Export Prices and Relative Location of Exporting Countries
Moonjung Choi
October 19, 2011
Abstract
This paper addresses how an exporting country's remoteness and its distance to a destination
market aect its export prices through the selection of exporting rms. Remoteness refers to how
far the exporting country is from all of its trading partners, while distance refers to how far it is
from a particular importing country. The heterogeneous-rms trade model predicts that the relationships of export prices to remoteness and distance dier depending on whether the industry
exhibits heterogeneity in quality among rms or merely heterogeneity in productivity. I nd that
the average export price decreases with remoteness, but increases with distance in an industry
where rms are heterogeneous in quality. The eects of remoteness and distance are magnied as
the degree of quality heterogeneity increases. In contrast, I nd ambiguous remoteness and negative distance eects in an industry with homogeneous quality. The empirical investigation using
product-level U.S. import data nds that remoteness and distance are signicant determinants
of export prices and the magnitude of their eects increases with the industry-specic scope for
quality dierentiation.
JEL Codes: F100, F120, F140
Keywords: Export Prices, Remoteness, Quality, Firm heterogeneity
1
1 Introduction
Ever since Melitz (2003), recent trade literature supports the claim that high productivity rms are
selected to be exporters. As more countries open to trade, survival of rms and their participation in export
markets become more competitive. However, the degree of competition faced by rms to enter an export
market may dier depending on their geographical circumstances. Thus, the geographical characteristics of
a country will be an important factor that determines the degree of selection of rms to be exporters and
subsequently aect its trade performance in the foreign market. Therefore, it is necessary to understand how
the geography of exporting countries determines their trade performance through this selection channel.
This study emphasizes two geographical characteristics: the remoteness of an exporting country and the
distance between exporting and importing countries. Remoteness refers to how far the exporting country is
from all of its trading partners, while distance refers to how far it is from a particular importing country.
The importance of these two geographical factors is discussed in numerous previous studies.
Wei (1996)
introduces remoteness to represent the relative location of a country. This relative location of a country has
been also examined as a signicant determinant of its economic performance by Anderson and van Wincoop
(2003) and Reddings and Venables (2004). On the other hand, the distance between exporters and importers
is directly related to transport costs, thus, it is taken into account in trade models as an essential factor that
determines trade outcomes.
Based on the discussion above, this paper specically addresses how the remoteness of an exporting
country and the distance between exporting and importing countries determine its export prices through the
selection channel in which high capability rms are selected to be exporters. Although previous literature
investigates the role of these two geographical conditions of exporting countries in their trade performance,
its eect on export prices via selection channel has not been thoroughly explored. Recent literature including Baldwin and Harrigan (2011), and Manova and Zhang (2010) addresses the eects of the remoteness
of importing countries and the distance between importing and exporting countries on export prices by
considering selection channels using export data. However, there have been few studies investigating those
eects from the exporting countries' viewpoint using import data.
Therefore, this paper theoretically in-
vestigates the impact of the remoteness of an exporting country and the distance between exporting and
importing countries on its export prices using the heterogeneous-rms trade model and empirically examines
the model's predictions using product-level U.S. import data.
In addition to nding the geographical eects on export prices, I also explore how these eects vary across
industries. The heterogeneous-rms trade model yields dierent predictions of geographical eects on export
prices depending on the source of heterogeneity among rms in an industry. If rms are heterogeneous in
2
terms of their product quality, the industry is referred to as a quality dierentiation (or quality-sorting)
industry, while if rms are heterogeneous in their productivity and homogeneous in their product quality,
the industry is referred to as a cost (or eciency-sorting) industry. The model yields that the relationships
of remoteness and distance to export prices dier depending on whether the industry is quality dierentiated
or not.
Also, the magnitude of the eects of remoteness and distance on export prices varies with the
industry-level degree of quality dierentiation. I nd the cross-industry variation of these eects from the
model and then test the predictions of the model using the U.S. import data in the empirical analysis.
In order to explain the impacts of remoteness and distance on export prices theoretically, I employ the
theoretical framework of the heterogeneous-rms trade model, supposing rms are heterogeneous in terms of
either productivity or quality. These two sources of rm heterogeneity are collectively referred to as capability
throughout the paper. Also, to allow consumers to care about product quality as well as varieties, I employ a
quality-augmented linear demand system following Kneller and Yu (2008) and Melitz and Ottaviano (2008).
Based on this model setting, I present an industry-partial equilibrium model of a four-country open economy,
where four countries that are same-sized and equidistant are located along a line. Relative to the two central
countries in the middle, the other two peripheral countries are remote. I assume that this relative location
1
among countries causes wages in remote countries to be lower than those in central countries .
Within this setting, I consider a case where one central country imports from three other exporting
countries. The central importing country has an equilibrium capability ranking threshold above which rms
cannot protably operate.
Given this threshold, a remote exporting country has an advantage of lower
wages relative to the other central exporting country. Thus, competition to enter the export market is less
challenging for rms in the remote country than those in the central country. On the other hand, a proximate
exporting country has an advantage of lower transport costs relative to the other distant exporting country.
Thus, it is more dicult for rms in the distant country to reach the export market than those in the
proximate country.
These dierent levels of selection caused by dierent geographical conditions across exporting countries
yield dierent predictions on average export prices between quality-sorting industries and eciency-sorting
industries.
In the quality-sorting industry, a high capability rm charges a high price.
stronger selection to export increases average export prices.
1 The
Consequently, a
Thus, the weaker selection in the remote
rationale of this assumption stems from the following previous literature. Redding and Venables (2004) document that
countries with poor market/supply access face higher transport costs in exporting their products and importing inputs relative
to the countries with better market/supply access. Due to this disadvantage of their relative location, rms in remote countries
can only aord to pay relatively low wages than in central countries. They empirically show the negative causal relationship
of market access to wage levels. Also, Anderson and van Wincoop (2003) document that multilateral trade resistance of a
exporting country reduces relative export price due to the low world demand for their products, implying low income per capita
in remote countries. These ndings provide supporting evidence for the assumption that central counties tend to have higher
wage relative to remote countries.
3
exporting country lowers the average export price, and at the same time, higher wages in the central exporting
country raise the average export prices. As a result, the remoteness of exporting countries and their average
export prices are negatively related.
On the other hand, the stronger selection in the distant exporting
country raises its average export prices, relative to the proximate exporting country, causing the distance
eect to be positive. In addition, these negative remoteness and positive distance eects on average export
prices are magnied as the degree of quality dierentiation deepens in the quality-dierentiated industry.
In contrast, if the industry is not engaged in quality dierentiation, the model yields dierent predictions.
In the eciency-sorting industry, a high capability rm charges a lower price. Thus, the weaker selection in
the remote exporting country raises its average export prices, but at the same time, the higher wage in the
central exporting country raises its average export prices. As a result, there is no signicant dierence in
export prices between remote and central exporting countries. On the other hand, the stronger selection in
the distant exporting country lowers its average export prices relative to the proximate exporting country,
causing the distance eect to be negative. Therefore, the model yields an ambiguous remoteness eect and
a negative distance eect on average export prices in the eciency-sorting industry.
I test the predictions of my model using U.S. import data with the Harmonized System (HS) 10-digit code
product categories in 2006. From this data set, free-on-board (f.o.b.) unit values can be calculated as f.o.b.
value of imports divided by their quantity for each product-country pair. The main explanatory variables
are a remoteness measure for each exporting country, constructed as the sum of GDP-weighted distance
over all other countries following Wei (1996), and distance between the U.S. and the exporting country. My
analysis controls for exporting country size, income per capita, border, landlocked, language, and tari rates.
Controlling for those factors, I regress the f.o.b. unit values on exporting countries' remoteness and distance
with product xed eects.
For the overall manufacturing sector, I nd both remoteness and distance are negatively correlated with
unit values of exports.
This result suggests that quality-sorting and eciency-sorting industries co-exist
within the manufacturing sector.
When the sample is divided into sub-groups by SITC-1 digit code, the
relationships of remoteness and distance to the average unit values vary across industries as the model
predicts. In the Chemical industry (SITC 5), one standard deviation increase in remoteness is associated
with a 0.133 standard deviation decrease in unit values of exports on average, while one standard deviation
increase in distance is associated with a 0.097 standard deviation increase in unit values of exports on
average. In the Machinery industry (SITC 7), one standard deviation increase in remoteness is statistically
insignicantly associated with a 0.058 standard deviation decrease in unit values of exports on average, while
one standard deviation increase in distance is associated with a 0.261 standard deviation decrease in unit
values of exports on average.
4
Then, I examine interaction eects of remoteness and distance with a proxy for industry-level scope for
quality dierentiation to identify whether this cross-industry variation of the eects are related to industrylevel degree of quality dierentiation.
I employ three measures as proxies for industry-specic degree of
quality dierentiation: the Rauch (1999) dummy, the quality ladder length by Khandelwal (2010), and the
slope estimates between export prices and thresholds by Johnson (2010). When remoteness and distance
measures are interacted with these proxies, I nd a signicant tendency that the negative remoteness and
positive distance eects are magnied as the scope for quality dierentiation increases.
The tendency is
captured more signicantly when using the proxies at more aggregated industry level.
The theoretical and empirical ndings of this paper imply that geographical factors of exporting countries
are signicant determinants of their aggregate export prices through the selection channel and their signicance is more pronounced in industries with a greater range of quality competition. However, although the
theoretical model yields clear predictions on the relationship between aggregate export prices and geographical factors, some of the empirical ndings that are not consistent with the model's predictions leave room for
more thorough exploration in both theoretical and empirical analysis. The limits and future improvement of
this study are discussed in the paper. The remainder of the paper is organized as follows: Section 2 discusses
related literature, Section 3 presents the theoretical model, Section 4 describes the data, Section 5 shows
empirical specications and results, and Section 6 reports the conclusion.
2 Related Literature
This work builds on recent studies that investigate the relationships of export prices to country characteristics through the selection mechanism. Baldwin and Harrigan (2011) and Manova and Zhang (2010) nd
remoteness and distance eects of importing countries on export prices based on heterogeneous-rm trade
model using product-level U.S. export data and Chinese rm-level data, respectively. Extending their work,
this paper explores the relationship of export prices to the exporting countries' geographical characteristics
using product-level U.S. import data.
This work also adds on recent empirical trade literature analyzing product-level import data to examine
the relationship of exporting country characteristics to export prices. Schott (2004) and Hummels and Klenow
(2005) nd that average export price is strongly correlated with exporting country characteristics, such as
GDP per capita and capital or skill endowments. Johnson (2011) investigates the selection eects of GDP
2
per capita of the exporting country on the export price variation . My research also analyze product-level
2 His nding implies that the strong correlation between average export price and income per capita of the exporting country
stems from variation in export thresholds across countries. He documents that poorer countries tend to have higher export
thresholds on average, based on that poor countries face high barriers to accessing foreign markets and relate this fact with
variation of aggregate export price across sectors. However, in this reasoning, there can be a problem of inverse causality that
poor market access leads a country to have low income as Redding and Venables (2004) point out.
5
import data adding remoteness as another exporting country characteristic that aects export prices.
The theoretical framework this paper employs is based on the heterogeneous-rms trade model presented
in Melitz and Ottaivano (2008), especially in terms of the linear demand system. My model incorporates
a feature of quality heterogeneity following Baldwin and Harrigan (2011) and Kneller and Yu (2008) to
explain both sources of rm heterogeneity, quality and productivity.
Based on this setting, I present an
industry-equilibrium model with a 4-country open economy. My model has an advantage of parsimony with
the minimum number of countries to examine both distance and remoteness eects on trade performance.
This paper also contributes to research on economic geography by examining the role of relative location
of exporting countries on export prices. The relative location has been introduced in gravity model literature.
Wei (1996) introduces remoteness as a measure of the overall distance of a country from all of its trading
partners and Anderson and van Wincoop (2003) interpret the relative location of a country as its multilateral
trade resistance. Redding and Venables (2004) consider the aspect of market/supply access and point out
that the relative location of a country determines its income. With these various approaches, the previous
studies nd that the relative location of a country signicantly inuences its economic performance. Based
on their discussion, I further explore the role of the relative location in export prices using the U.S. import
data.
Furthermore, this paper is related to recent empirical trade literature evaluating industry specic scope
for quality dierentiation and classifying industries into quality-sorting or eciency-sorting industries. Johnson (2011), Khandelwal (2010), and Mandel (2010) estimate measures that represent industry-specic scope
for quality dierentiation.
Manova and Zhang (2010), and Kneller and Yu (2008) classify industries into
quality-sorting and eciency-sorting industries based on the correlation between export prices and country
characteristics, such as market size and distance.
I utilize measures for degree of quality dierentiation
created by Khandelwal (2010) and Johnson (2011) and evaluate variations of remoteness and distance effects on export prices between quality-sorting and eciency-sorting industries. I contribute to these recent
studies by investigating the variation of remoteness eects on export prices relating to the scope for quality
dierentiation.
6
3 Model
3.1 Closed Economy
3.1.1 Consumers
In a closed economy, there are
L identical consumers and their preference over a continuum of vertically and
horizontally dierentiated varieties and a homogeneous good is expressed in the following utility function:
ˆ
(zi qi )di −
u = q0 + ρ
i∈Ω
where
q0
η
ˆ
(zi qi )2 di −
i∈Ω
denotes consumption of the homogeneous good,
for quantity of variety
and
γ
2
i.
zi
η
2
2
(zi qi )di
ˆ
3
(1)
i∈Ω
represents the quality of variety i, and
Consumers care about quality-adjusted quantity,
zi qi .
qi
stands
Demand parameters
ρ, γ ,
are all positive.
The demand for the numeraire good is assumed to be positive (q0 >0). This utility function yields the
inverse demand for each variety
i as follows:
pi
zi
= ρ−γzi qic −ηZ
denotes the aggregate (quality-adjusted) consumption.
consumed (qi
> 0).
i's
´
i∈Ω
(zi qi )di
is dened as the subset of varieties that are
residual demand becomes
qi ≡ Lqic =
pi
zi
Ω∗ ⊂ Ω
qi > 0 where Z =
An increase in the quality of the variety (zi ) increases consumers' willingness-to-pay and
decreases price elasticity. Therefore, rm
Thus,
for all i, so that
= P̂ − qi zLi γ
where
P̂ ≡
L
zi γ
pi
P̂ −
zi
ηN P̃ +ργ
is the quality-adjusted choke price common to all varieties,
ηN +γ
above which the demand for an individual variety becomes zero,
adjusted price of the varieties, and
N
(2)
P̃ =
1
N
´
i∈Ω∗
pi
zi
di
is the average quality-
is the number of varieties consumed.
3.1.2 Firms
Suppose there is a single industry in a closed economy in which
their products are indexed by
i.
Firm
i
N
rms oer a single product. Firms and
produces its product with the quality level
zi .
The rm needs
ci
units of labor to produce one unit of product. In producing its quality level, the rm needs cost with the
4
following form :
zi = c1+θ
i
3 Following Kneller and Yu (2008), quality is added in
4 This functional relationship between product quality
the utility function from Melitz and Ottaviano (2008).
and marginal cost is from Baldwin and Harrigan (2011).
7
(3)
1+θ
is referred to as the quality elasticity that governs the extent to which higher marginal costs are related
to higher quality.
When
When
−1 < θ < 0,
θ > 0,
quality increases elastically with respect to an increase in marginal cost.
quality responds inelastically to an increase in unit labor requirement. When
θ = −1,
this model collapses to the standard heterogeneous rm trade model, thus quality becomes homogeneous
across products and only horizontal dierentiation remains.
w.
Production in this industry requires only one factor, labor. All rms face the same wage rate,
rm i's cost for producing one unit of product is
is
wci
. When
zi
θ > −1,
the eective cost is
summarize these two cases, I use
capable the rm. When
rm
i's
ki
wci
zi
=
wci ,
wci
c1+θ
i
and its eective cost for producing one unit of quality
= wc−θ
i ,
capability and the lower the
ki ,
and when
θ > −1, ki ≡ c−1
i .
[0, kM ].
θ = −1,
wci
zi
it becomes
Thus, in both cases,
= wci .
ki ,
ki
To
the more
summarizes
the higher the capability is.
When a rm enters the industry, it randomly draws its
support of
while when
as an index of capability ranking such that the lower the
θ = −1, ki ≡ ci ,
Thus,
ki
from a distribution function
G(ki )
To learn their own level of capability, rms must pay a xed sunk entry cost,
fe .
on the
Firms
that can cover their marginal costs survive and maximize prots using their residual demand. All other rms
exit the industry immediately.
Subsequently, rm
L
zi r
P̂ −
pi
zi
. Let
kD
i
maximizes its prot
using the residual demand function
qi =
be the eective cost of the rm that makes zero prot. As the quality-adjusted price
decreases to its eective cost,
the eective cost
πi = [pi − wci ] qi
ki ≤ kD
p
z (kD )
θ
= wkD
= P̂ ,
this rm's demand
q(kD )
becomes zero. All rms with
earn non-negative prots and remain in the industry. Thus,
the survival condition. By solving the prot maximization problem of rm
i,
ki ≤ kD
becomes
output, revenue, and prot
functions can be obtained as follows:
wL 1+θ θ
k
kD − kiθ
2γ i
(4)
r(ci ) =
w2 L 2θ
kD − ki2θ
4γ
(5)
π(ci ) =
2
w2 L θ
kD − kiθ
4γ
(6)
q(ci ) =
8
Figure 1: Four-country open economy
τ3
A
τ1
B
τ1
τ1
C
τ2
D
τ2
Distance: 1 < τ1 < τ2 < τ3
3.1.3 Cut-o and Free Entry Condition
In the industry equilibrium, the free entry condition leads the expected prot to be equal to the xed
sunk entry cost as follows:
condition,
kD ≥ ki ,
E(π) =
´ kD
0
π(ki )dG(ki ) =
w2 L
4γ
2
´ kD θ
kD − kiθ dG(ki ) = fe .
0
the free entry condition pins down the cut-o level of the
With the survival
kD .5
3.2 Four-country open economy
Suppose there are four countries that are same-sized and equally distant located along a line as illustrated
in Figure 1. The distance between two adjacent countries is
τ1 ,
the distance between the rst (A) and the
third (C) countries or the second (B) and the fourth (D) countries is
peripheral countries (A and D) is
τ3 .
τ2 ,
and the distance between two
These distances and trade costs are assumed to be non-negligible and
the relationship between these distances are assumed as follows:
1 < τ1 < τ2 < τ3 .
All countries are trading
to one another.
This geographical arrangement of countries makes country B and C to be central but country A and D to
be remote from the rest of the world. I assume that the relative location of countries leads the wage level to be
lower in the remote countries than in the central countries. The wage level in the remote countries A and D
is set as one and the wage level in the central countries B and C is
w, so w > 1, without loss of generality.
In
this setting, therefore, the wage dierence only reects the degree of centrality or remoteness of the countries.
The argument for this assumption can be supported by previous literature such as Redding and Venables
(2004).
Remote countries face higher transport costs or trade costs, relative to the central countries, in
exporting their products and importing inputs. Due to the disadvantage of their relative location, rms in
remote countries can only aord to pay relatively low wages in comparison to central countries. Also, their
5 Refer
to Model Appendix 1 for the solutions of the cut-o.
9
empirical nding veries this negative causal relationship of market access of countries to their wage levels.
As Appendix Figure 2 shows, we can see that there is a negative correlation between per capita GDP and
remoteness across countries.
In this model, therefore, the remoteness is represented by and works through the wage dierences. Based
on this mechanism, the more polarized the relative location of the countries, indicating that the more
centralized the central countries while the more remote the remote countries, the greater the wage dierence.
In each country, there are
A, B, C, D)
consumers, sharing the same preferences. Firms in origin country
can sell in the destination country
of ice-berg cost.
becomes
L
wo τ c−θ
i .
d (d 6= o)
incurring a per-unit transport cost,
The variable cost per unit of exports is
A rm producing in a country
a delivered (c.i.f ) price,
pod
X.
o
w o τ ci
can export some output
od
o
= pod
X − w τ ci qX .
o (o =
as a form
and the eective cost per unit of quality
The demand for this rm's exports in country
od
and its exporting prot becomes πX
τ,
6
od
qX
to a destination country
od
d is given as qX
=
L
zγ
h
P̂ d −
d
at
pod
X
i
z
From the zero prot conditions, the choke price of
the destination country relates the origin country's exporting cut-o and the destination country's domestic
cut-o as follows:
od
d
.
= wo τ od kX
P̂ d = wd kD
As a result, the exporting cut-o of a country can be expressed
with the domestic cut-o of the other country as follows:
od
kX
=
h
wd
wo τ
i
d
kD
.
In a country, the capability
of domestic rms and the capability of exporters have identical distributions over this support, given by
G(k) =
k
kD
α
.
These conditions above yield performance functions of a rm of a country
o
in an export market
d.
The
prot maximizing export output, c.i.f. pricing rule, f.o.b pricing rule, exporting revenue, revenue based on
the f.o.b price, and prot functions are as follows:
pod
X =
od
rX
=
h
i
h
i
i
1
1
L 1+θ o od h od θ
o od
od θ
θ
o
od θ
θ
θ
od
od
w
τ
k
+
k
;
p
=
w
k
+
k
;
q
=
k
w
τ
k
−
k
X
i
X
i
X
i
Xf
ob
X
i
2k 1+θ
2k 1+θ
2γ
i
i
i2
2 h od 2θ
h od θ
L
L o 2 od h od 2θ
L
od
2θ
od
o od 2
θ
wo τ od
kX
− ki2θ ; rXf
=
(w
)
τ
k
−
k
;
π
=
w
τ
k
−
k
ob
X
i
X
X
i
4γ
4γ
4γ
6 Since this model is an industry-partial equilibrium model, the determination of the wage level is above the scope of the
model, but I provide evidence on how relative location of countries aects their wage levels based on the previous literature.
10
3.2.1 Free entry conditions and exporting cut-os
For each country, the domestic prot is
h
od 2
L
4γ
wo τ
od
P ´ kX
d 0
od
kX
θ
− kiθ
i2
o
πD
(k) =
i2
h
o θ
(kD
) − kiθ
(wo )2 L
4γ
and the exporting prot is
. Each country has its own free entry condition in a form of
od
πX
(ki )dG(ki ) = fe .
o
´ kD
0
od
πX
(k) =
o
πD
(ki )dG(ki ) +
Solving these four free entry conditions using the cut-o conditions yields the
domestic cut-os of the four countries,
A
B
C
kD
, kD , kD ,
and
D
kD
,
above which rms cannot operate protably in
central
each country. Due to the symmetry, domestic cut-os in the central countries are identical ,kD
C
kD
,
and those in the remote countries are identical,
B
≡ kD
=
remote
A
D 7
kD
≡ kD
= kD
.
Let us focus on one case where the central country B imports from the other three exporting countries,
A, C, and D. For a given domestic threshold
B
kD
in B, the exporting countries face dierent exporting cut-os
as follows:
The exporting cut-o of remote country A to B:
AB
=
kX
w B
τ1 k D
The exporting cut-o of central country C to B:
CB
kX
=
1 B
τ1 k D
The exporting cut-o of remote country D to B:
DB
kX
=
w B
τ3 kD
A remote county A has an advantage of lower wage relative to its competing central country C in the same
market B. Therefore, for the same central destination market, rms in the remote country face a relatively
higher cut-o level (less competition to export) than rms in the central country,
CB
AB
.
> kX
kX
On the other
hand, the county A has an advantage of lower transport costs relative to its competing country D in the
same market B. Therefore, for the same central destination market, rms in the proximate country face a
relatively higher cut-o level (less competition to export) than rms in the distant country,
DB
AB
.
> kX
kX
3.2.2 Output-Weighted Average F.O.B. Export Prices
In order to investigate how remoteness and distance determine the f.o.b.
export prices, I then formulate
output-weighted average f.o.b. export prices. The share of quantity of exports of an exporting rm relative
to the total exports of the industry is expressed as
od
ωX
≡
´
od
qX
od g(k )dk
qX
i
i
≡
od
qX
. Then, the output-weighted
Qod
X
average f.o.b. exports price can be calculated as the industry's total exporting revenue, calculated with f.o.b.
export prices, divided by the industry's total exports as follows:
ˆ
od
P̄Xf
ob
=
od
kX
´
od od
ωX
pXf ob dG(ki )
0
where
7 Refer
od
RX
and
Qod
X
od
rXf
Rod
ob dG(ki )
= ´ od
≡ X
qX dG(ki )
Qod
X
are the total exporting revenue and quantity from an origin country
to Model Appendix 2 for the solutions.
11
o (o = A, C, D)
to the destination
d (d = B),
respectively.
However, the expression of the average export prices diers depending on whether an industry follows
eciency-sorting or quality-sorting mechanisms. If the industry exhibits eciency-sorting,
are not engaged in quality dierentiation, thus,
θ + 1 = 0.
ki ≡ ci
and rms
Then, the output-weighted average f.o.b. export
o
o
price turns out to depend on the exporting cut-o (cX ) and the wage level (w ) of the origin county and the
shape parameter of the Pareto distribution (α) as follows:
od
P̄Xf
ob =
where
wo
od
RX
1 + α o od
w kX
=
od
2+α
QX
denotes the wage level in an exporting country
o.
On the other hand, if the industry exhibits quality-sorting,
dierentiation, thus,
θ + 1 > 0.
(7)
ki ≡ c−1
i
and rms are engaged in quality
Then, the output-weighted average f.o.b. export price depends on the quality
o
o
elasticity (θ ) as well as the exporting cut-o (kX ), the wage level (w ) of the exporting county, and the shape
parameter of the Pareto distribution (α) as follows.
od
P̄Xf
ob =
where
ξ(α, θ) ≡
od
od −1
RX
= ξ(α, θ)wo kX
od
QX
(8)
(α+θ+1)(α+2θ+1)
.
α(α+2θ)
3.2.3 Remoteness and Distance Eects on Export Prices
Using these expressions of the average export prices, I analyze how remoteness and distance determine the
export prices in both eciency-sorting and quality-sorting industries. By comparing average export prices of
the remote exporting country A and the central exporting country C that are equidistant from the importing
country B, I can verify remoteness eects on the average export prices. Also, by comparing average export
prices of the proximate exporting country A and the distant exporting country D that are equally remote
from the rest of the world, I can examine distance eects on the average export prices.
•
Since
When θ = −1 (Eciency-sorting industry; ki = ci )
wA = wD = 1 and w ≡ wC = wB > 1, the output-weighted average f.o.b.
country A, C and D to the same central destination market B are as follows:
12
export price of the exporting
AB
P̄Xf
ob
CB
P̄Xf
ob
=
=
1 + α AB
1+α w B
cX =
cD
2+α
2 + α τ1
| {z }
selection ef f ect
1 B
1 + α CB
1+α
wcX =
w
c
2+α
2 + α |{z} τ1 D
cost ef f ect
DB
P̄Xf
ob
When
AB
P̄Xf
ob
is compared to
CB
P̄Xf
ob ,
=
1+α w B
1 + α DB
cX =
cD
2+α
2 + α τ3
| {z }
selection ef f ect
we can nd that they are identical and there is no remoteness eect.
AB
CB
P̄Xf
ob = P̄Xf ob
The reason why they are identical is this: the weaker selection in country A raises the average export price
by a factor of
w
through the threshold term
cAB
X .
On the other hand, country C suers from higher costs
of production with higher wages, thus the average export price increases by a factor of
w,
too. Therefore,
these selection eect and cost eect cancel out the dierence in average export prices between remote and
central countries.
This result tells us that when an industry is not engaged in quality dierentiation, the remoteness eect
on the output-weighted average f.o.b. export price will not be observed. It is because the wage dierence
favors a remote exporting country by allowing less capable rms with high
ci
and high prices to export,
resulting in a high average export price. On the other hand, due to the higher wage level, exporting rms in
central countries set their price higher compared to the rms with the same level of
ci
in remote countries,
resulting in a high average export price. Consequently, the selection eect raises average export price from a
remote country, but the cost eect mitigates the price dierence by raising average export price from a central
country. As a result, the average export prices from remote and central counties are not dierent. Therefore,
in those industries with no quality competition, the eect of relative locations of exporting countries on the
13
average export prices is expected to be zero.
On the other hand, a distance eect on the average export prices can be found from a comparison between
AB
P̄Xf
ob
and
DB
P̄Xf
ob .
The higher transport costs for country D
country D than in A
DB
(cAB
X > cX ).
(τ3 > τ1 )
causes the export threshold lower in
Therefore,
AB
DB
P̄Xf
ob > P̄Xf ob
That is, as the distance increases, the average export price decreases;
od
∂ P̄Xf
ob
∂τ
w B
= − 1+α
2+α τ 2 cD < 0.
For an
1
eciency-sorting industry, the average price of exports decreases with distance. It is because the higher the
transport cost, the lower the exporting threshold is; then, it is more dicult for rms in a distant country
to export than those in a proximate country, thus more capable rms are selected to export. Since a more
capable rm charges lower price in an eciency-sorting industry, the average price of exports from the distant
country is lower than that of the close country.
•
When θ > −1 (Quality-sorting industry; ki = c−1
i )
In a quality-sorting industry, the output-weighted average f.o.b. export prices of country A, C and D to the
central destination market B are expressed as follows:
AB
P̄Xf
ob
CB
P̄Xf
ob
=
−1
AB −1
w B
ξ(α, θ) kX
= ξ(α, θ)
kD
τ1
| {z }
selection ef f ect
CB −1
= ξ(α, θ) |{z}
w
= ξ(α, θ)w kX
1 B
k
τ1 D
−1
cost ef f ect
DB
P̄Xf
ob
=
−1
DB −1
w B
= ξ(α, θ)
ξ(α, θ) kX
kD
τ3
| {z }
selection ef f ect
14
Unlike those in the eciency-sorting industry, the average export prices depend on quality elasticity, through
the term
θ, and a high capability rm charges a high price in the quality-sorting industry, thus average prices
are inversely related to the exporting thresholds.
The remoteness eect can be found from the comparison between
AB
P̄Xf
ob
and
CB
P̄Xf
ob .
AB
CB
P̄Xf
ob < P̄Xf ob
The exporting cut-o of country A,
eect leads
by
w,
AB
P̄Xf
ob
to be lower than
AB
kX
is greater than that of country B,
CB
P̄Xf
ob .
this cost eect additionally causes
CB
kX
by a factor of
w; this selection
At the same time, since the country C faces a higher wage level
AB
P̄Xf
ob
to be lower than
CB
P̄Xf
ob .
Therefore, these selection eect and
cost eect result in wider gap in export prices between central and remote countries, resulting in a negative
remoteness eect on average export prices.
The following comparative statics shows that as the remoteness increases, the average export price decreases.
−1
AB
∂ P̄Xf
1 B
ob
= −w−2 ξ(α, θ)
kD
<0
∂w
τ1
(9)
This result tells us that the output-weighted average f.o.b. export price from a remote country is observed to
be lower than that of a central country when the industry is engaged in quality dierentiation. The wage
dierence favors the remote exporting country by allowing less capable rms with high
ki
and low prices to
export, resulting in a low average export price. On the other hand, due to the higher wage level, exporting
rms in the central country set their price higher compared to the rms with the same level of capability
in the remote country, resulting in a high average export price in the central country.
Consequently, the
selection eect lowers the average export price of the remote country, but the cost eect raises the average
export price of the central country. Therefore, in the quality-sorting industry, the remoteness eect on the
average export prices is expected to be negative.
On the other hand, a distance eect on the average export prices can be found from a comparison between
AB
P̄Xf
ob
and
DB
P̄Xf
ob .
The higher transport costs for country D
higher in country D than in A
DB
(cAB
X > cX ).
(τ3 > τ1 )
causes the export threshold to be
Therefore,
AB
DB
P̄Xf
ob < P̄Xf ob
The following comparative statics shows that as the distance increases, the average export price increases
as well.
15
od
d −1
∂ P̄Xf
ob
= ξ(α, θ) wkD
>0
∂τ
(10)
This result is caused by the selection channel because a higher transport cost for the distant country
lowers the exporting threshold. Thus, only high capability rms that charge high prices are able to export
for the same destination country, which leads the average price of exports from a more distant country to
be higher.
3.2.4 Quality elasticity and eects of distance and remoteness
In addition to investigating the relationship of the average export prices to remoteness and distance of
exporting countries, I further explore a variation of the magnitude of remoteness and distance eects. When
the industry is engaged in quality competition, the quality elasticity parameter,
θ, determines the magnitude
of distance and remoteness eects on the average export prices. In this part, I analyze how the distance and
remoteness eects vary with the industry-specic quality elasticity. For that, I nd an interaction-eect of
remoteness and quality elasticity,
term
ξ(α, θ)
od
od
∂ P̄Xf
∂ P̄Xf
ob
ob
∂w∂θ , and that of distance and quality elasticity, ∂τ ∂θ . The multiplier
in the average export price equation (8) is given as follows:
ξ(α, θ) ≡
As in equation (3),
1+θ
(α + θ + 1) (α + 2θ + 1)
, f or θ > −1
α (α + 2θ)
represents the quality elasticity of an industry,
elastically with respect to an increase in marginal cost: when
regards to an increase in marginal cost. When
−1 < θ < 0,
θ = 0,
q .
When
θ > 0, quality increases
quality increases unit-elastically with
quality responds inelastically to an increase in
marginal cost.
•
When θ > 0 (q > 1)
Suppose an industry exhibits a technology such that quality increases elastically to an increase in unit
labor requirement.
Then, the multiplier
∂ξ(α, θ)/∂θ ≡ ξθ (α, θ) > 0.
ξ(α, θ)
is greater than one and increases with quality elasticity,
Therefore, in this industry, the magnitude of the negative remoteness eect and
the positive distance eect increase as the quality elasticity increases.
−1
od
od
h
i−1
∂ P̄Xf
∂ P̄Xf
1 d
ob
ob
−2
d
= −w ξθ (α, θ)
kD
< 0,
= ξθ (α, θ) wkD
>0
∂w∂θ
τ1
∂τ ∂θ
16
•
When θ = 0 (q = 1)
When quality is unit elastic with respect to a change in unit labor requirement, the multiplier is constant,
ξ(α, θ) =
α+1 2
.
α
Thus, it does not vary with the quality elasticity,
∂ξ(α, θ)/∂θ = 0.
Therefore, the
magnitude of distance and remoteness eects does not vary with the quality elasticity.
od
od
∂ P̄Xf
∂ P̄Xf
ob
ob
= 0,
=0
∂w∂θ
∂τ ∂θ
•
When −1 < θ < 0 (0 < q < 1)
When quality responds inelastically to a change in unit labor requirement,
may be either positive or negative depending on the ranges of
α ≤ 2.
negative if the shape parameter of Pareto distribution
√
θ<
2+a−a
, but turns to positive if
2
θ>
α
√
2+a−a
. When
2
and
θ.
When
α > 4.56,
ξ(α, θ) is positive but ∂ξ(α, θ)/∂θ
Specically,
∂ξ(α,θ)
is denitely
∂θ
2 < α ≤ 4.56,
it is denitely positive.
that if the distribution of capability of rms in the industry is close to uniform (if
α
it is negative if
8 This suggests
is close to 1), the
magnitude of the distance and remoteness eects decrease as quality elasticity increases; but as the greater
mass of rms concentrates more on low capability (as
α
increases), then the magnitude of those eects
increases.
Table 1: Eects of Remoteness and Distance on Average Export Prices
Quality-sorting
Eciency-sorting
where
8 Detail
q = 1 + θ,
quality elasticity
Remoteness
Remoteness
Distance
(q )
eect
× q
eect
q > 1
-
-
+
+
q = 1
-
0
+
0
0 < q < 1
-
+/-
+
+/-
q = 0
0
0
-
0
the elasticity of quality with respect to unit labor requirement.
is available in Model Appendix 3.
17
Distance
× q
In sum, all cases can be summarized as Table 1. In a quality-sorting industry where the quality elasticity is
greater than zero (q
> 0), the remoteness eect on average export prices is negative, while the distance eect
is positive. Furthermore, if
as
q
increases; if
q = 0 ,
q > 1,
the negative remoteness eect and positive distance eect are magnied
there is no additional interaction eects; and if
0 < q < 1,
the interaction eects
are indenite.
On the contrary, in an eciency-sorting industry where the quality elasticity is zero (q
= 0),
the
remoteness eect on average export prices is zero and the distance eect is negative.
4 Data
I use the U.S. import data with the Harmonized System (HS) 10-digit code product categories for the
year 2006, which is sourced from the U.S. Census Bureau and compiled by Feenstra. This data set provides
information on the quantity and f.o.b.
value of imports from each originating country in each product
category. The product categories are very narrowly dened at HS 10-digit code and some examples of them
include the following:
•
6206303010 Women's cotton blouses with more than 2 color warps
•
9503490025 Toys that represent animal or non-human creatures
Based on the information given in the data set, I construct the f.o.b. unit value (Upc ) of product
from country
c
p
shipped
by dividing the value of the imports by their quantity. I focus on manufactured products,
categorized in the Standard International Trade Classication 1-digit codes from 5 to 8, to which product
dierentiation reasonably applies. All products that report a quantity of one unit or a total value of less
than $1,000 dollars are dropped.
To remove outliers, the unit values above top 5% or below bottom 5%
within each product category are dropped.
The country-specic remoteness is measured as a GDP-weighted average distance, following Wei (1996):
Rc =
P
d sd Dcd , where
sd
denotes country
World Economic Outlook Database, and
d's
Dcd
GDP share relative to the world total, obtained from IMF
denotes the distance between country
c
and
d,
sourced from
CEPII. The sum of this GDP-weighted distance over all other countries (d's) is dened as the remoteness
of country
c.
This remoteness can be interpreted as country
18
c's
relative location from the rest of the world.
According to this measure, the most central country is the Netherlands and the most remote country is New
Zealand. Table 2 lists countries in the sample sorted by their remoteness.
The other key explanatory variable, the distance between an exporting country and the U.S., is sourced
from CEPII and measured as the distance between the most important cities, or agglomerations in terms of
population, in each country in
km.9
In order to make the empirical specication capture the remoteness and distance eects as in the theoretical model, I control for country-specic factors that can aect export price determination. I control for
characteristics of exporting countries such as whether the country is landlocked and whether the country
shares a border or the same language with the U.S. because these factors aect xed costs of export market
participation. The landlocked and language variables are obtained from CEPII and the data on the U.S.
border is obtained from the CIA world fact book. Also, to control for eects of demand, market size and
income of exporting countries on their export prices, I include GDP and GDP per capita obtained from the
World Development Indicators of the World Bank.
10
In addition to these country-level control variables, I also consider product-country level tari rates as
another potential determinant of f.o.b.
export prices because the linear demand in the theory exhibits
incomplete pass-through of the trade costs. Tari rates with HS-6 digit product categories across countries
under dierent tari regimes are obtained from WTO tari data.
Furthermore, to investigate how the cross-industry variation of the remoteness and distance eects are
related to the industry-specic degree of quality dierentiation, I employ three proxies for the scope for
quality dierentiation.
The rst proxy is the Rauch (1999) dummy for dierentiated goods for SITC-4
digit industry. This measure identies dierentiated goods as goods that are not traded on a commodity
exchange or that do not have a reference price in industry trade publications. The second measure is the
quality ladder length from Khandelwal (2010). The greater the quality ladder length, the greater the scope
for quality dierentiation. I use this measure at three dierent aggregation levels; SIC 2-digit, SIC 4-digit,
and HS 10-digit codes. The third proxy is the slope estimates from Johnson (2011). The measure shows
correlation between export prices and threshold of exporting at HS-2 digit industry. I utilize the positive
slope estimates as an indicator of quality-sorting industries and the negative estimates as an indicator of
11
eciency-sorting industries.
9 This data uses New York City as the most populated agglomeration in the U.S. CEPII: Research Center in International
Economics (Centre D'Etudes Prospectives et D'lnformations Internationales): www.cepii.fr.
10 Refer to Table 3 for summary statistics.
11 Table 4 reports summary statistics for these measures.
19
5 Empirical Specications and Results
In this section, I build empirical specications to test the model's predictions using the data described
in the previous section and report its result. First, I examine remoteness and distance eects on f.o.b. unit
values of exports with the following baseline empirical specication:
log(Upc ) = αp + β1 log(Remotenessc ) + β2 log(Distancec ) + Zγ + pc
The dependent variable is f.o.b. unit value of a HS 10-digit code product
(11)
p shipped from country c (Upc ).
The main explanatory variables are the remoteness of exporting countries, constructed as GDP-weighted
average distance of a country to all other countries
country
as
Z
c
and the importing country, the U.S.
(Remotenessc )
(Distancec ).
and includes the GDP per capita of exporting country
(GDPc ),
and the distance between the exporting
The control variables are collectively denoted
c (P CGDPc ),
the GDP of exporting country
landlocked, border, and language dummies. All continuous variables are transformed in logs.
c
αp
is a product xed eect to control for level dierences in unit values and unit dierences across products.
Robust standard errors are estimated by clustering exporting countries. Table 6 reports the results.
As shown in column (1), for overall manufacturing sectors, both remoteness and distance are negatively
associated with average unit values of exports. This result does not consistently support either quality-sorting
or eciency-sorting case because a negative remoteness eect is predicted in a quality-sorting industry but
a negative distance eect is predicted in an eciency-sorting industry. However, it conversely supports the
model, suggesting that the highly aggregated manufacturing sector includes both types of sub-industries
within it.
To look into more disaggregated industries, I divide the sample into 4 sub-categories according to the
1-digit Standard International Trade Classication (SITC1). Then, each industry exhibits dierent levels
and signs of remoteness and distance eects. In column (2), the Chemicals (SITC1= 5) industry signicantly
exhibits a negative remoteness eect and a positive distance eect, as predicted for a quality-sorting industry.
One standard deviation increase in remoteness is associated with a 0.133 standard deviation decrease in unit
values of exports on average, while one standard deviation increase in distance is associated with a 0.097
standard deviation increase in unit values of exports on average. In column (4), the Machinery (SITC1= 7)
industry exhibits an insignicant remoteness eect and a signicantly negative distance eect, as predicted
for an eciency-sorting industry. One standard deviation increase in remoteness is associated with a 0.058
standard deviation decrease in unit values of exports on average, but it is not statistically signicant, while
one standard deviation increase in distance is associated with a 0.261 standard deviation decrease in unit
20
values of exports on average.
In column (3) and (5), the results of the Manufactured Material (SITC1=
6) and Miscellaneous Manufacturing (SITC1= 8) industries are not consistent with the predictions of the
model. Again, these results may be attributed to composition between eciency-sorting and quality-sorting
industries within those SITC-1 digit industries.
Additionally, the linear demand system my model assumes has a property of incomplete pass-through of
trade costs. Thus, in addition to the country-level control variables, I also include tari rates as another
control variable to address a potential issue of omitted variables that might aect xed costs of entering the
U.S. market. Tari rates vary across both HS 6-digit code products and exporting countries. Table 7 shows
the results. When the tari rates are controlled for, there is a slight changes in point estimates, but the signs
and signicance of the remoteness and distance eects remain unchanged. In column (2) with the Chemicals
(SITC1= 5) industry, one standard deviation increase in remoteness is associated with a 0.141 standard
deviation decrease in unit values of exports on average, while one standard deviation increase in distance
is associated with a 0.113 standard deviation increase in unit values of exports on average. In column (4)
with the Machinery (SITC1= 7), one standard deviation increase in remoteness is associated with a 0.069
standard deviation decrease in unit values of exports on average, but it is not statistically signicant, while
one standard deviation increase in distance is associated with a 0.256 standard deviation decrease in unit
values of exports on average.
Even though these results suggest signicant cross-industry variation of the remoteness and distance
eects, they do not necessarily imply that the variation depends on whether the industry is quality dierentiated or not, because SITC 1-digit code does not classify the industries by their level of quality dierentiation. Therefore, the further empirical examination focuses on relating this cross-industry variation of the
remoteness and distance eects to industry-specic scope for quality dierentiation.
An empirical strategy to identify this cross-industry variation is to include interaction terms in the baseline
regression.
level
i.
QualityDif fi
term in (12) represents a proxy for scope for quality dierentiation at an industry
The coecient,δ1 , of the interaction term between
QualityDif fi
and
log(Remotenessc )
is expected
to capture the extent to which the remoteness eect changes depending on the scope for quality dierentiation
and predicted to be negative in the model. The coecient,
and
log(Distancec )
δ2 , of the interaction term between QualityDif fi
is expected to capture the extent to which the distance eect changes depending on the
scope for quality dierentiation and predicted to be positive in the model.
21
log(Upc ) = αp + β1 log(Remotenessc ) + β2 log(Distancec ) + Zγ
+δ1 [QualityDif fi × log(Remotenessc )] + δ2 [QualityDif fi × log(Distancec )] + pc
For the proxy for scope for quality dierentiation,
QualityDif fi ,
(12)
I make use of three dierent measures:
the Rauch (1999) dummy, the quality ladder length by Khandelwal (2010), and the slope estimates between
export prices and thresholds by Johnson (2011). The result is reported in Table 8.
First, I use the Rauch (1999) dummy to distinguish dierentiated products from homogeneous goods or
goods with reference prices
12 . In column (1), when the products with more than 30 exporting countries were
13 , the interaction term between remoteness and the Rauch dummy captures a signicant
selected as sample
negative remoteness eect for dierentiated products while non-dierentiated products exhibit insignicant
remoteness eect, supporting the model's prediction. The distance eect for non-dierentiated products is
signicantly negative as the model predicts, but its interaction eect with the Rauch dummy is insignicantly
negative, which is not consistent with the model's prediction. Since the Rauch dummy distinguishes only
dierentiated products from non-dierentiated products, the dierentiated products include both verticallydierentiated (quality-dierentiated) and horizontally-dierentiated (non-quality-dierentiated) products.
Therefore, this unclear distance eect may be driven by the horizontally-dierentiated products with no
quality dierentiation, rather than the vertically-dierentiated products in the sample.
Then, I employ a continuous measure for industry-specic quality ladder length estimated by Khandelwal
(2010). This measure is estimated at HS 10-digit product level using U.S. import data and also available at
more aggregated SIC 4-digit and SIC 2-digit levels. The longer the length, the greater the scope for quality
14
dierentiation.
In Table 8, column (2) reports the result when the quality ladder length by SIC 2-digit industry is used as
a proxy for scope for quality dierentiation. For products with no quality dierentiation, the point estimates
indicate that the remoteness eect is insignicant, but the distance eect is signicantly negative. Specically,
for those products with no quality dierentiation, one standard deviation increase in remoteness is associated
with a 0.009 decrease in export prices on average, but statistically insignicant, while one standard deviation
increase in distance is associated with a 0.212 standard deviation decrease in export prices on average. For
12 Rauch (1999) divides goods into three groups: goods that are traded in organized exchanges, goods that are not traded in
organized exchanges but have reference prices, and goods that have neither of them. The rst one is referred to as homogeneous
goods, the second one is close to homogeneous goods and the third one is referred to as dierentiated goods.
13 The result with the full sample turned out to be unclear and not consistent with the model's prediction. Thus, I limited the
sample to products with more diversied origins by selecting products with more than 30 exporting countries. The inconsistent
result with the full sample can be attributed to dierentiated products with homogeneous quality within the dierentiated
products category by Rauch dummy.
14 The Khandelwal's measure at SIC 2-digit level is obtained as average values of the measure at SIC 4-digit level.
22
products with quality dierentiation, the interaction terms of remoteness with quality ladder length captures
a signicant negative remoteness eect and the interaction of distance with quality ladder length captures
positive distance eect. For the products with positive quality ladder length, when the quality ladder length
increase by one standard deviation, one standard deviation increase in remoteness decreases the export price
by 0.044 of its standard deviation, while one standard deviation increase in distance raises the export prices
by 0.045 of its standard deviation. When tari rates are also controlled for, the result remains consistent,
as shown in column (1) of Table 9. These results strongly support both remoteness and distance eects in
quality-sorting and eciency-sorting industries.
To look more closely into the disaggregated industries, I use the quality ladder length measure at SIC-4
digit and HS-10 digit industry levels. When these measures are interacted with distance and remoteness as
shown in column (3) and (4) of Table 8, they do not signicantly capture the variation of the remoteness
and distance eects with the scope for quality dierentiation.
When tari rates are included as control
variables as shown in column (2) and (3), the signs of interaction eects of remoteness and distance with
the quality ladder length turn out to be consistent with the model's predictions, but the point estimates are
not statistically and economically signicant. This fact that the cross-industry variation is not signicantly
identied with more disaggregated levels of quality ladder length may suggest that there are more noises in
the quality ladder length at more disaggregated product levels and these potential noises are canceled out
as the measure is aggregated at a higher level. This fact can cause the regression result using the quality
ladder length at SIC 2-digit industry to be most consistent with the model's prediction.
To address this potential problem due to disaggregation and consider the possibility of non-linear interaction eects with the scope for quality dierentiation, I create step functions for quality ladder length at
HS 10-digit and SIC 4-digit industries. First, I divide HS 10-digit quality ladder length below and above
its median and generate
Q Dum above med
as an indicator that is one for quality ladder length above its
median. The result is shown in column (1) in Table 10. When the dummy is interacted with remoteness and
distance respectively, I nd a signicantly positive distance interaction eect but an insignicant negative
remoteness interaction eect.
Then, I also divide SIC 4-digit quality ladder length by its quartiles and generate interaction terms of
remoteness and distance with the dummy variables. In Table 10,
Q Dummy − q2
when the quality ladder length is between its bottom 25% and median,
median and top 75%, and
Q Dummy − q4
when it is above top 25%.
is an indicator that is one
Q Dummy − q3
when it is between
In column (4), the coecients of
the interaction terms for remoteness do not capture signicant and consistent variation depending on the
quality ladder length. However, the coecients of the interaction terms for distance suggest that there is a
signicant positive distance eect for quality dierentiated products and its magnitude increases as quality
23
ladder length increases, supporting the model's prediction for distance eects.
As the third proxy of the scope for quality dierentiation, I employ the slope estimates between export
prices and thresholds from Johnson (2011). This measure estimates the correlation between export prices and
threshold of exporting at HS-2 digit level industry. The negative slope estimate refers to a cost (eciencysorting) industry, while the positive slope refers to a quality-dierentiated (quality-sorting) industry. Column
(5) in Table 8 reports the result when the measure is introduced as a continuous measure. Its interaction term
with distance captures a positive distance eect for products with quality dierentiation and its increase as
the slope estimates increase. However, a variation in remoteness eect with slope estimate are not identied
as the model's prediction. This result remains almost unchanged when tari rates are also controlled for, as
shown in column (4) in Table 9.
I transform Johnson's slope estimates as step functions by partitioning it with its median and quartiles,
respectively. In Table 10, column (2) reports the result with dummy variables above its median. Again, this
result provides supportive evidence for distance eects while inconclusive evidence for remoteness eects.
Column (4) presents the result with dummy variables based on quartiles above the second quartile.
The
results supports the model's prediction of the distance eects. Products with negative or low slope estimates
exhibit negative distance eects on their export prices, but products with positive and larger slope estimates
exhibit positive and greater distance eects on their export prices. The remoteness eects for products with
negative or low slope estimates exhibit turned out to be negative (-0.74). This negative remoteness eect
can be driven by products with quality elasticity less than 1. In the theoretical model, a product with its
quality elasticity between 0 and 1 is predicted to exhibit negative remoteness eects and its magnitude can
increase or decrease depending on the relative size between Pareto parameter and quality elasticity. Thus,
if products with bottom 25% of the slope estimates include the products with very low quality elasticity,
the model still explains this negative remoteness eects. Then, the baseline remoteness eect is -0.74 and
interaction estimates decreases from 0.37 to 0.34 and 0.31 as the Johnson's measure increases. This implies
that total magnitude of negative remoteness eect tends to increase for the upper quartile dummies. This
result is consistent with the model's prediction by showing that the negative remoteness eect increases as
the degree of quality dierentiation increases, for products with quality elasticity is greater than 1.
From these empirical results, I nd that industries with no quality dierentiation tend to exhibit insignificant remoteness eects and negative distance eects on export prices. In contrast, industries with quality
dierentiation tend to exhibit signicant negative remoteness eects and positive distance eects on export
prices. Also, the magnitude of these eects tend to increase as the industry's degree of quality dierentiation
increases. This set of results that are consistent with the model's prediction support the theoretical ndings
of this paper.
24
However, as discussed on each of the regression results, some of the ndings do not consistently support
the model's predictions. This issue either leaves room for more thorough empirical investigation or challenges
the theoretical model. To make the empirical specication close enough to the theoretical model, control
variables can be employed more exibly or other measures for industry-level scope for quality dierentiation,
such as R&D intensity, can be also utilized.
Theoretically, the linear demand system this paper assumes
may be too strict to fully represent consumer's behavior in some industries, or a clear-cut division between
quality-sorting and eciency-sorting industries may not be easily applicable to all industries. Based on this
discussion, further investigation for theoretical and empirical improvement will be conducted in the future.
6 Conclusion
This paper investigates how relative location of exporting countries and distance between exporting and
importing countries determine their export prices through the selection channel that high capability rms
are selected to be exporters. I theoretically analyze the eects of these geographical factors on average export
prices based on the heterogeneous-rms trade model with a four-country open economy, and examine the
model's predictions using product-level U.S. import data. My model yields that the average export prices are
negatively related to remoteness but positively related to distance in a quality dierentiation industry, and
the strength of the relationships increases as the industry exhibits a greater scope for quality dierentiation.
In contrast, for an industry with no quality dierentiation, the remoteness and distance eects become
zero and negative, respectively.
The empirical results suggest that the relative location of exporters and
the distance between exporter and importer are signicant determinants of their export prices. Using the
interaction eects of remoteness and distance with industry-specic scope for quality dierentiation, I also
nd that the importance of the remoteness and distance eects are more pronounced in industries with a
greater scope of quality competition. This work will be further extended by addressing the empirical ndings
that do not signicantly support the predictions of the model by employing other proxies for degrees of quality
dierentiation and modifying the setting of the theoretical model.
25
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26
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27
Model Appendix
1. Closed economy
kD :
θ = −1,
The solutions of the cut-o,
kD = cD =
kD =
1/(α+2)
γ
if
w2 L φ
1/(α+2θ)
γ
if
w 2 L φ2
θ > −1,
where
where
α
φ = 2 (α + 1) (α + 2) kM
fe
α
φ2 = 2θ−2 (a + 2θ) (a + θ) kM
fe .
2. Four-country open economy
•
When
θ = −1
(Eciency-sorting industry;
k i = ci )
Domestic cut-os of four countries:
D
remote
cA
D = cD = cD
central
C
cB
D = cD = cD
where
•
=
=
1
w
τ1α τ2α τ3α (τ2α (1+τ1α )−wα (τ2α +τ1α ))
γ
φ
τ1α τ22α (1+τ1α )+(−τ12α +(τ12α +τ1α −1)τ22α )τ3α −2(τ1 τ2 τ3 )α L
τ1α τ2α (wα (τ1α τ2α )(1+τ3α )−τ3α (τ1α +τ2α ))
τ1α τ22α
(
1+τ1α
)+(
−τ12α +
(
τ12α +τ1α −1
)
τ22α
)
τ3α −2(τ1 τ2 τ3 )α
1/(α+2)
γ
φ
L
1/(α+2)
φ = (α + 1) (α + 2) cα
M fe
When
θ > −1
(Quality-sorting industry;
ki = c−1
i )
Domestic cut-os of four countries:
remote
D
A
= kD
= kD
kD
1/(α+2θ)
(τ1 τ2 τ3 )a+2θ [τ2α+2θ (τ12 +τ1α+2θ )−wα+2θ−2 (τ1α+2θ τ22 +τ12 τ2a+2θ )]
i γ φ2
i
hh
=
(τ1 τ22 )α+2θ τ32 (τ12 +τ1α+2θ )+τ3α+2θ τ12+α+2θ +τ12(α+2θ) −τ14 τ22(α+2θ) −τ12(α+2θ) τ24 −2(τ1 τ2 )2+α+2θ L
central
C
B
kD = kD = kD
1/(α+2θ)
(τ1 τ2 )a+2θ [(τ1 τ2 )a+2θ (τ32 +τ3α+2θ )wα+2θ−2 −τ3α+2θ (τ1α+2θ τ22 +τ12 τ2a+2θ )]
i γ φ2
hh
i
= w1
(τ1 τ22 )α+2θ τ32 (τ12 +τ1α+2θ )+τ3α+2θ τ12+α+2θ +τ12(α+2θ) −τ14 τ22(α+2θ) −τ12(α+2θ) τ24 −2(τ1 τ2 )2+α+2θ L
where
α
fe
φ2 = 2θ−2 (a + 2θ) (a + θ) kM
3. Quality elasticity and distance and remoteness eects
When −1 < θ < 0; (0 < Quality elasticity < 1)
(2)
(3)
(4)
(5)
(6)
1≤a<2
a=2
2 < a < 4.562
2 < a < 4.562
a > 4.562
ξ(α, θ)
∂ξ(α, θ)/∂θ
+
-
+
-
+
-
+
+
+
+
if θ > −a/2
√
if θ <
if θ >
28
2+a−a
√ 2
2+a−a
2
AB
∂ P̄Xf
ob
∂w∂θ
+
DB
∂ P̄Xf
ob
∂τ ∂θ
-
+
-
+
-
-
+
-
+
Figure 2:
The inverse relationship between GDP per capita and Remoteness
X axis: Remoteness measure, Y axis: GDP per capita
X axis: ln(Remoteness), Y axis: ln(GDP per capita)
Correlation coecient (X,Y) = -0.2972
Table 2. Countries by remoteness
This table shows countries in the sample in order of their remoteness. The remoteness measure is constructed as a GDPweighted average distance, following Wei (1996).
Remoteness
500K-600K
600K-700K
700K-800K
800K-900K
900K-1000K
1000K-1100K
1100K-1200K
1200K-
Countries
The Netherlands, Denmark, Germany, Norway, Belgium-Luxembourg, Sweden, UK, Czech Rep.,
Latvia, Estonia, France, Finland, Poland, Ireland, Switzerland, Austria, Slovakia, Belarus, Hungary,
Slovenia, Croatia, Iceland, Ukraine, Russia, Bosnia-Herzegovina, Moldova, Italy, Romania, Bulgaria,
Macedonia, Albania
Spain, Turkey,Tunisia, Greece, Algeria, Portugal, Malta, Armenia, Libya, Morocco, Cyprus, Azerbaijan,
Lebanon, Syria, Israel, Jordan, Egypt, Iran, Turkmenistan, Uzbekistan, Canada, Kyrgyzstan, Kazahstan,
Tajikistan, Kuwait
Mongolia, Bahrain, Pakistan, Saudi Arabia, Qatar, Arab Emirate, Oman, Mauritania, India, Niger, Chad,
China, Burkina Faso, Senegal, Yemen, Mali, Nepal, Gambia, Guinea Bissau, South Korea, Djibouti
Guinea, Nigeria, Ethiopia, Sierra Lion, Togo, Bangladesh, Ghana, Liberia, Ivy Cost, Central Africa, St.
Kitts and Nevis, Cameroon, Dominica Rep., Haiti, Japan, Jamaica, Gabon, Uganda, Trinidad, Macao,
Hong Kong, Rwanda, Kenya, Lao, Congo, Belize, Venezuela, Burundi, Mexico, Guyana, Suriname,
Honduras
Thailand, Guatemala, Sri Lanka, El Salvador, Angola, Nicaragua, Tanzania, Panama, Costa Rica,
Cambodia, Seychelles, Philippines, Colombia, Malawi, Zambia, Malaysia, Ecuador
Singapore, Madagascar, Mozambique, Mauritius, Indonesia, Peru, South Africa, Bolivia, Brazil
Kiribati, Paraguay, New Guinea, Uruguay, Argentina
Chile, Samoa, Fiji, Australia, New Zealand
Table 3. Summary Statistics
This table reports summary statistics for continuous variables in the analysis. * For log of unit
value, product fixed effects were removed. ** Standard deviation across products and exporting
countries. *** Standard deviation within each HS 10-digit product
# obs.
Mean
St. Dev.
Min
Max
(ln) Remoteness
170,184
13.48
0.25
13.22
14.16
(ln) Distance
170,184
8.84
0.68
6.31
9.7
(ln) PCGDP
170,184
8.94
1.42
4.71
10.7
(ln) GDP
170,184
26.56
1.60
18.14
29.26
(ln) Unit Value*
170,184
0
1.25**
-8.22
9.83
Std. Dev. (ln) Unit Value***
# of Countries
144
1.09
0.61
0
# of HS 10-digit products
4.53
11,899
Table 4. Summary Statistics of Proxies for Scope for Quality Differentiation
This table reports the summary statistics for two measures of scope for quality differentiation, quality ladder length by
Khandelwal (2010) and slope estimates by Johnson (2011). The quality ladder length is available at 3 different industry
aggregation levels.
Khandelwal
(2010)
Johnson
(2011)
# Obs
Mean
Std.Dev.
Min
Max
Median
Quality ladder length HS10
15,088
1.717
1.068
0
5.803
1.776
Quality ladder length SIC4
325
2.120
0.754
0
4.571
2.123
Quality ladder length SIC2
19
2.030
0.398
1.33
2.640
2.080
Slope measure rho
68
0.074
0.114
-0.217
0.383
0.094
HS2
Table 5. Product Categories in Manufacturing Sectors
This table reports the number of industries at SIC4 level and products at HS10 level
within the entire manufacturing sectors.
Industry
# SIC 4
industry
%
# HS 10
products
%
All Manufacturing
SITC5
SITC6
SITC7
SITC8
342
40
117
103
82
100
11.70
34.21
30.12
23.98
11,899
1,977
4,360
2,481
3,081
100
16.61
36.64
20.85
25.89
Table 6. Statistical Determinants of Product-level Average Export Prices
with Country Characteristics
This table examines the effects of remoteness and distance of exporting countries, controlling for country characteristics such
as size, income, and dummies for landlocked, border, and language. Column (1) presents the result for the full sample of all
manufacturing sectors, while columns (2)-(5) show estimates from separate regressions for each of SITC-1 digit industries.
All regressions include product fixed effects, and cluster errors by county. Robust standard errors are reported below the
coefficient estimates. ***, **, and * indicate significance at the 1%, 5%, and 10% level.
Dependent variable: (ln) average f.o.b unit value, by HS-10 product and country
Model's Prediction
SITC 5-8
All
Quality- Efficiencymanufacturing
sorting
sorting
(1)
SITC 5
Chemicals
(2)
SITC 6
Manuf.
Materials
(3)
SITC 7
SITC 8
Machinery
Misc. Manuf.
(4)
(5)
(ln) Remoteness
-
0
-.517***
0.187
-.663***
0.186
-.400**
0.165
-0.291
0.266
-.714***
0.232
(ln) Distance
+
-
-.208*
0.112
.178**
0.079
-0.112
0.125
-.480***
0.158
-.251**
0.104
(ln) GDP per capita
.180***
0.059
.151***
0.05
.182***
0.056
.212***
0.078
.178***
0.057
(ln) GDP
-0.023
0.043
-0.029
0.043
-0.019
0.046
-0.077
0.053
-0.003
0.042
Border
-.625**
0.252
-0.246
0.163
-.592*
0.313
-.929***
0.332
-.546***
0.2
Landlocked
.218***
0.061
.610***
0.107
.246***
0.079
-0.019
0.099
.229***
0.071
English
0.195
0.171
.448***
0.122
0.214
0.168
-0.115
0.216
0.137
0.169
Product FE
(Adj) R-squared
# observations
# country clusters
# products
Y
Y
Y
Y
Y
0.78
170,184
144
11,899
0.61
20,922
127
1,977
0.70
56,829
141
4,360
0.79
38,851
136
2,481
0.72
53,582
142
3,081
Table 7. Statistical Determinants of Product-level Average Export Prices
with Country Characteristics and Tariff rates
This table examines the effects of remoteness and distance of exporting countries on HS-10 digit product-level average
unit values, controlling for tariff rates as well as other characteristics of exporting countries. The tariff rates are HS-6 digit
product-specific for MFN tariff regimes but HS-6 product and country specific for different tariff regimes such as FTA's.†
Column (1) presents the result for the full sample of all manufacturing sectors, while columns (2)-(5) show estimates from
separate regressions for each of SITC-1 digit industries. All regressions include product fixed effects product, and cluster
errors by county. Robust standard errors are reported below the coefficient estimates. ***, **, and * indicate significance
at the 1%, 5%, and 10% level.
Dependent variable: (ln) average f.o.b unit value, by HS-10 product and country
Model's Prediction
(ln) Remoteness
SITC 5-8
All
Quality- Efficiencymanufacturing
sorting
sorting
(1)
0
-0.512***
0.182
(ln) Distance
+
-
-0.194*
0.107
(ln) GDP per capita
0.183***
0.057
(ln) GDP
-0.016
0.042
Border
-0.601**
0.24
Landlocked
0.221***
0.059
English
0.197
0.165
Tariff
Product FE
(Adj) R-squared
# observations
# country clusters
# products
0.005
SITC 5
Chemicals
(2)
-0.705***
0.184
0.207***
0.075
0.146***
SITC 6
Manuf.
Materials
(3)
-0.399**
0.171
-0.116
0.123
0.181***
SITC 7
Machinery
(4)
-0.346
0.253
-0.47***
0.156
0.222***
SITC 8
Misc.
Manuf.
(5)
-0.67***
0.217
-0.244**
0.095
0.18***
0.05
0.056
0.078
0.053
0.002
-0.008
-0.073
0.002
0.043
0.046
0.055
0.039
-0.357*
0.187
0.636***
0.11
0.43***
0.121
-0.053***
-0.63**
0.317
0.26***
-0.975***
0.338
-0.023
-0.456**
0.181
0.215***
0.083
0.094
0.074
0.202
0.132
0.139
0.167
0.214
0.155
-0.008
-0.024
0.006
0.015
0.007
0.034
0.011**
0.005
Y
Y
Y
Y
Y
0.799
127,989
144
9,166
0.670
15,386
126
1,562
0.701
44,834
139
3,551
0.796
28,097
130
1,931
0.727
39,672
139
2,122
† For the country-product pairs under two different FTA tariff regimes, the minimum tariff rates are used. (There are 1,147 observations
with two different tariff regimes.) The tariff data does not specify product-country pair information for regional FTA with multiple
countries. When the maximum tariff rates are used for those with more than two different FTA schedules, the results do not change
much. The results are available upon request.
Table 8. Interaction Effects of Remoteness and Distance
with Scope for Quality Differentiation
This table examines the interaction effects of remoteness and distance of exporting countries with the industryspecific scope for quality differentiation, controlling for size, income, and dummies for landlocked, border,
and language. Column (1) presents the result when Rauch dummy is used as proxy for scope for quality
differentiation and products with more than 30 origin countries are selected as sample. Columns (2)-(4) report
the result when quality ladder length by Khandelwal (2010) is used as the proxy for scope for quality
differentiation. In column (2), (3), and (4), the measures by SIC2, SIC4, and HS10 product categories are
used, respectively. Column (5) reports the result when slope estimates by Johnson (2011) is used as the proxy.
All regressions include product fixed effects, and cluster errors by county. Robust standard errors are reported
below the coefficient estimates. ***, **, and * indicate significance at the 1%, 5%, and 10% level.
Dependent variable: (ln) average f.o.b unit value, by HS-10 product and country
Rauch
Dummy
(1)
-0.158
SIC2
SIC4
Full Sample
Slope
by
Estimate by
Johnson
(2011)
HS10
HS2
(2)
-0.043
(3)
-.464*
(4)
-.546***
0.161
0.305
0.193
0.195
-.201*
-.226**
0.105
0.112
Full Sample
products with
>30 exporting Quality Ladder length
countries
Khandelwal (2010)
Model's
Prediction
(ln) Remoteness
(ln) Distance
0
-
-.177**
0.08
(ln) GDP per capita
.178***
(ln) GDP
Border
QulaityDiff
x (ln) Remoteness
QulaityDiff
x (ln) Distance
Product FE
(Adj) R-squared
# observations
# country clusters
# products
+
.181***
.182***
0.112
.176***
0.059
0.061
-0.027
-0.022
-0.022
-0.018
0.032
0.043
0.043
0.044
0.042
-0.632
-.547**
.141**
-
.181***
-.301***
0.058
0.156
English
0.184
0.24
0.045
-.590***
Landlocked
-.390**
(5)
-.742***
-.628**
0.251
.222***
-.631**
0.251
.221***
0.247
.220***
0.058
-0.02
0.232
.234***
0.057
0.061
0.062
0.068
0.066
0.149
0.198
0.198
0.196
0.217
0.142
0.171
0.172
0.176
0.169
0.136
0.111
0.047
-0.034
.083*
-0.006
0.039
0.047
0.019
0.008
.294**
0.146
.246***
0.036
Y
Y
Y
Y
Y
0.74
49,080
144
1,311
0.78
168,925
144
11,815
0.78
169,429
144
11,685
0.79
140,971
144
9,825
0.75
150,313
144
10,526
-.333**
-.221**
-0.024
0.14
0.024
0.00003
Table 9. Interaction Effects of Remoteness and Distance
with Scope for Quality Differentiation
This table examines the interaction effects of remoteness and distance of exporting countries with the
industry-specific scope for quality differentiation, controlling for tariff rates as well as size, income, and
dummies for landlocked, border, and language. Columns (1)-(3) report the result when quality ladder
length by Khandelwal (2010) is used as the proxy for scope for quality differentiation. In column (1), (2),
and (3), the measures by SIC2, SIC4, and HS10 product categories are used, respectively. Column (4)
reports the result when slope estimates by Johnson (2011) is used as the proxy. All regressions include
product fixed effects, and cluster errors by county. Robust standard errors are reported below the
coefficient estimates. ***, **, and * indicate significance at the 1%, 5%, and 10% level.
Dependent variable: (ln) average f.o.b unit value, by HS-10 product and country
(ln) Remoteness
(ln) Distance
Model's
Prediction
0
-
Khandelwal
SIC2
(1)
0.146
Khandelwal
SIC4
(2)
-0.491**
Khandelwal
HS10
(3)
-0.481**
0.279
0.189
0.19
-0.334**
0.158
0.183***
(ln) GDP per capita
(ln) GDP
QulaityDiff
x (ln) Remoteness
QulaityDiff
x (ln) Distance
Product FE
(Adj) R-squared
# observations
# country clusters
# products
+
0.104
0.178***
-0.015
-0.015
-0.014
-0.013
0.042
0.042
0.043
0.042
0.198
-
0.186***
-0.292***
0.056
0.06
Tariff
0.105
0.059
0.225***
English
0.183***
-0.223**
0.057
0.24
Landlocked
0.097
0.229
0.057
-0.604**
Border
-0.214**
Johnson
HS2
(4)
-0.71***
-0.604**
0.24
0.223***
0.06
0.232
0.212***
-0.513**
0.225
0.238***
0.065
0.067
0.201
0.216
0.165
0.165
0.169
0.164
0.004
0.005
0.005
0.008*
0.006
0.006
0.005
0.005
-0.305***
0.2
-0.599**
-0.009
-0.018
0.271*
0.108
0.047
0.026
0.141
0.063*
0.007
0.007
0.035
0.017
0.01
0.039
Y
Y
Y
Y
0.799
126,894
144
9,091
0.799
125,818
144
8,995
0.805
107,148
144
7,630
0.767
113,561
144
8,095
0.246***
Table 10. Interaction Effects of Remoteness and Distance
with Scope for Quality Differentiation (of Step Functions)
This table examines the interaction effects of remoteness and distance of exporting countries with the industryspecific scope for quality differentiation, controlling for size, income, tariff rates, and dummies for landlocked,
border, and language. Columns (1) and (2) respectively report the results with quality ladder length by Khandelwal
(2010) at HS10 product level and slope estimate by Johnson (2011) are used as proxies for degree of quality
differentiation. Both measures are divided below and above its median and taken as a step function. Q Dum_ above
median is an indicator that is one for values above its median. Columns (3) and (4) respectively report the results
when quality ladder length by Khandelwal (2010) at SIC4 industry level and the slope estimates by Johnson (2011)
are used as the proxy for scope for quality differentiation. Q Dum_ q2 , Q Dum_ q3 , and Q Dum_ q4 represent
dummy variables of those proxies for their 25%-50%, 50%-75%, and above 75% values. respectively. All
regressions include product fixed effects, and cluster errors by county. Robust standard errors are reported below the
coefficient estimates. ***, **, and * indicate significance at the 1%, 5%, and 10% level.
Dependent variable: (ln) average f.o.b unit value, by HS-10 product and country
Model's
Prediction
(ln) Remoteness
0
Khandelwal
HS10
(1)
-0.499***
Johnson
HS2
(2)
-0.591***
Khandelwal
SIC4
(3)
-0.564***
Johnson
HS2
(4)
-0.735***
0.184
0.208
0.185
-0.221**
-0.274**
-0.212**
0.106
0.108
0.101
0.105
(ln) GDP per capita
0.183***
0.186***
0.183***
0.186***
0.057
0.057
0.057
0.057
(ln) GDP
-0.016
-0.019
-0.016
-0.021
Border
-0.599**
(ln) Distance
-
0.042
Landlocked
0.224
0.221***
0.217***
0.22***
0.216***
0.059
0.06
0.059
0.005
0.164
0.006
x (ln) Remoteness
+
-0.023
0.209
0.165
0.008
0.005
0.039
0.129
0.043***
0.223***
0.014
0.031
-
0.373***
-
0.06
0.079
0.211
0.008
0.014
0.162
0.311**
0.157
0.09*
0.053
+
0.028
0.032
0.044
+
0.232***
0.255***
0.048
0.07
Y
0.799
127,989
144
9,166
Y
0.800
127,758
144
9,142
x (ln) Distance
(Adj) R-squared
# observations
# country clusters
# products
0.173***
0.121
x (ln) Distance
Product FE
0.005
0.34**
x (ln) Distance
Q Dum_ q4
0.006
-0.009
+
Q Dum_ q3
0.164
0.009**
-
x (ln) Remoteness
Q Dum_ q2
0.164
0.005
0.061
0.212
0.06
x (ln) Remoteness
Q Dum_ q4
0.197
0.192
x (ln) Remoteness
Q Dum_ q3
0.042
-0.551**
0.239
Tariff
Q Dum_ above median
x (ln) Distance
Q Dum_ q2
0.042
-0.593**
0.228
0.197
-
-0.309***
0.24
English
Q Dum_ above median
0.042
-0.56**
0.233
Y
0.799
127,989
144
9,166
Y
0.814
127,758
144
9,142
0.261***