ECONOMICS
THE GRAVITY OF RESOURCES AND THE
TYRANNY OF DISTANCE
by
Peter E Robertson
Business School
University of Western Australia
and
Marie-Claire Robitaille
The University of Nottingham
Ningbo China
DISCUSSION PAPER 15.01
The Gravity of Resources and the
Tyranny of Distance
Peter E. Robertson ∗
The University of Western Australia
Marie-Claire Robitaille
The University of Nottingham Ningbo China
November 2014
DISCUSSION PAPER 15.01
Abstract
Falling transport costs and the rise of global production networks have reshaped
world trade. But endowments still determine production locations for fuels and
minerals. Moreover, because they are often bulky or difficult to store, unit transport
costs for natural resources may still be very large. To what extent, therefore,
does geography remain an important determinant of comparative and absolute
advantage in these markets? We estimate gravity models and show that some
minerals and fuels, particularly Iron Ore and Gas, have very high elasticities of
trade with respect to distance. We then consider counterfactuals, how trade would
differ if location advantages were eliminated. We find that for a few resource
intensive countries, distance barriers have a large impact of their market share and
are equivalent to a 70-100% ad valorem export tax relative to an average country.
Similar implicit subsidies apply for a large number of well located countries.
Keywords: International Trade, Energy, Resources, Gravity Model, Geography.
JEL: F14, Q02
∗
Robertson acknowledges the hospitality of St. Antony’s College Oxford and The Centre for the
Study of African Economies (CSAE) in the Department of Economics at The University of Oxford. We
are grateful for comments from Peter Hartley and the participants at the Asia Pacific Trade Seminars
(APTS) at Southeast University, Nanjing, China, 2013 and the International Workshop on Distance
and Border Effects in Economics, Loughborough University, January 2014. Corresponding Author;
Peter E. Robertson, Economics, School of Business, University of Western Australia, Perth. Email:
[email protected]. Phone: +61 6488 5633.
1
Introduction
Historically only the most precious commodities, such as silk and spices, were traded
over very long distances, due to high transport costs. Remote antipodean colonies thus
suffered from a “tyranny of distance”.1 Technological changes, however, have allowed
dramatic reductions in transport costs. Consequently, world manufacturing is now characterized by global production networks and intra-industry trade in product varieties.
The importance of endowments as a source of comparative advantage is thus believed
to have been eroded and preferences and institutions are argued to now be critical in
understanding trade patterns (Romalis 2004, Levchenko 2007, Chor 2010).
But for resource exporting countries, supply is indelibly linked to that country’s endowments. Moreover many resources, such as iron ore and coal, are bulky and have high
unit transport costs. Others, such as as fresh food and gas, have high storage costs.
These two facts – the link between endowments and supply and the high transport costs
– suggest that geography remains a very important factor in explaining the pattern of
resources trade as well as the sources of absolute advantage, or “competitiveness”, in
resource sectors.
Understanding these issues is important since the impact of distance on each country’s
absolute advantage, will influence the degree of market power wielded by that country.
Likewise it will influence the impact of tax and other policies aimed at resource companies
in terms of their competitiveness and ability to seek alternative host countries. It is also
important for understanding how future changes in technology that effect transport costs
might affect world trade patterns and country’s competitiveness in international markets.
The aim of this paper, therefore, is to quantify the impact of distance on world food,
minerals and fuels trade patterns. Specifically we show how world commodity trade
patterns would differ if the natural barriers due to country location did not exist. We
estimate gravity models for several energy and resource commodities and then consider
a counterfactual set of distance values that removes any advantage or disadvantage of
distance for any single exporting country. Thus we consider what would trade patterns in
food, minerals and fuels would look like, if no country had an advantage or disadvantage
in terms of geographical location.
We find that several resource intensive economies’ exports are significantly suppressed
1
The term is due to Blainey (2001). He notes that the net inflow of diggers during the gold rush in
Australia provided empty ships in Melbourne which could be used for wool exports. Blainey argues that
the availability of empty ships reduced transport costs enough to make wool exports, which also had low
perishability and a high value to weight ratio relative to wheat, a feasible export.
2
by their geographical disadvantage. Specifically there are five countries in which exports
would be 35-50% higher if their location were an average distance from their markets. For
these countries transport costs are the equivalent of a 70-100% ad valorem resource export
tax relative to the average country in terms of geographical location. Moreover there are
many countries that enjoy similar, or larger, implicit “subsidy” due to their location.
The difference between being well located or remote is therefore very substantial. These
differences are therefore very important in explaining the global pattern of resources
trade, tax decisions of governments and location decision of firms.
The remainder of this paper is organized as follows. In Section 2 we discuss the geography
of world demand for different commodities. In Section 3 we consider gravity models for
food, different raw materials and fuels. Sections 4 and 5 then consider the implications
of a counterfactual experiment where distance barriers are equalized across countries and
Section 6 concludes.
2
The Geography of World Resources Demand
Our first task is to characterize the geography of world demand patterns for resources.
This differs substantially by commodity. For example, although the USA is the world’s
largest importing country overall, China is the world’s largest importer of Minerals followed by Japan. Likewise, with respect to Coal, Japan is by far the largest importer
accounting for almost a quarter of world import demand while China is only the 10th
largest country, behind countries like Italy and India.
Thus measures of the distance of a country from their export market will differ by commodity. Table 1 summarizes the import shares for selected resource commodities based
on COMTRADE/WITS data, following the Standard International Trade Classification
1 (SITC-1), for 2006 while Figures 1 to 7 (Panel A) map the major importers for selected
commodity groups.
It can be seen that overall world import demand is largest in Europe-Central Asia (44%)
followed by roughly equal shares from East Asia (23%) and North America (22%).2
However, with respect to Minerals, world import demand is roughly split between EuropeCentral Asia (38%) and East Asia (46%). Within this category world demand for Iron
Ore is predominantly from East Asia (70%), of which China alone accounts for two thirds
(48%). Thus East Asia’s world share of Minerals imports is more than twice as large as
2
The list of countries included in each region is provided in Table A.1 while the definitions of the
commodity groups are provided in Table B.1.
3
its overall world import share while in North America’s Minerals import share is a mere
third of its overall world import share.
Likewise world Coal demand is mostly split geographically between East Asia and EuropeCentral Asia, with relatively little demand from North America. However, for Gas and
Petroleum, world demand is more evenly divided between Europe-Central Asia (35 and
36%), North America (27 and 25%) and East Asia (29 and 29%).
Thus the world import demand is very biased toward East Asia for Minerals, especially
Iron Ore, and for Coal. Conversely North American demand for these same commodities
is much lower than its total trade shares. With respect to fuels other than Coal, however,
world import demand shifts toward North America. Europe and Central Asia, in contrast,
has a very large demand for Manufactured goods and Food, but relatively little demand
for Minerals, except Coal.
Finally as can be seen in Table 1, Food and Raw materials import patterns are similar
to Manufacturing patterns and the overall average, though Europe can be seen to be a
relatively large importer of Food and Raw Materials, while North America and Asia tend
to import less food relative to other commodities.
To what extent, therefore, does geography account for world trade patterns in these
resources? One way of addressing this is to consider what the world pattern of trade
would be if there was no location advantage or disadvantage for any country. This is our
aim in the rest this paper.
[Table 1 and Figures 1-8 about here]
3
3.1
The Gravity Model
Estimation Strategy and Data
To estimate the impact of distance on the patterns of trade and specialization, we use
the gravity model. Given our focus on commodities, we adopt Santos Silva and Tenreyro
(2006) specification of the gravity model as our preferred specification. This has the advantage of handling the numerous zero trade flow observations, which are highly prevalent
in the Minerals and Fuel sectors. Specifically in our sample, 61% of the country-pair-year
do not trade Minerals, but only 18% do not trade Manufactured Goods.
Our data set is constructed from COMTRADE/WITS yearly data for the 104 economies
4
for which all variables are available, following the SITC-1 classification for the period
1998-2003 and 2006.3, 4 We use import data to measure trade flows between country-pair
as imports data are believed to be less at risk of double-counting and misreporting of the
country of origin/destination than exports data (Athukorala 2009).
There are two problems with the traditional log-linearization of the gravity model equation. The first is that the data usually contain many zero values. This may arise
from missing values or represent genuine instances of zero trade between country-pairs.
The common solution of omitting the zero trade observations leads to selection bias
(Santos Silva and Tenreyro 2006, Disdier and Head 2008). Santos Silva and Tenreyro
(2006) also point out that the log linearization of the gravity equation leads to biased coefficient estimates in the presence of heteroscedasticity. They propose the use of a Poisson
Pseudo-Maximum-Likelihood (PPML) model which, by avoiding log-linearization, thus
avoids the problem of zeroes and bias.5
Thus following Santos Silva and Tenreyro (2006), we estimate the gravity model using
the PPML estimator.
c
Xijt
= αGRAVgγg F Ef δf
(1)
c
the trade volume in million USD (constant 2000 USD) between exporting
with Xijt
country i and importing country j for commodity c in year t.
GRAV is a matrix including the standard gravity variables. From the World Development Indicators (WDI), we include the log of GDP of the exporting country (ln gdp) and
of the importing country (p ln gdp), the log of the population of the exporting country
(ln pop) and of the importing country (ln p pop) as well as the log of the land area of the
country-pair (ln land paire). From the Centre d’Etudes Prospectives et d’Informations
3
As in Greenaway, Mahabir and Milner (2008), we include Hong Kong’s exports with China’s exports
as the two economies are closely integrated. As pointed out by Greenaway et al. (2008) many exports
originating from those two countries combine management and distribution skills from Hong Kong and
labour from China.
4
Those years and classification system have been selected so as to include, as far as possible, all large
natural resources exporters.
5
An alternative approach is to use the Tobit model. However, as pointed out by Linders and Groot
(2006) this approach relies on assumptions on the data generating process that do not hold in the gravity
model of trade. More precisely, the Tobit model assumes that the data suffer from rounding, which is
highly uncommon for trade data, or that the desired outcome may not be measured by the actual
outcome, for example, negative value, again a characteristic not applicable to trade data. Helpman,
Melitz and Rubinstein (2008) similarly consider a selection model where only the most productive firms
export. We find that their model is compelling for understanding manufacturing trade but less convincing
for resources trade.
5
Internationales (CEPII) data set, we take the log of the weighted great-circle distance
between the country-pair, with the weight depending on the population distribution
within both partner countries (ln distwces), a set of dummy variables classifying the
pair of country as none is landlocked (reference category), one country is landlocked
(landlocked 1) and two countries are landlocked (landlocked 2), a dummy variable taking the value of one if the two countries in the country-pair are contiguous (contig), a set
of dummy variables classifying the country-pair into none of the countries is an island
(reference category), one country in the country-pair is an island (island 1), and the
two countries in the country-pair are islands (island 2), a dummy variable taking the
value of one if at least one language is spoken by at least 9% of the population in both
countries (comlang ethno) and, finally, a dummy variable taking the value of one if the
two countries in the country-pair have ever been in a colonial relationship (colony).
We also include the log of human capital level in the exporting country (hum k) and
of the importing country (p hum k).6 Finally, FE is a set of fixed effects for exporting
countries (COUNTRY ), for importing countries (PCOUNTRY ) and for years (YEAR).
The standard errors are adjusted for clustering at the country-pair level.
The commodities have been classified into broad categories, that is, All, Food, Raw
Materials, Minerals, Coal, Petrol, Gas and Manufactured Goods. Given its importance
in world trade, we also estimate a model for Iron Ore (24% of Minerals exports in 2006).
The exact SITC-1 categories included into each element of this classification are presented
in Table B.1.
3.2
Trade-Distance Elasticities by Commodity
The results are given in Table 2. We find that an increase of 1% in distance leads to a
drop of approximately 0.77% in total exports and a 0.75% fall in Manufacturing trade.
These results are consistent with the existing literature (Disdier and Head 2008). It can
be seen, moreover, that distance is highly significant for all of the different commodity
groups. Nevertheless it can also be seen that the elasticities for some commodities differ
substantially from Manufacturing. They range from -0.86 for Raw Materials to -1.96 for
Iron Ore and -2.56 for Gas. In general the trade elasticities for Iron Ore and fuels are
substantially larger than the elasticities of other commodities and Manufacturing.
Interestingly we do not find much evidence than geography matters in other respects
6
Human capital is measured using the Mincerian relationship e0.15s where s is the average years of
schooling in the labour force (Barro and Lee 2010).
6
than distance and the contiguous border dummy. Sharing a border increases the volume
of trade by 26%. Moreover the impact of sharing a border is particularly large in the
case of Minerals and Gas, increasing the volume of trade by approximately 60%.
[Table 2 about here]
3.3
Quantifying the Impact of Distance
To quantify the implications of geographical location in determining the pattern of world
trade we consider a counterfactual experiment, where each export market is at the same
distance from every country. In designing this experiment, we keep the average distance
to importing country the same so as to keep the geography of demand constant.
More precisely, let denote the actual distance from exporting country i to import country
j as dij . We then replace all of the dij with a common value d¯j where d¯j is the average
P
distance for all exporters to destination market j, that is d¯j = i dij /(n − 1), where n is
the number of countries. One way to think of this is that we impose a destination specific
export tax, or subsidy, on all countries in proportion to their distance from the export
market, such that that no country has any transport cost advantage or disadvantage due
to the distance from its export markets.
Thus we calculate the average distance of all countries in our sample to each destination
country. We hence have an average value specific to each importing country. These values
are presented in Table C.1. We then use these counterfactual values d¯j to recalculate
total world trade for each commodity group using the coefficients from (1).
4
Geography and Global Resource and Energy Export Shares
The aggregate change in trade implied by this experiment for each commodity group is
shown in Table 3. It can be seen that although average trade weighted distance only
increases by 5%, total world trade in the experiment falls by 34%.7
7
The average trade weighted distance is calculated for the year 2006 using the following formula. Let
J be the set of world markets and J−i be the set of export markets for
P country i ∈ J. We define the
average distance to market for country i for commodity c as Di,c = j dij sjc , j ∈ J−i , where sjc is
country j’s share of imports for all countries
P j ∈ J−i . Likewise the average distance to market implied
0
by the counterfactual distances is Di,c
= j d¯j sjc , where it will be recalled that d¯j is the counterfactual
common distance between all exporters and the destination market j.
7
Thus as we redistribute distance equally across countries, but preserve total distance between all country pairs, trade volumes fall substantially. Hence, despite keeping the total
average distance constant, the total trade costs have increased. This reflects the fact
that neighboring countries trade much more with each other than with distant countries,
and that the relationship between trade and distance is non-linear. In particular Europe
consists of many large countries with a lot of trade, while many remote southern hemisphere countries are small. Effectively by breaking up Europe, we reduce world trade in
the counterfactual.
Nevertheless there are significant differences by commodity. For example it can be seen
that equalizing export distance across countries causes Gas trade to collapse – falling by
78%. In contrast Iron Ore trade increases by 48%.
A detailed visual description of the changes in each market is given in panel B of Figures
1-8. Table 4 summarizes the actual and counterfactual exports share by broad region
while Table 5 presents the actual and counterfactual export shares by country.
[Tables 3 to 5 about here]
4.1
Food
First we consider the pattern of change in world Food supply. As Europe and Central
Asia alone accounts for over 50% of world Food imports with the two other major regions,
East Asia and North America, have, respectively, a world share of 16% and 19% (Figure
2, Panel A), European and Central Asian countries have a clear locational advantage
when it comes to food exports.
Thus, in our experiment, European countries lose their world share of Food exports,
which falls from 48% to 25% (Table 4 and Figure 2, Panel B). Most other regions gain
exports shares but particularly South America and East Asia and the Pacific. Thus in
this market there is a very clear locational advantage for the European and Central Asian
region. As we shall see, this last result is very important when we come to look at how
distance affects individual country’s overall advantage or disadvantage in world natural
resources markets.
8
4.2
Minerals
Recall from the preceding discussion that Minerals imports are focused geographically
on East Asia, particularly China, Japan and South Korea (Figure 4, Panel A). Panel B
of Figure 4 shows the actual and counterfactual export patterns. It can be seen that
Australia and the Americas (Brazil, Chile, Canada and the USA) dominate the world
supply of Minerals, accounting for 42% of world Minerals exports.
In the counterfactual there are large increases in exports in the southern hemisphere –
South America, Australia and South Africa – roughly neutral impact in North America
and a collapse of exports in Europe-Central Asia and East Asia. The shares of Australia,
Brazil and Chile increase dramatically with the collective share doubling from 27% to
44% of world exports of Minerals (see Tables 4 and 5).
Hence in the world Minerals market there is a relatively large dispersion between producers and their main destination markets and this dispersion has a very significant cost
on the southern Minerals giants such as Australia and Brazil. This also suggests that
these regions still stand to make substantial gains in terms of world market shares with
new transport technologies and reduction in transport costs.
4.3
Iron Ore
Iron Ore is a subset of Minerals that, as noted above, is mainly imported by East Asia,
especially China. It is dominated on the supply side by Brazil and Australia, which
mutually account for 61% of world exports (Figure 5). Because of this concentration of
demand in East Asia, Brazil is much farther from the world’s largest Iron Ore importers
than Australia. So in relative terms Australia is close to the Iron Ore market.8
In the counterfactual, because of its relative proximity to East Asia, Australia market
share falls by approximately one third to 23% of world trade. Likewise European based
exporters such as Sweden and Ukraine are driven out from the market. Brazil however
almost doubles its export share from 31% to 59% of world exports. Similar results are
found for the other Latin American countries – Peru, Venezuela and Chile – though their
shares of world trade are very small. Moreover total Iron Ore trade flows increase in the
counterfactual, suggesting that Brazil’s distance from China is an important impediment
to world Iron Ore trade.
8
In absolute terms the Iron Ore market is the most dispersed with trade weighed distance falling by
19% in our counterfactual experiment, as shown in Table 3.
9
The large changes in world Iron Ore trade shares reflect not only Brazil’s rather large
distance from its market but also the very large elasticity of Iron Ore trade with respect
to distance of -1.96. The experiments thus endorse the popular view that Australia
has benefited from its locational advantage to China and that Brazil is particularly
disadvantaged, at least in the Iron Ore market.
4.4
Coal
Whereas the world Coal market is relatively dispersed across northern hemisphere countries in Europe-Central Asia and East Asia, Panel B of Figure 6 shows that supply is
very concentrated in the South with one country, Australia, accounting for 28% of the
world exports. Removing any distance disadvantage increases Australia’s share of world
exports substantially to 47%.
Hence with respect to Coal the popular notion that Australian resource exports have
benefited from its proximity to Asia can be seen in a somewhat different light. For Coal,
Australia’s relative proximity to Japan and China does not offset the cost of remoteness
from Europe. Moreover, unlike Iron Ore, there are apparently no major South American suppliers that would be in a position to expand substantially supply if distance
disadvantages were removed.
The results also show that South Africa faces a similar geographical disadvantage as
Australia - though its Coal exports are substantially smaller. The two second largest
exporters, however, China and Russia, have significant geographical advantages being
relatively close to both East Asian and European-Central Asian markets.
4.5
Petroleum
The demand for imported Petroleum is almost equally split between Europe-Central
Asia (36%), North America (25%) and East Asia (29%) while the supply of Petroleum
is essentially split equally between the Middle-East (32%) and Europe-Central Asia,
including Russia, (34%) (Figure 7).
The European-Central Asian exporters are therefore strongly advantaged by their geographical location within the largest Petroleum import market. This is confirmed by the
counterfactual results which show that Saudi Arabia’s share of world Petroleum exports
increases from 15 to 25% and Kuwait’s share increases from 4 to 7% in the counterfactual
case. Overall the Middle-East share rises to 46%, while Europe and Central Asia’s share
10
falls to 25% (Table 4). Thus the European-Central Asian exporters have a significant
advantage relative to the Middle-East. The exporters on the American continent, though
small on the world market, nevertheless are also shown to be gaining significantly from
their proximity to the USA.
4.6
Gas
It can be seen immediately in Figure 8 that the Gas market lacks the South-North pattern
seen in the preceding commodity markets due to significant Gas exporters in the northern
hemisphere. The exception is the Australia-Indonesia-Malaysia East Asia LNG corridor
which accounts for 17% of world Gas exports.
Central to the Gas market is the issue of the delivery mode. Over a short distance
pipelines are the cheapest mode of transport, while over long distances, shipping liquified
gas (LNG) is the only viable solution. Indeed, estimating the model separately for LNG
and for Gaseous gas, we find that the elasticity of distance for Gaseous Gas (-5.7) is
approximately double the LNG elasticity (-2.7) (Table 6).9 In either case however the
elasticity of trade to distance is much higher than for other commodities.
Thus countries that share a border with a large Gas importer benefit significantly from
their geographical location, with notably Canada and the USA market and, to a smaller
extent, Algeria and the European market. At the other extreme the Middle-Eastern
countries, in particular Saudi Arabia and Qatar, but also Australia, suffer from their
incapacity to be connected via pipelines to large import markets.
Our counterfactual of removing distance advantages or disadvantages would lead to major
changes in the Gas market, with current big players, such as Canada and Algeria, to be
almost completely eliminated from the market. Conversely Australia’s world exports
share rise from 3% to 13% and Saudi Arabia and Qatar’s world share would more than
double. This is of interest given that there have been recent technological advances that
have lowered the cost of transporting LNG (Ruester 2010). Consequently there is also
evidence that the elasticity of trade to distance has started dropping significantly in the
LNG sector (see Table 7).
[Tables 6 and 7 about here]
9
To maximise the sample size and to ensure that as many major natural resource exporters are
included in our sample, we use SITC-1 classification throughout this paper. However, for Gas, as
the SITC-1 classification does not distinguish between LNG and Gaseous Natural Gas, we use SITC-3
classification for this set of results.
11
5
The Tyranny of Distance
Given the preceding discussion it is clear that a country’s location has an important effect
on its volume of resource trade. Brazil probably exports much more Petroleum and much
less Minerals than it would if it were located elsewhere. Australia is close to Asia and
this is typically regarded as being very advantageous in terms of resource exports. We
have found that this is true for Iron Ore but Australia still suffers a tyranny of distance
in terms of Coal.
How then do these costs and benefits of location add up for each country? Specifically,
which countries face the greatest disadvantage of location in resources? Table 5 reports
the total percentage change in resources trade for each country as a result of removing
the relative distance barriers across countries. The countries with the largest gains are
thus the countries that currently suffer the greatest competitive disadvantage from their
geographical location. For each country we also report the composition of this change by
commodity, so we can see which particular markets contribute to that country’s remoteness.
Figure 9 shows this information for all the countries where the total increase in trade
exceeds 10%. There are 11 such countries out of a sample total of 104. It can be seen
that the countries at the greatest disadvantage are the antipodean countries: Australia,
New Zealand and the South American and South African resource exporters.
For the 5 most disadvantaged countries the losses are very significant, exceeding 30%.
Chile and New Zealand are particularly affected with a loss of around 50%. That is Chile
and new Zealand’s resources trade flows are predicted to be around 50% larger if their
distance to exports markets were at the world average for each commodity.10
To convert these quantity changes to ad valorem tax equivalents, a useful rule of thumb
is that the elasticity of demand is approximately -1/2 across a wide range of commodities
(Deaton 1974, Clements 2008). Thus the change in export quantities of around 50% for
Chile and New Zealand implies that the cost disadvantage of distance, relative to the
average country, is approximately the equivalent of a 100% export tax. Likewise the
magnitude for South Africa, Brazil and Australia is similar, with a handicap equivalent
to approximately a 70% export tax across their resources trade as a whole.
10
These values are all percentage changes. In terms of absolute changes the countries that stand the
most are Australia in terms of Minerals, Brazil in terms of Coal and the USA in terms of everything else.
Thus the absolute gains and losses tend to reflect the country’s size in the market. In general Brazil,
Australia and the USA are the biggest remote countries.
12
These values however are relative to a country that suffers no particular advantage or
disadvantage. Kenya is an example of such a country (Table 5). The disadvantage of these
antipodean countries relative to Kenya is much smaller than their disadvantage relative
to the least remote countries. For convenience Figure 10 thus shows the 20 countries
that experience the greatest fall in their resource trade volumes in the counterfactual.
These countries have the opposite of a tyranny of distance, that is, the “benevolence of
proximity”.
It can be seen that, in our counterfactual, trade volumes fall by around 70% in these countries. Thus we can reconsider the disadvantage of distance implied by our experiments
by comparing for example New Zealand with a well-located economy such as France. In
France the total percentage loss in trade is 70% whereas for New Zealand the total gain
is 46%. This suggests that the combination of implicit “tax” on New Zealand producers and an implicity “subsidy” to French producers adds up to a 106 percent change in
volumes and a 212% ad valorem tax disadvantage for New Zealand producers relative to
France. In general the results show that many country, mostly European, benefit from
an enormous locational advantage over more remote countries. This advantage is often
in the dimension of a “subsidy” of over 100%.
It can also be seen that the reasons behind these changes differs by country. For New
Zealand the key factor is its distance from European Food markets. For Chile and
Brazil the distance from world Minerals demand is the key component. As we have
seen above this is due to their distance from Asia relative to other countries. Likewise
Australia and South Africa have a very similar pattern in terms of the composition of
their distance disadvantage with Food, Minerals and also Coal being similarly important
for each country. It can be seen further that Argentina is not as remote overall as, for
example, Chile and Brazil. This is in part because it exports Petroleum and Gas to North
America, and in part because minerals is a relative small component of its exports, so it
is not affected by the distance to Asia in the same way Brazil is.
Finally for the least remote countries we can see in, Figure 9, that their advantage is
mostly in Food and Petroleum. Thus the Slovak Republic, Korea, Algeria, Norway and
Mexico are shown to have particular luck in terms of having both Petroleum endowments
and proximity to large markets. In each case the benevolence of proximity adds around
30-50% to their Petroleum export sales. Two exceptions from the general pattern are
Poland and the Czech Republic which also benefit considerably from having coal deposits
and being within Europe. Similarly Canada and Algeria have significant advantages in
terms of the world Gas market.
13
[Figures 9 and 10 about here]
6
Conclusion
While a broad literature exists on the importance of geography in explaining the volume
of trade across countries, little is known on the importance of geography in explaining
the predominance of some countries in resource exports. In contrast to manufacturing
trade we have find that geography plays a very important role in explaining trade in
commodities. This is due to both the relatively high transports costs and the fact that
resource export supply is limited by natural endowments.
We show first that many resources, particularly Iron-Ore and Gas, have very large elasticities of trade with respect to distance. However the impact of geography also depends
the geographic separation of the export sources and the geographic distribution of demand. Thus we also show show that equalizing distances to markets would have large
effects in some markets and on a country shares of world resource exports.
In particular we found that the several southern resource exporting countries are significantly disadvantaged by their location. However Southern Food producers such as
New Zealand were also found to have a large disadvantage, while many Latin American
petroleum exporters were found to have a smaller total disadvantage due to the proximity
to the USA.
Finally, we also show that the impact of distance on exports is very large. The counterfactual resulted in an increase in export sales in the range of 35-50% for several countries.
This implies the equivalent of an export tax in the order of 70-100%. Likewise for many
countries the location advantage is very large. Especially the changes in trade volumes in
the counterfactual for many countries are also consistent with subsidies exceeding 100%.
Consequently, we conclude that the ability of many countries to be competitive in the
world resource markets derives, to a large extent, from their location. This suggests that
the responsiveness of extraction firms to national policies, for example with respect to
resource taxation and regulation, may be quite low, particularly in Europe. Nevertheless
the results also show that this advantage differs by sector, due to the differing geographical
distribution of world demand by commodity. Finally the results also suggest that future
technological changes that reduce transport costs have the potential to substantially alter
country market shares in world resource markets.
14
Appendix A: Country Coverage by Region
[Table A.1 about here]
Appendix B: Definition of Variables
[Table B.1 about here]
Appendix C: Average Distance
[Table C.1 about here]
Figure 1: Trade Shares: All
Import Shares
Export Shares
Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.
Figure 2: Trade Shares: Food
Import Shares
Export Shares
Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.
Figure 3: Trade Shares: Raw Materials
Import Shares
Export Shares
Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.
Figure 4: Trade Shares: Minerals
Import Shares
Export Shares
Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.
Figure 5: Trade Shares: Iron Ore
Import Shares
Export Shares
Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.
Figure 6: Trade Shares: Coal
Import Shares
Export Shares
Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.
Figure 7: Trade Shares: Petroleum
Import Shares
Export Shares
Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.
Figure 8: Trade Shares: Gas
Import Shares
Export Shares
Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.
Food
Raw
Minerals
Coal
Petrol
Argentina
Zambai
Peru
Mauritius
Uruguay
Paraguay
Australia
Brazil
South Africa
New Zealand
Chile
-20
% Change of Export Values
40
0
20
60
Figure 9: The Tyranny of Distance: Percentage Change of Export Values when Location Advantages/Disadvantages Are Removed
Gas
Food
Raw
Minerals
Coal
Petrol
Slovak Rep.
Switzerland
Czech Rep.
Netherlands
Austria
Korea
Germany
Algeria
Hungary
Denmark
Poland
Slovenia
Croatia
France
Ireland
Canada
Norway
Mexico
United Kingdom
Malaysia
Italy
% Change of Export Values
-80
-60
0
-40
-20
Figure 10: The Benevolence of Proximity: Percentage Change of Export Values when Location Advantages/Disadvantages Are
Removed
Gas
Table 1: Regional Share of World Imports in 2006
Europe and
North America
South America
Central Asia
All
44.23
21.78
5.57
Food
52.96
16.15
5.42
Raw
41.92
13.61
5.25
Min.
38.30
6.72
3.52
Iron Ore
22.51
2.89
2.35
Coal
40.62
6.83
5.03
Petrol
35.88
25.33
3.61
Gas
34.57
26.85
5.64
Manuf.
44.89
22.13
6.02
Source: COMTRADE/WITS data. Authors’ calculation.
Middle-East
2.51
4.65
3.10
1.12
1.03
2.14
1.48
1.96
2.54
East Asia and
the Pacific
23.26
18.80
31.95
46.30
70.91
37.38
29.22
29.06
22.14
South Asia
1.56
0.58
3.02
3.76
0.20
7.44
3.21
1.81
1.26
Sub-Saharan
Africa
1.09
1.44
1.16
0.28
0.11
0.55
1.27
0.11
1.03
Table 2: PPML Results
ln gdp
p ln gdp
ln pop
p ln pop
ln distwces
ln land paire
landlocked 1
landlocked 2
contig
island 1
island 2
comlang ethno
colony
hum k
p hum k
Constant
Observations
Pseudo R2
(1)
All
1.502***
(0.000)
1.289***
(0.000)
0.330
(0.249)
-0.846***
(0.002)
-0.767***
(0.000)
-0.090
(0.112)
0.790
(0.554)
1.918
(0.474)
0.264***
(0.000)
-1.519
(0.359)
-2.717
(0.408)
0.305***
(0.000)
-0.101
(0.324)
0.209***
(0.000)
0.093***
(0.000)
-61.765***
(0.000)
74,984
0.952
(2)
Food
0.479***
(0.000)
0.803***
(0.000)
-1.274***
(0.000)
-0.126
(0.703)
-0.968***
(0.000)
-0.027
(0.620)
-0.410
(0.802)
-0.178
(0.957)
0.248***
(0.006)
-0.378
(0.843)
-0.559
(0.882)
0.362***
(0.001)
0.179
(0.147)
0.177***
(0.000)
0.015
(0.487)
-13.704**
(0.017)
74,984
0.908
(3)
Raw
0.458**
(0.025)
1.553***
(0.000)
-1.214***
(0.008)
-0.863*
(0.083)
-0.864***
(0.000)
-0.141**
(0.018)
3.827*
(0.083)
8.302*
(0.061)
0.358***
(0.000)
0.437
(0.876)
0.790
(0.887)
0.115
(0.298)
0.101
(0.430)
0.096*
(0.088)
0.068
(0.244)
-31.129***
(0.000)
74,984
0.897
(4)
Min.
-0.406*
(0.093)
2.384***
(0.000)
2.109***
(0.005)
-1.714**
(0.021)
-0.959***
(0.000)
0.043
(0.613)
8.455***
(0.007)
17.431***
(0.005)
0.590***
(0.000)
3.789
(0.381)
7.965
(0.358)
0.236*
(0.055)
0.411***
(0.005)
0.041
(0.456)
0.180***
(0.000)
-49.403***
(0.000)
74,984
0.870
(5)
Iron Ore
-0.008
(0.991)
3.032***
(0.000)
2.117
(0.268)
-1.754
(0.119)
-1.964***
(0.000)
-0.135
(0.572)
8.077*
(0.065)
15.205*
(0.081)
0.839**
(0.013)
8.866
(0.163)
17.876
(0.159)
0.169
(0.556)
1.734***
(0.000)
0.203
(0.306)
-0.016
(0.854)
-73.869**
(0.022)
74,984
0.932
(6)
Coal
0.792**
(0.016)
1.089***
(0.006)
0.115
(0.942)
0.176
(0.863)
-1.220***
(0.000)
0.082
(0.742)
2.707
(0.553)
7.225
(0.430)
0.361
(0.139)
1.848
(0.757)
4.215
(0.723)
0.006
(0.979)
0.382*
(0.073)
0.189*
(0.089)
0.004
(0.968)
-45.247*
(0.052)
74,984
0.892
(7)
Petrol
0.904***
(0.000)
1.006***
(0.001)
-0.925***
(0.002)
0.926
(0.264)
-1.372***
(0.000)
0.095
(0.397)
3.806
(0.255)
9.652
(0.149)
0.158
(0.348)
6.708
(0.139)
13.254
(0.146)
0.549***
(0.004)
0.228
(0.241)
0.029
(0.716)
-0.038
(0.417)
-39.501***
(0.000)
74,984
0.859
(8)
Gas
0.212
(0.796)
-0.240
(0.746)
1.029
(0.150)
6.935**
(0.022)
-2.557***
(0.000)
0.110
(0.540)
9.772
(0.202)
19.490
(0.205)
0.596*
(0.051)
25.882*
(0.058)
52.465*
(0.055)
-0.087
(0.746)
0.419
(0.201)
0.350
(0.266)
-0.117
(0.400)
-19.879
(0.473)
74,984
0.902
(9)
Manuf.
1.638***
(0.000)
1.342***
(0.000)
-0.828**
(0.028)
-1.004***
(0.000)
-0.752***
(0.000)
-0.139***
(0.004)
0.679
(0.602)
1.571
(0.547)
0.194**
(0.011)
-2.306
(0.158)
-4.346
(0.179)
0.306***
(0.000)
-0.142
(0.162)
0.197***
(0.000)
0.118***
(0.000)
-59.360***
(0.000)
74,984
0.963
Notes: Country, partner country and year fixed effects are controlled for. Standard-errors are adjusted for clustering at the country-pair
level. ***, p-value< 0.01; **, p-value< 0.05; *, p-value< 0.10.
Table 3: Distance and Total Trade
All
Food
Raw
Min.
Iron Ore
Coal
Petrol
Gas
Manuf.
Unweighted Distance
Actual
Count.
Change in %
7726
7730
0.05
7374
7381
0.09
7762
7768
0.07
7927
7939
0.15
8594
8617
0.26
7859
7866
0.08
8086
8087
0.00
8061
8062
0.01
7714
7717
0.04
Actual
7310
7216
7943
8858
10589
8896
7462
7475
7251
Weighted Distance
Count.
Change in %
7703
5.37
7385
2.34
7748
-2.46
7932
-10.45
8618
-18.61
7856
-11.69
8084
8.33
8054
7.75
7687
6.00
Actual
8245530
493876
193021
156250
37322
52327
867712
113589
6191161
Trade Value
Count.
Change in %
5445163
-33.96
310746
-37.08
140350
-27.29
133808
-14.36
55200
47.90
51711
-1.18
444992
-48.72
24685
-78.27
4122514
-33.41
Table 4: Regional Share of World Exports in 2006: Actual and Counterfactual
All
Food
Raw
Min.
Iron Ore
Coal
Petrol
Gas
Manuf.
Europe and
Central Asia
Actual Count.
41.40
28.98
47.82
25.24
35.25
19.91
29.15
12.70
10.55
0.99
20.11
5.69
34.07
25.15
24.86
13.42
42.50
29.59
North America
Actual
14.93
15.50
25.90
15.18
6.17
13.52
6.39
27.70
15.04
Count.
16.41
19.03
29.68
15.41
3.26
12.21
3.48
4.86
16.75
South America
Actual
6.16
14.48
11.00
19.77
34.41
6.49
12.91
4.63
4.33
Count.
7.58
24.94
19.92
35.08
65.04
8.20
12.42
6.81
4.79
Middle-East
Actual
4.21
1.45
1.12
3.19
0.29
0.22
32.06
24.99
1.22
Count.
4.91
1.24
0.90
2.25
0.06
0.08
45.74
41.10
1.33
East Asia and
the Pacific
Actual
Count.
31.14
38.78
16.68
23.20
23.28
25.18
24.51
25.03
31.87
23.09
51.42
61.50
12.47
9.45
17.78
33.51
35.15
44.81
South Asia
Actual
1.28
1.67
1.06
3.63
12.57
0.02
0.67
0.01
1.26
Count.
1.77
2.19
1.00
2.64
2.26
0.01
0.92
0.01
1.76
Sub-Saharan
Africa
Actual
Count.
0.88
1.57
2.41
4.16
2.39
3.41
4.57
6.89
4.13
5.31
8.22
12.30
1.43
2.85
0.04
0.29
0.51
0.96
Table 5:
Percentage Change of
tages/Disadvantages Are Removed
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
Export
Values
When
Locational
Country
Food
Raw
Minerals
Coal
Petrol
Gas
Total
Chile
New Zealand
South Africa
Brazil
Australia
Paraguay
Uruguay
Mauritius
Peru
Zambia
Argentina
Guyana
Gabon
Tanzania
Burundi
Kenya
Cameroon
Uganda
Maldives
Ghana
CAF
Cote d’Ivoire
Benin
Mali
Yemen
Senegal
Jamaica
Sudan
Gambia
United States
Costa Rica
Bolivia
Kuwait
Niger
Saudi Arabia
Colombia
Ecuador
Honduras
Nicaragua
Qatar
India
Panama
El Salvador
Pakistan
Guatemala
Iran
Belize
Thailand
Barbados
Philippines
14.61
33.48
8.41
12.61
9.35
6.93
16.68
22.35
4.24
4.28
12.43
-1.35
-0.01
2.59
0.58
-0.34
-1.23
-3.07
-3.67
-5.19
-1.15
-4.85
-1.62
-0.76
-0.65
-6.93
-11.02
-1.38
-10.54
-4.21
-15.01
3.02
-0.04
-1.04
-0.09
-2.34
-0.36
-23.17
-18.97
-0.01
-6.18
-9.72
-23.01
-13.33
-21.35
-0.69
-24.54
-14.70
-2.25
-11.76
5.31
10.24
1.85
6.97
2.36
19.61
7.07
0.32
1.36
2.94
7.12
0.67
0.23
0.14
-0.02
-0.51
-1.01
-0.72
-0.28
-0.25
-4.50
-0.15
-2.93
-7.51
-0.06
-0.73
0.00
-1.31
-2.15
0.01
0.52
3.75
-0.01
-0.21
-0.02
0.49
0.70
-0.43
-0.01
0.00
-2.69
0.09
-0.07
-6.07
-0.59
-0.18
-0.14
-6.66
0.06
-5.97
30.53
0.64
12.57
16.31
6.19
0.55
0.38
0.23
21.03
6.65
2.23
8.46
0.57
0.77
0.36
0.00
0.00
0.00
-0.31
0.47
-0.02
0.03
0.01
-0.02
-0.07
0.15
4.97
-0.07
0.06
-0.54
0.24
8.29
-0.18
-0.14
-0.16
0.21
0.09
0.79
0.29
-0.08
-10.48
0.70
0.34
-2.21
0.21
-0.53
0.04
-1.40
0.16
-12.73
0.01
1.40
12.77
0.02
14.48
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
-0.02
0.00
0.00
0.00
0.00
0.00
0.00
-0.01
0.00
-0.02
0.00
0.00
0.00
0.00
0.00
4.69
0.00
0.00
0.00
0.00
-0.01
0.07
0.00
-0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.13
0.73
2.38
2.24
3.31
-0.25
-0.75
0.03
-3.73
0.15
-6.10
0.00
5.33
0.30
-0.03
1.23
0.83
0.03
0.00
-0.10
0.00
-1.07
-1.93
-0.03
-7.53
-2.46
-4.38
-8.80
-0.12
-6.84
-0.31
-2.50
-18.29
-19.75
-19.14
-24.50
-22.62
-0.16
-4.99
-13.16
-7.88
-17.58
-4.91
-7.34
-8.14
-28.08
-8.31
-9.62
-31.71
-5.74
-0.21
0.00
0.19
0.01
-0.71
-0.05
-0.03
0.01
-0.11
0.05
-3.71
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
-0.03
0.00
-0.02
-0.17
0.00
-0.06
-0.18
-0.03
-0.06
0.00
-2.85
-0.13
-29.52
-1.97
-0.06
-2.02
-0.07
-0.24
-0.34
0.00
-13.57
-0.02
-1.35
-0.95
0.00
-0.13
-0.88
-0.03
-1.03
-0.09
-0.92
50.38
46.50
38.17
38.15
34.99
26.79
23.35
22.93
22.80
14.07
11.97
7.77
6.13
3.79
0.90
0.40
-1.42
-3.76
-4.26
-5.10
-5.69
-6.06
-6.64
-8.32
-8.36
-10.15
-10.46
-11.61
-12.74
-14.44
-14.68
-16.97
-20.50
-21.20
-21.45
-21.53
-22.42
-23.31
-23.70
-26.82
-27.25
-27.79
-28.60
-28.98
-30.00
-30.36
-32.99
-33.41
-33.82
-37.11
Advan-
Rank
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
Country
Food
Raw
Minerals
Coal
Petrol
Gas
Total
Egypt
Indonesia
Iceland
Kyrgyz Rep.
Kazakhstan
Israel
Trinidad
Venezuela
Syria
Jordan
Morocco
Cyprus
Vietnam
China
Turkey
Russia
Malta
Portugal
Greece
Finland
Spain
Japan
Ukraine
Singapore
Tunisia
Romania
Albania
Bulgaria
Latvia
Estonia
Sweden
Macao
Lithuania
Italy
Malaysia
United Kingdom
Mexico
Norway
Canada
Ireland
France
Croatia
Slovenia
Poland
Denmark
Hungary
Algeria
Germany
Korea
Austria
Netherlands
Czech Rep.
Switzerland
Slovak Rep.
-4.99
-2.23
-37.09
-11.94
-1.57
-20.06
-0.25
-0.13
-3.80
-10.01
-26.23
-29.34
-10.27
-18.76
-29.50
-1.88
-9.93
-24.10
-28.53
-7.43
-37.19
-4.80
-15.22
-2.59
-8.89
-10.51
-16.56
-23.50
-5.69
-8.57
-12.18
-27.85
-12.82
-37.11
-4.30
-16.96
-9.90
-4.88
-10.19
-56.54
-48.12
-22.16
-34.31
-32.49
-40.90
-41.99
-0.08
-38.64
-8.80
-37.57
-38.09
-25.42
-39.48
-14.20
-1.81
-2.83
-1.45
-9.36
-0.58
-7.03
0.01
0.01
-1.83
-1.23
-1.95
-2.69
-1.50
-4.13
-4.26
-2.04
-1.31
-11.80
-9.09
-22.56
-5.95
-10.78
-6.49
-1.06
-7.94
-13.33
-16.16
-8.42
-15.09
-9.64
-20.59
-9.11
-5.17
-6.37
-10.75
-2.25
-0.45
-0.66
-6.99
-2.55
-5.80
-15.57
-21.99
-5.61
-6.30
-8.55
-0.02
-9.15
-6.34
-18.70
-10.54
-15.76
-8.95
-8.87
-1.95
-3.93
-0.80
-18.62
-4.79
-6.90
0.15
0.59
-0.69
-33.90
-12.08
-10.47
-0.80
-4.28
-8.34
-2.11
-1.69
-7.29
-4.51
-3.72
-3.29
-17.33
-15.03
-1.24
-5.32
-11.30
-18.62
-8.59
-1.46
-4.41
-8.58
-23.09
-5.10
-3.16
-1.28
-3.55
-0.23
-1.27
-2.03
-7.25
-4.43
-9.67
-12.85
-6.11
-2.02
-4.57
-0.31
-6.68
-2.07
-9.26
-4.13
-9.73
-15.75
-6.45
-0.82
-0.40
-0.01
-0.01
-1.43
-0.01
0.00
0.23
-0.01
0.00
0.00
-0.30
-1.22
-8.91
-0.03
-1.88
-0.02
-0.10
0.00
-0.19
-0.42
-1.93
-3.76
-0.05
-0.01
-0.10
-0.02
-0.16
-2.04
-2.28
-0.14
0.00
-1.04
-0.11
0.00
-0.17
0.00
-0.14
-0.62
-0.66
-0.12
-0.09
-0.13
-20.37
-0.05
-0.58
0.00
-0.62
-0.02
-0.07
-0.37
-15.60
-2.13
-0.43
-19.35
-18.00
-0.15
-0.39
-30.64
-10.06
-26.21
-44.40
-38.84
-0.80
-5.76
-6.93
-36.58
-15.17
-8.67
-37.54
-40.09
-9.81
-10.76
-20.60
-5.78
-19.89
-12.85
-51.32
-35.56
-24.36
-9.16
-21.70
-38.03
-38.18
-21.26
-3.72
-38.48
-17.93
-36.43
-40.24
-56.56
-53.55
-26.30
-2.49
-10.74
-18.11
-2.79
-7.75
-21.98
-17.81
-46.48
-14.55
-57.93
-9.30
-22.83
-12.22
-12.89
-52.82
-10.07
-12.10
-0.01
0.00
-1.44
-0.01
-17.89
-0.92
0.00
-0.16
-0.24
-0.46
-0.01
-0.08
-0.53
-7.08
-0.38
-0.52
-1.64
-0.10
-2.39
-0.58
-2.34
-1.29
0.00
-0.54
0.00
-0.09
-0.19
-0.02
-0.45
0.00
-1.52
-0.81
-12.76
-3.49
-0.24
-7.31
-22.35
-0.15
-1.53
-5.32
-0.08
-0.06
-1.34
-0.46
-27.33
-4.62
-1.05
-1.56
-2.06
-0.43
-0.96
-0.15
-38.99
-39.49
-39.51
-40.32
-40.47
-44.07
-44.20
-44.62
-45.17
-46.10
-46.26
-50.18
-50.38
-51.33
-51.33
-52.53
-53.43
-53.62
-54.54
-54.61
-55.03
-55.31
-55.69
-57.55
-57.71
-60.14
-60.51
-62.46
-62.50
-63.09
-63.21
-63.77
-64.13
-65.49
-65.51
-66.66
-67.37
-67.81
-68.48
-69.63
-70.73
-70.92
-72.14
-72.38
-72.59
-73.98
-74.22
-74.25
-76.20
-76.46
-78.02
-79.16
-80.17
-82.92
Table 6: PPML Results: Natural Gas
ln gdp
p ln gdp
ln pop
p ln pop
ln distwces
ln land paire
landlocked 1
landlocked 2
contig
island 1
island 2
comlang ethno
colony
hum k
p hum k
Constant
Observations
(1)
Nat. Gas
0.184
(0.873)
0.390
(0.772)
0.836
(0.349)
8.388**
(0.044)
-3.713***
(0.000)
0.110
(0.699)
11.216**
(0.013)
22.200**
(0.015)
0.780
(0.150)
27.179**
(0.023)
54.999**
(0.021)
0.145
(0.754)
0.558
(0.241)
0.291
(0.453)
-0.057
(0.739)
-30.152
(0.455)
52,876
(2)
LNG
1.331
(0.235)
1.774
(0.224)
-0.052
(0.949)
11.381*
(0.094)
-2.672***
(0.000)
-0.332
(0.183)
16.958***
(0.003)
39.743***
(0.000)
0.709
(0.605)
38.292*
(0.051)
76.786**
(0.050)
-0.312
(0.514)
1.471*
(0.096)
-0.318
(0.327)
-0.654
(0.144)
-97.625**
(0.018)
52,876
(3)
Gaseous
-3.303
(0.156)
0.126
(0.956)
-3.576
(0.648)
4.609
(0.463)
-5.674***
(0.000)
0.324
(0.624)
10.299
(0.226)
19.520
(0.253)
-0.708
(0.204)
18.686
(0.297)
42.732
(0.232)
0.545
(0.484)
-0.912
(0.211)
0.029
(0.936)
0.091
(0.594)
79.478
(0.335)
52,876
Notes: WITS/COMTRADE DATA using SITC-3 classification, for the period 1999-2003 and 2006. Country, partner country and year fixed effects are controlled
for. Standard-errors are adjusted for clustering at the
country-pair level. ***, p-value< 0.01; **, p-value< 0.05;
*, p-value< 0.10. To allow the impact of distance to
vary over time, interaction variables between distance
and time are included.
Table 7: PPML Results: Natural Gas over Time
ln gdp
p ln gdp
ln pop
p ln pop
ln distwces
ln land paire
landlocked 1
landlocked 2
contig
island 1
island 2
comlang ethno
colony
hum k
p hum k
dist y2000
dist y2001
dist y2002
dist y2003
dist y2006
Constant
Observations
(1)
All
1.203***
(0.000)
0.821***
(0.000)
-0.260***
(0.000)
-0.016
(0.845)
-0.782***
(0.000)
-0.084
(0.142)
0.438
(0.544)
1.180
(0.416)
0.237***
(0.001)
0.257**
(0.032)
0.876***
(0.000)
0.306***
(0.000)
-0.105
(0.304)
0.172***
(0.000)
0.119***
(0.000)
0.018***
(0.000)
0.008
(0.151)
-0.008
(0.343)
-0.002
(0.847)
0.006
(0.671)
-38.905***
(0.000)
52,876
(3)
Nat. Gas
-0.100
(0.928)
0.688
(0.625)
0.711
(0.420)
9.210*
(0.062)
-3.696***
(0.000)
0.109
(0.699)
12.825**
(0.038)
25.417**
(0.041)
0.779
(0.149)
30.586**
(0.046)
61.813**
(0.044)
0.144
(0.755)
0.555
(0.244)
0.342
(0.408)
-0.072
(0.688)
0.030
(0.586)
-0.143
(0.131)
-0.095
(0.283)
-0.033
(0.783)
0.064
(0.777)
-33.642
(0.439)
52,876
(4)
LNG
0.764
(0.393)
-0.160
(0.928)
-0.263
(0.700)
29.756***
(0.003)
-3.185***
(0.000)
-0.329
(0.184)
34.200***
(0.002)
74.251***
(0.001)
0.605
(0.633)
96.711***
(0.004)
193.615***
(0.004)
-0.351
(0.461)
1.399*
(0.093)
0.218
(0.610)
-0.046
(0.935)
0.137
(0.220)
0.129
(0.361)
0.201
(0.188)
0.288
(0.122)
0.849**
(0.011)
-108.177***
(0.001)
52,876
(5)
Gaseous
-2.145
(0.283)
-0.137
(0.950)
-3.133
(0.673)
5.187
(0.419)
-5.583***
(0.000)
0.329
(0.620)
9.856
(0.231)
18.631
(0.260)
-0.699
(0.211)
19.781
(0.282)
44.877
(0.220)
0.540
(0.489)
-0.902
(0.217)
0.056
(0.892)
0.037
(0.856)
0.043
(0.751)
-0.115
(0.563)
-0.029
(0.901)
0.114
(0.688)
-0.310
(0.552)
60.250
(0.422)
52,876
Notes: WITS/COMTRADE DATA using SITC-3 classification.
Country, partner country and year fixed effects are controlled for.
Standard-errors are adjusted for clustering at the country-pair level.
***, p-value< 0.01; **, p-value< 0.05; *, p-value< 0.10.
Table A.1: Country Coverage by Region
Europe and
Central Asia
Albania
Austria
Bulgaria
Croatia
Cyprus
Czech Rep.
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Iceland
Ireland
Italy
Kazakstan
Kyrgyzstan
Latvia
Lithuania
Netherlands
Norway
Poland
Portugal
Romania
Russia
Slovakia
Slovenia
Spain
Sweden
Switzerland
Turkey
Ukraine
United Kingdom
Notes: * Countries
North America
South America
Middle-East
Canada
United States
Argentina
Barbados
Belize
Bolivia
Brazil
Chile
Colombia
Costa Rica
Ecuador
El Salvador
Guatemala
Guyana
Honduras
Jamaica
Mexico
Nicaragua
Panama
Paraguay
Peru
Trinidad
Uruguay
Venezuela
Algeria
Egypt
Iran*
Israel
Jordan
Kuwait
Malta
Morocco
Qatar
Saudi Arabia
Syria*
Tunisia*
Yemen*
East Asia and
the Pacific
Australia
China
Indonesia
Japan
Korea
Macau
Malaysia
New Zealand
Philippines
Singapore
Thailand
Viet Nam*
available only in SITC-1. ** Country available only in SITC-3.
South Asia
India
Maldives
Pakistan
Sub-Saharan
Africa
Benin
Burundi
Cameroon
CAF
Cote d’Ivoire
Gabon
Gambia
Ghana
Kenya
Mali
Mauritius
Niger
Senegal
South Africa
Sudan
Tanzania
Tunisia**
Uganda
Zambia
Table B.1: Variables’ Definition
Variable
Definition
Dependent Variables
All
SITC-1: 00-96.
Food
SITC-1: 00-09 and 11-12.
Raw
SITC-1: 21-26, 29 and 41-43.
Minerals
SITC-1: 27-28.
Iron Ore
SITC-1: 281.
Coal
SITC-1: 32.
Petrol
SITC-1: 33.
Gas
SITC-1: 34.
Nat. Gas
SITC-3: 343.
LNG
SITC-3: 3431.
Gaseous
SITC-3: 3432.
Manuf.
SITC-1: 51-89.
Independent Variables
ln gdp and p ln gdp
Log of GDP.
ln pop and p ln pop
Log of population (in millions).
ln land paire
Log of total land area of country pair.
ln distwces
Weighted great-circle distance (based on population distribution) between country pair.
landlocked 1
1: 1 country in the country pair is landlocked, 0 otherwise.
landlocked 2
1: 2 countries in the country pair are landlocked, 0 otherwise.
contig
1: countries are continguous, 0 otherwise.
island 1
1: 1 country in the country pair is an island, 0 otherwise.
island 2
1: 2 countries in the country pair are islands, 0 otherwise.
comlang ethno
1: a language is spoken by at least 9% of the population in both countries, 0: otherwise.
colony
1: country pair has ever been in a colonial relationship, 0: otherwise.
hum k and p hum k
Exponent of 0.15 times the average years of schooling among the 25+ years old.
Note: The subscript p indicates that the variable refers to the partner country.
Source
COMTRADE/WITS
COMTRADE/WITS
COMTRADE/WITS
COMTRADE/WITS
COMTRADE/WITS
COMTRADE/WITS
COMTRADE/WITS
COMTRADE/WITS
COMTRADE/WITS
COMTRADE/WITS
COMTRADE/WITS
COMTRADE/WITS
WDI
WDI
WDI
CEPII
CEPII
CEPII
CEPII
CEPII
CEPII
CEPII
CEPII
Barro and Lee (2010)
Table C.1: Average Distance
Country
New Zealand
Australia
Chile
Indonesia
Philippines
Argentina
Singapore
Uruguay
Malaysia
Peru
Japan
Bolivia
Mexico
Paraguay
Macao
Vietnam
Ecuador
Korea
Guatemala
El Salvador
Thailand
Nicaragua
Costa Rica
Honduras
Belize
Panama
China
Colombia
Brazil
Mauritius
Jamaica
United States
Venezuela
South Africa
Maldives
Distance
14508
13390
10948
10938
10743
10482
10446
10369
10311
10244
10235
9997
9994
9961
9959
9957
9930
9834
9754
9709
9666
9611
9610
9564
9514
9454
9419
9323
9030
9012
8924
8861
8788
8612
8596
Country
Guyana
Trinidad
Canada
Barbados
India
Zambia
Tanzania
Pakistan
Kyrgyz Rep.
Kenya
Burundi
Kazakhstan
Uganda
Gabon
Yemen
Gambia
Cote d’Ivoire
Senegal
Qatar
Ghana
CAF
Iceland
Cameroon
Benin
Mali
Iran
Kuwait
Sudan
Saudi Arabia
Russia
Niger
Finland
Norway
Estonia
Ireland
Distance
8498
8483
8379
8301
7966
7677
7384
7384
7234
7136
7053
6985
6898
6706
6646
6581
6571
6557
6542
6479
6462
6420
6389
6373
6323
6304
6279
6233
6232
6100
6072
5899
5827
5814
5800
Country
Jordan
Portugal
Morocco
Syria
Israel
Sweden
Latvia
Egypt
United Kingdom
Cyprus
Ukraine
Lithuania
Denmark
Spain
Turkey
Netherlands
Algeria
Poland
France
Germany
Romania
Tunisia
Greece
Bulgaria
Czech Rep.
Switzerland
Malta
Slovak Rep.
Hungary
Austria
Albania
Slovenia
Croatia
Italy
Distance
5776
5774
5769
5757
5746
5732
5706
5699
5667
5654
5643
5642
5617
5587
5557
5552
5510
5486
5483
5477
5446
5431
5427
5426
5425
5423
5414
5410
5392
5384
5373
5365
5358
5353
References
Athukorala, Prema-Chandra (2009) ‘The rise of china and east asian export performance:
Is the crowding-out fear warranted?’ World Economy 32(2), 234–266
Barro, Robert, and Jong-Wha Lee (2010) ‘A new data set of educational attainment in
the world, 1950-2010.’ NBER Working Paper 15902
Blainey, Geoffrey (2001) The tyranny of distance: How distance shaped Australia’s history
(Australia: Pan Macmillan)
Chor, Davin (2010) ‘Unpacking sources of comparative advantage: A quantitative approach.’ Journal of International Economics 82(2), 152–167
Clements, Kenneth W (2008) ‘Price elasticities of demand are minus one-half.’ Economics
Letters 99(3), 490–493
Deaton, Angus (1974) ‘A reconsideration of the empirical implications of additive preferences.’ The Economic Journal 84(June), 338–348
Disdier, Anne-Celia, and Keith Head (2008) ‘The puzzling persistence of the distance
effect on bilateral trade.’ Review of Economics and Statistics 90(1), 37–48
Greenaway, David, Aruneema Mahabir, and Chris Milner (2008) ‘Has China displaced
other Asian countries’ exports?’ China Economic Review 19(2), 152–169
Helpman, Elhanan, Marc Melitz, and Yona Rubinstein (2008) ‘Estimating trade flows:
Trading partners and trading volumes.’ The Quarterly Journal of Economics
123(2), 441–487
Levchenko, Andrei A (2007) ‘Institutional quality and international trade.’ The Review
of Economic Studies 74(3), 791–819
Linders, Gert-Jan M., and Henri L.F. de Groot (2006) ‘Estimation of the gravity equation
in the presence of zero flow.’ Tinbergen Institute Discussion Paper 06-072/3
Romalis, John (2004) ‘Factor proportions and the structure of commodity trade.’ American Economic Review 94(1), 67–97
Ruester, Sophia (2010) ‘Recent dynamics in the global liquefied natural gas industry.’
Resource Markets Working Paper No. RM-19
Santos Silva, J. M. C., and Silvana Tenreyro (2006) ‘The log of gravity.’ Review of
Economics and Statistics 88(4), 641–658
Editor, UWA Economics Discussion Papers:
Sam Hak Kan Tang
University of Western Australia
35 Sterling Hwy
Crawley WA 6009
Australia
Email: [email protected]
The Economics Discussion Papers are available at:
1980 – 2002: http://ecompapers.biz.uwa.edu.au/paper/PDF%20of%20Discussion%20Papers/
Since 2001:
http://ideas.repec.org/s/uwa/wpaper1.html
Since 2004:
http://www.business.uwa.edu.au/school/disciplines/economics
ECONOMICS DISCUSSION PAPERS
2013
DP
NUMBER
AUTHORS
TITLE
13.01
Chen, M., Clements, K.W. and
Gao, G.
THREE FACTS ABOUT WORLD METAL PRICES
13.02
Collins, J. and Richards, O.
EVOLUTION, FERTILITY AND THE AGEING
POPULATION
13.03
Clements, K., Genberg, H.,
Harberger, A., Lothian, J.,
Mundell, R., Sonnenschein, H. and
Tolley, G.
LARRY SJAASTAD, 1934-2012
13.04
Robitaille, M.C. and Chatterjee, I.
MOTHERS-IN-LAW AND SON PREFERENCE IN INDIA
13.05
Clements, K.W. and Izan, I.H.Y.
REPORT ON THE 25TH PHD CONFERENCE IN
ECONOMICS AND BUSINESS
13.06
Walker, A. and Tyers, R.
QUANTIFYING AUSTRALIA’S “THREE SPEED” BOOM
13.07
Yu, F. and Wu, Y.
PATENT EXAMINATION AND DISGUISED PROTECTION
13.08
Yu, F. and Wu, Y.
PATENT CITATIONS AND KNOWLEDGE SPILLOVERS:
AN ANALYSIS OF CHINESE PATENTS REGISTER IN
THE US
13.09
Chatterjee, I. and Saha, B.
BARGAINING DELEGATION IN MONOPOLY
13.10
Cheong, T.S. and Wu, Y.
GLOBALIZATION AND REGIONAL INEQUALITY IN
CHINA
13.11
Cheong, T.S. and Wu, Y.
INEQUALITY AND CRIME RATES IN CHINA
13.12
Robertson, P.E. and Ye, L.
ON THE EXISTENCE OF A MIDDLE INCOME TRAP
13.13
Robertson, P.E.
THE GLOBAL IMPACT OF CHINA’S GROWTH
13.14
Hanaki, N., Jacquemet, N.,
Luchini, S., and Zylbersztejn, A.
BOUNDED RATIONALITY AND STRATEGIC
UNCERTAINTY IN A SIMPLE DOMINANCE SOLVABLE
GAME
13.15
Okatch, Z., Siddique, A. and
Rammohan, A.
DETERMINANTS OF INCOME INEQUALITY IN
BOTSWANA
13.16
Clements, K.W. and Gao, G.
A MULTI-MARKET APPROACH TO MEASURING THE
CYCLE
13.17
Chatterjee, I. and Ray, R.
THE ROLE OF INSTITUTIONS IN THE INCIDENCE OF
CRIME AND CORRUPTION
13.18
Fu, D. and Wu, Y.
EXPORT SURVIVAL PATTERN AND DETERMINANTS
OF CHINESE MANUFACTURING FIRMS
13.19
Shi, X., Wu, Y. and Zhao, D.
KNOWLEDGE INTENSIVE BUSINESS SERVICES AND
THEIR IMPACT ON INNOVATION IN CHINA
13.20
Tyers, R., Zhang, Y. and
Cheong, T.S.
CHINA’S SAVING AND GLOBAL ECONOMIC
PERFORMANCE
13.21
Collins, J., Baer, B. and Weber, E.J.
POPULATION, TECHNOLOGICAL PROGRESS AND THE
EVOLUTION OF INNOVATIVE POTENTIAL
13.22
Hartley, P.R.
THE FUTURE OF LONG-TERM LNG CONTRACTS
13.23
Tyers, R.
A SIMPLE MODEL TO STUDY GLOBAL
MACROECONOMIC INTERDEPENDENCE
13.24
McLure, M.
REFLECTIONS ON THE QUANTITY THEORY: PIGOU IN
1917 AND PARETO IN 1920-21
13.25
Chen, A. and Groenewold, N.
REGIONAL EFFECTS OF AN EMISSIONS-REDUCTION
POLICY IN CHINA: THE IMPORTANCE OF THE
GOVERNMENT FINANCING METHOD
13.26
Siddique, M.A.B.
TRADE RELATIONS BETWEEN AUSTRALIA AND
THAILAND: 1990 TO 2011
13.27
Li, B. and Zhang, J.
GOVERNMENT DEBT IN AN INTERGENERATIONAL
MODEL OF ECONOMIC GROWTH, ENDOGENOUS
FERTILITY, AND ELASTIC LABOR WITH AN
APPLICATION TO JAPAN
13.28
Robitaille, M. and Chatterjee, I.
SEX-SELECTIVE ABORTIONS AND INFANT
MORTALITY IN INDIA: THE ROLE OF PARENTS’
STATED SON PREFERENCE
13.29
Ezzati, P.
ANALYSIS OF VOLATILITY SPILLOVER EFFECTS:
TWO-STAGE PROCEDURE BASED ON A MODIFIED
GARCH-M
13.30
Robertson, P. E.
DOES A FREE MARKET ECONOMY MAKE AUSTRALIA
MORE OR LESS SECURE IN A GLOBALISED WORLD?
13.31
Das, S., Ghate, C. and
Robertson, P. E.
REMOTENESS AND UNBALANCED GROWTH:
UNDERSTANDING DIVERGENCE ACROSS INDIAN
DISTRICTS
13.32
Robertson, P.E. and Sin, A.
MEASURING HARD POWER: CHINA’S ECONOMIC
GROWTH AND MILITARY CAPACITY
13.33
Wu, Y.
TRENDS AND PROSPECTS FOR THE RENEWABLE
ENERGY SECTOR IN THE EAS REGION
13.34
Yang, S., Zhao, D., Wu, Y. and
Fan, J.
REGIONAL VARIATION IN CARBON EMISSION AND
ITS DRIVING FORCES IN CHINA: AN INDEX
DECOMPOSITION ANALYSIS
ECONOMICS DISCUSSION PAPERS
2014
DP
NUMBER
AUTHORS
TITLE
14.01
Boediono, Vice President of the Republic
of Indonesia
THE CHALLENGES OF POLICY MAKING IN A
YOUNG DEMOCRACY: THE CASE OF INDONESIA
(52ND SHANN MEMORIAL LECTURE, 2013)
14.02
Metaxas, P.E. and Weber, E.J.
AN AUSTRALIAN CONTRIBUTION TO
INTERNATIONAL TRADE THEORY: THE
DEPENDENT ECONOMY MODEL
14.03
Fan, J., Zhao, D., Wu, Y. and Wei, J.
CARBON PRICING AND ELECTRICITY MARKET
REFORMS IN CHINA
14.04
McLure, M.
A.C. PIGOU’S MEMBERSHIP OF THE
‘CHAMBERLAIN-BRADBURY’ COMMITTEE.
PART I: THE HISTORICAL CONTEXT
14.05
McLure, M.
A.C. PIGOU’S MEMBERSHIP OF THE
‘CHAMBERLAIN-BRADBURY’ COMMITTEE.
PART II: ‘TRANSITIONAL’ AND ‘ONGOING’ ISSUES
14.06
King, J.E. and McLure, M.
HISTORY OF THE CONCEPT OF VALUE
14.07
Williams, A.
A GLOBAL INDEX OF INFORMATION AND
POLITICAL TRANSPARENCY
14.08
Knight, K.
A.C. PIGOU’S THE THEORY OF UNEMPLOYMENT
AND ITS CORRIGENDA: THE LETTERS OF
MAURICE ALLEN, ARTHUR L. BOWLEY, RICHARD
KAHN AND DENNIS ROBERTSON
14.09
Cheong, T.S. and Wu, Y.
THE IMPACTS OF STRUCTURAL RANSFORMATION
AND INDUSTRIAL UPGRADING ON REGIONAL
INEQUALITY IN CHINA
14.10
Chowdhury, M.H., Dewan, M.N.A.,
Quaddus, M., Naude, M. and
Siddique, A.
GENDER EQUALITY AND SUSTAINABLE
DEVELOPMENT WITH A FOCUS ON THE COASTAL
FISHING COMMUNITY OF BANGLADESH
14.11
Bon, J.
UWA DISCUSSION PAPERS IN ECONOMICS: THE
FIRST 750
14.12
Finlay, K. and Magnusson, L.M.
BOOTSTRAP METHODS FOR INFERENCE WITH
CLUSTER-SAMPLE IV MODELS
14.13
Chen, A. and Groenewold, N.
THE EFFECTS OF MACROECONOMIC SHOCKS ON
THE DISTRIBUTION OF PROVINCIAL OUTPUT IN
CHINA: ESTIMATES FROM A RESTRICTED VAR
MODEL
14.14
Hartley, P.R. and Medlock III, K.B.
THE VALLEY OF DEATH FOR NEW ENERGY
TECHNOLOGIES
14.15
Hartley, P.R., Medlock III, K.B.,
Temzelides, T. and Zhang, X.
LOCAL EMPLOYMENT IMPACT FROM COMPETING
ENERGY SOURCES: SHALE GAS VERSUS WIND
GENERATION IN TEXAS
14.16
Tyers, R. and Zhang, Y.
SHORT RUN EFFECTS OF THE ECONOMIC REFORM
AGENDA
14.17
Clements, K.W., Si, J. and Simpson, T.
UNDERSTANDING NEW RESOURCE PROJECTS
14.18
Tyers, R.
SERVICE OLIGOPOLIES AND AUSTRALIA’S
ECONOMY-WIDE PERFORMANCE
14.19
Tyers, R. and Zhang, Y.
REAL EXCHANGE RATE DETERMINATION AND
THE CHINA PUZZLE
ECONOMICS DISCUSSION PAPERS
2014
DP
NUMBER
AUTHORS
TITLE
14.20
Ingram, S.R.
COMMODITY PRICE CHANGES ARE
CONCENTRATED AT THE END OF THE CYCLE
14.21
Cheong, T.S. and Wu, Y.
CHINA'S INDUSTRIAL OUTPUT: A COUNTY-LEVEL
STUDY USING A NEW FRAMEWORK OF
DISTRIBUTION DYNAMICS ANALYSIS
14.22
Siddique, M.A.B., Wibowo, H. and
Wu, Y.
FISCAL DECENTRALISATION AND INEQUALITY IN
INDONESIA: 1999-2008
14.23
Tyers, R.
ASYMMETRY IN BOOM-BUST SHOCKS:
AUSTRALIAN PERFORMANCE WITH OLIGOPOLY
14.24
Arora, V., Tyers, R. and Zhang, Y.
RECONSTRUCTING THE SAVINGS GLUT: THE
GLOBAL IMPLICATIONS OF ASIAN EXCESS
SAVING
14.25
Tyers, R.
INTERNATIONAL EFFECTS OF CHINA’S RISE AND
TRANSITION: NEOCLASSICAL AND KEYNESIAN
PERSPECTIVES
14.26
Milton, S. and Siddique, M.A.B.
TRADE CREATION AND DIVERSION UNDER THE
THAILAND-AUSTRALIA FREE TRADE
AGREEMENT (TAFTA)
14.27
Clements, K.W. and Li, L.
VALUING RESOURCE INVESTMENTS
14.28
Tyers, R.
PESSIMISM SHOCKS IN A MODEL OF GLOBAL
MACROECONOMIC INTERDEPENDENCE
14.29
Iqbal, K. and Siddique, M.A.B.
THE IMPACT OF CLIMATE CHANGE ON
AGRICULTURAL PRODUCTIVITY: EVIDENCE
FROM PANEL DATA OF BANGLADESH
14.30
Ezzati, P.
MONETARY POLICY RESPONSES TO FOREIGN
FINANCIAL MARKET SHOCKS: APPLICATION OF A
MODIFIED OPEN-ECONOMY TAYLOR RULE
14.31
Tang, S.H.K. and Leung, C.K.Y.
THE DEEP HISTORICAL ROOTS OF
MACROECONOMIC VOLATILITY
14.32
Arthmar, R. and McLure, M.
PIGOU, DEL VECCHIO AND SRAFFA: THE 1955
INTERNATIONAL ‘ANTONIO FELTRINELLI’ PRIZE
FOR THE ECONOMIC AND SOCIAL SCIENCES
14.33
McLure, M.
A-HISTORIAL ECONOMIC DYNAMICS: A BOOK
REVIEW
14.34
Clements, K.W. and Gao, G.
THE ROTTERDAM DEMAND MODEL HALF A
CENTURY ON
ECONOMICS DISCUSSION PAPERS
2015
DP
NUMBER
AUTHORS
TITLE
15.01
Robertson, P.E. and Robitaille, M.C.
THE GRAVITY OF RESOURCES AND THE
TYRANNY OF DISTANCE
15.02
Tyers, R.
FINANCIAL INTEGRATION AND CHINA’S GLOBAL
IMPACT
15.03
Clements, K.W. and Si, J.
MORE ON THE PRICE-RESPONSIVENESS OF FOOD
CONSUMPTION
15.04
Tang, S.H.K.
PARENTS, MIGRANT DOMESTIC WORKERS, AND
CHILDREN’S SPEAKING OF A SECOND
LANGUAGE: EVIDENCE FROM HONG KONG
15.05
Tyers, R.
CHINA AND GLOBAL MACROECONOMIC
INTERDEPENDENCE
15.06
Fan, J., Wu, Y., Guo, X., Zhao, D. and
Marinova, D.
REGIONAL DISPARITY OF EMBEDDED CARBON
FOOTPRINT AND ITS SOURCES IN CHINA: A
CONSUMPTION PERSPECTIVE
15.07
Fan, J., Wang, S., Wu, Y., Li, J. and
Zhao, D.
BUFFER EFFECT AND PRICE EFFECT OF A
PERSONAL CARBON TRADING SCHEME
15.08
Neill, K.
WESTERN AUSTRALIA’S DOMESTIC GAS
RESERVATION POLICY THE ELEMENTAL
ECONOMICS
15.09
Collins, J., Baer, B. and Weber, E.J.
THE EVOLUTIONARY FOUNDATIONS OF
ECONOMICS
15.10
Siddique, A., Selvanathan, E. A. and
Selvanathan, S.
THE IMPACT OF EXTERNAL DEBT ON ECONOMIC
GROWTH: EMPIRICAL EVIDENCE FROM HIGHLY
INDEBTED POOR COUNTRIES
15.11
Wu, Y.
LOCAL GOVERNMENT DEBT AND ECONOMIC
GROWTH IN CHINA
15.12
Tyers, R. and Bain, I.
THE GLOBAL ECONOMIC IMPLICATIONS OF
FREER SKILLED MIGRATION
15.13
Chen, A. and Groenewold, N.
AN INCREASE IN THE RETIREMENT AGE IN
CHINA: THE REGIONAL ECONOMIC EFFECTS