND COAL PRODUCTS 0 0.0393371 HOUSEHOLD AND INSTITUTIONAL FURNITURE AND KITCHEN CABINETS 0 0.0363327 BOLTS, NUTS, SCREWS ETC. PRODUCTS 0 0.0353121 BEVERAGES 0 0.0273219 OTHER WOOD PRODUCTS 0 0.0213312 STEEL PRODUCTS FROM PURCHASED STEEL 0 0.0193115 DAIRY PRODUCTS 0 0.0183315 FOUNDRIES 0 0.0163323 ARCHITECTURAL AND STRUCTURAL METALS 0 0.0163118 BAKERY AND TORTILLA PRODUCTS 0 0.0133274 LIME AND GYPSUM PRODUCTS 0 0.0133379 FURNITURE RELATED PRODUCTS, NESOI 0 0.0123273 CEMENT AND CONCRETE PRODUCTS 0 0.004
Notes: Trade balance equals one if exp orts exceed imports over 1997-2000, and zero otherw ise. (EXP=Q) is exp ort/output ratio .
39