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Shuo.jan.dave
The Link between Trade and Income: Export Effect, Import Effect, or Both?
Shuo Zhang
Jan Ondrich
J. David Richardson1
Department of Economics
Syracuse University
March 2003
Abstract
A new framework is developed to evaluate how cross-country differences in
export openness and import openness in 1990 affected the level of real per capita income.
Familiar and novel instruments are used to extract the exogenous components of total
trade (exports plus imports) and of net exports (exports minus imports), which in turn
imply distinct export and import effects. We build on an existing literature (FrankelRomer and others) that uses aspects of a country’s geography as instrumental variables
for total trade openness. We build on a country’s demography and net wealth abroad to
develop a novel instrument for net export openness. Our new estimates reveal that export
openness alone correlates with income cross-sectionally, not import openness.
1
Department of Economics, 110 Eggers Hall, Syracuse, New York 13244. All responsibility for errors
remains with the authors.
I.
Introduction
The doctrine of mercantilism views trade, at least in part, as a zero-sum game.
Trade is considered favorable if exports bring in more money than what is paid out for
imports. Adam Smith and David Ricardo challenged the mercantilism theory by arguing
that overall openness is what matters – nations grow more prosperous through the process
of specialization and trade through imports as well as exports.
Many developing
countries have replaced their strategy of government-protected import substitution
industrialization with a market-focused export oriented strategy. They seek to promote
economic growth by exporting more and more of their products; this has the feel of neomercantilism.
This paper re-poses the question whether trade “raises” a country’s income per
person (or per worker); and if it does, what are the channels through which trade affects
income, exports or imports? Numerous theoretical and empirical studies have attempted
to answer these questions. Theoretical support for the positive link between different
sorts of openness and economic growth were provided by Romer (1986) and Lucas
(1988). Barro and Sala-i-Martin (1995) and Romer (1992) emphasized how countries
that are more open have a greater ability to absorb (import) technological advances,
which ultimately leads to higher real per capita income. Krugman (1974), Rodrik (1995),
and Rodriguez and Rodrik (2000) on the other hand, cast doubt on the impact of openness
on growth. Warner (2003) casts doubt on their doubts and views our central question as a
frontier issue.2
“Proposition [to be assessed] 10. Passive liberalization [balanced export and import openness] works but
only assures moderate growth; fast growth requires more active liberalization-promoting exports rather
than just creating an enabling environment for open trade.” Warner (2003), pp. 20.
2
2
Empirical studies construct measures of openness variable based on exports 3 ,
imports4, or the sum of the exports and imports5. Levine and Renelt (1992) argue that
“all findings using the share of exports in GDP could be obtained almost identically using
the total trade or import share. Thus, studies that use export indicators should not be
interpreted as studying the relationship between growth and exports per se but rather as
studying the relationship between growth and trade defined more broadly.” (p.959)
Grossman and Helpman (1991) stated that technological spillovers could come via
imports as easily as exports. Lawrence and Weinstein (2001) argue that imports, not
exports, contribute importantly to the productivity growth of Japan and Korea.
Frankel and Romer (1999), on which chapter one of this dissertation is based
(Zhang 2002a), alleviates many of the conceptual and econometric barriers to these issues
by showing how geographical characteristics provide an arguably good instrument for a
country’s intrinsic openness. Yet they remark that their trade and income investigation
cannot separate the import effect and export effect. Wei (with Wu 2001, 2002a, 2002b)
consciously follows Frankel’s and Romer’s lead in a study of how globalization affects
Chinese city-level growth and inequality. Wei conflates export and import influences by
selecting Chinese cities’ distanced to two major Chinese ports as his instrument for a
city’s natural openness.
3
See Krueger (1978), Balassa (1978), Feder (1982) for some of the earliest studies, and the collection of
Asian-Miracle (re)considerations in Stiglitz and Yusuf (2000) for more recent references. Micro-data
studies that correlate firm performance with export openness are summarized in Lewis and Richardson
(2001).
4
See Ram (1990), Masumdar (2000), Mody and Yilmax (2002). Micro-data studies that correlate firm
performance with import openness include Ozler and Yilmax (2000), Pavcnik (2000), and Levinsohn
(2003).
5
For example, Franker and Romer (1999) and Harrison (1996) are based on the assumption that the
coefficients on exports and imports are the same.
3
No one to our knowledge has yet figured a way to do what seems initially the
most natural thing. That is to construct a measure of export openness, then an arguably
independent measure of import openness, and investigate whether one has a different
effect on income than the other, ceteris paribus.
That is the principal objective of this paper -- to identify the separate influences of
export openness and import openness on income levels after controlling for endogeneity.
In particular, we develop a framework that is slightly different from the Frankel and
Romer (1999) income determination model by considering an additional “net trade
effect” on income levels. When combined with Frankel and Romer’s “total-trade” effect,
the two effects together imply separable export and import effects. The extended income
model is intended to contribute to three empirical challenges related to trade and income.
The first is the Frankel-Romer determination of instruments for total trade. The second is
the determination of instruments for net trade. Thirdly, the model and data employed
permit the identification of the impact of exports and imports on income separately.
Our concern about endogeneity in this chapter differs slightly from our concern in
the previous chapter. When considering exports and imports separately, the expected
bias from ignoring endogeneity is, respectively, caused by the unobserved income
determinants that are correlated with exports and imports.
This paper (Zhang 2002b) is one part of my three dissertation papers (Zhang
2002a, Zhang 2002b, Zhang 2003). Zhang (2002a) explores the sensitivity of the Frankel
and Romer (1999) empirical relationship between country’s total-trade openness and
income level to heteroscedasticity and sample selection in their first-stage bilateral
instrumenting regressions.
The results support their hypothesis that trade has a
4
significant and positive, yet relatively small impact on income. The present chapter tries
to differentiate Frankel’s and Romer’s export openness from import openness. Export
openness plays the dominant role. Unfortunately, the unique instrumenting techniques
for “net trade” in this chapter cannot be implemented bilaterally, so there remains some
doubt about the exact onformity of our conclusions to Frankel and Romer’s. Zhang
(2003) employs established and new panel econometrics techniques to further examine
the robustness of the findings presented in Frankel and Romer (1999), Zhang (2002a),
Zhang (2002b) and other literature.
The rest of the paper is as follows. Section II describes the models. Section III
provides data definitions and sources.
Section IV reports the empirical results and
Section VI contains the conclusions of the paper.
II.
Empirical Models
Extensive research has been devoted to investigating factors that influence the
per-capita income levels of countries. Recently, an enormous literature has developed on
the influence of trade openness. 6 The major shortcoming of many of these empirical
studies is their inability to separate the impact of exports and imports. Some focus on
one to the neglect of the other; others focus on openness measures that force them to have
equal weight. In particular, by regressing income on total trade, many studies embody an
underlying assumption that exports and imports contribute equally to income growth. In
this section, we develop a simple model based on one of them, the Frankel-Romer study,
6
See notes 3 through 5 above. Plus, Dollar (1992), Sachs and Warner (1995), and Edwards (1998) find
significant relationship between the lower barriers to trade and higher growth. Both on theoretical and
empirical grounds, Rodriguez and Rodrik (2000) cast doubt on the robustness of this linkeage.
5
by separating the total trade share into export share and import share in order to
distinguish between export effect and import effect.
(1)
ln( yi )   0  1 ( X i )   2 (M i )   3 (Z i )   i
where y i represents per capita income. X i and M i are exports and imports scaled by
real GDP.7 Z i stands for other control variables.
As written, the “true” effects of export openness and import openness,
respectively, are 1 and  2 . Yet in practice, researchers usually omit one of the openness
terms or force the coefficients to be identical on the two measures of openness (calling
the sum of the two “overall” or total “openness”). In principle, this could cause omittedvariable bias in the remaining coefficients of interest or specification bias, due to
constraining two coefficients to have the same value. Yet the natural temptation to “just
run equation (1)” usually creates misleading inferences because export openness and
import openness are indeed highly correlated, as any general-equilibrium thinker knows
and as Levine and Renelt observe (above), and because both are acknowledged to be
endogenously related income per capita, the focus variable on which they are thought to
“operate.”8 Confronting these challenges, what’s a researcher to do?
Both exports and imports contribute to income growth.
Exports promote
specialization and exploit economies of scale. An increase in demand for country’s
output raises real income. Imports allow countries to take advantage of other countries’
technology embodied in imported inputs. Through intensive involvement in international
competition, countries become more productive. But income also has a causal effect on
7
The scaling introduces special estimation challenges, discussed below.
Focusing on the cases of Japan and Korea, Lawrence and Weinstein (2001) found that imports rather than
exports are the beneficial conduit of faster productivity growth. However, directly using the import and
8
6
exports and imports. More supply of output leads potentially to more exports. Higher
income facilitates consumption of both domestic and foreign products which ultimately
raises imports. Therefore, we cannot directly regress income on exports and imports as
shown in equation (1). Standard regression is inconsistent in the presence of mutual
endogeneity.
Instrumental variable estimation provides a theoretically appealing way to handle
the endogeneity problem. The important practical question posed in this paper is how to
find two sets of instrumenting variables that can not only capture the exogenous
components of exports and imports but also distinguish the export effect from the import
effect. Country A’s exports to country B is country B’s imports from country A. There
is a tendency both within countries and across them for exports and imports to co-vary
for general-equilibrium reasons. Thus it is difficult in practice for instrumental-variable
groups to ideally capture the distinction.
This paper proposes an alternative method to distinguishing the effects of imports
and exports.
The challenge of endogeneity in standard models is maintained by
algebraically re-arranging equation (1). Letting T be the total trade (exports plus imports)
divided by real GDP and E be the net trade (exports less imports) divided by real GDP, to
control for scale effects, equation (1) transforms to
(2)
ln( y i )   0   1 (Ti )   2 ( Ei )   3 (Z i )   i
We argue that finding good instruments for T and E in order to estimate equation (2) is
much easier than finding good instruments for X and M in order to estimate equation (1).
In Figure 1, the average export share X from 1970 to 1998 is plotted against the average
export variables on the right hand side of the regression function as they did may have led to inconsistent
estimates due to the effect of reverse causation.
7
import share M from 1970 to 1998. The World Development Indicator 2000 data cover
174 countries.9 The figure exhibits a strong positive relationship between export share
and import share (with the correlation of 0.85).
In Figure 2, we plot the sum of the export share and the import share along the
vertical axis. Along the horizontal axis, we plot the difference between the export share
and the import share. The chart shows no evident relationship between the two variables
in the long run (the correlation is equal to -0.13.)
Frankel and Romer (1999) have already provided good instrument candidates for
the total trade share. The major task of the next section is to present the Frankel-Romer
model with the heteroscedasticity correction, propose instruments for the net trade share
variable by capturing cross-country differences in borrowing and lending behavior, and
investigate the impact of total trade and net trade on income after instrumenting for both
endogenous independent variables. Since Ti  X i  M i and Ei  X i  M i , the estimation
will shed implicit light on the distinctive export effect and the distinctive import effect.
A. Bilateral Trade Regression
Trade promotes growth through increased specialization, efficient resource
allocation, diffusion of international knowledge, and heightened domestic competition
(Sachs and Warner 1995). On the other hand, countries that produce more output tend to
trade more with the rest of the world. To correct for the simultaneity bias, Frankel and
Romer (1999) proposed the geographical characteristics of countries as instruments for
total trade. They argued that geography is a powerful determinant of bilateral trade as
9
The observations on Netherlands Antilles (X = 495%, M = 486%, T = 981%, E = 9%) are dropped for
graphical purpose.
8
well as overall trade. Furthermore, countries’ geographical features are not affected by
their incomes, or by government policies and other factors that influence income.
This section applies the extended version of the Frankel-Romer bilateral trade
model as appeared in Frankel and Rose (2002). We also employ the updated bilateral
trade data set which “is estimated to cover at least 98 percent of all trade.” (Frankel and
Rose 2002, pp. 462)
The first stage regression is based on the international trade
extension of the gravity model, namely, that trade volume between two trading partners
rises with an increase of national incomes or a reduction of the distance between them:
(3) ln t ij  ln(  ij / GDPi )  a' X ij  eij
 a0  a1 ln( dist ij )  a2 ln( pop j )  a3 ln( areai  area j )  a4 (languageij )  a5 (borderij )
 a6 (landlockedij )  eij .
In equation (3),  ij denotes the bilateral trade between countries i and j (measured as
exports plus imports), a is the vector of coefficients, Xij is the vector of the covariates,
distij measures the great circle distance between the principle cities of countries i and j,
pop and area represent population and area respectively, languageij is a dummy variable
which takes the value of 1 if people in country i and country j speak the same language,
landlockedij is the number of landlocked countries within the country pair, and borderij is
the dummy variable for a common border between two countries. Countries far away
from each other tend to trade less. Since country size is inversely related to proximity,
the impact of area is expected to be negative. Larger trading partner’s population implies
higher demand for the domestic country’s export. Thus, the sign for popj should be
positive. Countries are expected to trade more with each other if they have the same
9
official language or share a border. If countries have access to the ocean, shipping costs
are significantly reduced which makes it less difficult to trade.
Due to the possibility of heteroscedasticity in large cross-country analysis, this
paper conducts the Breusch-Pagan test on equation (3). The test statistic rejects the
homoscedasticity assumption with a high level of confidence. To counteract the problem
of heteroscedasticity, weighted least square (WLS) estimation is used based on the
procedure proposed by Harvey (1976).10 Assuming the variance of the disturbance term
eij in equation (3) has the following hypothesized specification:
(4)
 ij2  exp(  ' X ij ).
The estimator of  is:
(5)


   X ijX ij '
 i j

~
1
X
i j
ij
ln( eˆij2 )
based on the estimation
(6)
ln( eˆij2 )   ' X ij  ij
where eˆij2 is the residual resulting from the OLS regression of equation (3) and
ij  ln( eˆij2 /  ij2 ) . The values of both the dependent and independent variables are divided
by the square root of the predicted variance of the disturbance term, which corresponds to
the pattern of the residuals. This normalizes the residuals so that they are homoscedastic.
The next step is to obtain the predicted value of the dependent variable in levels.
Simply exponentiating ln tˆij underestimates the expected value of t ij because
10
We also adopt the robust ordinary least square (ROLS) estimation to correct for heteroscedascity by
applying the Huber-White sandwich estimator of variance in place of the traditional variance calculation
(Huber 1967, White 1982). This approach allows the correlation of errors for any one country, while errors
10
(7)
E (t ij | X ij )   ij  exp( a ' X ij )
where  ij is the expected value of exp( eij ) , which is greater than unity.
Thus, the bilateral trade share is predicted as:
(8)
tˆij  ˆij  exp( â ' X ij ).
Assuming  ij has the following functional form
(9)
ˆij  exp( ˆ ' X ij ) ,
the following nonlinear regression is conducted to estimate the value of  :
(10)
t ij  exp( ' X ij )  exp( â ' X ij )  u ij .
The value of ˆ ij is obtained from equation (9).11 Therefore, the predicted value of the
overall trade share for country i becomes:
(11)
Tˆi  ˆij exp( â ' X ij )
i j
B. Net Trade Regression
While aspects of the time-series relationship between net exports and income are
well-established, e.g., its cyclicality, the cross-section relationship is not. What kind of
national and international variables determine which countries have overall trade
surpluses and which have overall trade deficits? What variables determine bilateral net
exports between two countries? And are those cross-sectional determinants suitable for
between countries remain uncorrelated. ROLS generates similar results as we have derived using OLS and
WLS.
11
Detailed explanation of the adjustment procedure is presented in Zhang (2002a).
11
first-stage instruments in the current study, or do they too vary cross-sectionally with
income levels? This section aims to answer.
To begin, current account balance is consisted of three parts: (1) balance of trade
in goods and services E expressed as the difference between exports and imports; (2) net
rents, interest, profits and dividends represented as rB where B (-B) is the net foreign
credit (debt) that earns (pays) interest at some appropriate interest rate r; (3) transfer
payments TP such as foreign workers’ remittances.
In the open economy, current
account balance is also determined by the saving-investment gap (S – I). Changes in
official reserves R may offset the surplus or deficit in the overall balance of the capital
account KA and the current account CA.
CA  E  rB  TP  S  I  R  KA .
(12)
All variables in the above equation are divided by GDP.12 In principle, equation (12)
describes both overall and bilateral current-account balances. As we explain in the next
section, bilateral empirical implementation of our approach to (12) unfortunately
founders on data limitations (few nations or global data-collection agencies publish more
than a handful of the variables in equation (12) on a bilateral basis, including the current
account itself.)
It is clear that we can re-write equation (12) to conceive of E as determined crosssectionally by S, I, r, B, remittances, and reserve changes. Though S, I, and possibly r are
also correlated with income levels, transfer payments and reserve changes are less likely
to be. And there are at least some deeper determinants of S and E itself that might qualify
as good instrument candidates, especially in financially open economies.
Even if all the variables proposed and GDP are reasonably measured, the resulting ratio has the “division
bias”.
12
12
With respect to S and I, in a closed economy with zero international capital
movement, saving is equal to investment. Economic growth depends on both saving and
investment directly. As countries open up, however, domestic investment is financed by
the worldwide pool of savings, while domestic saving seeks the highest returns in the
global capital market.
When domestic saving is not sufficient to finance domestic
investment, such as in the US, the difference is made up by foreign capital inflow. When
domestic saving exceeds domestic investment, such as in Japan, the extra capital is
invested abroad. Higher saving is associated with capital outflow and lower saving
indicates capital inflow. Thus, while investment remains as a direct determinant of
countries’ income level in the open market scenario, saving is related to income through
the channel of global borrowing and lending, and domestic saving alone is arguably
detached from its closed-economy influence on income. That in turn makes it or its
deeper determinants reasonably good candidates for net-trade (E) instruments.
Yet before this strategy becomes persuasive, there is an important question to
answer: How integrated is the international capital market? Using the data for 16 OECD
countries from 1960-1974, Feldstein and Horioka (1980) initially answered “not very.”
They cross-sectionally regressed national gross domestic investment ratios on gross
domestic saving ratios and presented a high saving-investment coefficient.
They
concluded that the strong sectional correlation between investment and saving is likely to
be a result of low capital mobility and thus low capital market integration.
Their
skeptical conclusion is challenged by various studies. The major theoretical argument is
that the high correlation may be explained by the real short-run shock (productivity,
growth, globalization, government behavior, etc.) toward both saving and investment
13
(Obstfeld 1986; Obstfeld 1995; Obstfeld and Rogoff 1995; Baxter and Crucini 1993;
Coakley, Kulasi and Smith 1998). Based on the empirical study of extended sample and
improved approaches, recent cross-country and time-series study discovered a low
saving-investment coefficient, in accord with the belief in high capital mobility.
Moreover, almost all commentators agree that however “low” it is, the degree of
international capital mobility has increased significantly, especially during the last 20-25
years.
The widespread capital control removal in the industrial countries enhanced
financial integration. The financial sector reforms and the opening up of the capital
account to private capital inflows help to predict lower and lower correlation between
saving and investment, even in developing countries. (With less diversified production
and export structures, oil-exporting countries and small countries tend to have a low
saving-investment correlation.)
High capital mobility allows us to use saving or factors that determine saving as
instruments for net trade or current account balance. 13 Yet the relationship between
saving and net trade or current account balance reflects a complex interaction of
households, firms, and governments both at home and oversea. A random shock specific
to the net trade balance could potentially affect a country’s saving ratio. For instance,
governments tend to adopt contractionary fiscal policy to prevent sustained large capital
flows when the countries are in trade deficit. In order to avoid these new endogeneity
problems, we propose a country’s demographical characteristics as more fundamental
13
Glick and Rogoff (1995) showed that country-specific shocks rather than global productivity shocks are
important determinants of current account fluctuations. Empirical work extended dynamic optimizing
models proposed by Ghosh (1995) and Ghosh and Ostry (1995) to the open economy context (Razin 1995,
Obstfeld and Rogoff 1998). From a saving-investment perspective, Debelle and Faruqee (1996)
investigated the determinants of current account using the structural approach. Given certain investment
level, countries with a higher saving rate tend to be net exporters and net importers with a lower saving rate.
14
and cleaner instruments for net trade – recognizing that differences of saving between
two countries can be very well explained by their differences in demography.
Demography matters in both age structure and in steady-state population growth.
With regard to structure, both theoretical and empirical literature proposes the empirical
linkage between net trade and the age structure of the population. Life cycle models in
an intertemporal approach suggest that net trade balance is the outcome of forwardlooking dynamic saving and investment decisions. People save during the earning span
at productive age and dissave when they are young or old. Thus, aggregate national
savings will be relatively high if the size of the dependent population is low comparing to
the size of the working-age population. High savings build up domestic and foreign
assets, reflected as a large net trade surplus.
In the cross-sectional context, real growth of output (GDP) is expected to have a
positive impact on the net trade balance due to the fast growing supply in the global
market. We use the population growth rate as the proxy for the real growth of output to
capture the impact. Population growth rate may also serve as the determinant of saving.
Balanced population growth leads to the growth of income, which generates greater gap
between the target consumption levels of the current working generation and the
dissaving of retirees from a less prosperous generation. (Hussein and Thirlwall 1999,
Knight and Scacciavillani 1998) Consequently, the increased population growth rate
leads to higher savings. The unbalanced population growth impact, which changes the
demographic composition of the society, is captured by the dependency ratio. Saving
ratio is independent of the level of per capita income but is a function of the population
growth rate as shown in the life-cycle hypothesis of Ando and Modigliani (1963).
15
Urbanization is also a deeper fundamental determinant of saving, like
demography, and less likely than saving itself to be subject to feedback influences from
exogenous shocks to income. The influence of the urban population ratio on saving is
mixed. The trend toward urbanization leads to lower private saving rate because of the
precautionary saving motive. (Edward 1996, Loayza, Schmidt-Hebbel and Serven 2000)
On the other hand, urbanization exerts a positive and statistically significant impact on
national saving (Rodrik 1998).
An important determinant of net trade balance is the net income from abroad.
Negative current account balance increases foreign indebtedness. A country can run a
steady-state trade deficit equal to the investment income. The deterioration in investment
income requires improvement in the trade balance.
Therefore, negative net foreign
liability requires countries to run trade surplus while positive net foreign asset enables
countries to run trade deficits.
International reserves flow into the home country when country exports and flow
out of the home country when country imports. The volume of imports largely depends
on the amount of international reserves available to finance imports, especially for
developing countries. A country can run a steady-state trade deficit equal to its decline in
official reserves. Negative net export and negative changes in reserves go together.
Net trade balance is also influenced by a nation’s relative price of tradeable output
to non-tradeable output (compared in turn to the comparable world price ratio). The
higher is a nation’s relative tradeables price (by world standards), the larger will be its
relative output of both exportables and import substitutes, output that gets exported in the
first instance and displaces imports in the second (Obstfeld and Rogoff 1996, pp. 199-
16
257). Yet there is no obvious reason why a nation’s income level varies systematically
vis-à-vis other nations with this relative price, because every nation produces both
tradeables and non-tradeables and earns income from both.14
Current transfers, such as workers’ remittances, are an important source of foreign
exchange for many countries. Positive transfers of resources increase country’s real
exchange rate and thereby hurt country’s competitiveness in the world markets by
reducing the range of home goods for exports (Obstfeld and Rogoff 1996, pp. 255.)
The net export determination equation is shown as the following:
(13)
Eˆ i  ˆ0  ˆ1 (depi ,t )  ˆ2 ( popg i ,t )  ˆ3 (oil i ,t )  ˆ4 (urpop i ,t )  ˆ5 (nii ,t )  ˆ6 (dres i ,t )
 ˆ7 (relp i ,t / relp i ,t 5 )  ˆ8 (nct i ,t )
where depi,t is calculated as the proportion of people under 14 or over 65 in country i.
popgi,t stands for the population growth rate. oili,t takes the value of 1 if country i
depends heavily on oil revenues as her main source of income. The dummy variable for
oil exporting countries (primarily Gulf States) is included due to their unique
demographic trends such as the intra-regional and international migration. The dummy
variable for oil exporting countries is expected to be positive since these countries
typically have a more favorable current account position on average (Chinn and Prasad
2000.) urpopi,t is defined as the percentage of the total population living in urban area.
nii,t is the stock of net income from abroad as ratio of GDP, measured in 1990. It
14
One could object that several of these variables, e.g., net remittances or returns on cross-border capital
placement are co-determined endogenously with net exports (E) as part of the deeper fundamentals of
current account behavior – how the current account sum of all of them, E, remittances, capital income,
responds to shifts and shocks to output, investment prospects, and government spending needs (Obstfeld
and Rogoff 1999, pp. 74-116). We agree, but can think of no feasible measures of global and countryspecific productivity and fiscal shocks that could serve as deeper, more fundamental instruments than those
we choose. Furthermore, we think this objection affects net capital income and flows of official reserves
more than it affects remittances.
17
captures the term rB in equation (12). dresi,t denotes changes in net official reserves as a
ratio of GDP. Price relative (relp i ,t / relp i ,t 5 ) is measured as the ratio of ratios of export
prices to GDP deflators from 1985 to 1990. ncti,t represents current transfers (from
abroad), such as migrants’ remittances, divided by GDP.
C. Income Regression
Frankel-Romer study estimated the impact of total trade on per capita income
controlling for the size of each country. To incorporate the net trade extension, this paper
estimates the income equation
(14)
ln( yi )   0  1 (Ti )   2 ( Ei )   3 ln( popi )   4 ln( area i )  u i .
where ui is a proxy for all the uncertain factors that may also affect per capita income
level. Since both Ti and Ei are likely to be correlated with the error term, this paper
utilizes the instrumental variable (IV) estimation technique, substituting these two
variables with their predicted values from section A and section B.
The first stage regression to estimate total trade share is based on the bilateral
observations. Ideally, we would also hope to obtain the bilateral determinants for net
trade balance between country pairs. To estimate the net trade balance on a bilateral
basis, we need to obtain observations on a bilateral basis as well, for example, relative
price of the tradeables. However, variables of this kind are not available. Due to the
limit of good instrument candidate, we employ a set of instruments for each country’s
overall net trade balance while aggregating the bilateral trade observations as instruments
for each country’s total trade.
18
The size and significance of the total trade and net export variables determines if
exports or imports improve prediction of per capita output.
(15)
ln( yˆ i )  ˆ0  (ˆ1  ˆ2 )( X i )  (ˆ1  ˆ2 )( M i )  ˆ3 ln( popi )  ˆ4 ln( areai )
where ˆ1  ˆ 2 measures the predicted partial effect of exports on the per capita income
and ˆ1  ˆ 2 measures the predicted partial effect of imports on the per capita income,
holding country size constant. The relative importance of exports and imports depends
on the sign, magnitude, and the joint significance of ˆ1 and ̂ 2 estimated for equation
(14).
For instance, if ˆ1  0, ˆ 2  0 and ˆ1  ˆ 2 , both exports and imports raise
income. If ˆ1  0, ˆ 2  0 and ˆ1  ˆ 2 , exports but imports raise income. If ˆ1 and ̂ 2
are jointly insignificant, the regression produces imprecise estimates. The estimation
result is shown in Section V.
III. Data
The bilateral trade regression is based on the data set obtained from the Andrew
Rose Web site. This data set covers the bilateral trade share, distance, population, area,
common language dummy variable, border dummy variable, and landlocked dummy
variable for 186 countries in year 1990. The original source for the trade data is the
World Trade Database and United Nation’s International Trade Statistics Yearbook. The
population and real GDP per capita data come from Penn World Table 5.6. Area data are
from the World Reference Atlas. The information of the distance and common language
dummy variable is from the CIA’s web site.
19
The net trade data set including net trade share, population aged 0-14, population
aged 65 and above, total population, population growth rate, net income, changes in net
reserves, GDP at market prices, official exchange rate, and current transfers is taken from
World Bank’s World Development Indicator 2000. Net income from abroad includes the
net labor income and net property entrepreneurial income components of the SNA.
Changes in net reserves are the net change in a country’s holdings of international
reserves resulting from transactions on the current, capital, and financial accounts.
Current transfers take three forms. The net current transfers from abroad comprise
transfers of income between residents of the reporting country and the rest of the world
that carry no provisions for repayment. The net current transfers are recorded in the
balance of payments whenever an economy provides or receives goods, services, income,
or financial items without a quid pro quo. The third measure comes from the net
workers’ remittances as recorded by IMF Balance of Payments Statistics. The relative
price of tradeables is calculated based on the data from the World Table. It refers to the
1990 to 1985 ratio of export-to-GDP-deflator price indexes. Since the export prices
index is in 1987 current US dollars while the GDP deflator is in 1987 local currencies, we
create an exchange rate index for year 1990 and year 1985 to adjust the prices into the
same currencies. The exchange rate index for 1990 is 100 times the ratio of the 1990
exchange rate and 1987 exchange rate. The data on official exchange rate is taken from
the World Development Indicator 2000. Oil country list includes 11 OPEC member
countries (Algeria, Libya, Nigeria, Indonesia, Iran, Iraq, Kuwait, Qatar, Saudi Arabia, the
United Arab Emirates), additional Gulf Cooperation Council countries (Oman, Bahrain),
and other non-OPEC oil producing countries (Russia, Norway, Mexico).
20
Data on trade, gross domestic product (GDP), population, and area for the income
regression are also extracted from the Andrew Rose’ web site. This data set consists of
the year 1990 observation for 210 “countries” from the World Development Indicators
1998, merged with data from the Penn World Table 5.6.
Summary statistics and detailed description of all variables are provided in the
appendix.
IV. Estimation results
A. Bilateral Trade Regression
This paper runs the bilateral trade regression by conducting the Breusch-Pagan
tests on the ordinary least square (OLS) estimate. The resulting test statistic of 88.38 is
statistically significant at the p = 0.000 level.
That is, the null hypothesis of
homoscedasticity is rejected. OLS results are adjusted using the approach proposed by
Harvey (1976), where the square of the weight (  ij2 ) is constructed as the exponential
function of the predicted ln eˆij2 . All the variables in the regression are weighted by the
inverse of the variance.
WLS regressions reveal a positive and statistically significant relationship
between bilateral trade and country’s geographic characteristics. All else being equal,
large distance leads to less trade. If the official languages in country i and country j are
the same, trade rises by 55 percent. Two countries that share a border trade 62 percent
more than countries pairs which do not. Landlocked countries tend to trade less due to
the high transportation cost. 41% of the variation in the bilateral trade share is explained
21
by the estimated bilateral trade equation. Frankel-Romer results are confirmed by using a
larger sample.15
B. Net Trade Regression
To understand how the results may be driven by the specification and possible
omitted variable bias, the net export regressions are estimated by including variables to
control for countries’ demographic characteristics and balance of payment features.
Table 2 provides the result of the net trade regression based on equation (13).
The results of the first specification indicate that an increase in the age
dependency ratio leads to a reduction in net trade. The impact of population growth rate
is positive but not significant.
Controlling for country’s demography, oil-exporting
countries tend to have a higher net trade share relative to non-oil-exporting countries.
The second specification adds the urban population ratio variable. Urban population ratio
is positively related to the net trade balance at 1% significance level. Demography
appears to explain approximately 15 percent of the per capita GDP level in 1990. These
estimates imply that a country’s demography affects her net trade.
In specification (3), (4) and (5), we suggest a different set of net trade
determination factors. As expected, the net income from abroad variable has a significant
and negative impact on net export share. Changes in reserves and the relative price of
tradeables do not appear to play a major role in determining the trade balance, possibly
because of the inability of international trade flow to adjust to changes in the market
15
Data on bilateral trade are only available for 62 countries in the Frankel and Romer (1999) study. Based
on the coefficients estimated from the gravity model, Frankel and Romer impute the bilateral trade share
for country pairs whose recorded trade share is missing. The quality of the instruments and the precision of
22
condition due to the non-market forces in some planned economics. Current transfers
take three forms. All three measures indicate that transfers affect the net trade balance
negatively. A one percentage point difference in the current transfers is associated with
an approximately one percentage point difference in the net trade share balance. From
specification (6) to (11), we combine the two sets of instruments. In most cases, lower
dependency ratio, higher urban population ratio, lower net income from abroad, and
lower net current transfers are significantly associated with an improved trade balance.
The binary variable of oil-exporting countries is associated with the net trade balance at
the 1% significance levels, while we do not find a significant relationship between trade
balance and population growth rate, changes in reserves, as well as relative price of
tradeables. 14% to 80% of the variation in the net export share is accounted for by the
joint predictive power of the net trade regression according to different specification
results.
C. Income Regression
In running the regression of equation (14), endogeneity problem arises since the
per capita income may affect both the total trade value and net trade value. Endogeneity
renders biased OLS estimates. Higher income raises both export and import. The OLS
estimation would be biased upward. As increased levels of the income promote domestic
production of tradeables while stimulate higher import demand, the ultimate effect on the
net trade balance is uncertain. OLS estimates for the net export coefficient may bias
either upward or downward. In order to cope with the simultaneity between openness
the estimated effects are brought into questions, especially if the gravity relation is systematically different
for countries in the sample than for countries added through imputation.
23
and income, the income equation is estimated using the instrumental variables procedure.
The objective of this section is to test whether, after controlling for the size of the
economy, total trade and net trade balance contributes to explaining the cross-country
differences in per capita income. Before running the income regression, it is worthwhile
to investigate the quality of the instruments suggested in section A and B.
In the correlation matrix presented in Figure 3, a positive relationship between the
selected variables and their predicted values is suggested.
Next to the correlation
matrixes, we present the correlation tables. Overall, it shows that total trade share is
positively linked with the aggregated exponents of estimated bilateral log equations
(around 0.53 to 0.77.) Regarding the net exports share, we find that the actual values are
in general positively correlated with predicted values with the correlation between 0.51
and 0.90. Considerable information about countries overall trade and trade balance is
provided by the predicted values.
The scatter plot matrix in Figure 3 helps visually identify the positive correlations
between total trade share, net trade share, and their predictions. Additional evidence of
the correlation is displayed in the correlation tables. The correlation between the total
trade share and the net trade share, as well as the correlation between the predicted total
trade share and the predicted net trade share, are weak and nonlinear, implying that the
multi-collinearity problem does not occur in the IV regressions. Both the predicted total
trade share and the predicted net trade share are proved to be effective instruments with
no apparent outliers.
Table 3 presents the two-stage least squares estimation results based on the eleven
specifications of the first stage net trade balance estimation. The primary focus of this
24
table is the coefficient estimates of the total trade share and net trade share. When
controlling for the size of the economy, we find that higher levels of both the overall
trade share and the net trade share are typically linked with more per capita income. Size
of countries, as measured by population and area, has insignificant effect. This suggests
that the main influence of geography on income is through the channel of international
flow of goods and services. Table 3 shows that the regressions account for 28 to 60
percent of the cross-country variation in per capital real GDP.
The main results from Table 3 are summarized in Table 4, where we record the
coefficients estimates for T and E. The F statistics reject the joint hypothesis that both
coefficients in Row 1 and 2 are zero. Coefficients of X in Row 3 are the sum of the Row
1 coefficients of T and Row 2 coefficients of E. Coefficients of M in Row 4 are the
difference of the Row 1 coefficients of T and Row 2 coefficients of E. The relative
standard errors for the sum/difference are computed and recorded in the parenthesis. The
size of the coefficients for X changes moderately but remains positive and highly
significant. The coefficients for M are significantly most of the time. Table 4 leads to
two main conclusions: first, comparable and equally open countries that differ between
themselves in X-openness and M-openness have different per capita GDP; those that are
1 percent more X-open than M-open have approximately 0.1 higher GDP per person.
Second, increasing the import trade share by 1 percentage point is associated with a
reduction of the per capita GDP by approximately 0.1 percent. In other words, countries
with higher net export share are also countries with higher income per person, ceteris
paribus.16
16
Very similar results emerge implicitly from Miller’s and Upadhavy’s (2000) study of the effect of
openness on a country’s total factor productivity (intimately related, of course, to its income per person).
25
For ordinary least squares to generate the best linear unbiased estimator, it is
assumed that all variables on the right hand side of equation (14) are purely exogenous.
This paper examines the hypotheses using two Hausman tests. Hausman specification
test follows the method outlined in Hausman (1978). The test statistics reject the null that
IV and OLS estimates are equal. The second, and arguably better, Hausman test simply
adds the predicted endogenous variables into the income equation. T-test results reject
the hypothesis that the additional regression parameters equal zero, indicating no
misspecification in our model. In other words, both total trade and net trade are shown to
be endogenous.
Figure 4 graphs the confidence ellipse around the mean of our estimated
coefficients on 1 and  2 at the 95% confidence level. The orientation of the ellipses
shows that ˆ1 and ˆ2 are negatively correlated.
Most of the points lie inside the
northeast panel and far beyond the Y-coordinate. To test the conjecture that income is
related to trade, this paper runs an F-test to find the joint significance of the coefficients
on total trade share and net trade share.
The test statistics offers support for the
conjecture that income is associated with both total trade and net trade.
VI. Conclusion
Rather than staying closed, many countries gain substantial amounts of income
through their intensive involvement in international trade. This phenomenon is widely
recognized and has received considerable attention from analysts who wish to learn
whether countries should enhance or detract from trade. Although many recent empirical
Their “trade orientation” measure, based on the countries deviations from Purchasing-Power-Parity
exchange rates proxies for import shares.
26
studies attempt to capture the trade-income nexus using cross-sectional and time series
data, they lack the power to distinguish between the impact of exports on income and the
impact of imports on income.
What causes different levels of per capita income in various countries, export
openness or import openness? This study explores empirical evidence on this issue by
disaggregating both exports and imports. In particular, the conventional wisdom views
that trade (both exports and imports) are beneficial for economic growth is challenged.
Using instrumental variable estimation with the cross-country variations in overall trade
explained by geographic factors and the cross-country variations in net trade balance
explained by demography as well as balance of payment features, we investigate the
impact of both exports and imports on income. A significantly positive effect of export
share on per capita income level confirms the findings of earlier studies in which export
matters in determining the cross-country variation in income. Imports, rather, affect
growth negatively. Several studies in the literature help explain the phenomena. One
explanation is that import pattern matters.
Countries import primarily consumption
goods grow slower than countries with a large proportion of their imports as intermediate
goods. Among the latter group of countries, importing primarily from follower countries
leads to less technological improvement than importing primarily from leaders (Keller
2000). A more popular argument is that importing from established foreign firms may
discourage the development of domestic infant industries, especially in the third world
countries. Therefore, reducing the imports of infant industry products may encourage the
internalization of costs of natural resources and help increase the total factor productivity,
hence countries’ overall per capita income level.
27
The growth of exports provides productivity and technology benefits to national
economies. There are a number of channels through which the rapid expansion in
exports can contribute to the increased economic output. First, export growth offers
greater economies of scale, encourages specialization, and increases production
efficiency. The improved production techniques and management styles acquired by the
export sectors generate positive externalities to non-export sectors.
Unlike import
substitution economies, export-oriented countries tend to have relatively less price
distortions and more efficient allocation of resources. Second, increased exports ease the
foreign exchange constraint. By providing a source of foreign exchange for countries
that wish to import intermediate inputs that embody domestically unavailable technology,
export expansion leads to cost reduction, increased efficiency and greater access to
international market. Third, exports enhance diffusion of knowledge through learning by
doing. Intensive interaction with the international market encourages communication of
ideas, intensify competition, and stimulate domestic imitation and innovation. Thus,
outward-oriented strategy, with the emphasis on exports, is increasingly preferred by the
policymakers.
28
Appendix
Table A1: Summary Statistics of the Total Trade Share Regression

General Statistics
Variable
bilateral trade share
distanceij
populationj
areai  areaj
languageij
borderij
landlockedij

Obs.
10940
13264
11034
14746
14750
14746
14746
Mean
0.005
4667.31
57986.14
8.86e+11
0.12
0.02
0.19
Std. Dev.
0.025
2735.97
168141.90
6.41e+12
0.32
0.14
0.41
Min
5.31e-09
19.43
40
1883
0
0
0
Max
0.793
12351.26
1133683
2.08e+14
1
1
2
Variable Description
Variable
bilateral trade share
distanceij
populationj
areai  areaj
languageij
borderij
landlockedij
Description
Bilateral trade value divided by real GDP
The great circle distance between the principle cities of
country i and j
Country j’s population in 000’s
Product of real areas.
1 for common official language between country i and j
1 for common land border between country i and j
Number of landlocked countries in pair (0, 1 or 2)
Data source: Frankel and Rose (2002).
Table A2: Summary Statistics of the Net Trade Share Regression

General Statistics
Variable
Net trade share
Age dependency ratio
Population growth rate
Oil-exporting countries
Urban population ratio
Net income from abroad
Changes in reserves
Relative prices
Net current transfers from abroad
Net current transfers
Remittances

Obs.
145
179
196
206
196
168
148
116
96
142
91
Mean
-7.01
40.01
1.89
0.08
50.69
-1.40
-1.24
1.00
3.57
5.60
1.41
Std. Dev.
17.63
6.71
1.64
0.27
23.75
11.51
4.05
0.40
10.78
10.45
4.90
Min
-199.46
26.98
-4.87
0
5.2
-30.62
-19.87
0.14
-22.82
-26.86
-10.70
Max
26.84
52.11
14.12
1
100
74.74
20.95
3.05
81.29
64.03
27.24
Variable Description
Variable
Net trade share
Age dependency ratio
Description
1990 Net trade divided by real GDP
1990 Population aged 0-14 + population aged 65 and above (% of total)
29
Population growth rate
Oil-exporting countries
Urban population ratio
Net income from abroad
Changes in reserves
Relative prices
Net current transfers
Net current transfers from abroad
Remittances
1990 Population growth (annual %)
1 for OPEC, Abab-OPEC, and non-OPEC countries
1990 Urban population (% of total)
1990 net income from abroad (% of GDP)
Net changes in reserves (% of GDP)
1990 to 1985 ratio of export-to-GDP-deflator price indexes
1990 net current transfers (% of GDP)
1990 net current transfers from abroad (% of GDP)
1990 Workers’ remittances (% of GDP)
Data source: Frankel and Rose (2002).
World Development Indicators 2000.
International Monetary Fund Balance of Payments Statistics
World Bank Table.
Table A3: Summary Statistics of the Income Regression

General Statistics
Variable
Per capita GDP
Total trade share
Net trade share
Population
Area

Obs.
115
145
145
115
210
Mean
4913.78
80.67
-7.01
39177.84
631418.7
Std. Dev.
4945.33
63.57
17.63
134104.40
1813066
Variable Description
Variable
Per capita GDP
Total trade share
Net trade share
Population
Area
Description
Real per capita GDP chain index
Total trade divided by real GDP
Net trade divided by real GDP
Population in 000’s
Area in sq. km.
Data source: Frankel and Rose (2002).
World Development Indicators 2000.
30
Min
399
13.62
-109.47
40
2
Max
18054
538.674
26.84
1133683
1.71e+07
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35
Table 1: The Bilateral Trade Equation, 1990
Dependent variable: ln (bilateral trade share)
ln (distanceij)
-1.21***
(0.03)
ln (populationj)
1.01***
(0.02)
-0.29***
ln (areai  areaj)
(0.01)
languageij
0.55***
(0.08)
borderij
0.62***
(0.15)
landlockedij
-0.70***
(0.06)
Constant
-0.58*
(0.33)
Number of Observations
8104
R2
0.41
SE of regression
2.28
Standard errors recorded in parentheses.
The symbols *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
36
Table 2: The Net Export Equation, 1990
Dependency ratio
Population growth rate
Oil-exporting
countries
(1)
-0.73**
(0.28)
0.93
(1.59)
14.39***
(4.64)
Urban population ratio
(2)
-0.34
(0.36)
0.60
(1.59)
12.16**
(4.79)
(3)
Dependent variable: net export share
(4)
(5)
(6)
-0.65***
(0.19)
1.52
(1.14)
8.10***
(2.39)
-0.16
(0.16)
-0.72***
(0.27)
-4.08*
(2.66)
-1.00***
(0.13)
Relative price
Net transfers from
abroad
Net current transfers
-0.79***
(0.10)
-0.35*
(0.19)
-0.43
(1.64)
-1.09***
(0.16)
-0.37
(0.38)
-1.97
(3.17)
-0.69***
(0.19)
-0.45*
(0.24)
-2.36
(2.10)
-0.70***
(0.13)
-1.14***
(0.10)
Net remittances
Number of
Observations
R2
SE of regression
(8)
-0.38
(0.32)
-0.90
(2.03)
10.41**
*
(3.65)
0.13*
(0.07)
Net income from
abroad
Changes in reserves
Constant
(7)
-0.06
(0.15)
-0.89
(0.82)
4.26**
(2.09)
-0.64***
(0.14)
-0.16
(0.17)
0.23
(1.35)
-0.79***
(0.26)
0.15
(0.35)
-1.27
(2.78)
(9)
-0.43**
(0.22)
1.51
(1.11)
6.86***
(2.40)
(10)
0.09
(0.18)
-1.02
(0.82)
3.56*
(2.12)
(11)
0.13
(0.41)
-2.23
(2.10)
9.28**
(3.61)
0.08**
(0.04)
-0.58***
(0.19)
-0.45*
(0.24)
-2.10
(2.05)
-0.68***
(0.13)
0.05*
(0.03)
-0.59***
(0.14)
-0.16
(0.17)
0.47
(1.35)
0.15*
(0.07)
-0.67**
(0.26)
0.15
(0.34)
-0.52
(2.74)
-1.03***
(0.10)
22.30**
(9.94)
134
-0.64
(16.08)
134
0.83
(2.71)
76
-2.31
(1.73)
113
-1.14***
(0.26)
-5.90*
(3.31)
74
0.14
14.43
0.16
14.33
0.80
7.19
0.78
6.85
0.65
10.19
Robust standard errors recorded in parentheses.
The symbols *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
21.01***
(6.57)
70
0.69
5.42
-1.02***
(0.10)
2.04
(5.34)
106
-0.82***
(0.29)
12.80
(10.55)
67
7.91
(8.87)
70
-6.84
(7.49)
106
-0.84***
(0.28)
-13.85
(17.12)
67
0.73
5.50
0.49
8.46
0.71
5.28
0.73
5.45
0.52
8.27
Table 3: Trade, Net Trade, and Income, 1990
Total trade share
Net trade share
Ln (population)
Ln (area)
Constant
Number of Observations
R2
RMSE
(1)
0.004
(0.007)
0.147***
(0.044)
0.00
(0.10)
-0.06
(0.13)
8.83***
(2.17)
102
0.56
0.74
(2)
0.004
(0.007)
0.150***
(0.035)
0.00
(0.11)
-0.06
(0.13)
8.93***
(2.04)
102
0.65
0.66
Dependent variable: ln (per capita real GDP)
(3)
(4)
(5)
(6)
(7)
0.019
0.024
0.107**
0.007
0.017
(0.016) (0.017)
(0.041)
(0.010)
(0.012)
0.020
0.046**
-0.076
0.102***
0.068***
(0.057) (0.016)
(0.072)
(0.038)
(0.016)
0.06
0.12
1.14**
0.03
0.12
(0.18)
(0.18)
(0.48)
(0.12)
(0.14)
0.20
0.17
0.36
-0.02
0.10
(0.23)
(0.14)
(0.35)
(0.18)
(0.20)
4.01
3.32
-14.00
7.96**
4.65*
(4.65)
(3.42)
(9.87)
(3.16)
(2.59)
65
94
61
64
92
0.28
0.35
0.28
0.45
0.45
0.91
0.87
0.91
0.80
0.81
Robust standard errors recorded in parentheses.
The symbols *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
38
(8)
0.097*
(0.055)
0.035
(0.068)
0.98*
(0.51)
0.32
(0.37)
-11.10
(11.79)
59
0.52
0.76
(9)
0.006
(0.010)
0.112***
(0.035)
0.02
(0.12)
-0.04
(0.17)
8.38***
(2.98)
64
0.50
0.76
(10)
0.017
(0.011)
0.072***
(0.016)
0.11
(0.14)
0.10
(0.12)
4.82*
(2.57)
92
0.48
0.79
(11)
0.096*
(0.054)
0.039
(0.058)
0.97*
(0.50)
0.31
(0.36)
-10.82
(11.49)
59
0.60
0.69
Table 4: The impact of export share and import share on per capita GDP, 1990
Total trade
share
Net trade
share
F test
Export share
Import share
(1)
0.004
(0.007)
0.147***
(0.044)
19.37
(0.000)
0.151***
(0.037)
-0.143***
(0.048)
(2)
0.004
(0.007)
0.150***
(0.035)
25.88
(0.000)
0.154***
(0.03)
-0.146***
(0.040)
(3)
0.019
(0.016)
0.020
(0.057)
2.88
(0.064)
0.039
(0.046)
-0.001
(0.070)
(4)
0.024
(0.017)
0.046**
(0.016)
6.31
(0.003)
0.076***
(0.021)
-0.022
(0.026)
Coefficient estimates
(5)
(6)
0.107**
0.007
(0.041)
(0.010)
-0.076
0.102***
(0.072)
(0.038)
3.35
13.19
(0.042)
(0.000)
0.031
0.109***
(0.064)
(0.030)
0.183*
-0.095**
(0.099)
(0.046)
Robust standard errors recorded in parentheses.
The symbols *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
39
(7)
0.017
(0.012)
0.068***
(0.016)
12.43
(0.000)
0.085***
(0.017)
-0.050**
(0.022)
(8)
0.097*
(0.055)
0.035
(0.068)
5.19
(0.009)
0.132***
(0.046)
0.062
(0.115)
(9)
0.006
(0.010)
0.112***
(0.035)
13.79
(0.000)
0.117***
(0.029)
-0.106**
(0.043)
(10)
0.017
(0.011)
0.072***
(0.016)
13.40
(0.000)
0.089***
(0.018)
-0.055**
(0.022)
(11)
0.096*
(0.054)
0.039
(0.058)
6.11
(0.009)
0.135***
(0.040)
0.056
(0.104)
Figure 1: Export Share vs. Import Share, Mean 1970 – 1998
200
180
BHS
SGP
160
Export (% of GDP)
140
MDV
SUR
HKGABW
120
100
BHR
MLT
GUY
ATG
PAN
SWZ
LCA
MYS
BEL SYC
SVK VCT
SVN
IRL
MNG
BLZ SLBKNA
KWT
BRB
MUS
GAB
BWA
CZE
JAM
COG
OMN
NLD
MKD
EST
NAM
GNQ
FJI
AGO
BGR
CYP GMB
VUT
IRQSAU
LTU
DMA
LBRTKM
GRD
HRV
LBY
TTO
CHEPNG
MDA
TGO
DJI
HUN
YEM
NOR
MRT
JOR
CIV
LVA
CRI
NGA
TUN
ZMB
AUT
UKR
ISL
VNM
DNK
STP
HND
KOR
SWE
TJK
SENISR
THA
FIN
WSM ERI
LKA
TON
KEN
CAN
NZL
PHL
DOM
ZAF
KAZ
DEU
IDN
CHL
PRY
PRT
VEN
GBR
ZWE
ECU
BLR
ROM
SLV
MWI NICBTN
DZA
SYR
KHM
BEN
CMR
BOL
GIN
FRA
POL
RUS
MAR
AZE
URY
GHA
ITA
KGZ
EGY
ZAR
MEX
ESP
NER
IRN
AFG
CAF
SOM
SLE
GTM
CPV
COM
MLI
CHN
UZB
LAO
MDG
AUS
PER
TZA
GRC
ARM
GEO
ALB
TUR
COL
TCD
NPL
HTI
PAK
LBN
JPN
UGA
MOZ GNB
ETH
ARG
BDIBFA
RWA
BRA
USA
SDN
IND
BGD
80
60
40
20
KIR
LSO
0
0
20
40
60
80
100
120
Import (% of GDP)
140
160
180
200
Figure 2: Total Trade Share vs. Net Trade Share, Mean 1970 – 1998
350
BHS
SGP
Total Trade (% of GDP)
300
MDV
SUR
ABW
250
200
BHR
GUY
MLT
ATG
SWZ
KIR
150
HKG
LSO
GNQ
ERI
100
LBN
50
PAN
LCA
KNAVCT SYC
SLB
MYS
SVK BEL
MKD
MNGSVN
NAMBLZ
BRB
MUS
DMA GMB
JAM
IRL
COG
CZE
EST
BWA
JOR DJIGRD
CYP FJI NLD
VUT BGR
KWT
WSM
YEM
MDA LTU
HRV
OMN
PNGAGOSAUGAB
TON
STP MRTTGO
TKM
LBR
CHE
HUN
BTN
LVA
CRI
LBY IRQ
TTO
ISR TUN
SOM
VNM
NOR
CIV
UKR
ZMB
NIC
HNDAUT
NGA
SEN
ISL
CPV
LKA
DNK
TJK
KOR
THASWE
MWI
KHM
PRT
BEN
COM
KEN
PHL
DOM
SLV
FIN
AZE
PRY
NZL
KAZ
SYR
EGY
CAN
MAR
ZWE
GNB ARM
BLR
ROM
KGZ
GBR
GIN
CHL
DEU
IDN
BOL
TZA
ZAF
MLI CAF
GHA
ECU
NER
CMR
VEN
ALB
DZA
POL
FRA
AFG
MOZ
BFALAO
GEOSLE
URY
ITA
RUS
ZAR
TCD
GTM
ESP
MDG
MEX
GRC
IRN
RWA
BDIHTI
NPLTUR
UZB
PAK
AUS
PER
CHN
UGA
ETH
SDN COL
JPN
BGDUSA
ARG
BRA
IND
0
-120 -110 -100 -90
-80
-70 -60 -50 -40 -30
Net Trade (% of GDP)
40
-20
-10
0
10
20
Figure 3: Correlation Matrix for Specification (1) to (11)
(1)
total_trade
50
0
NOG
A
MN
VEN
SYR
TTO
G AB
MD
HV
KG
EC
U
C
O
G
N
O
R
U
PO
R
AG
KW
Y
LN
T
AR
D
ZAF
G
ZA
D
N
K
IR
LMYS
C
O
L
BH
LD
SPAN
C
H
SAU
LO
H
D
MR
H
N
EU
U
N
BH R
BR
ID
A
ISL
AU
N
TBLZ
BEL
ATG
JC
PN
ZAR
C
IV
PER
ZW
FR
C
ITA
N
VN
SW
AN
ZL
C
A
E
M
BW
H
E
A
SLE
BR
B
U
AU
MEX
SA
KO
S
R
IR
FIN
N
IN
BO
G
D
BR
L
TU
G
H
TM
R
N
DA
ESP
PN
JE
AM
G
YP
G
MR
IN
T
BEN
BG
R
FJ
I SAZ
D
MAR
MW
PH
KEN
O
ISR
M
L
IC
H
ALB
TI
SEN
C
R
ISYC
SD
N
TU
N
MU
BG
G
PAK
N
R
D
ER
TH
C
G
T
MB
R
W
A
LKA
R
G
O
H
PR
M
A
Y
SW
UETH
G
BTN
LC
N
EG
PL
Y
MD
G
TG
O
VC
T
LAO
TC
D
C
AF
SLV
YEM
MLT
BFA
ZMB
MLI
ABW
VU
BD
IM
GN
RAM
DT
CTZA
OPV
G
N
C
D
MA
SO
MB
STP
SLB
NSM
IC
MO
Z
TO
N
W
G
NKN
QJ OAR
SG P
SU R
net_trade
-50
T
E
Tˆ
T
E
Tˆ
Ê
1.0000
0.1344
0.5277
1.0000
0.0844
1.0000
0.1683
0.5134
0.2866
1.0000
T
E
Tˆ
Ê
T
E
Tˆ
Ê
1.0000
0.1344
0.5277
1.0000
0.0844
1.0000
0.2180
0.5579
0.3060
1.0000
T
E
Tˆ
Ê
T
E
Tˆ
1.0000
-0.2253
0.7473
1.0000
-0.1731
1.0000
Ê
-0.1790
0.8983
-0.0890
LBN
-100
LSOKIR
MLT
MLT
H KG
H KG
SG P
SG P
KN A
KN A
BR B BH R
BEL
MU S
C H SYC
E
CTLD
YP
AUN
IR L KIR
DTTO
NJK
SLV
TG
AM
O
ISR
BH
S
D
EU
KW
G
C
R
O
C
MRG
MB
H
TM
PR
U
T
N
C
N
DBLZ
IT
BG
R
JPAN
ITA
RG
O
M
G
BR
FR
A
LKA
TU
N
BG
D
SYR
N
FJ
IC
MYS
IO R
N
H
PL
KO
TI
SEN
RE
D
O
G
N
Q
ESP
N
O
FIN
PO
LM
SW
BTN
U
G
R
Y
B
R
W
PAK
ZW
PH
A
ISL
E
TU
BEN
EC
MAR
R
U
SLB
BD
SLE
IA
G
MW
H
TH
O
MN
ILYR
A
JC
C
PN
EG
O
L
Y
IN
LAO
D
N
K
G
EN
ZL
IN
U
G
VEN
A
PER
VN
C
C
H
IV
M
C
MR
PR
G
YEM
AB
MD
D
MEX
TZA
ZA
N
G
G
AG
AR
ETH
IR
BFA
H
G
N
C
ZMB
O
BO
ZAF
ID
N
MR
L
PN
SAU
TG
SD
SO
MO
N
M
Z
C
MLI
AN
U
C
SA
AF
AG
O
BR
AU
TC
N
ZAR
A
ER
S
D
BR
BHBR
BEL
MU S
SYC
CHE
CAU
YP
T
N
DIRLD
NLTTO
K
SLV
TG
J
O
AM
ISR
BH
ST
D
EU
KW
M
G
R
C
G
MB
PR
G
H
TM
T
N
C
H
BLZ
R
N
D
JN
OCIC
ROR
BG
RIIU
ITA
PAN
O
M
G
BR
FR
TU
LKA
N
BG
D
FJ
MYS
N
SEN
H
PL
KO
TI
RA
G NSLB
O
M
ESP
N
O
R
FIN
PO
LY
BTN
SW
GQN
B
U
R
R
PAK
PH
W
ZW
ISL
A
L
ELSYR
MAR
BEN
TU
EC
R
U
BD
ID
SLE
G
TH
MW
H
A
IIV
O
MN
EG
J
Y
C
PN
O
LAO
KEN
G
IN
N
IN
D
ZL
U
G
A
VEN
VN
PER
C
SU
H
M
RAB
MR
YEM
PR
Y
G
MD
MEX
G
D
ZA
N GA
ZMB
BFA
ETH
IR
C
AR
C
N
H
G
MR
PN
BO
ID
SAU
ZAF
G
T
L
N
MO
SOTZA
ZM
SD
N
MLI
C
AN
C
AF
U
SA
AG
OG
TC
N
ER
D
AU
ZAR
BR
SO
AN
KIR
SU R
predicted_total_trade
40
Q AT
20
0
BH R
NKW
O RT
ID
N
VEN
SAU
MEX
D ZA
NOG
A
MN
IRKO
N
R
H KG
JCG
PN
C
D
AN
EU
C
HKTLD
EBEL
N
A
ITA
S
H
N
FIN
R
C
D
NU
UAU
SA
N
ZL
FR
A
RESP
O
M
C
H
PR
ISL
TH
H
U
BH
LR
T
NAYP
MU
S MLT
S
G
BR
PO
SW
E
C
BG
BR
BMYS
ALB
TU
R
LKA
R
ISR
YLM
BR
A
C
R
LBN
IG
AR
CU
O
G
O
LPN
IN
ZAF
D
TU
N
G
AB
IR
MB
LPAN
TTO
FJ
I
PER
MAR
EC
U
EG
PH
Y
L
VN
M
JG
AM
MD
PR
G
Y
BO
LA
SO
M
LSO
PAK
SLV
N
AM
SD
N
ZW
N
PL
BTN
E
LAO
G
N
B
SM
SLB
C
SLE
AF
BW
G
H
MR
N
D
T
SW
C
MR
SEN
TG
O
BG
BD
TZA
D
IW
C
G
O
N
BLZ
G
QJAT
H
MW
TI
C
G
PV
IN
IN
VU
O RZ MD V
ZAR
AG
O
TC
G
N
SYR
TM
C
ER
D
IV
IC
UETH
C
MO
MLI
O
YEM
M
Z
R
W
BFA
A
BEN
ZMB
KEN
SG P
SU R
LSO
LBN
BH
R
KW
NNO T
R
ID
VEN
SAU
MEX
D
ZAP
SG
N GA
O MN
IR N
KO
R
C
J
D
AN
PN
HH
EU
E
N
LD
AU
TKG
AU
ITA
S
C
H
N
G
R
FIN
C
D
N
K
BEL
MLT
ESP
U
N
SA
ZL
FR
A
R
O
M
MU
PR
TH
H
C
T
ISL
A
BH
S
U
HB
S
LL
G
BR
SW
PO
E
C
YP
BG
BR
R
ALB
LKA
ISR
TU
MYS
U
R
R
YAB
BR
ALN
C
R
IR
D
O
C
AR
PAN
M
O
L
G
IN
D
ZAF
TU
N
G
MB
IG
G
TTO
FJ
IEC
SU
R
MAR
PN
PER
U
EG
PH
YY
L
JPL
VN
AM
MD
PR
G
BO
LM
SO
M
SLV
NB
AM
N
BTN
SD
ZW
N
E
WJMO
SLB
GQ
N
LAO
ETH
C
AF
B
SLE
W
AO
G
SW
MR
H
N
A
Z
T
TG
SEN
O
C
G
NSM
BD
BG
IPAK
BLZ
D
CMR
GV
C
O
PV
R
VU
T
MW
H
G
TI
IN
ID
ZAR
AG
MD
N
IC
TC
N
ER
D
C
TM
IVO
SYR
CTZA
ZO
MLI
YEM
U
M
G
A
BFA
R
W
A
ZMB
BEN
KEN
AR E
BH R
KW T
N OR
ID
N
VEN
SAU
MEX
DOIR
ZA
Q
N
A
MN
IRGN
KO
R
C
AN
JHU
PN
DRG
EU
BYU
NN
LDTC H E
AU
AU
S
ITA
CC
N
FIN
G
D
U
SA
ZL
ESP
FR
AM
R
O
CN
N
TH
H
ISL
C
LBG
A
H
L
PR
U
BH
N
TCS
SBR B
G
BR
SW
PO
E
LC
CKYP MUBEL
RZE
TU
U
MYS
LKA
R
R
Y
ISR
BR
A
G
U
Y
C
R
IIIR
AR
C
D
O
GD
O
L
PAN
ZAF
IN
MMR
TU
G
AB
L
TTO
SU
R
FJ
DN
IJMB
JAM
PN
PER
MAR
G
EC
UG
EG
PH
Y
LEM
VN
M
MD
PR
G
Y
BO
L
MN
SO
G
M
PAK
SLV
SD
ZW
N
BTN
N
PL
ETH
LAO
G
SLB
N
C
AF
SLE
MR
T
H
A
N
C
MR
SEN
TG
C
TZA
O
BD
G
G
BG
N
ITI
BLZ
Q
D
G
MW
IN
H
JH
IB
O
AG
ZAR
O
TC
N
ER
C
D
IV
SYR
N
G
IC
TM
MO
MLI
YEM
U
G
Z
A
C
ORDMO
BFA
R
LBR
W
A
ZMB
BEN
KEN
SG P
H KG
predicted_net_trade
MLT
-20
0
200
400
600
0
100
200
Ê
300
(2)
total_trade
50
0
NOG
A
MN
VEN
SYR
TTO
G AB
MD
HV
KG
EC
U
C
O
G
N
O
R
U
PO
R
AG
KW
Y
LN
T
AR
D
ZAF
G
ZA
D
N
K
IR
LMYS
C
O
L
BH
LD
SPAN
C
H
SAU
LO
H
D
MR
H
N
EU
U
N
BH R
BR
ID
A
ISL
AU
N
BEL
ATG
JC
PN
ZAR
C
IV
PER
ZW
FR
C
ITA
N
VN
SW
AN
ZL
C
A
E
M
BW
H
E
A
SLE
BR
B
U
AU
MEX
SA
KO
S
R
IR
FIN
N
IN
BO
G
D
BR
L
TU
G
H
TM
R
N
DTBLZ
ESP
PN
JE
C
G
YP
G
MR
IN
T
BEN
BG
R
FJ
I SAZ
D
MAR
MW
PH
KEN
O
ISR
M
L
IG
H
ALB
TI
SEN
C
R
ISYC
SD
N
TU
N
MU
BG
G
PAK
N
R
D
PR
ER
TH
C
T
MB
R
W
A
LKA
R
G
O
H
M
A
YAAM
SW
UETH
G
BTN
LC
N
EG
PL
Y
MD
G
TG
O
VC
T
LAO
TC
D
C
AF
SLV
YEM
MLT
BFA
ZMB
MLI
ABW
VU
BD
IM
GN
RAM
DT
CTZA
OPV
G
N
C
D
MA
SO
MB
STP
SLB
NSM
IC
MO
Z
TO
N
W
G
NKN
QJ OAR
SG P
SU R
net_trade
-50
LBN
-100
LSOKIR
MLT
MLT
H KG
H KG
SG P
SG P
KN A
KN A
BR B BH R
BEL
MU S
C H SYC
E
CTLD
YP
AUN
IR L KIR
DTTO
NJK
SLV
TG
AM
O
ISR
BH
S
D
EU
KW
G
C
R
O
C
MRG
MB
H
TM
PR
U
T
N
C
N
DBLZ
IT
BG
R
JPAN
ITA
RG
O
M
G
BR
FR
A
LKA
TU
N
BG
D
SYR
N
FJ
IC
MYS
IO R
N
H
PL
KO
TI
SEN
RE
D
O
G
N
Q
ESP
N
O
FIN
PO
LM
SW
BTN
U
G
R
Y
B
R
W
PAK
ZW
PH
A
ISL
E
TU
BEN
EC
MAR
R
U
SLB
BD
SLE
IA
G
MW
H
TH
O
MN
ILYR
A
JC
C
PN
EG
O
L
Y
IN
LAO
D
N
K
G
EN
ZL
IN
U
G
VEN
A
PER
VN
C
C
H
IV
M
C
MR
PR
G
YEM
AB
MD
D
MEX
TZA
ZA
N
G
G
AG
AR
ETH
IR
BFA
H
G
N
C
ZMB
O
BO
ZAF
ID
N
MR
L
PN
SAU
TG
SD
SO
MO
N
M
Z
C
MLI
AN
U
C
SA
AF
AG
O
BR
AU
TC
N
ZAR
A
ER
S
D
BR
BHBR
BEL
MU S
SYC
CHE
CAU
YP
T
N
DIRLD
NLTTO
K
SLV
TG
J
O
AM
ISR
BH
ST
D
EU
KW
M
G
R
C
G
MB
PR
G
H
TM
T
N
C
H
BLZ
R
N
D
J OCIC
ROR
BG
RIIU
ITA
PAN
O
M
G
BR
FR
TU
LKA
N
BG
D
FJ
MYS
N
SEN
H
PL
KO
TI
RA
G NN
O
M
ESP
N
O
R
FIN
PO
LY
BTN
SW
GQN
B
U
R
R
PAK
PH
W
ZW
ISL
A
L
ELSYR
SLB
MAR
BEN
TU
EC
R
U
BD
ID
SLE
G
TH
MW
H
A
IIV
O
MN
EG
J
Y
C
PN
O
LAO
KEN
G
IN
N
IN
D
ZL
U
G
A
VEN
VN
PER
C
SU
H
M
RAB
C
MR
YEM
Y
G
MD
MEX
G
D
N GA
ZMB
BFA
ETH
IR
AR
C
N
H
O
G
MR
PN
BO
ID
SAU
ZAF
G
T
L
N
MO
SOTZA
ZM
SD
N
MLI
C
AN
CPR
AF
U
SA
AG
OG
TC
N
ER
D
AU
ZAR
BR
SZA
AN
KIR
SU R
predicted_total_trade
Q AT
20
0
KW T
BH R
VEN
NSAU
OR
MEX
MN
D
ZA
IRID
NO
N
H KG
N
GNALD
BELMLT
D
EU
S
ISL
C
D
AN
N
JAU
PN
G
N
KO
ISR
ZL
BH
S
C
H
LK
U
RBR
YRE
SW
U
SA
AR
ESP
G
LBN
FR
A
ITA
AU
T
H
BR
A
FIN
G
R
C
C
O
H
LC
ULM
NE
PO
BG
R
G
AB
TU
R
PER
TTO
RD
O
M
O
C
YP
PR
TIR
LMYS
EC
UL
MU
JPAN
OSR
ZAF
BR
B
CLTU
R
IN
CBO
H
N
MAR
PH
J
AM
ALB
PR
Y
EG
Y
FJ
CBW
O
GAI
N
IC
SLV
SYR
ABLZ
H
MR
N
G
D
T
MB
IN
D
LKA
C
MR
C
PV
SEN
C
PAK
AF
CTH
IV
G
SO
H
M
A
G
TM
MD
ZW
G
E
G
ZMB
NG
QSW Z MD V
SLE
NSM
AM
SD
VN
N
TG
O
H
BEN
TI
PN
ZAR
AG
LSO
C
OYEM
IN
M
G
N
B
TZA
W
LAO
MO
ZMO
BG
MLI
DG
SLB
TC
D
KEN
N
N
MW
PL
BTN
UETH
G
AER
BFA
BD
I IVU T
R
W
A
SG P
LBN
SU R
LSO
KWRT
BH
VEN
SAU
N OR
MEX
O MN
D
SG
ZAP
IRID
N
HNKG N G A
BEL
N
LD
EU
AU
S
MLT
ISL
C
D
AN
N
ISR
G
KO
N
JD
BR
PN
ZL
R
BH
S
C
H
LK
U
R
Y
SW
E
U
SA
ESP
AR
FR
A
AU
ITA
TG
H
BR
A
FIN
GBG
RC
CR
H
C
U
OE
N
LTTO
PO
LRAB
G
TU
R
PER
R
O
M
SU
DC
CO
YP
M
TU
N
MYS
PR
T
PAN
IR
J O R PR
MU
S
EC
U
BR
ZAF
BL
R
I
BO
L
C
H
N
MAR
PH
L
J
AM
ALB
Y
EG
Y
FJ
IT
O
G
N
SLV
TH
BLZ
AC
G
MR
H
MB
BW
N
D
ASYR
LKA
IN
D
CQIC
PV
C
MR
SEN
C
PAK
AF
C
IV
SO
M
G
H
A
G
TM
G
NSM
ZMBZW
MD
G
E
N
AM
SLE
SD
VN
N
M
TG
O
H
BEN
PN
TI
G
SW
ZAR
Z
AG
O V
M
G
IN
GCTZA
N
B
MD
WMO
ZO
YEM
LAO
SLB
MLI
BG
D
TC
ETH
D
VU
T
KEN
N
N
MW
PL
ER
I
BTN
U
BFA
BD
RI G
WAA
AR E
KW T
BH R
VEN
SAU
N OR
MEX
IR Q
OZA
MN
D
IR
N
ID
N
NG
A D EU
BEL
AU
S
ISL
CAR
AN
DNS
NLD
K
JU
PN
ZL
KO
G
BR
RBH
ISR
CN
H
B
LITA
R
Y
SW
E
U
SA
G
ESP
FR
CR
AJZE
AU TC H E
D
BR
A
FIN
G
CITTO
CR
L
CN
O
LR
H
UM
N
PO
BG
LM
RG
G
AB
TU
PER
YU
O
SU
RO
D
CL YP MU SBR B
TU
MYS
PAN
TIIR
EC
JULPR
ON
RAM
ZAF
MN
G
C
R
BO
L
C
H
N
MAR
PH
J
PR
Y
EG
Y
FJ
IMB
CCG
O
G
U
N
Y
IC
SLV
TH
SYR
A
BLZ
MR
T
G
N
D
IN
D
LKA
MR
MMR
SEN
C
AF
PAK
C
IV
SO
G
M
H
A
LBR
G
TM
MD
ZMB
ZW
G
G
NH
EB
Q
SLE
SD
VN
N
M
TG
PN
BEN
G
HN
TI
AG
ZAR
O
G
IN
C
O MO
G
TZA
YEM
LAO
Z
MLI
SLB
BG
D
TC
ETH
D
KEN
NMO
ER
MW
N
BTN
U
G
APL
BFA
IIA
RBD
W
SG P
H KG
MLT
predicted_net_trade
-20
0
200
400
600
0
100
200
300
(3)
total_trade
50
0
NOG
A
MN
VEN
SYR
TTO
G AB
MD
HV
KG
EC
U
C
O
G
N
O
R
U
PO
R
AG
KW
Y
LN
T
AR
D
ZAF
G
ZA
D
N
K
IR
LMYS
C
O
L
BH
LD
SPAN
C
H
SAU
LO
H
D
MR
H
N
EU
U
N
BH R
BR
ID
A
ISL
AU
N
TBLZ
BEL
ATG
JC
PN
ZAR
C
IV
PER
ZW
FR
C
ITA
N
VN
SW
AN
ZL
C
A
E
M
BW
H
E
A
SLE
BR
B
U
AU
MEX
SA
KO
S
R
IR
FIN
N
IN
BO
G
D
BR
L
TU
G
H
TM
R
N
DA
ESP
PN
JE
C
AM
G
YP
G
MR
IN
T
BEN
BG
R
FJ
I SAZ
D
MAR
MW
PH
KEN
O
ISR
M
L
IG
H
ALB
TI
SEN
C
R
ISYC
SD
N
TU
N
MU
BG
G
PAK
N
R
D
ER
TH
C
T
MB
R
W
A
LKA
R
G
O
H
PR
M
A
Y
SW
UETH
G
BTN
LC
N
EG
PL
Y
MD
G
TG
O
VC
T
LAO
TC
D
C
AF
SLV
YEM
MLT
BFA
ZMB
MLI
ABW
VU
BD
IM
GN
RAM
DT
CTZA
OPV
G
N
C
D
MA
SO
MB
STP
SLB
NSM
IC
MO
Z
TO
N
W
G
NKN
QJ OAR
SG P
SU R
net_trade
-50
LBN
-100
LSOKIR
MLT
MLT
H KG
H KG
SG P
SG P
KN A
KN A
BR B BH R
BEL
MU S
C H SYC
E
CTLD
YP
AUN
IR L KIR
DTTO
NJK
SLV
TG
AM
O
ISR
BH
S
D
EU
KW
G
C
R
O
C
MRG
MB
G
TM
PR
U
T
NBLZ
H
C
N
D
IT
BG
R
JPAN
ITA
RESP
O
M
G
BR
FR
A
LKA
TU
N
BG
D
SYR
N
FJ
IC
MYS
IO R
N
H
PL
KO
TI
SEN
RE
D
O
G
M
N
Q
N
O
FIN
PO
L
SW
BTN
U
G
R
Y
B
R
W
PAK
ZW
PH
A
ISL
E
TU
BEN
EC
MAR
R
U
SLB
BD
SLE
I
G
MW
H
TH
O
A
MN
ILYR
A
JC
C
PN
EG
O
L
Y
IN
LAO
D
N
K
G
EN
ZL
IN
U
G
VEN
A
PER
VN
C
C
H
IV
M
C
MR
PR
G
YEM
AB
MD
D
MEX
TZA
ZA
N
G
G
AG
AR
ETH
IR
BFA
H
G
N
C
ZMB
O
BO
ZAF
ID
N
MR
L
PN
SAU
TG
SD
SO
MO
N
M
Z
C
MLI
AN
U
C
SA
AF
AG
O
BR
AU
TC
N
ZAR
A
ER
S
D
0
ID
N
PN
G
SW
C
D
H
N
LK
FIN
N
MR
ZL
O
R
MYS
TH
A
MU
PAN
S
BR
C
IR
ESP
U
C
ITA
O
A
VEN
R
N
AN
L
Y
FR
ZAF
G
SEN
BR
A
N
LD
SD
U
N
AU
SA
EC
N
PL
K
ISL
EN
A
S
U
U
JE
T
AM
JC
MEX
PN
H
KO
N
C
R
H
E
BG
U
BO
G
D
A
LU
FJ
IATG
IN
D
D
PH
ZA
H
LR
N
N
IC
ETH
PR
C
M
R
T
IR
ID
T
L
LC
N
D
TU
LKA
R
R
D
G
PAK
SLV
R
CG
TC
G
TM
D
MA
KN
AA
SLB
MD
DNOGBMVU
G
T
C PV
W SM
KIR
SU R
SG P
BR
BHBR
BEL
MU S
SYC
CHE
CAU
YP
T
N
DIRLD
NLTTO
K
SLV
TG
J
O
AM
ISR
BH
ST
D
EU
KW
M
G
R
C
G
MB
PR
G
H
TM
T
N
C
H
BLZ
R
N
D
JN
OCIC
ROR
BG
RIIU
ITA
PAN
O
M
G
BR
FR
TU
LKA
N
BG
D
FJ
MYS
N
SEN
H
PL
KO
TI
RA
G NSLB
D
O
M
ESP
N
O
R
FIN
PO
LY
BTN
SW
GQN
B
U
R
R
PAK
PH
W
ZW
ISL
A
L
ELSYR
MAR
BEN
TU
EC
R
U
BD
I
SLE
G
TH
MW
H
A
IIV
O
MN
EG
J
Y
C
PN
O
LAO
KEN
G
IN
N
IN
D
ZL
U
G
A
VEN
VN
PER
C
SU
H
M
RAB
MR
YEM
PR
Y
G
MD
MEX
G
D
ZA
N GA
ZMB
BFA
ETH
IR
C
AR
C
N
H
G
MR
PN
BO
ID
SAU
ZAF
G
T
L
N
MO
SOTZA
ZM
SD
N
MLI
C
AN
C
AF
U
SA
AG
OG
TC
N
ER
D
AU
ZAR
BR
SO
AN
predicted_total_trade
SG
P
ID
N
PN
G
SW
C
D
H
N
E
LK
FIN
N
C
MYS
N
MR
O
R
MU
TH
A
PAN
S
ESP
IR
C
BR
ITA
C
U
AN
N
O
R
A
Y
VEN
SEN
G
FR
BR
N
ZAF
LD
A
N
SD
KEN
JPL
U
AU
AM
AU
ISL
SA
S
TL
U
KO
MEX
C
J
PN
H
H
R
E
NL
U
BG
G
FJ
BO
ATG
D
IEC
L
N
IC
PH
IN
H
L
D
U
ZA
LC
PR
C
ETH
MR
R
A
T
IZL
T
H
N
D
DG
LKA
TU
R
SLV
G
PAK
R
CD
DG
MA
D
TM
KN
ARTC
SLB
MD
G
DO
M
GN
BT
VU
C PV
W SM
ID
N
PN
G
C
SW
H
L
E
D
NLD
K HES
FIN
C
N
MR
N
ZL
MYS
O
R
TH
PAN
A
BR
C
IR
VEN
C
AN
U
A
O
ESP
R
ITA
Y
ZAF
SEN
G
FR
BR
AU
U
SD
SA
KEN
EC
S
N
ISL
N
U
PL
JA
AM
C
MEX
JH
PN
KO
N
RD
BO
U
G
L
BG
FJ
AL
ITM
D
IN
ZA
PH
D
N
LIC
U
N
MR
ETH
T
PR
C
RSLV
TC
IN
IRAU
L TC MU
H
N
D
TU
LKA
R
PAK
R
TC
D
G
SLB
MD
DGN
OB
M
G
SG P
KN A
-50
predicted_net_trade
-100
KIR
KIR
KIR
-150
0
200
400
600
0
100
200
300
41
1.0000
(4)
total_trade
50
N GA
0
O MN
VEN
SYR
TTO
G AB
MD
HV
KG
EC
U
C
O
G
N
O
R
U
PO
R
AG
KW
Y
LN
T
AR
D
ZAF
G
ZA
D
N
K
IR
LMYS
C
O
L
BH
LD
SPAN
C
H
SAU
LO
H
D
MR
H
N
EU
U
N
BH R
BR
ID
A
ISL
AU
N
TBLZ
BEL
ATG
JC
PN
ZAR
C
IV
PER
ZW
FR
C
ITA
N
VN
SW
AN
ZL
C
A
E
M
BW
H
E
A
SLE
BR
B
U
AU
MEX
SA
KO
S
R
IR
FIN
N
IN
BO
G
D
BR
L
TU
G
H
TM
R
N
DA
ESP
PN
JE
C
AM
G
YP
G
MR
IN
T
BEN
BG
R
FJ
I SAZ
D
MAR
MW
PH
KEN
O
ISR
M
L
IG
H
ALB
TI
SEN
C
R
ISYC
SD
N
TU
N
MU
BG
G
PAK
N
R
D
ER
TH
C
T
MB
R
W
A
LKA
R
G
O
H
PR
M
A
Y
SW
UETH
G
BTN
LC
N
EG
PL
Y
MD
G
TG
O
VC
T
LAO
TC
D
C
AF
SLV
YEM
MLT
BFA
ZMB
MLI
ABW
VU
BD
IM
GN
RAM
DT
CTZA
OPV
G
N
C
D
MA
SO
MB
STP
SLB
NSM
IC
MO
Z
TO
N
W
G
NKN
QJ OAR
SG P
SU R
net_trade
-50
LBN
-100
LSOKIR
MLT
MLT
H KG
H KG
SG P
SG P
KN A
KN A
BR B BH R
BEL
MU S
C H SYC
E
CTLD
YP
AUN
IR L KIR
DTTO
NJK
SLV
TG
AM
O
ISR
S
D
EU
KW
G
C
R
O
C
MRBH
G
MB
G
H
TM
PR
U
T
N
C
N
DBLZ
IT
BG
R
JPAN
ITA
RESP
O
M
G
BR
FR
A
LKA
TU
N
BG
D
SYR
N
FJ
IC
MYS
IO R
N
H
PL
KO
TI
SEN
RE
D
O
G
N
Q
N
O
FIN
PO
LM
SW
BTN
U
G
R
Y
B
R
W
PAK
ZW
PH
A
ISL
E
TU
BEN
EC
MAR
R
U
SLB
BD
SLE
I
G
MW
H
TH
O
A
MN
ILYR
A
J
C
PN
EG
O
L
Y
IN
LAO
D
N
K
G
EN
ZL
IN
U
G
VEN
A
PER
VN
C
C
H
IV
M
C
MR
PR
G
YEM
AB
MD
D
MEX
TZA
ZA
N
G
G
AG
AR
C
ETH
IR
BFA
H
G
N
C
ZMB
O
BO
ZAF
ID
N
MR
L
PN
SAU
TG
SD
SO
MO
N
M
Z
C
MLI
AN
U
C
SA
AF
AG
O
BR
AU
TC
N
ZAR
A
ER
S
D
KIR
SU R
BR
BHBR
BEL
MU S
SYC
CHE
CAU
YP
T
N
DIRLD
NLTTO
K
SLV
TG
J
O
AM
ISR
BH
ST
D
EU
KW
M
G
R
C
G
MB
PR
G
H
TM
T
N
C
H
BLZ
R
N
D
JN
OCIC
ROR
BG
RIIU
ITA
PAN
O
M
G
BR
FR
TU
LKA
N
BG
D
FJ
MYS
N
SEN
H
PL
KO
TI
RA
G NSLB
D
O
M
ESP
N
O
R
FIN
PO
LY
BTN
SW
GQN
B
U
R
R
PAK
PH
W
ZW
ISL
A
L
ELSYR
MAR
BEN
TU
EC
R
U
BD
I
SLE
G
TH
MW
H
A
IIV
O
MN
EG
J
Y
C
PN
O
LAO
KEN
G
IN
N
IN
D
ZL
U
G
A
VEN
VN
PER
C
SU
H
M
RAB
C
MR
YEM
PR
Y
G
MD
MEX
G
D
ZA
N GA
ZMB
BFA
ETH
IR
AR
C
N
H
G
MR
PN
BO
ID
SAU
ZAF
G
T
L
N
MO
SOTZA
ZM
SD
N
MLI
C
AN
C
AF
U
SA
AG
OG
TC
N
ER
D
AU
ZAR
BR
SO
AN
predicted_total_trade
50
BH R
0
C
O
IV
CSLE
OC
G
LSAU
AB
N
G
A G ATG
ZAR
TTO
C
H
LK
EC
U
N
ZL
SW
VEN
SYR
MYS
AR
C
AU
U
FIN
ID
PO
G
MR
D
R
N
S
N
N
Y
O
LR
R
LC
ZAF
C
AN
ISL
IR
L
FJ
PAN
I SA
BR
PER
D
G
ITA
N
A
ZA
BR
ER
TH
MEX
FR
A
N
JE
AM
LD
BO
ESP
H
H
N
AU
L
U
N
T
SD
IN
U
SA
D
N
SEN
U
JC
PN
KO
A
D
MU
MA
PH
K
EN
C
G
L
R
R
ISYC
D
O
C
PN
M
H
G
E
YP
N
IR
G
PL
TM
N
ETH
H
N
BR
DA
KN
B
A
MW
IC
TU
PR
R
TU
Y
N
BG
MAR
D
ZMB
PAK
BEN
LKA
MR
BLZ
T
MD
G
H
G
A
T
G
H
R
TI
C
TG
C
AF
MLI
TZA
MLT
EG
SLV
YG O
TC
D
BFA
G
NISR
MB
BD
IB
SLB
N
ICJTO R
VU
C PV
BH R
CIV
GAB
C
C
OOG
LTTO
SAU
SLE
ZAR
C
H
LSYR
ATG
U N GA
N
ZL
SW
MYS
SG
E
P
VEN
LC
AU
FIN
C
A
ID
AR
D
U
N
PO
MR
N
S
N
R
O
L
K
Y
R
C
ISL
AN
IR
ZAF
FJ
PAN
IEC
N
TH
G
ER
PER
BR
ITA
BR
A
D
ZA
A
JPL
MEX
FR
AM
N
A
ESP
BO
C
AU
H
H
LLD
TL
NG
SD
SEN
IN
U
SA
D
DG
MA
U
MU
G
KO
J
A
S
PN
R
RMD
DG
C
KEN
PH
R
L
IU
D
PN
C
C
O
YP
H
M
G
E
N
SYC
TM
N
KN
AZMB
ETH
H
BR
N
B
MW
ID
PR
TU
TU
Y
N
R
BG
MAR
D
LKA
PAK
MR
BEN
BLZ
T
PR
HIR
G
T
A
TG
G
H
R
O
TI
C
C
AF
ISR
MLI
MLT
SLV
EG
TC
DY
BFA
GTZA
N
B
G
BD
I MB
J SLB
OIC
R
N
VU T
C PV
W SM
SG P
W SM
-50
-100
KIR
0
BH R
C
O
G
IV
GC
AB
O
N
G
ALLU
SAU
SLE
ZAR
TTO
C
H
EC
N
ZL
VEN
SW
MYS
SYR
E
SG P
AU
AR
ID
C
MR
U
N
S
PO
G
FIN
R
O
Y
LR
C
ZAF
AN
ISL
LK TC MU
FJ
PAN
ITM
N
BR
D
ER
PER
TH
ZA
A
G
A
ITA
BR
MEX
FR
JA
AM
NN
LD
C
BO
L
N
ESP
H
U
NIIR
AU
U
SD
SA
IN
N
SEN
D
U
JH
G
PN
KO
A
RD
KEN
PH
LPR
RD
PN
D
G
O
H E SBR B KN A
IR
N
N
PL
G
SYC
ETH
H
N
D
MW
ICM
PR
TU
Y
TU
R
ZMB
MAR
BG
MR
BEN
PAK
T
LKA
BLZ
MD
G
H
G
A
TCOC YP
H
TI
TG
R
C
AF
ISR
MLI
TZA
EG
YG
SLV
TC
D
BFA
GN
BMB
G
BD
OR
SLB
NJIIC
KIR
200
400
MLT
predicted_net_trade
KIR
600
0
100
200
T
E
Tˆ
Ê
T
E
Tˆ
1.0000
-0.1820
0.7105
1.0000
-0.1185
1.0000
-0.1445
0.9028
-0.0775
1.0000
T
E
Tˆ
Ê
1.0000
-0.3336
0.6590
1.0000
-0.1956
1.0000
-0.1748
0.8000
-0.1799
1.0000
T
E
Tˆ
Ê
1.0000
0.0752
0.7719
1.0000
0.0959
1.0000
0.2711
0.7534
0.1697
Ê
300
(5)
total_trade
50
0
NOG
A
MN
VEN
SYR
TTO
G AB
MD
HV
KG
EC
U
C
O
G
N
O
R
U
PO
R
AG
KW
Y
LN
T
AR
D
ZAF
G
ZA
D
N
K
IR
LMYS
C
O
L
BH
LD
SPAN
C
H
SAU
LO
H
D
MR
H
N
EU
U
N
BH R
BR
ID
A
ISL
AU
N
TBLZ
BEL
ATG
JC
PN
ZAR
C
IV
PER
ZW
FR
C
ITA
N
VN
SW
AN
ZL
C
A
E
M
BW
H
E
A
SLE
BR
B
U
AU
MEX
SA
KO
S
R
IR
FIN
N
IN
BO
G
D
BR
L
TU
G
H
TM
R
N
DA
ESP
PN
JE
C
AM
G
YP
G
MR
IN
T
BEN
BG
R
FJ
I SAZ
D
MAR
MW
PH
KEN
O
ISR
M
L
IG
H
ALB
TI
SEN
C
R
ISYC
SD
N
TU
N
MU
BG
G
PAK
N
R
D
ER
TH
C
T
MB
R
W
A
LKA
R
G
O
H
PR
M
A
Y
SW
UETH
G
BTN
LC
N
EG
PL
Y
MD
G
TG
O
VC
T
LAO
TC
D
C
AF
SLV
YEM
MLT
BFA
ZMB
MLI
ABW
VU
BD
IM
GN
RAM
DT
CTZA
OPV
G
N
C
D
MA
SO
MB
STP
SLB
NSM
IC
MO
Z
TO
N
W
G
NKN
QJ OAR
SG P
SU R
net_trade
-50
LBN
-100
LSOKIR
MLT
MLT
H KG
H KG
SG P
SG P
KN A
KN A
BR B BH R
BEL
MU S
C H SYC
E
CTLD
YP
AUN
IR L KIR
DTTO
NJK
SLV
TG
AM
O
ISR
BH
S
D
EU
KW
G
C
R
O
C
MRG
MB
G
H
TM
PR
U
T
N
C
N
DBLZ
IT
BG
R
JPAN
ITA
RESP
O
M
G
BR
FR
A
LKA
TU
N
BG
D
SYR
N
FJ
IC
MYS
IO R
N
H
PL
KO
TI
SEN
RE
D
O
G
N
Q
N
O
FIN
PO
LM
SW
BTN
U
G
R
Y
B
R
W
PAK
ZW
PH
A
ISL
E
TU
BEN
EC
MAR
R
U
SLB
BD
SLE
IA
G
MW
H
TH
O
MN
ILYR
A
JC
C
PN
EG
O
L
Y
IN
LAO
D
N
K
G
EN
ZL
IN
U
G
VEN
A
PER
VN
C
C
H
IV
M
C
MR
PR
G
YEM
AB
MD
D
MEX
TZA
ZA
N
G
G
A
AR
ETH
IR
BFA
H
G
N
C
ZMB
O
G
BO
ZAF
ID
N
MR
L
PN
SAU
G
T
SD
SO
MO
N
M
Z
C
MLI
AN
UAU
C
SA
AG
BR
TC
N
ZAR
AAF
ER
SD O
KIR
SU R
BR
BHBR
BEL
MU S
SYC
CHE
CAU
YP
T
N
DIRLD
NLTTO
K
SLV
TG
J
O
AM
ISR
BH
ST
D
EU
KW
M
G
R
C
G
MB
PR
G
H
TM
T
N
C
H
BLZ
R
N
D
JN
OCIC
ROR
BG
RIIU
ITA
PAN
O
M
G
BR
FR
TU
LKA
N
BG
D
FJ
MYS
N
SEN
H
PL
KO
TI
RA
G NSLB
O
M
ESP
N
O
R
FIN
PO
LY
BTN
SW
GQN
B
U
R
R
PAK
PH
W
ZW
ISL
A
L
ELSYR
MAR
BEN
TU
EC
R
U
BD
ID
SLE
G
TH
MW
H
A
IIV
O
MN
EG
J
Y
C
PN
O
LAO
KEN
G
IN
N
IN
D
ZL
U
G
A
VEN
VN
PER
C
SU
H
M
RAB
MR
YEM
PR
Y
G
MD
MEX
G
D
ZA
N GA
ZMB
BFA
ETH
IR
C
AR
C
N
H
G
MR
PN
BO
ID
SAU
ZAF
G
T
L
N
MO
SOTZA
ZM
SD
N
MLI
C
AN
CNAF
U
SA
AG
OG
TC
ER
D
AU
ZAR
BR
SO
AN
predicted_total_trade
50
BH R
0
BH R
C
CCIV
OOG
LGAB
ZMB
N GA
ATG
SLE
JBO
AM
PAN
TTO
N
ZL
MR
C
MR
TLN
N
KEN
SYC
ER
SD
ID
N
VEN
GGR
NRTC
B
C
AF
SW
E
MD
LC
SEN
PER
G
A
N
O
R
BR
D
ZA
A
TG
D
MEX
O
G
H
ITA
SAU
A
ESP
FR
A
SLB
DG
N
LD
D
KO
IN
O
AU
M
D
TSYR
JD
O
PH
U
BLZ
SA
L
TU
BEN
TM
BFA
MA
MAR
G
LKA
R
C
PR
PAK
C
T
H
E
TU
RR
KN
AMLI
SLV
BG
D
MLT
EG
VU
T Y
C PV
W SM
IVO G
C OCG
LC
AB
N
G
ZMB
AAM
ATG
SLE
TTO
JESYC
PAN
N
ZL
C
MR
MR
T
BO
N
KEN
ER
L
SD
ID
VEN
N
C
G
SW
SYR
AF
BG
PER
MD
SEN
N
G
O
R
LC
BR
D
A
ZA
MEX
TC
D
TG
OD
G
ID
TA
H
SAU
A
ESP
FR
R
LD
IN
KO
O
AU
M
R
T
U
SA
PH
LN
BLZ
JMA
OAA
R
G
BEN
N
BFA
MLI
MAR
D
G
R
LKA
PAK
PR
CTU
HTSLB
EKN
TU
R
BG
SLV
DTM
EG
Y
MLT
C PVVU T
W SM
CC
OO
GL
C
IV
GG
AB
N
ZMB
APAN
SLE
J AM
TTO SYC
N
ZL
MR
C
MR
T
N
BO
ER
KEN
L
SD
ID
VEN
N
N
C
AF
G
SW
SYR
N
B
E
MD
PER
SEN
G
O
R
BR
D
ZA
A
TC
MEX
D
SAU
G
H
A
ITA
ESP
FR
ARTCNOAU
SLB
LDTC H E
IN
D
KO
D
O
M
RTG
U
SA
PH
JR
LPR
BLZ
O
BEN
TU
G
N
TM
MLI
BFA
MAR
LKA
R
PAK
TU
EGBG
Y D SLV
BH R
KN A
MLT
predicted_net_trade
-50
KIR
-100
0
KIR
200
400
KIR
600
0
100
200
T
E
Tˆ
Ê
300
(6)
total_trade
50
0
NOG
A
MN
VEN
SYR
TTO
G AB
MD
HV
KG
EC
U
C
O
G
N
O
R
U
PO
R
AG
KW
Y
LN
T
AR
D
ZAF
G
ZA
D
N
K
IR
LMYS
C
O
L
BH
LD
SPAN
C
H
SAU
LO
H
D
MR
H
N
EU
U
N
BH R
BR
ID
A
ISL
AU
N
TBLZ
BEL
ATG
JC
PN
ZAR
C
IV
PER
ZW
FR
C
ITA
N
VN
SW
AN
ZL
C
A
E
M
BW
H
E
A
SLE
BR
B
U
AU
MEX
SA
KO
S
R
IR
FIN
N
IN
BO
G
D
BR
L
TU
G
H
TM
R
N
DA
ESP
PN
JE
C
AM
G
YP
G
MR
IN
T
BEN
BG
R
FJ
I SAZ
D
MAR
MW
PH
KEN
O
ISR
M
L
IG
H
ALB
TI
SEN
C
R
ISYC
SD
N
TU
N
MU
BG
G
PAK
N
R
D
ER
TH
C
T
MB
R
W
A
LKA
R
G
O
H
PR
M
A
Y
SW
UETH
G
BTN
LC
N
EG
PL
Y
MD
G
TG
O
VC
T
LAO
TC
D
C
AF
SLV
YEM
MLT
BFA
ZMB
MLI
ABW
VU
BD
IM
GN
RAM
DT
C
O
TZA
G
NM
B
C
PV
D
MA
SO
STP
SLB
NSM
IC
MO
Z
TO
N
W
G
NKN
QJ OAR
SG P
SU R
net_trade
-50
LBN
-100
LSOKIR
MLT
MLT
H KG
H KG
SG P
SG P
KN A
KN A
BR B BH R
BEL
MU S
C H SYC
E
CTLD
YP
AUN
IR L KIR
DTTO
NJK
SLV
TG
AM
O
ISR
BH
S
D
EU
KW
G
C
R
O
C
MRG
MB
TM
PR
U
T
NBLZ
H
C
N
D
IT
BG
R
JPAN
ITA
RG
O
M
G
BR
FR
A
LKA
TU
N
BG
D
SYR
N
FJ
IC
MYS
IO R
N
H
PL
KO
TI
SEN
RE
D
O
G
N
Q
ESP
N
O
FIN
PO
LM
SW
BTN
U
G
R
Y
B
R
W
PAK
ZW
PH
A
ISL
E
TU
BEN
EC
MAR
R
U
SLB
BD
SLE
IA
G
MW
H
TH
O
MN
ILYR
A
JC
C
PN
EG
O
L
Y
IN
LAO
D
N
K
G
EN
ZL
IN
U
G
VEN
A
PER
VN
C
C
H
IV
M
C
MR
PR
G
YEM
AB
MD
D
MEX
TZA
ZA
N
G
G
AG
AR
ETH
IR
BFA
H
G
N
C
ZMB
O
BO
ZAF
ID
N
MR
L
PN
SAU
TG
SD
SO
MO
N
M
Z
C
MLI
AN
U
C
SA
AF
AG
O
BR
AU
TC
N
ZAR
A
ER
S
D
KIR
SU R
BR
BHBR
BEL
MU S
SYC
CHE
CAU
YP
T
N
DIRLD
NLTTO
K
SLV
TG
J
O
AM
ISR
BH
ST
D
EU
KW
M
G
R
C
G
MB
PR
G
H
TM
T
N
C
H
BLZ
R
N
D
JN
OCIC
ROR
BG
RIIU
ITA
PAN
O
M
G
BR
FR
TU
LKA
N
BG
D
FJ
MYS
N
SEN
H
PL
KO
TI
RA
G NSLB
O
M
ESP
N
O
R
FIN
PO
LY
BTN
SW
GQN
B
U
R
R
PAK
PH
W
ZW
ISL
A
L
ELSYR
MAR
BEN
TU
EC
R
U
BD
ID
SLE
G
TH
MW
H
A
IIV
O
MN
EG
J
Y
C
PN
O
LAO
KEN
G
IN
N
IN
D
ZL
U
G
A
VEN
VN
PER
C
SU
H
M
RAB
MR
YEM
PR
Y
G
MD
MEX
G
D
ZA
N GA
ZMB
BFA
ETH
IR
C
AR
C
N
H
G
MR
PN
BO
ID
SAU
ZAF
G
T
L
N
MO
SOTZA
ZM
SD
N
MLI
C
AN
C
AF
U
SA
AG
OG
TC
N
ER
D
AU
ZAR
BR
SO
AN
predicted_total_trade
20
0
ID N
NLO R
VEN
CO
C
H
L
MEX
N
ZL
C
AU
S
FIN
DAN
NN
DITA
ZA
MYS
SW
EKA
LD
KO
TH
AU
TAM
PN
G
ISL
U
H
EC
R
N
Y
URH
MU
S
JCIR
PN
JN
ESP
N
FR
ZAF
A
BR
G
A
BR
IR
LPAN
U
SA
H
U
C
C
C
MR
H
DIE
LNLR
IN
D
FJ I
PR
T
LKA
SD
N
SEN
GBO
R
C
PH
T
TU
KEN
RMR
UETH
N
G
A
IC
PAK
DPL
OGMNSLB
MD
BG
DTM
GSLV
TC
D
GN B
-20
IDSG
NP
VEN
CNOOLR
CD
H
LK
MEX
NMYS
ZL
C
AN
AU
S
FIN
N
D
ZA
SW
E
ITA
N
LD
TH
KO
AU
A
R
TL
PN
PAN
G
ISL
MU
C
S
U
EC
H
R
NY
U
JG
AM
J
PN
ESP
IR
N
FR
ZAF
A
BR
BR
IR
A
U
SA
H
C
H
C
R
IU
C
HR
N
DEN
BO
FJ
IN
D
IMR
PR
T
LKA
SD
SEN
NL
G
C
PH
MR
TL
KEN
TU
R
U
ND
ETH
G
PL
N IC SLV
PAK
OA
MD
BG
DM
SLBTC
GDG
TM
GN B
SG P
VU T
C PV
W SM
ID N
VEN
CNOO
LR
CN
HZL
L DNK
CMEX
AN
AU
S
FIN
D
ZA
MYS
SW
E
ITA
NAU
LDT
TH
KO
A
RJAAM
PN
G
PAN
ISL
C
EC
U
N
R
U
Y
JH
PN
IR
N
ESP
ZAF
FR
BR
A
G
BR
U
H
URD
NIIR L C MU
HES
C
CSA
MR
H
N
BO
L
IN
DLKA
FJ
IR TC
SD
N
G
PH
LPR
MR
TSEN
TU
R
ETH
UKEN
G
NA
IC
PAK
DN
OPL
M SLV
MD
G
BG
SLB
GDTM
TC D
GN B
SG P
predicted_net_trade
VU T
C PV
W SM
-40
0
200
400
600
0
100
200
300
42
T
E
Tˆ
Ê
1.0000
(7)
total_trade
50
N GA
0
O MN
VEN
SYR
TTO
G AB
MD
HV
KG
EC
U
C
O
G
N
O
R
U
PO
R
AG
KW
Y
LN
T
AR
D
ZAF
G
ZA
D
N
K
IR
LMYS
C
O
L
BH
LD
SPAN
C
H
SAU
LO
H
D
MR
H
N
EU
U
N
BH R
BR
ID
A
ISL
AU
N
TBLZ
BEL
ATG
JC
PN
ZAR
C
IV
PER
ZW
FR
C
ITA
N
VN
SW
AN
ZL
C
A
E
M
BW
H
E
A
SLE
BR
B
U
AU
MEX
SA
KO
S
R
IR
FIN
N
IN
BO
G
D
BR
L
TU
G
H
TM
R
N
DA
ESP
PN
JE
C
AM
G
YP
G
MR
IN
T
BEN
BG
R
FJ
I SAZ
D
MAR
MW
PH
KEN
O
ISR
M
L
IG
H
ALB
TI
SEN
C
R
ISYC
SD
N
TU
N
MU
BG
G
PAK
N
R
D
ER
TH
C
T
MB
R
W
A
LKA
R
G
O
H
PR
M
A
Y
SW
UETH
G
BTN
LC
N
EG
PL
Y
MD
G
TG
O
VC
T
LAO
TC
D
C
AF
SLV
YEM
MLT
BFA
ZMB
MLI
ABW
VU
BD
IM
GN
RAM
DT
CTZA
OPV
G
N
C
D
MA
SO
MB
STP
SLB
NSM
IC
MO
Z
TO
N
W
G
NKN
QJ OAR
SG P
SU R
net_trade
-50
LBN
-100
LSOKIR
MLT
MLT
H KG
H KG
SG P
SG P
KN A
KN A
BR B BH R
BEL
MU S
C H SYC
E
CTLD
YP
AUN
IR L KIR
DTTO
NJK
SLV
TG
AM
O
ISR
S
D
EU
KW
G
C
R
O
C
MRBH
G
MB
G
H
TM
PR
U
T
N
C
N
DBLZ
IT
BG
R
JPAN
ITA
RESP
O
M
G
BR
FR
A
LKA
TU
N
BG
D
SYR
N
FJ
IC
MYS
IO R
N
H
PL
KO
TI
SEN
RE
D
O
G
N
Q
N
O
FIN
PO
LM
SW
BTN
U
G
R
Y
B
R
W
PAK
ZW
PH
A
ISL
E
TU
BEN
EC
MAR
R
U
SLB
BD
SLE
I
G
MW
H
TH
O
A
MN
ILYR
A
J
C
PN
EG
O
L
Y
IN
LAO
D
N
K
G
EN
ZL
IN
U
G
VEN
A
PER
VN
C
C
H
IV
M
C
MR
PR
G
YEM
AB
MD
D
MEX
TZA
ZA
N
G
G
AG
AR
C
ETH
IR
BFA
H
G
N
C
ZMB
O
BO
ZAF
ID
N
MR
L
PN
SAU
TG
SD
SO
MO
N
M
Z
C
MLI
AN
U
C
SA
AF
AG
O
BR
AU
TC
N
ZAR
A
ER
S
D
BH R
20
0
-20
KIR
SU R
BR
BHBR
BEL
MU S
SYC
CHE
CAU
YP
T
N
DIRLD
NLTTO
K
SLV
TG
J
O
AM
ISR
BH
ST
D
EU
KW
M
G
R
C
G
MB
PR
G
H
TM
T
N
C
H
BLZ
R
N
D
JN
OCIC
ROR
BG
RIIU
ITA
PAN
O
M
G
BR
FR
TU
LKA
N
BG
D
FJ
MYS
N
SEN
H
PL
KO
TI
RA
G NSLB
D
O
M
ESP
N
O
R
FIN
PO
LY
BTN
SW
GQN
B
U
R
R
PAK
PH
W
ZW
ISL
A
L
ELSYR
MAR
BEN
TU
EC
R
U
BD
I
SLE
G
TH
MW
H
A
IIV
O
MN
EG
J
Y
C
PN
O
LAO
KEN
G
IN
N
IN
D
ZL
U
G
A
VEN
VN
PER
C
SU
H
M
RAB
C
MR
YEM
PR
Y
G
MD
MEX
G
D
ZA
N GA
ZMB
BFA
ETH
IR
AR
C
N
H
G
MR
PN
BO
ID
SAU
ZAF
G
T
L
N
MO
SOTZA
ZM
SD
N
MLI
C
AN
C
AF
U
SA
AG
OG
TC
N
ER
D
AU
ZAR
BR
SO
AN
NLSAU
GO
AG
C
C
IV
C ID
ON
R
G
AB
NO
TTO
VEN
SLE
D
ZA
MEX
N
C
ZL
H
LK
FIN
SW
D
N
E
EC
U
AU
ZAR
PO
ISL
S
LN
IR
LMYS
U
ITA
RBR
Y
C
G
AN
AR
G
FR
A
LD
FJ
I
H
U
ZAF
ESP
BR
JC
C
PN
MR
A
SYR
TH
U
SA
H
N
AU
TAM
IR
N
PER
KO
R
JNA
IN
D
C
H
E
MU
S
BO
N
ER
L
BR
BPAN
SD
N
C
R
SEN
PH
L
CIG
YP
D
O
M
G
TM
UTU
A
PN
KEN
N
PL
R
LKA
PR
T
BG
G
R
D
H
C
TU
N IY
DBLZ
N
ETH
MW
MAR
MR
T MLT
PAK
MD
G
H
TI
G
H
A
ZMB
BEN
C
AF
TG
O
ISR
MLI
TZA
EG
Y
TC
SLV
BFA
G
NDB
BD I G MB
SLB
N
ICJTO R
VU
C PV
BH R
SAU
N GA
OLG
C
CC
NIV
O
RAB
ID
NG
VEN
SLE
D
ZA
MEX
N
C
ZL
H
LTTO
SW
FIN
D
N
ELN
K
U
AU
ZAR
ISL
IR
PO
S
L
ITA
U
R
Y
G
C
BR
AN
AR
FJ
FR
N
IEC
LD
A
H
U
MYS
ZAF
SG
P
C
J
BR
PN
MR
A
TH
A
SYR
U
AU
SA
H
TNG
IR
N
KO
PER
R
JESP
AM
PAN
IN
D
C
H
MU
S
N
ER
BR
SD
N
C
R
ILBE
SEN
PH
CBO
YP
L
D
O
M
TM
U
PN
G
A
KEN
N
PL
TU
R
PR
PR
LKA
Y
T
G
BG
TU
H
RBLZ
N
D
C
NG
ETH
MW
ID
MAR
MR
MD
G
H
TI
GPAK
HO
AT
ZMB
BEN
MLT
C
TG
AF
ISR
MLI
TZA
EG
TC
SLV
DY
G N BFA
B
BDG
I MB
N
VU T
J SLB
OIC
R
SG P
BH R
SAU
NC
GNO
AL R
C
O
G
GVEN
AB
ID
NIVO
TTO
SLE
D
ZA
MEX
CN
H
ZL
LU
SW
FIN
E
D
NLD
EC
ZAR
AU
S
PO
ISL
LU
IR
LK
U
R
ITA
Y
C
AN
G
BR
AR
G
FJ
FR
H
NN
ZAF
MYS
ESP
BR
C
JH
MR
A
PN
TH
SYR
A
U
C
SA
N
AU TC MU
IR
N
PER
KO
RIJAAM
PAN
IN
D
H E SBR B
N
BO
ER
L
SD
N
R I C YP
SEN
PH
LCMTM
D
OR
G
PN
U
G
G
A
KEN
N
PL
TU
PR
Y
LKA
PR
TC
BG
TU
G
NRD
ETH
MW
ID
MR
MAR
T
PAK
MD
G
HH
BLZ
G
H
ATI
ZMB
BEN
CMLI
AF
TG O
TZA
EG
Y ISR
TC
D
BFA
G N B SLV
BD G
I MB
SLB
NJIC
OR
SG P
MLT
predicted_net_trade
C PV
W SM
-40
predicted_total_trade
W SM
0
200
400
600
0
100
200
T
E
Tˆ
Ê
T
E
Tˆ
1.0000
0.0114
0.7075
1.0000
0.0111
1.0000
0.0257
0.8242
0.0246
1.0000
T
E
Tˆ
Ê
1.0000
-0.0641
0.6312
1.0000
-0.0595
1.0000
0.1637
0.6058
0.0271
1.0000
T
E
Tˆ
Ê
1.0000
0.0752
0.7719
1.0000
0.0959
1.0000
0.2753
0.7712
0.1787
Ê
300
(8)
total_trade
50
0
NOG
A
MN
VEN
SYR
TTO
G AB
MD
HV
KG
EC
U
C
O
G
N
O
R
U
PO
R
AG
KW
Y
LN
T
AR
D
ZAF
G
ZA
D
N
K
IR
LMYS
C
O
L
BH
LD
SPAN
C
H
SAU
LO
H
D
MR
H
N
EU
U
N
BH R
BR
ID
A
ISL
AU
N
TBLZ
BEL
ATG
JC
PN
ZAR
C
IV
PER
ZW
FR
C
ITA
N
VN
SW
AN
ZL
C
A
E
M
BW
H
E
A
SLE
BR
B
U
AU
MEX
SA
KO
S
R
IR
FIN
N
IN
BO
G
D
BR
L
TU
G
H
TM
R
N
DA
ESP
PN
JE
C
AM
G
YP
G
MR
IN
T
BEN
BG
R
FJ
I SAZ
D
MAR
MW
PH
KEN
O
ISR
M
L
IG
H
ALB
TI
SEN
C
R
ISYC
SD
N
TU
N
MU
BG
G
PAK
N
R
D
ER
TH
C
T
MB
R
W
A
LKA
R
G
O
H
PR
M
A
Y
SW
UETH
G
BTN
LC
N
EG
PL
Y
MD
G
TG
O
VC
T
LAO
TC
D
C
AF
SLV
YEM
MLT
BFA
ZMB
MLI
ABW
VU
BD
IM
GN
RAM
DT
CTZA
OPV
G
N
C
D
MA
SO
MB
STP
SLB
NSM
IC
MO
Z
TO
N
W
G
NKN
QJ OAR
SG P
SU R
net_trade
-50
LBN
-100
LSOKIR
MLT
MLT
H KG
H KG
SG P
SG P
KN A
KN A
BR B BH R
BEL
MU S
C H SYC
E
CTLD
YP
AUN
IR L KIR
DTTO
NJK
SLV
TG
AM
O
ISR
BH
S
D
EU
KW
G
C
R
O
C
MRG
MB
G
H
TM
PR
U
T
N
C
N
DBLZ
IT
BG
R
JPAN
ITA
RESP
O
M
G
BR
FR
A
LKA
TU
N
BG
D
SYR
N
FJ
IC
MYS
IO R
N
H
PL
KO
TI
SEN
RE
D
O
G
N
Q
N
O
FIN
PO
LM
SW
BTN
U
G
R
Y
B
R
W
PAK
ZW
PH
A
ISL
E
TU
BEN
EC
MAR
R
U
SLB
BD
SLE
IA
G
MW
H
TH
O
MN
ILYR
A
JC
C
PN
EG
O
L
Y
IN
LAO
D
N
K
G
EN
ZL
IN
U
G
VEN
A
PER
VN
C
C
H
IV
M
C
MR
PR
G
YEM
AB
MD
D
MEX
TZA
ZA
N
G
G
A
AR
ETH
IR
BFA
H
G
N
C
ZMB
O
G
BO
ZAF
ID
N
MR
L
PN
SAU
G
T
SD
SO
MO
N
M
Z
C
MLI
AN
UAU
C
SA
AG
BR
TC
N
ZAR
AAF
ER
SD O
BH R
20
0
-20
KIR
SU R
BR
BHBR
BEL
MU S
SYC
CHE
CAU
YP
T
N
DIRLD
NLTTO
K
SLV
TG
J
O
AM
ISR
BH
ST
D
EU
KW
M
G
R
C
G
MB
PR
G
H
TM
T
N
C
H
BLZ
R
N
D
JN
OCIC
ROR
BG
RIIU
ITA
PAN
O
M
G
BR
FR
TU
LKA
N
BG
D
FJ
MYS
N
SEN
H
PL
KO
TI
RA
G NSLB
O
M
ESP
N
O
R
FIN
PO
LY
BTN
SW
GQN
B
U
R
R
PAK
PH
W
ZW
ISL
A
L
ELSYR
MAR
BEN
TU
EC
R
U
BD
ID
SLE
G
TH
MW
H
A
IIV
O
MN
EG
J
Y
C
PN
O
LAO
KEN
G
IN
N
IN
D
ZL
U
G
A
VEN
VN
PER
C
SU
H
M
RAB
MR
YEM
PR
Y
G
MD
MEX
G
D
ZA
N GA
ZMB
BFA
ETH
IR
C
AR
C
N
H
G
MR
PN
BO
ID
SAU
ZAF
G
T
L
N
MO
SOTZA
ZM
SD
N
MLI
C
AN
CNAF
U
SA
AG
OG
TC
ER
D
AU
ZAR
BR
SO
AN
predicted_total_trade
BH R
N OR
ID
N
CMEX
OC
NLSAU
GO
AG
IV
C
DVEN
ZA
G
NTTO
ZLAB
ITA
N
JETAM
LDPAN
ESP
SW
KO
R
FR
AU
SLE
BR
A
U
SA
MD
BO
MR
G
LA
T
PER
G
RAF
C
HE
SD
N
C
G
MR
N
BZMB
C
IN
D
O
M
PR
N
SEN
ER
TC
KEN
LKA
D
PH
SYR
LTSLB
TU
N
TU
G
H
R
A
TG
O MLT
G
PAK
MAR
BFA
BG
DTMY BLZ
MLI
SLV
EG
BEN
J OR
C PVVU T
N OR
ID
NL
C IV
O
MEX
N GA
C
VEN
C ZA
OG
D
SAU
G
AB
TTO
N
ZL
ITA
JESP
AM
N
LD
SW
E
KO
R
FR
AU
T
SLE
BR
PAN
A
U
SA
MD
MR
BO
G
TLA
PER
R
C
C
H
E
SD
N
G NZMB
BG
CT
MR
C
AF
D
IN
O
M
D
PR
N
SEN
ER
TC
LKA
KEN
D
SLBMLT
PH
TU
NLR SYR
TG
G
TU
H
OTM
A
G
PAK
MAR
BFA
BLZ
BG
MLI
SLV
EG
YD
BEN
J OR
VU T
C PV
W SM
BH R
N OR
IDCNO L
N
MEX
G
A
C
IV
C
O
G
DVEN
ZA
SAU
GNAB
TTO
ZLITA
NAU
LDT
SW
ESP
E
KO
RJAAM
FR
SLE
BR
A
PAN
U
SA
MR
BO
MD
L
TD
GN
PER
CHE
SD
ZMB
N
CER
MR
G
B
AF
IN
D
OG
MR TC
NC
SEN
TC
KEN
D
LKA
SLB
PH
SYR
LPR
TU
N O
G
TU
H
R
A
TG
G
PAK
MAR
BFA
BG
DTM
MLI
SLV
EG
YBLZ
BEN
J OR
MLT
predicted_net_trade
W SM
-40
0
200
400
600
0
100
200
T
E
Tˆ
Ê
300
(9)
total_trade
50
0
NOG
A
MN
VEN
SYR
TTO
G AB
MD
HV
KG
EC
U
C
O
G
N
O
R
U
PO
R
AG
KW
Y
LN
T
AR
D
ZAF
G
ZA
D
N
K
IR
LMYS
C
O
L
BH
LD
SPAN
C
H
SAU
LO
H
D
MR
H
N
EU
U
N
BH R
BR
ID
A
ISL
AU
N
TBLZ
BEL
ATG
JC
PN
ZAR
C
IV
PER
ZW
FR
C
ITA
N
VN
SW
AN
ZL
C
A
E
M
BW
H
E
A
SLE
BR
B
U
AU
MEX
SA
KO
S
R
IR
FIN
N
IN
BO
G
D
BR
L
TU
G
H
TM
R
N
DA
ESP
PN
JE
C
AM
G
YP
G
MR
IN
T
BEN
BG
R
FJ
I SAZ
D
MAR
MW
PH
KEN
O
ISR
M
L
IG
H
ALB
TI
SEN
C
R
ISYC
SD
N
TU
N
MU
BG
G
PAK
N
R
D
ER
TH
C
T
MB
R
W
A
LKA
R
G
O
H
PR
M
A
Y
SW
UETH
G
BTN
LC
N
EG
PL
Y
MD
G
TG
O
VC
T
LAO
TC
D
C
AF
SLV
YEM
MLT
BFA
ZMB
MLI
ABW
VU
BD
IM
GN
RAM
DT
C
O
TZA
G
NM
B
C
PV
D
MA
SO
STP
SLB
NSM
IC
MO
Z
TO
N
W
G
NKN
QJ OAR
SG P
SU R
net_trade
-50
LBN
-100
LSOKIR
MLT
MLT
H KG
H KG
SG P
SG P
KN A
KN A
BR B BH R
BEL
MU S
C H SYC
E
CTLD
YP
AUN
IR L KIR
DTTO
NJK
SLV
TG
AM
O
ISR
BH
S
D
EU
KW
G
C
R
O
C
MRG
MB
TM
PR
U
T
NBLZ
H
C
N
D
IT
BG
R
JPAN
ITA
RG
O
M
G
BR
FR
A
LKA
TU
N
BG
D
SYR
N
FJ
IC
MYS
IO R
N
H
PL
KO
TI
SEN
RE
D
O
G
N
Q
ESP
N
O
FIN
PO
LM
SW
BTN
U
G
R
Y
B
R
W
PAK
ZW
PH
A
ISL
E
TU
BEN
EC
MAR
R
U
SLB
BD
SLE
IA
G
MW
H
TH
O
MN
ILYR
A
JC
C
PN
EG
O
L
Y
IN
LAO
D
N
K
G
EN
ZL
IN
U
G
VEN
A
PER
VN
C
C
H
IV
M
C
MR
PR
G
YEM
AB
MD
D
MEX
TZA
ZA
N
G
G
AG
AR
ETH
IR
BFA
H
G
N
C
ZMB
O
BO
ZAF
ID
N
MR
L
PN
SAU
TG
SD
SO
MO
N
M
Z
C
MLI
AN
U
C
SA
AF
AG
O
BR
AU
TC
N
ZAR
A
ER
S
D
KIR
SU R
BR
BHBR
BEL
MU S
SYC
CHE
CAU
YP
T
N
DIRLD
NLTTO
K
SLV
TG
J
O
AM
ISR
BH
ST
D
EU
KW
M
G
R
C
G
MB
PR
G
H
TM
T
N
C
H
BLZ
R
N
D
JN
OCIC
ROR
BG
RIIU
ITA
PAN
O
M
G
BR
FR
TU
LKA
N
BG
D
FJ
MYS
N
SEN
H
PL
KO
TI
RA
G NSLB
O
M
ESP
N
O
R
FIN
PO
LY
BTN
SW
GQN
B
U
R
R
PAK
PH
W
ZW
ISL
A
L
ELSYR
MAR
BEN
TU
EC
R
U
BD
ID
SLE
G
TH
MW
H
A
IIV
O
MN
EG
J
Y
C
PN
O
LAO
KEN
G
IN
N
IN
D
ZL
U
G
A
VEN
VN
PER
C
SU
H
M
RAB
MR
YEM
PR
Y
G
MD
MEX
G
D
ZA
N GA
ZMB
BFA
ETH
IR
C
AR
C
N
H
G
MR
PN
BO
ID
SAU
ZAF
G
T
L
N
MO
SOTZA
ZM
SD
N
MLI
C
AN
C
AF
U
SA
AG
OG
TC
N
ER
D
AU
ZAR
BR
SO
AN
predicted_total_trade
20
0
-20
VEN
NLN
OLR
C ID
O
C
H
MEX
N
ZL
AU
S
C
D
AN
N
SW
EKLD
D
ZA
ISL
N
UITA
FIN
R
Y
KO
G
BR
BR
A
N
PAN
EC
AU
UR
T MYS
J IR
PN
ESP
FR
A
U
SA
JN
ZAF
IR
L S
PN
MU
HN
C
U
H
E
TH
L
AAM
CBO
C
IG
NR
D
C
HMR
SEN
FJ
PH
TU
RMR
GD
R
PR
CLN
TT
SD
IN
N
KEN
IC I
LKA
UETH
G
DPL
AO M
SLV
N
PAK
G
BG
MD
DTM
G
TC D SLB
GN B
VU T
C PV
SG P
SG P
VEN
ID
NOL
CNO
H
LR
MEX
NCD
ZL
AU
S
C
AN
N
SW
EK
D
ZA
ISL
N
LD
FIN
MYS
U
R
KO
R
BR
IR
ITA
PAN
N
AU
EC
TY
U
ESP
JBR
PN
FR
AA
U
SA
JG
AM
ZAF
IR
MU
PN
G
C
H
H
N
TH
BO
LEL
C
C
R
IU
H
NAS
D
H
N
SEN
FJ
IMR
PH
MR
TU
R
T
PR
R
C
SD
KEN
INT
NL
D
N IC G
LKA
U
D
ETH
G
OA
M
SLV
NBG
PAK
PL
G
TM
MD
G
D
SLBTC
D
GN B
SG P
VEN
O
ID
N
LR
CC
HO
L
NSW
ZL
AU
S
CMEX
K
E DNNLD
DAN
ZA
ISL
U
FIN
MYS
RPAN
Y
KO
RJAAMAU T
G
BR
BR
IR
A
N
ITA
U
JEC
PN
ESP
FR
U
SA
ZAF
PN
G
URD
NIIR L C MU
HES
BO
TH
L
A
CH
MR
HLCN
C
NFJ
IR TC
PH
MR
TU
G
PR
SD
KEN
IN
NTSEN
D
NR
IC
ETH
UG
DNLKA
A
OGMTM
PAK
MDSLB
BG
G PL
D SLV
TC D
GN B
predicted_net_trade
VU T
C PV
W SM
W SM
-40
0
200
400
600
0
100
200
300
43
T
E
Tˆ
Ê
1.0000
(10)
total_trade
50
N GA
0
O MN
VEN
SYR
TTO
G AB
MD
HV
KG
EC
U
C
O
G
N
O
R
U
PO
R
AG
KW
Y
LN
T
AR
D
ZAF
G
ZA
D
N
K
IR
LMYS
C
O
L
BH
LD
SPAN
C
H
SAU
LO
H
D
MR
H
N
EU
U
N
BH R
BR
ID
A
ISL
AU
N
TBLZ
BEL
ATG
JC
PN
ZAR
C
IV
PER
ZW
FR
C
ITA
N
VN
SW
AN
ZL
C
A
E
M
BW
H
E
A
SLE
BR
B
U
AU
MEX
SA
KO
S
R
IR
FIN
N
IN
BO
G
D
BR
L
TU
G
H
TM
R
N
DA
ESP
PN
JE
C
AM
G
YP
G
MR
IN
T
BEN
BG
R
FJ
I SAZ
D
MAR
MW
PH
KEN
O
ISR
M
L
IG
H
ALB
TI
SEN
C
R
ISYC
SD
N
TU
N
MU
BG
G
PAK
N
R
D
ER
TH
C
T
MB
R
W
A
LKA
R
G
O
H
PR
M
A
Y
SW
UETH
G
BTN
LC
N
EG
PL
Y
MD
G
TG
O
VC
T
LAO
TC
D
C
AF
SLV
YEM
MLT
BFA
ZMB
MLI
ABW
VU
BD
IM
GN
RAM
DT
CTZA
OPV
G
N
C
D
MA
SO
MB
STP
SLB
NSM
IC
MO
Z
TO
N
W
G
NKN
QJ OAR
SG P
SU R
net_trade
-50
LBN
-100
LSOKIR
MLT
MLT
H KG
H KG
SG P
SG P
KN A
KN A
BR B BH R
BEL
MU S
C H SYC
E
CTLD
YP
AUN
IR L KIR
DTTO
NJK
SLV
TG
AM
O
ISR
S
D
EU
KW
G
C
R
O
C
MRBH
G
MB
G
H
TM
PR
U
T
N
C
N
DBLZ
IT
BG
R
JPAN
ITA
RESP
O
M
G
BR
FR
A
LKA
TU
N
BG
D
SYR
N
FJ
IC
MYS
IO R
N
H
PL
KO
TI
SEN
RE
D
O
G
N
Q
N
O
FIN
PO
LM
SW
BTN
U
G
R
Y
B
R
W
PAK
ZW
PH
A
ISL
E
TU
BEN
EC
MAR
R
U
SLB
BD
SLE
I
G
MW
H
TH
O
A
MN
ILYR
A
J
C
PN
EG
O
L
Y
IN
LAO
D
N
K
G
EN
ZL
IN
U
G
VEN
A
PER
VN
C
C
H
IV
M
C
MR
PR
G
YEM
AB
MD
D
MEX
TZA
ZA
N
G
G
AG
AR
C
ETH
IR
BFA
H
G
N
C
ZMB
O
BO
ZAF
ID
N
MR
L
PN
SAU
TG
SD
SO
MO
N
M
Z
C
MLI
AN
U
C
SA
AF
AG
O
BR
AU
TC
N
ZAR
A
ER
S
D
BH R
20
0
-20
KIR
SU R
BR
BHBR
BEL
MU S
SYC
CHE
CAU
YP
T
N
DIRLD
NLTTO
K
SLV
TG
J
O
AM
ISR
BH
ST
D
EU
KW
M
G
R
C
G
MB
PR
G
H
TM
T
N
C
H
BLZ
R
N
D
JN
OCIC
ROR
BG
RIIU
ITA
PAN
O
M
G
BR
FR
TU
LKA
N
BG
D
FJ
MYS
N
SEN
H
PL
KO
TI
RA
G NSLB
D
O
M
ESP
N
O
R
FIN
PO
LY
BTN
SW
GQN
B
U
R
R
PAK
PH
W
ZW
ISL
A
L
ELSYR
MAR
BEN
TU
EC
R
U
BD
I
SLE
G
TH
MW
H
A
IIV
O
MN
EG
J
Y
C
PN
O
LAO
KEN
G
IN
N
IN
D
ZL
U
G
A
VEN
VN
PER
C
SU
H
M
RAB
C
MR
YEM
PR
Y
G
MD
MEX
G
D
ZA
N GA
ZMB
BFA
ETH
IR
AR
C
N
H
G
MR
PN
BO
ID
SAU
ZAF
G
T
L
N
MO
SOTZA
ZM
SD
N
MLI
C
AN
C
AF
U
SA
AG
OG
TC
N
ER
D
AU
ZAR
BR
SO
AN
SAU
N
G
O
AG
CO
C
LC
IV
N
G
O
AB
R
VEN
TTO
C
H
LK
MEX
SLE
N
SW
D
ZL
N
E
U
ID
RBR
ISL
N
Y
D
ZA
AR
G
G
AU
EC
S
U
FIN
IR
LMYS
ZAR
PO
LN
LD
FR
C
ITA
AN
A
BR
A
ESP
SYR
FJ
I
H
U
JPER
U
CIR
PN
SA
ZAF
MR
N
AU
KO
RH
JNTAM
PAN
BO
LM
CG
H
N
TH
C
IN
D
N
SEN
ER
MU
PH
TM
L
BR
B S
SD
N
O
C
RYAIE
YP
R
KEN
UTU
G
A
PR
H
TU
NC
DG
N
N
PL
PN
BG
G
R
D
PR
C
MR
T MLT
ETH
MAR
LKA
PAK
MW
ZMB
ITBLZ
H
G
TI
H
A
BEN
ISR
MD
G
C
AF
TG
O
MLI
EG
Y
TZA
SLV
TC
BFA
G
NDB
BD I N
GSLB
MB
IC
VUJTO R
BH R
SAU
CIV
OG
GAB
N GA
C
CN
OO
LTTO
R
VEN
C
H
LK
MEX
SW
N
SLE
D
ZL
N
ELG
ID
ISL
U
N
R
Y
D
ZA
GAU
BR
AR
S
U
FIN
IR
ZAR
N
PO
LD
LP
C
FR
ITA
AN
SG
A
BR
ESP
IEC
H
U
U
PER
C
J
MYS
SA
PN
ZAF
MR
IR
N
AU
TNSYR
KO
RA
JFJ
AM
PAN
BO
TH
C
C
A
H
H
IN
D
N
MU
SEN
S
PH
G
BR
TM
L
SD
D
C
O
R
N
M
ILBEN
CER
YP
TU
R
KEN
U
PR
G
A
Y
TU
H
N
NG
D
N
PN
PL
G
BG
PR
R
D
T
C
MR
BLZ
MAR
ETH
MLT
LKA
ZMB
PAK
MW
I
G
H
HO
TI
AT
BEN
ISR
MD
G
C
AF
TG
MLI
EG Y
TZA
SLV
TC
G N BFA
B D
BDG
I MB
N
J SLB
OIC
R
VU T
C PV
SG P
C PV
W SM
-40
predicted_total_trade
BH R
SAU
C
N
O
G
G
AO
C
IV
L R TTO
GVEN
AB
NO
CNEC
HZL
LUBR
MEX
SLE
SW
E
DIR
NLD
ID
U
N
ISL
R
Y
D
ZA
AR
G
G
AU
S
FIN
LK
ZAR
PO
LU
C
AN
FR
ITA
ANN
BR
A
ESP
SYR
FJ
H
UBO
ZAF
C
PER
SA
JMR
PN
MYS
IR
N
AU TC H E
KO
RIJ AM
PAN
L
CER
H
TH
N
AG
IN
DO
NSD
SEN
MU SBR B
PH
LCMTM
D
N
R I C YP
TU
R
PR
UKEN
G
Y
AH
TU
NRD
PN
G
N
PL
BG
G
PR
TC
MR
T
BLZ
ETH
MAR
LKA
ZMB
MW
PAK
ID
G
H
H
A
TI
BEN
ISR
G
CMD
AF
TG
O
MLI
EG
Y SLV
TZA
TC
D
BFA
GN B
BD
IIC
NJG
OMB
R
SLB
SG P
MLT
predicted_net_trade
W SM
0
200
400
600
0
100
200
T
E
Tˆ
Ê
T
E
Tˆ
1.0000
0.0114
0.7075
1.0000
0.0111
1.0000
0.0407
0.8295
0.0376
1.0000
T
E
Tˆ
Ê
1.0000
-0.0641
0.6312
1.0000
-0.0595
1.0000
0.2058
0.6414
0.0752
Ê
300
(11)
total_trade
50
0
NOG
A
MN
VEN
SYR
TTO
G AB
MD
HV
KG
EC
U
C
O
G
N
O
R
U
PO
R
AG
KW
Y
LN
T
AR
D
ZAF
G
ZA
D
N
K
IR
LMYS
C
O
L
BH
LD
SPAN
C
H
SAU
LO
H
D
MR
H
N
EU
U
N
BH R
BR
ID
A
ISL
AU
N
TBLZ
BEL
ATG
JC
PN
ZAR
C
IV
PER
ZW
FR
C
ITA
N
VN
SW
AN
ZL
C
A
E
M
BW
H
E
A
SLE
BR
B
U
AU
MEX
SA
KO
S
R
IR
FIN
N
IN
BO
G
D
BR
L
TU
G
H
TM
R
N
DA
ESP
PN
JE
C
AM
G
YP
G
MR
IN
T
BEN
BG
R
FJ
I SAZ
D
MAR
MW
PH
KEN
O
ISR
M
L
IG
H
ALB
TI
SEN
C
R
ISYC
SD
N
TU
N
MU
BG
G
PAK
N
R
D
ER
TH
C
T
MB
R
W
A
LKA
R
G
O
H
PR
M
A
Y
SW
UETH
G
BTN
LC
N
EG
PL
Y
MD
G
TG
O
VC
T
LAO
TC
D
C
AF
SLV
YEM
MLT
BFA
ZMB
MLI
ABW
VU
BD
IM
GN
RAM
DT
CTZA
OPV
G
N
C
D
MA
SO
MB
STP
SLB
NSM
IC
MO
Z
TO
N
W
G
NKN
QJ OAR
SG P
SU R
net_trade
-50
LBN
-100
LSOKIR
MLT
MLT
H KG
H KG
SG P
SG P
KN A
KN A
BR B BH R
BEL
MU S
C H SYC
E
CTLD
YP
AUN
IR L KIR
DTTO
NJK
SLV
TG
AM
O
ISR
BH
S
D
EU
KW
G
C
R
O
C
MRG
MB
G
H
TM
PR
U
T
N
C
N
DBLZ
IT
BG
R
JPAN
ITA
RESP
O
M
G
BR
FR
A
LKA
TU
N
BG
D
SYR
N
FJ
IC
MYS
IO R
N
H
PL
KO
TI
SEN
RE
D
O
G
N
Q
N
O
FIN
PO
LM
SW
BTN
U
G
R
Y
B
R
W
PAK
ZW
PH
A
ISL
E
TU
BEN
EC
MAR
R
U
SLB
BD
SLE
IA
G
MW
H
TH
O
MN
ILYR
A
JC
C
PN
EG
O
L
Y
IN
LAO
D
N
K
G
EN
ZL
IN
U
G
VEN
A
PER
VN
C
C
H
IV
M
C
MR
PR
G
YEM
AB
MD
D
MEX
TZA
ZA
N
G
G
A
AR
ETH
IR
BFA
H
G
N
C
ZMB
O
G
BO
ZAF
ID
N
MR
L
PN
SAU
G
T
SD
SO
MO
N
M
Z
C
MLI
AN
UAU
C
SA
AG
BR
TC
N
ZAR
AAF
ER
SD O
20
0
-20
KIR
SU R
BR
BHBR
BEL
MU S
SYC
CHE
CAU
YP
T
N
DIRLD
NLTTO
K
SLV
TG
J
O
AM
ISR
BH
ST
D
EU
KW
M
G
R
C
G
MB
PR
G
H
TM
T
N
C
H
BLZ
R
N
D
JN
OCIC
ROR
BG
RIIU
ITA
PAN
O
M
G
BR
FR
TU
LKA
N
BG
D
FJ
MYS
N
SEN
H
PL
KO
TI
RA
G NSLB
O
M
ESP
N
O
R
FIN
PO
LY
BTN
SW
GQN
B
U
R
R
PAK
PH
W
ZW
ISL
A
L
ELSYR
MAR
BEN
TU
EC
R
U
BD
ID
SLE
G
TH
MW
H
A
IIV
O
MN
EG
J
Y
C
PN
O
LAO
KEN
G
IN
N
IN
D
ZL
U
G
A
VEN
VN
PER
C
SU
H
M
RAB
MR
YEM
PR
Y
G
MD
MEX
G
D
ZA
N GA
ZMB
BFA
ETH
IR
C
AR
C
N
H
G
MR
PN
BO
ID
SAU
ZAF
G
T
L
N
MO
SOTZA
ZM
SD
N
MLI
C
AN
CNAF
U
SA
AG
OG
TC
ER
D
AU
ZAR
BR
SO
AN
BH R
predicted_total_trade
BH R
N OR
VEN
CMEX
O
LSAU
C
O
C
TTO
DID
ZA
N
G
AG
N
ZL
NIV
G
AB
SW
E LD
N
ESP
FR
ITA
AR
J AM
BR
A
PER
U
SA
KO
BO
LZMB
SLE
MR
AU
TT PAN
C
CD
MR
AF
G
R
O
SEN
SYR
C
E MLT
SD
N
MD
G
N
BM
TC
PH
KEN
PR
D
LHTBLZ
G
TM
TU
R
TU
N
G
H
A
N
ER
IN
D
TGSLB
OJ O R
MAR
SLV
PAK
BEN
BFA
MLI
BG
DLKA
EG
Y
C PV
VU T
N OR
MEX
VEN
C
OOLG
SAU
CIV
TTO
D
N GA
NC
ID
NZA
SW
EG AB
NZL
LD
JESP
FR
AM
ITA
A
BR
UPER
SA
KO
BO
LA
MR
SLE
TR
ZMB
AU
PAN
T
MLT
CG
AF
C
MR
RC
C
D
O
M
SEN
SD
NH
MD
G
G N TC
B
PR
KEN
PH
D
T
L
G
TM
TU
TU
N
RESYR
G
H
A
N
ER
BLZ
IN
D
TG
O
J SLB
O RMLI
MAR
SLV
LKA
PAK
BEN
BFA
BG
EG
YD
C PV
VU T
W SM
BH R
N OR
MEX
VEN
C
O
L
SAU
C
O
G
C
IV
TTO
N
DN
G
ZA
A
ID
N
ZL
G
AB
SW
E N LD
ESP
FR
ITA
BR
AKO
PER
U
SA
RJAAM
BO
L
MR
SLE
T PAN
ZMB
C
CAF
MR
GMR C AU TC H E
D
OG
SYR
SD
NSEN
G
G
N
TC
KEN
D
PH
R
T
TM
TU
TU
R
N
G
H
ALPBTG
NMD
ER
IN
D
SLB
JBLZ
OR O
MAR
LKA
PAK
BEN
BFA
MLI
BG
EG
Y D SLV
MLT
predicted_net_trade
W SM
-40
0
200
400
600
0
100
200
300
44
T
E
Tˆ
Ê
1.0000
Figure 4: Estimated Total Trade Share Coefficient vs. Estimated Net Trade Share
Coefficient for Specification (1) to (11) (95% Confidence Interval Ellipse)
(2)
(1)
.02
.02
.015
.01
Estimated ptt1
Estimated ptt1
.015
.005
0
.01
.005
0
-.005
-.005
0
.05
.1
Estimated pnt1
.15
.2
.05
.1
Estimated pnt1
.15
.2
0
.05
.1
Estimated pnt1
.15
.2
0
.05
.1
Estimated pnt1
.15
.2
(4)
Estimated ptt1
(3)
Estimated ptt1
0
.02
.015
.01
.005
.02
.015
.01
.005
0
0
-.005
0
.05
.1
Estimated pnt1
.15
-.005
.2
(6)
(5)
Estimated ptt1
Estimated ptt1
.02
.015
.01
.005
0
-.005
.02
.015
.01
.005
0
-.005
0
.05
Estimated pnt1
.1
.15
.2
45
(8)
.02
Estimated ptt1
Estimated ptt1
(7)
.015
.01
.005
0
.02
.015
.01
.005
0
-.005
-.005
0
.05
.1
Estimated pnt1
.15
.2
0
.05
.1
Estimated pnt1
.15
.2
(10)
(9)
Estimated ptt1
Estimated ptt1
.02
.015
.01
.005
0
.02
.015
.01
.005
-.005
0
-.005
0
.05
.1
Estimated pnt1
.15
.2
.1
Estimated pnt1
.15
.2
0
Estimated ptt1
(11)
.02
.015
.01
.005
0
-.005
0
.05
46
.05
.1
Estimated pnt1
.15
.2