WORLD TRADE
WT/COMTD/SE/W/5
23 October 2002
ORGANIZATION
(02-5793)
Committee on Trade and Development
Dedicated Session
TRADE AND ECONOMIC PERFORMANCE:
THE ROLE OF ECONOMIC SIZE?
Note by the Secretariat
1.
The General Council, at its meeting of 1 March 2002, instructed the CTD to establish a
programme of work on small economies, to be conducted in Dedicated Sessions1. The General
Council also instructed the WTO Secretariat to provide relevant information and factual analysis,
inter alia, on the constraints faced by small economies as well as their shortfalls in institutional and
administrative capacities, including in the area of human resources and the effects of trade
liberalization on small economies. A recent document (WT/COMTD/SE/W/4) prepared by the
Secretariat provided an overview of the discussion on small economies in the existing literature.
Members requested that the Secretariat provide a more in depth analysis on the variables that are of
relevance to small economies in light of the multilateral trading system. This note, which has been
prepared under the Secretariat's own responsibility, and which is without prejudice to the positions of
Members or to their rights and obligations under the WTO, aims to respond to aspects of those
requests.
EXECUTIVE SUMMARY
2.
A recent document prepared by the Secretariat (document WT/COMTD/SE/W/4: "Small
Economies: A Literature Review") provided an overview of the discussion on small economies in the
existing literature. The overview identified a number of key variables that seem to be of particular
relevance to small economies, such as transport costs, openness to trade, diversification of exports and
volatility of export earnings.
3.
The present note attempts to look more closely at the key variables emphasized in the
above-mentioned document. Relevant data have therefore been collected for the WTO Membership
and are presented and analysed in this note. The present note attempts to clarify the relationship
between the different variables and to assess their relevance for the ongoing discussion in the
Dedicated Session on Small Economies.
4.
For expositional reasons this paper mainly uses data analysis tools relying on sample or group
averages. It should therefore not be excluded that the behaviour of single economies differs
significantly from what the findings in this note would predict. To control for the robustness of the
presented results, these have been compared whenever possible with the results of more sophisticated
econometric analyses found in recent literature.
1
Document WT/L/447.
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5.
The main findings of the present note can be summarized as follows:
Measures of economic size

Different variables have been proposed as measures for "economic size". These measures
include population, land area, GDP and share in global trade. Population and land area can be
considered to reflect the size of an economy's endowments. GDP is a frequently used measure
for market size, as it reflects domestic demand.

The analysis in the present note shows that the variables population size, GDP and geographic
size are closely related. It is therefore not unreasonable to assume that each of the three
measures for economic size relates in a similar way to other economic variables analysed in
this note. In order to avoid repeating each exercise for three variables throughout the paper,
one variable has been selected as being representative. In accordance with the approach most
frequently taken in the literature, population size will be used as an indicator for size
throughout the rest of the paper if not indicated otherwise. No threshold for "smallness" will
be defined, however.

Recent literature (Davenport, 2001) has suggested using the share in global trade as an
indicator of economic size in the context of WTO issues. The relationship between this
variable and population size turns out to be rather weak, but with GDP it is very strong. Share
in global trade (measured as share in WTO trade) has been included in the analysis here as a
separate measure for economic size on account of its direct relevance to the current discussion.
Competitiveness of small economies: transport costs and scale economies

An economy’s size is likely to affect its possibilities to be a successful exporter of products
subject to large economies of scale. This can be considered one of the restrictions small
economies face when trying to diversify exports. This paper provides a list and ranking of
those manufacturing industries where economies of scale play an important role for the spread
of development costs and for production costs.

It has been argued that small economies are characterized by large transport costs. The
analysis in the present note indicates that more than size, it is geographic location that affects
transport costs. Landlocked economies tend to be subject to higher transport costs than
islands. Islands in turn have higher transport costs than economies that are neither landlocked
nor islands.

When relating Members' share in WTO trade to transport costs, a negative relationship is
found. If share in WTO trade is considered to be a measure for economic size, this finding
would imply that transport costs are indeed higher for smaller economies. Interpreted
differently, the finding could also indicate that transport costs have a significant effect on an
economy's potential to export.

In order to assess the role of transport costs for export diversification the present paper
presents data on transport costs across different product groups.
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Small economies and trade: high reliance on imports and lack of export diversification

"Smallness" is likely to limit an economy’s possibilities to diversify local production. This
explains why smaller WTO Members tend to rely more heavily on trade than larger Members.
Besides, the available data on export diversification indicate that the exports of smaller
Members tend to be more heavily concentrated in a few commodities or services.

The argument that smaller economies experience higher volatility in their export earnings is
not confirmed by the relevant data for the WTO Membership. These findings are in line with
those in recent economic literature.
Vulnerability and economic performance of small economies

Because small economies tend to be very open, any given level of variation in export earnings
will tend to have a large impact on their economies. The analysis in the present note confirms
that volatility of GDP decreases with economic size. This indicates that smaller economies do
indeed operate in a more volatile economic environment.

When relating economic size to GDP per capita, the present note finds that smaller economies
are not systematically poorer than larger ones. However, GDP per capita turns out to be
positively related with the variable "share in WTO trade".

Economic size does not seem to have a significant effect on GDP per capita growth. Recent
economic literature (Easterly and Kraay, 1999) points out that two offsetting effects may be at
work. Openness to trade, while leading to higher volatility in smaller economies, also
stimulates economic activity and growth.

Smaller economies, however, may face bigger problems in adjusting to changes in the trade
policy regime than larger economies because of the lack of diversification of small countries'
export structure. If a change in policy regime is to lead to a shrinkage of a small economy's
main export sector, this is likely to have repercussions for private sector activity in general. At
the same time, it may be more difficult for a small economy to expand exports in alternative
activities.
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TRADE AND ECONOMIC PERFORMANCE:
THE ROLE OF ECONOMIC SIZE?
I.
INTRODUCTION
6.
A recent document prepared by the Secretariat (document WT/COMTD/SE/W/4: "Small
Economies: A Literature Review") provided an overview of the discussion on small economies in the
existing literature. The overview identified a number of key variables that seem to be of particular
relevance to small economies. The present note attempts to look more closely at these key variables.
Relevant data have therefore been collected for the WTO Membership and are presented and analysed
in this note. This note attempts to clarify the relationship between the different variables and to assess
their relevance for the ongoing discussion in the Dedicated Session on Small Economies.
7.
In particular, this note discusses the role of transport costs and economies of scale as
determinants of small economies' competitiveness. It assesses whether economic size affects
economies' reliance on trade and level of export diversification. The issue of vulnerability is
discussed as the note analyses how economic size relates to the volatility of export earnings and to
GDP volatility. Last but not least the note discusses the evidence on the relationship between
economic size and economic performance, measured by GDP per capita and GDP per capita growth
respectively.
8.
For expositional reasons this paper mainly uses data analysis tools relying on sample or group
averages. It should therefore be borne in mind that the behaviour of single economies differs
significantly from what the findings in this note would predict. To control for the robustness of the
presented results, they have been compared whenever possible with the results of more sophisticated
econometric analysis presented in recent literature.
II.
MEASURES OF ECONOMIC SIZE
9.
Several indicators have been used to measure the "smallness" of an economy, the most
popular one being population size. Other indicators of economic size include: GDP, land area and
share in global trade. Tables 1 and 2 in the Appendix present the relevant figures for each of the four
"measures of size" for the WTO Membership.
10.
Population and land area can be considered to reflect the size of an economy's endowments.
GDP rather reflects market size, as it is an indicator for the size of demand in an economy. An
economy's share in global trade is likely to reflect two things: the economy's size (larger economies
trade more) and an economy's openness (more open economies trade more).
11.
With respect to population size, the literature uses different thresholds when referring to
"small economies". Some suggest to use a population of 1.5 million as a threshold (Commonwealth
Secretariat – World Bank Joint Task Force, 2000), others 5 million or even more (Streeten, 1993,
Collier and Dollar, 1999, Brautigam and Woolcock, 2001), and still others something in between
(Armstrong et al, 1998). Table 1 of the Appendix shows that 30 of the 143 WTO Members have a
population below 1.5 million.2 Five of these countries are Least-Developed Countries (LDCs).
Twenty-seven WTO Members have a population between 1.5 and 5 million, four of which are LDCs.
The remaining 86 Members have a population above 5 million, 20 of which are LDCs.
2
The European Communities have been excluded from our analysis, as its member countries are
included separately.
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12.
Although GDP per capita income may vary it is, in general, the case that countries with a
larger population are also characterized by a larger GDP. This positive relationship between
population and GDP can be measured by the correlation coefficient. This coefficient takes values
between 0 and 1 if the relationship between the relevant variables is positive.3 The stronger the
relationship, the closer the coefficient's value will be to 1. For WTO Members the correlation
between GDP and population size is 0.59, which is relatively strong. Table 2 in the Appendix shows
the GDP of all WTO Members.
13.
The rank-correlation is another indicator that can be used to measure the relationship between
two variables.4 The rank-correlation for GDP and population size is even higher than the normal
correlation coefficient: 0.80. This implies that chances are high, for instance, that the country with
the tenth largest population also has the tenth largest GDP. Figure 1 reflects this relationship
graphically.
Figure 1: Correlation between WTO Members' position in the
population ranking and their position in the GDP ranking
14.
The relationship between land area and population size turns out to be less strong that the one
between GDP and population. The normal correlation between countries' population and their land
area is 0.55. Again, the rank correlation is higher: 0.72. See Table 2 for information of the land area
of WTO Member countries.
15.
Population size, land area and GDP have been identified by the literature as measures for
economic size. The analysis in the previous paragraphs has shown that these three variables are
closely related. It is therefore not unreasonable to believe that each of the three measures for
economic size relates in a similar way to other economic variables analysed in this note. In order to
avoid repeating each exercise for three variables throughout the paper, one variable has therefore been
selected as being representative. In accordance with the approach most frequently taken in the
literature, population size will be used as an indicator for size throughout the rest of the paper, if not
The coefficient takes values between 0 and –1 if the relationship is negative.
The correlation coefficient measures the closeness of the relationship between the value of one
variable and the value of another variable. The rank correlation coefficient instead measures the closeness of the
relationship between two sets of rankings – that is, between rankings of one variable and the rankings of the
other variable.
3
4
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indicated otherwise. No threshold for "smallness" will be defined, however. Instead, the entire WTO
Membership will be included in the analysis.
16.
In a recent contribution, Davenport (2001) suggested using "share in world trade" as an
indicator for smallness. He finds that the group of countries with a share in overall merchandise trade
of less than 0.02 corresponds more or less to the group of 42 "small vulnerable states" as defined by
the World Bank. The question therefore arises whether "share in trade" is an appropriate measure for
economic size.
17.
Table 2 reflects Members' shares in total WTO trade.5 This variable turns out to be much less
correlated with population size than GDP and land area. For all WTO Members the correlation is
only 0.26. This implies that differences in participation in WTO trade only to a limited extent reflect
differences in population size.6 The rank correlation for population size and share in WTO trade is
0.57.7 Figure 2 reflects this rank correlation. The comparison between Figure 1 and Figure 2 shows
that the relationship between population size and Members' share in WTO trade is indeed weaker than
that between population size and Members' GDP. In Figure 1 the data points are rather closely ranged
around an (imaginary) line with an increasing slope. In Figure 2, the positive relationship between the
two variables is less clear as data points cover nearly the entire plotting area.
Figure 2: Correlation between WTO Members' position
in the population ranking and their position
in the ranking of share in WTO trade
18.
WTO Members' share in total WTO trade is, however, very closely related to GDP. The
correlation coefficient between the two variables is 0.85 and the rank correlation is 0.92. The latter
relationship is depicted in Figure 3 below.
5
Both trade in commodities and trade in services have been taken into account.
However, this correlation is higher, 0.50, when only Members in Africa, Asia, the Middle East and
Latin America are taken into account (excluding also Australia, Japan and New Zealand).
7
The rank correlation is 0.54 for the restricted sample.
6
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Figure 3: Correlation between WTO Members' position
in the GDP ranking and their position
in the ranking of share in WTO trade
19.
This finding supports the arguments presented in Davenport (2001). Yet, as has been pointed
out before, the variable "share in trade" is a rather indirect measure of economic size. Besides it is not
closely related to population size. Share in WTO trade has been included in the analysis for this paper
as a separate measure for economic size. It turns out to relate quite differently to several key
economic variables (e.g. GDP per capita) than the variable population size. Whenever this is the case,
it will be pointed out explicitly in this note.
III.
COMPETITIVENESS OF SMALL ECONOMIES: TRANSPORT COSTS AND
SCALE ECONOMIES
20.
It has been argued that many small states are rendered less competitive in world markets
because they are situated far away from major centres of trade and commerce. Remoteness does have
an impact on competiveness because it increases transport costs for imports and exports. In
competitive global markets, higher transport costs would have to be offset either by lower wages or by
reduced costs somewhere else in the production process in order to allow firms to compete. 8
The literature has identified several factors that affect the size of transport costs:9
21.

Economies that are located further from major markets are likely to face higher shipping costs
than proximate economies;10
8
See Radelet and Sachs (1998).
See Radelet and Sachs (1998). These factors may apply to any type of economy, independent of its
size or development stage.
10
Radelet and Sachs (1998) find that a 10 per cent increase in sea distance is associated with a
1.3 per cent increase in shipping costs. Clark et al. (2002) relate a 100 per cent increase in distance to a
20 per cent increase in transport costs.
9
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
Overland transport costs tend to be considerably higher than sea freight costs. Thus for a
given distance from main markets, economies with a higher proportion of transit by land will
tend to have higher overall transport costs;11

There are extra costs to inter-modal transport (e.g. in which freight must be shipped both by
land and sea), because of extra costs of transferring between transport modes;

Transport costs differ because of differences in the quality of ports administration and/or ports
infrastructure; and

Transport costs will depend on the composition of trade. The costs of shipping agricultural
products, for instance, will differ depending on whether they are perishable or dry bulk and the
extent to which they have been processed.
22.
Data on transport costs are not readily available. The most frequently used measure is the
so-called "CIF/FOB ratio". The FOB (free on board) price measures the cost of an imported item at
the point of shipment by the exporter as it is loaded on to a carrier for transport. The CIF (costinsurance-freight) price measures the cost of the imported item at the point of entry into the importing
country, inclusive of the costs of transport, including insurance, handling, and shipment costs, but not
including customs charges. The higher the value of the ratio, the higher the share of transport cost in
the value of traded goods.
23.
Figure 4 represents the CIF/FOB ratio for WTO Members divided into three groups:
landlocked Members, islands and other Members. The Figure is based on the data presented in
Table 3 in the Appendix. It is clear that landlocked Members tend to face significant disadvantages
when it comes to transport costs. This is due to the fact that they must pay the high costs of overland
transport from the neighbouring ports. These costs are increased by the costs of crossing at least one
additional border. Islands also face higher transport costs than "other Members", probably due to the
large distance of most of them from the major centres of trade. The finding that landlocked
economies face particular transport cost disadvantage is confirmed by the econometric literature on
the subject. Limão and Venables (1999) and Radelet and Sachs (1998) both emphasise this
phenomenon.
11
Limão and Venables (1999) find that an extra 1000 km by sea adds $190 to transport costs whereas a
similar increase in land distance adds $1380.
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Figure 4: Transport costs for WTO Members
Average CIF/FOB ratios for landlocked Members, islands and other Members
24.
For the WTO Membership the correlation coefficient between population size and CIF/FOB
ratios is –0.07. This value is very close to zero and indicates that there is hardly any relationship
between economic size and transport costs. In other words, transport costs do not decrease with
population size. This finding can to a large extent be explained by the distribution of islands and
landlocked economies across population groups in the WTO Membership. Figure 5 shows that out
of 30 WTO Members having a population of less than 1.5 million, 17 Members are islands, but only
three are landlocked. The average CIF/FOB ratio for this population group is 0.127. The previous
paragraph has shown that landlocked economies are characterized by the highest transport costs and
relatively high numbers of landlocked Members are situated in the groups with a population
between 5 and 10 millions (8 out of 26) and between 10 and 25 million (9 out of 29). These
population groups are also characterized by the highest CIF/FOB ratios: 0.130 and 0.152
respectively.12
12
The average CIF-FOB ratio for the population range of 1.5-5 million is 0.108 and the one for the
group with highest populations (25+) is 0.092.
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Figure 5: Number of islands, landlocked and other WTO Members
in different population groups and average CIF-FOB ratios in percentages
25.
Figure 6, however, shows that transport costs do decrease with higher shares in WTO trade. 13
If share in WTO trade is considered to be a measure of economic size, this figure would indicate that
transport costs are indeed higher for smaller economies. Yet interpreted differently, the figure could
also indicate that transport costs have a significant effect on the potential of an economy to export.
This latter interpretation would be in line with findings in recent economic literature (e.g. Limão and
Venables, 1999). This double interpretation indicates that one needs to be cautious when using share
of trade as an indicator for economic size, as this variable is also a reflection of trade performance
and/or openness to trade. It also serves as a reminder of the fact that figures like the one below do not
say anything about causation.
13
The shape of this figure strongly depends on the thresholds being used to define the different groups,
as the correlation between the two variables is only –0.26. The first threshold has been chosen according to the
criterium used by the WTO to set the minimum level of WTO budgetary contributions. The other thresholds
have been set in order to create groups of similar sizes.
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Figure 6: CIF-FOB ratios grouped according to WTO Members share in WTO trade
CIF-FOB ratios
0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
<0.015
0.0151- 0.05
0.051- 0.2
0.21- 1
>1
Share of WTO Trade
26.
Transport costs will reduce the possibilities for landlocked countries and, to some extent,
islands to compete in certain types of products. This will for instance be the case for products with a
high import content, such as electronics.14 It will also be the case for products facing high transport
and/or insurance costs when exported. Table 4 in the Appendix gives an overview of the CIF/FOB
ratios for different product groups.15 The SITC groups of crude materials (includes cork and wood)
and food and live animals (includes fruits, nuts and sugar) turn out to have the highest CIF/FOB
ratios.16
27.
Small economies may also face difficulties in exporting products that are subject to large
economies of scale.17 Serving the home market will not allow producers to exploit economies of scale
and they will produce at high unit costs. Table 1 presents a ranking of manufacturing industry groups
according to the importance of scale economies in the production process. It reveals that motor
vehicles, other vehicles and chemicals are particularly subject to large economies of scale.
14
See Radelet and Sachs (1998).
For this table ratios have been computed using a U.S. data set for imports into the U.S..
16
Exports of many small countries tend to be determined by natural resources. A comparison of this
Table with Table 6 in the Appendix shows that several small Members are actually exporters of products
characterized by relatively high transport costs.
17
That is, products for which unit costs fall when they are produced on a larger scale.
15
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Table 1: Ranking of manufacturing industry by economies of scale
Industry groups are listed in order of the importance of economies of scale for spreading
developing costs and for production costs
1
2
3
4
5
6
7
8
9
10
Motor vehicles
Other vehicles
Chemicals
Man-made fibres
Metals
Office machinery
Mechanical engineering
Electrical engineering
Instrument engineering
Paper printing and publishing
11
12
13
14
15
16
17
18
19
20
Non metalic mineral products
Metal goods
Rubber and plastic products
Drink and tobacco
Food
Other manufacturing industries
Textiles
Timber and furniture
Footwear and clothing
Leather goods
Source: Pratten (1988)
28.
It has been argued, however, that the home market is not the relevant market for open
economies. Their relevant market would also be determined by the market size of foreign partners. 18
In theory, at least, trade may allow small economies to exploit economies of scale. Non-traded goods
and services, particularly infrastructure, are not subject to this rule and should they represent an
important share of inputs in traded good production, small state competitiveness in international
markets might be affected (Srinavasan, 1986).
IV.
SMALL ECONOMIES AND TRADE: HIGH RELIANCE ON IMPORTS AND LACK
OF EXPORT DIVERSIFICATION
29.
It has been argued that "smallness" limits an economy’s possibilities to diversify local
production. As a consequence small economies are expected to rely heavily on imports to support
local consumption. At the same time, small economies' exports will be characterized by a lack of
diversification. Transport costs and the problems of exploiting economies of scale may be additional
factors affecting small economies' potential to diversify exports.
30.
Smaller economies tend to rely more heavily on external trade and foreign investment to
overcome their inherent scale and resource limitations. The high degree of openness brings real
benefits that accrue from trade – consumers in small states are able to obtain a greater variety of
goods at lower costs than if their choices were confined to domestically produced goods, while
producers in small states can sell on world markets – provided they have effective market access –
earning more than if they were confined to meeting limited domestic demand. Participation in the
world market also helps to channel new ideas and information about opportunities to firms and
consumers in small states. Figure 7 shows the average ratio of trade to GDP for WTO Members
grouped according to population size. It reveals that Members are indeed more open to trade the
smaller they are.
18
See for instance Alesina and Spolaore (1997).
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Figure 7: Small economies' reliance on trade
Based on data for all WTO Members presented in Table 5 Appendix
31.
Small size limits an economy's opportunities to diversify. Smaller economies thus have to
rely on imports in order to increase the choice of goods and services supplied to their population. At
the same time their exports tend to be less diversified than those of larger countries. As discussed in
the previous section, transport costs and the problems to exploit economies of scale may be additional
factors restricting the scope for small economies to diversify exports. A lack in export diversification
is considered to be a disadvantage because it makes smaller countries more vulnerable to changes in
demand for or prices of the few commodities or services they export.
32.
Table 6 in the Appendix shows the first and second commodity or service exported by WTO
Members and their respective share in total exports. Table 6.A reveals that exports of Members with
a population of less than 1.5 million tend to be highly concentrated in one product or service. Also,
the second most important export good or service tends to represent a high share in total exports. A
look at tables 6.B-6.E, however, shows that other factors also seem to affect countries' level of export
diversification. Petrol producers tend to be highly dependent on exportation of petroleum and
petroleum related products, independent of country size (e.g. Venezuela, rank 112 in population and
Nigeria, rank 136 in population). Also, the level of development seems to play a role. Several large
LDCs are characterized by a high export dependence on one good. This is the case, for instance, for
Tanzania (37.7 per cent travel), Uganda (coffee, 41.8 per cent), Burkina Faso (cotton, 51.8 per cent),
Mali (cotton, 75.7 per cent) and Malawi (tobacco, 58.6 per cent).
33.
It has been argued that smaller economies experience higher volatility in their export earnings
because of the afore-mentioned lack of diversification. It has also been argued that smaller economies
are more likely to export goods particularly subject to price volatility. This would be another cause
for high volatility in export earnings.
34.
Figure 8 reveals that our data for WTO Members do not confirm this argument. Volatility of
export earnings does not seem to decrease with economic size.19 A related finding by the
Commonwealth Secretariat/World Bank Joint Task Force (2000) goes in the same direction: "The
terms of trade of small countries – the price of their exports (which tend to be concentrated in a
relatively narrow range of goods and services) relative to the price of their imports (which tend to
19
Economic size is measured by population size in Figure 8. If measured by share of WTO trade the
results are similar, the correlation coefficient between volatility of export earnings and share of WTO trade
being negative but rather small: -0.21.
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cover a broad range of goods and services) – are not significantly more volatile than those of other
developing countries." Easterly and Kraay (1999) come to the same result in their econometric
analysis.
Figure 8: Population size and volatility of export earnings
Based on data presented in table 5 Appendix
35.
Yet, because small economies tend to be very open, a large proportion of domestic economic
activity tends to be accounted for by exports and imports. Even minor variations in world markets –
such as fluctuating demand and prices for exports – can then have a large impact on their economies.
To take this into account Easterly and Kraay (1999) also analyze whether the volatility of the
"weighted" terms of trade is affected by economic size. They define "weighted" terms of trade shocks
as the growth in the local currency price of export times the share of exports in GDP less the growth
in the local currency price of imports times the share of imports in GDP. This measure thus captures
both the magnitude of price fluctuations (changes in export and import prices) and their importance
for the domestic economy (weighted by the shares of exports and imports in GDP). They find that the
standard deviation of their measure is indeed significantly higher for smaller economies with the fact
of being small adding around thirty percent to the average volatility.
36.
This section has shown that smaller economies indeed tend to rely more heavily on trade and
tend to be characterized by a lack of export diversification. It is however not the case that this lack of
export diversification leads to a significantly higher volatility of their export earnings. It is rather the
case that any level of volatility has stronger repercussions on their domestic economy because trade
represents such a high share of domestic activity. The next section will have a closer look at this
issue.
V.
VULNERABILITY AND ECONOMIC PERFORMANCE OF SMALL
ECONOMIES
37.
According to the Commonwealth Secretariat/World Bank Joint Task Force (2000): "Overall,
the range of per capita incomes and rates of growth are not significantly different in small and large
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developing countries. However, the residents of small states experience higher volatility in their
incomes."20
38.
The findings for WTO Members confirm the above statement only to a limited extent.
Figure 9 reflects the volatility in GDP for WTO Members in Africa, Asia, the Middle East and Latin
America grouped according to population size.21 Focusing on this group of Members has the
advantage of excluding particularly rich small economies (like Iceland, Liechtenstein and
Luxemburg) from the analysis and to focus on the more vulnerable small economies. 22 Figure 9 is
based on the data presented in Table 5 of the Appendix. According to this Figure the group of
Members with the smallest population sizes has only the second largest GDP volatility. Apart from
this, however, the figure shows that GDP volatility decreases with population size.23
Figure 9: Volatility of GDP for WTO Members in Africa, Asia,
the Middle East and Latin America24
Based on data presented in table 5 Appendix
39.
A simple Figure like the one above does not take into account that variables other than
economic size will tend to affect GDP volatility. Certain regions, for instance, tend to be more
volatile than others. Whether a country is an oil exporter or not is another important factor. When
taking these variables into account in their econometric analysis, Easterly and Kraay (1999) find a
clearly significant and negative relationship between economies' size and GDP volatility.
40.
This finding can be related to those of the previous section. Small economies tend to be more
open to trade and to be less diversified. Their export earnings seem not to be significantly more
20
The term "income" refers to real per capita GDP.
GDP volatility has been measured by the standard deviation of per capita GDP growth rates, a
standard measure in the economic literature.
22
Focusing on this group of economies also facilitates the comparison with the findings of the
Commonwealth Secretariat/World Bank Joint Task Force (2000) study quoted in paragraph 37, the analysis of
which is restricted to developing economies. When including the entire WTO Membership in the analysis, the
relationship between GDP volatility and population size remains negative but is weaker than the one found for
the restricted sample.
23
The figures for all WTO Members are similar with the averages of standard deviation being 4.8, 5.99
and 4.57 for the first three population groups.
24
Australia, Japan and New Zealand are not included.
21
WT/COMTD/SE/W/5
Page 16
volatile than those of larger countries. Yet because trade represents such an important part of
economic activity, overall GDP volatility tends to be higher in smaller countries.
41.
Another characteristic unrelated to trade also affects GDP volatility of small economies.
According to Commonwealth Secretariat/World Bank Joint Task Force (2000) many small states are
in regions susceptible to natural disasters such as hurricanes, cyclones, drought and volcanic
eruptions. Because of the small size of the country, these natural disasters will easily affect the whole
population and economy, leading to high fluctuations in GDP.
42.
Although a volatile environment can be considered to be an unfavourable influence on the
functioning of an economy, it is not the case that smaller countries are systematically poorer than
larger ones. The examples of very rich, small economies like Liechtenstein and Luxembourg are
well-known. Yet even when excluding this type of economy, GDP per capita does not increase with
economic size. Figure 10 shows that this also holds for WTO Members in Africa, Asia, the Middle
East and Latin America.25 The correlation coefficient between GDP per capita and population size for
this group of members is –0.08 and thus insignificant.26
Figure 10: GDP per capita for WTO Members in Africa, Asia,
the Middle East and Latin America grouped according to population size 27
Based on data presented in table 7 Appendix
43.
Figure 11 shows that GDP per capita relates very differently to economic size, when share in
WTO trade is taken as a measure for economic size. The figure reflects a clearly positive relationship
between share in WTO trade and GDP per capita. The correlation coefficient is also positive and
rather high: 0.46.28 A look at the composition of the group of "largest economies" in each figure gives
some indications as to the reasons for the difference between Figure 10 and Figure11. Only five
WTO Members in the relevant regions have a trade share larger than 1 per cent: China, Hong Kong,
China, Republic of Korea, Malaysia, Mexico and Singapore (See Table 2 in the Appendix). Only
China and the Republic of Korea also figure in the group of Members with the largest population
25
Australia, New Zealand and Japan have been excluded from the sample.
The correlation coefficient for all WTO Members is –0.05.
27
Australia, New Zealand and Japan have been excluded from the sample.
28
It increases to 0.58 when the entire WTO Membership is taken into account.
26
WT/COMTD/SE/W/5
Page 17
(larger than 25 million). Malaysia figures in the second largest group, Hong Kong, China in the
middle group and Singapore in the second smallest (see Table 1 Appendix). Both Hong Kong, China
and Singapore are very open economies. This confirms the argument that "share in trade" is an
indicator reflecting both economic size and openness to trade. Openness to trade is expected to have a
positive effect on GDP per capita, which could explain the positive relationship depicted in Figure 11.
This idea is confirmed by the subsequent discussion on GDP growth. However, further analysis would
be needed to explain the differences between Figure 10 and 11.
Figure 11: GDP per capita for WTO Members in Africa, Asia,
the Middle East and Latin America grouped according to share in WTO trade 29
16000
14000
GDP per capita
12000
10000
8000
6000
4000
2000
0
<0.015
0.0151- 0.05 0.051- 0.2
0.21- 1
>1
Share of WTO Trade
44.
Econometric analysis has shown that economic size has no significant effect on GDP growth
(Easterly and Kraay, 1999). A look at the relevant data for the WTO Membership seems to confirm
this finding. Figure 12 shows the average GDP growth rate for the periods 1980-2000 for WTO
Members in Africa, Asia, the Middle East and Latin America. The correlation coefficient for the
relevant group is 0.35, which reduces to 0.09 when the outliers China and India are excluded. When
share of WTO trade is used as a measure of economic size the correlation coefficient is 0.46 for the
relevant group. This high value is mainly due to the strong performance of the five economies with
the largest trade shares mentioned in the above paragraph. The correlation reduces to 0.24 when they
are excluded.
29
Australia, New Zealand and Japan have been excluded from the sample.
WT/COMTD/SE/W/5
Page 18
Figure 12: Average growth rate of GDP per capita for WTO Members
in Africa, Asia, the Middle East and Latin America30
45.
Easterly and Kraay (1999) give an explanation for this weak relationship between economic
size and growth. Their analysis shows that openness to trade has a significantly positive effect on
GDP growth. They also find that volatility in GDP is bad for GDP growth. It has been shown before
that small states are particularly open. This openness stimulates economic activity and growth. At
the same time, however, openness to trade is one of the main reasons for the GDP volatility that
characterizes small economies and GDP volatility is bad for economic growth. Easterly and Kraay
(1999) show that the positive and negative effects from openness roughly offset each other in the case
of small states. They therefore do not find any significant effect of smallness on economic growth.
46.
Smaller economies, however, may face bigger problems in adjusting to changes in the trade
policy regime than larger countries. In other words, while having an open trade regime tends to be
beneficial for small economies, changing the trade regime may be more cumbersome. The reason for
this is linked to the lack of diversification of the export structure of small economies. If a change in a
policy regime leads to a shrinkage of a small economy's main export sector, for example, this is likely
to have repercussions for private sector activity in general. This leads to externalities that represent an
additional adjustment burden in economies characterized by a lack of export diversification.31 At the
same time, it may be more difficult for a small economy to expand exports in alternative activities.
An expansion in exports can take the form of an expansion of existing export activities or of the startup of new export activities. The less diversified an economy, the more probable it is that new
activities need to be developed. It has been argued that starting new export activities is more costly
and involves more risks than expanding existing ones.32 As a consequence, a change of policy regime
may lead to more severe adjustment problems when countries are characterized by a lack of
diversification in their export structure.
VI.
CONCLUSIONS
30
Australia, Japan and New Zealand have been excluded.
See Rama (1999) and Bacchetta and Jansen (2002).
32
See Bacchetta and Jansen (2002).
31
WT/COMTD/SE/W/5
Page 19
47.
This note presents and discusses evidence on the relationship between economic size and
selected economic indicators for the WTO Membership. In particular, the note discusses the role of
transport costs and economies of scale as determinants of small economies' competitiveness. It
assesses whether economic size affects economies' reliance on trade and their level of export
diversification. The issue of vulnerability is discussed as the note analyses how economic size relates
to the volatility of export earnings and to GDP volatility. Last but not least the note discusses the
evidence on the relationship between economic size and economic performance, measured by GDP
per capita and GDP per capita growth respectively.
48.
The analysis presented in the paper suggests that "smallness" limits an economy’s
possibilities to diversify local production. As a result smaller WTO Members tend to rely more
heavily on trade than larger Members. The available data on export diversification also indicate that
the exports of smaller Members tend to be more heavily concentrated in a few commodities or
services.
49.
Transport costs turn out to depend more heavily on geographic location than on economic
size. Landlocked economies tend to be subject to higher transport costs than islands. Islands in turn
have higher transport costs than economies that are neither landlocked nor islands. Among the WTO
membership islands tend to be among the Members with small population size. Landlocked Members
instead tend to have rather large population sizes as compared to islands.
50.
This note examines the vulnerability of small economies by analysing how economic size
relates to volatility in export earnings and in GDP. It finds that the volatility of export earnings does
not clearly decrease with economic size. This result is in line with findings in relevant recent
literature. But, because small economies tend to be very open, a large proportion of domestic
economic activity tends to be accounted for by exports and imports. Even minor variations in world
markets, such as fluctuating demand and prices for exports, can then have a large impact on their
economies. The analysis in the present note confirms that volatility of GDP decreases with economic
size. This indicates that smaller economies do indeed operate in a more volatile economic
environment.
51.
Although a volatile environment can be considered to be bad for the functioning of an
economy, it is not the case that smaller economies are systematically poorer than larger ones. The
findings in this note also confirm the results in the relevant literature, that economic size does not
have a significant effect on GDP per capita growth.
52.
Smaller economies, however, may face bigger problems to adjust to changes in the trade
policy regime than larger economies because of the lack of diversification of small countries' export
structures. If a change in policy regime is to lead to a shrinkage of a small economy's main export
sector, this is likely to have repercussions for private sector activity in general. At the same time, it
may be more difficult for a small economy to expand exports in alternative activities.
WT/COMTD/SE/W/5
Page 20
BIBLIOGRAPHY
Alesina, A. and E. Spolaore (1997), "On the Number and Size of Nations." Quarterly Journal of
Economics. November 1997.
Armstrong, H.W., R.J. de Kervenoael, X. Li and R. Read (1998), "A Comparison of the Economic
Performance of Different Micro-states, and Between Micro-states and Larger Countries", World
Development, 26(4):639-56.
Brautigam, D. and M. Woolcock (2001), "Small States in a Global Economy", WIDER Discussion
Paper 2001/37.
Commonwealth Secretariat/World Bank Joint Task Force (2000), "Small States: Meeting Challenges
in the Global Economy", report of the Commonwealth Secretariat, Commonwealth Secretariat/World
Bank Joint Task Force.
Clark, X., D. Dollar and A. Micco (2002), "Maritime Transport Costs and Port Efficiency", World
Bank Working Paper 2781, World Bank, Washington D.C.
Collier, P. and D. Dollar (1999), "Aid, Risk and Special Concerns of Small States", paper presented at
the Conference on Small States, Saint Lucia, 17-19 February.
Davenport, M. (2001), "A Study of Alternative Special and Differential Arrangements for Small
Economies", Interim Report, Commonwealth Secretariat.
Easterly, W. and A. Kraay (1999), "Small States, Small Problems?", World Bank Working Paper
2139, The World Bank, Washington D.C.
Limão, N. and A. Venables (1999), "Infrastructure, Geographical Disadvantage and Transport Costs",
World Bank Working Paper 2257, The World Bank, Washington D.C.
Pratten, Cliff (1988), "A Survey of the Economies of Scale", Chapter two in "Research on the Cost of
Non-Europe: Basic Findings, Volume 2 of Studies on the Economics of Integration, Commission of
the European Communities.
Radelet, S. and J. Sachs (1998), "Shipping Costs, Manufactured Exports and Economic Growth"
mimeo, Harvard Institute for International Development.
Rama, M. (1999), "Public Sector Downsizing: An Introduction", The World Bank Economic Review,
13(1):1-22.
Srinivasan T.N. (1986), "The Costs and Benefits of Being a Small, Remote, Island, Landlocked or
Ministate Economy." World Bank Research Observer, 1(2), 205-218.
Streeten, P. (1993), "The Special Problems of Small Countries", World Development, 21(2):197-202.
UNCTAD (2001): "Handbook of Statistics", United Nations Conference on Trade and Development,
Geneva.
DATA APPENDIX
Table1: Population Data of WTO Members ranked according to population size (in 2000)
0-1.5
Liechtenstein
St. Kitts and Nevis
Antigua and Barbuda
Dominica
Grenada
St. Vincent and the Grenadines
St. Lucia
Belize
Barbados
Maldives *
Iceland
Brunei
Malta
Suriname
Macao, China
Luxembourg
Solomon Islands *
Qatar
Djibouti *
Bahrain
Cyprus
Guyana
Fiji
Swaziland
Mauritius
Guinea-Bissau *
Gabon
Trinidad and Tobago
Gambia, The *
Estonia
32,000
41,000
68,000
73,000
98,000
115,000
156,000
240,000
267,000
276,000
281,000
338,000
390,000
417,000
438,000
438,400
447,000
584,890
632,000
691,000
757,000
761,000
811,900
1,045,000
1,186,140
1,199,000
1,230,000
1,301,000
1,303,000
1,369,000
Members
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
Botswana
Namibia
Kuwait
Slovenia
Lesotho *
Latvia
Oman
Mongolia
Jamaica
Mauritania *
Panama
United Arab Emirates
Congo, Rep.
Uruguay
Albania
Lithuania
Central African Rep.*
Ireland
Costa Rica
New Zealand
Singapore
Moldova
Croatia
Norway
Togo *
Jordan
Kyrgyz Republic
Source: World Bank, World development Indicators 2002.
Chinese Taipei population estimates are from EIU 2001.
Note: * Indicates Least Developed Countries as defined by the UN.
1.51-5
1,602,000
1,757,000
1,984,400
1,988,000
2,035,000
2,372,000
2,395,000
2,398,000
2,633,000
2,665,000
2,856,000
2,905,080
3,018,000
3,337,000
3,411,000
3,695,000
3,717,000
3,794,000
3,811,000
3,830,800
4,018,000
4,282,000
4,380,000
4,491,000
4,527,000
4,886,810
4,915,000
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
Members
5.1-10
Georgia
Sierra Leone *
Nicaragua
Papua New Guinea
Finland
Denmark
Slovak Republic
Paraguay
Israel
Benin *
El Salvador
Honduras
Hong Kong, China
Burundi *
Switzerland
Guinea *
Chad *
Haiti *
Austria
Bulgaria
Bolivia
Dominican Rep.
Rwanda *
Sweden
Senegal
Tunisia
5,024,000
5,031,000
5,071,000
5,130,000
5,177,000
5,336,000
5,401,790
5,496,000
6,233,210
6,272,000
6,276,000
6,417,000
6,797,000
6,807,000
7,180,000
7,415,000
7,694,000
7,959,000
8,110,240
8,166,960
8,328,700
8,373,000
8,508,000
8,869,000
9,530,000
9,563,500
Members
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
Portugal
Hungary
Zambia *
Belgium
Czech Republic
Malawi *
Greece
Niger *
Mali *
Cuba
Burkina Faso *
Guatemala
Zimbabwe
Ecuador
Angola *
Cameroon
Chile
Madagascar *
Netherlands
Cote d'Ivoire
Mozambique*
Australia
Ghana
Sri Lanka
Uganda *
Taipei, Chinese
Romania
Malaysia
Venezuela, RB
10.1-25
10,008,000
10,022,000
10,089,000
10,252,000
10,273,300
10,311,000
10,560,000
10,832,000
10,840,000
11,188,000
11,274,000
11,385,300
12,627,000
12,646,000
13,134,000
14,876,000
15,211,300
15,523,000
15,919,000
16,013,000
17,691,000
19,182,000
19,306,000
19,359,000
22,210,000
22,400,000
22,435,000
23,270,000
24,170,000
Members
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
25.1>
Peru
25,661,000
Morocco
28,705,000
Kenya
30,092,000
Canada
30,750,000
Tanzania *
33,696,000
Argentina
37,032,000
Poland
38,650,000
Spain
39,465,000
Colombia
42,299,300
South Africa
42,800,992
Korea, Rep.
47,275,000
Myanmar *
47,749,000
Congo, Dem. Rep.*
Italy
57,690,000
France
58,892,000
United Kingdom
59,738,900
Thailand
60,728,000
Egypt, Arab Rep.
63,976,000
Turkey
65,293,000
Philippines
75,580,000
Germany
82,150,000
Mexico
97,966,000
Japan
126,870,000
Nigeria
126,910,000
Bangladesh *
131,050,000
Pakistan
138,080,000
Brazil
170,406,000
Indonesia
210,420,992
United States
281,550,016
India
1,015,923,008
China
1,262,460,032
WT/COMTD/SE/W/5
Page 21
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
Members
WT/COMTD/SE/W/5
Page 22
Table 2: GDP, land area and share in WTO tradea for WTO Members for year 2000
A. Members with Population < 1.5 million
GDP, PPP
(Millions, current
international $)
1,216
1 Liechtenstein
513
2 St. Kitts and Nevis
717
3 Antigua and Barbuda
268
4 Dominica
743
5 Grenada
639
6 St. Vincent and the Grenadines
890
7 St. Lucia
1,346
8 Belize
4,137
9 Barbados
1,238
10 Maldives *
8,312
11 Iceland
1,707
12 Brunei
6,736
13 Malta
1,584
14 Suriname
7,967
15 Macao, China
21,947
16 Luxembourg
737
17 Solomon Islands *
12,628
18 Qatar
513
19 Djibouti *
10,053
20 Bahrain
15,764
21 Cyprus
3,016
22 Guyana
3,790
23 Fiji
4,694
24 Swaziland
11,882
25 Mauritius
905
26 Guinea-Bissau *
7,672
27 Gabon
11,663
28 Trinidad and Tobago
2,149
29 Gambia, The *
13,780
30 Estonia
Source: World Bank, World Development Indicators 2002.
a
Exports and imports of merchandise and commercial services.
Macau, China land surface area are from the EIU 2000.
Rank
Members
Land area
(sq km)
160
360
440
750
340
390
610
22,800
430
300
100,250
5,270
320
156,000
24
2,586
27,990
11,000
23,180
690
9,240
196,850
18,270
17,200
2,030
28,120
257,670
5,130
10,000
42,270
Share in WTO
Trade
N.A
0.003
0.007
0.002
0.003
0.003
0.006
0.006
0.020
0.006
0.045
0.038
0.049
0.007
0.054
N.A
0.003
0.083
0.002
0.069
0.061
0.012
0.015
0.016
0.039
0.001
0.033
0.047
0.003
0.064
WT/COMTD/SE/W/5
Page 23
Table 2: GDP, land area and share in WTO tradea for WTO Members for year 2000
B. Members with Population 1.51 – 5 million
GDP, PPP
Land area
Share in WTO
(Millions, current
(sq km)
Trade
international $)
11,508
566730
0.039
31 Botswana
11,300
823290
0.027
32 Namibia
31,351
17820
0.192
33 Kuwait
34,526
20120
0.161
34 Slovenia
4,133
30350
0.008
35 Lesotho *
Latvia
16,711
62050
0.049
36
19,748
212460
0.105
37 Oman
4,277
1566500
0.009
38 Mongolia
9,582
10830
0.055
39 Jamaica
4,468
1025220
0.006
40 Mauritania *
17,137
74430
0.115
41 Panama
48,855
83600
0.538
42 United Arab Emirates
2,196
341500
0.021
43 Congo, Rep.
Uruguay
30,150
175020
0.058
44
11,959
27400
0.012
45 Albania
26,257
64800
0.077
46 Lithuania
4,355
622980
0.003
47 Central African Rep.*
113,311
68890
1.205
48 Ireland
32,963
51060
0.114
49 Costa Rica
76,884
267990
0.244
50 New Zealand
93,846
610
1.980
51 Singapore
Moldova
9,032
32910
0.012
52
35,441
55920
0.134
53 Croatia
134,362
306830
0.823
54 Norway
6,526
54390
0.008
55 Togo *
19,380
88930
0.063
56 Jordan
13,324
191800
0.010
57 Kyrgyz Republic
Source: World Bank, World Development Indicators 2002.
a
Exports and imports of merchandise and commercial services
Note: United Arab Emirates and Oman data for GDP was available from WDI for the year 1998.
Rank
Members
WT/COMTD/SE/W/5
Page 24
Table 2: GDP, land area and share in WTO tradea for WTO Members for year 2000
C. Members with Population 5.1 – 10 million
GDP, PPP
(Millions, current
international $)
13,386
58 Georgia
2,467
59 Sierra Leone*
11,999
60 Nicaragua
11,698
61 Papua New Guinea
129,405
62 Finland
147,417
63 Denmark
60,735
64 Slovak Republic
24,325
65 Paraguay
125,481
66 Israel
6,212
67 Benin *
28,226
68 El Salvador
15,743
69 Honduras
170,964
70 Hong Kong, China
4,021
71 Burundi *
206,561
72 Switzerland
14,696
73 Guinea *
6,704
74 Chad *
11,677
75 Haiti *
217,075
76 Austria
46,630
77 Bulgaria
20,190
78 Bolivia
50,515
79 Dominican Republic
8,024
80 Rwanda *
215,312
81 Sweden
14,386
82 Senegal
60,849
83 Tunisia
Source: World Bank, World Development Indicators 2002.
a
Exports and imports of merchandise and commercial services
Note: Hong Kong China GDP data are from the WDI for the year 1999.
Rank
Members
Land area
(sq km)
69700
71620
121400
452860
304590
42430
48080
397300
20620
110620
20720
111890
1098
25680
39550
245720
1259200
27560
82730
110550
1084380
48380
24670
411620
192530
155360
Share in WTO
Trade
0.013
0.003
0.020
0.030
0.644
0.920
0.199
0.052
0.570
0.010
0.059
0.039
3.164
0.001
1.641
0.012
0.006
0.011
1.378
0.094
0.025
0.128
0.003
1.441
0.023
0.127
WT/COMTD/SE/W/5
Page 25
Table 2: GDP, land area and share in WTO tradea for WTO Members for year 2000
D. Members with Population 10.1 – 25 million
GDP, PPP
(Millions, current
international $)
173,033
84 Portugal
124,431
85 Hungary
7,868
86 Zambia *
278,629
87 Belgiumb
143,734
88 Czech Republic
6,336
89 Malawi *
174,252
90 Greece
8,079
91 Niger *
8,640
92 Mali *
24,099
93 Cuba
11,005
94 Burkina Faso *
43,501
95 Guatemala
33,270
96 Zimbabwe
40,506
97 Ecuador
28,726
98 Angola *
25,334
99 Cameroon
143,242
100 Chile
13,041
101 Madagascar *
408,436
102 Netherlands
26,100
103 Cote d'Ivoire
15,101
104 Mozambique*
492,850
105 Australia
37,913
106 Ghana
68,330
107 Sri Lanka
26,839
108 Uganda *
313,900
109 Taipei, Chinese
144,098
110 Romania
211,019
111 Malaysia
140,036
112 Venezuela, RB
Source: World Bank, World Development Indicators 2002.
a
Exports and imports of merchandise and commercial services.
b
Refers to Belgium and Luxembourg for share to WTO trade data.
Rank
Members
Land area
(sq km)
91,500
92,340
743,390
32,820
77,280
94,080
128,900
1,266,700
1,220,190
109,820
273,600
108,430
386,850
276,840
1,246,700
465,400
748,800
581,540
33,880
318,000
784,090
7,682,300
227,540
64,630
197,100
36,000
230,340
328,550
882,050
Share in WTO
Trade
0.572
0.418
0.019
2.838
0.501
0.008
0.416
0.005
0.010
0.069
0.005
0.063
0.043
0.077
0.073
0.033
0.291
0.015
3.493
0.064
0.014
1.147
0.043
0.094
0.019
2.039
0.168
1.276
0.317
WT/COMTD/SE/W/5
Page 26
Table 2: GDP, land area and share in WTO tradea for WTO Members for year 2000
E. Members with Population > 25 million
GDP, PPP
Land area
Share in WTO
(Millions, current
(sq km)
Trade
international $)
123,157
1280000
0.127
113 Peru
101,798
446300
0.157
114 Morocco
30,752
569140
0.044
115 Kenya
856,090
9220970
3.958
116 Canada
17,606
883590
0.024
117 Tanzania *
Argentina
458,344
2736690
0.465
118
349,838
304420
0.697
119 Poland
768,454
499440
2.430
120 Spain
264,267
1038700
0.212
121 Colombia
402,380
1221040
0.482
122 South Africa
821,652
98730
2.373
123 Korea, Rep.
348,484
657550
0.032
124 Myanmar *
36,877
2267050
0.024
125 Congo, Dem. Rep.*
Italy
1,363,003
294110
4.150
126
1,426,548
550100
5.291
127 France
1,404,385
240880
5.742
128 United Kingdom
388,792
510890
0.954
129 Thailand
232,539
995450
0.260
130 Egypt, Arab Rep.
455,336
769630
0.762
131 Turkey
300,136
298170
0.556
132 Philippines
2,062,239
356680
8.951
133 Germany
Mexico
883,974
1908690
2.276
134
3,394,373
364500
6.381
135 Japan
113,663
910770
0.185
136 Nigeria
209,928
130170
0.106
137 Bangladesh *
266,159
770880
0.153
138 Pakistan
1,299,353
8456510
0.929
139 Brazil
640,345
1811570
0.776
140 Indonesia
9,612,680
9158960
15.954
141 United States
2,395,376
2973190
0.853
142 India
5,019,395
9327420
3.166
143 China
Source: World Bank, World Development Indicators 2002.
a
Exports and imports of merchandise and commercial services
Note: Myanmar and Congo, Dem. Rep GDP data was taken from the WDI for the year 1995 and 1998
accordingly.
Rank
Members
Table 3: Transport Costs described by CIF-FOB input ratios sorted by population groups
1
2
3
4
Members
LL I
Liechtenstein
St. Kitts and Nevis
Antigua and Barbuda
Dominica
X
5 Grenada
6 St. Vincent and the
Grenadines
7 St. Lucia
8
9
10
11
12
Belize
Barbados
Maldives *
Iceland
Brunei
13 Malta
14
15
16
17
Suriname
Macao, China
Luxembourg
Solomon Islands *
18
19
20
21
22
Qatar
Djibouti *
Bahrain
Cyprus
Guyana
23
24
25
26
27
Fiji
Swaziland
Mauritius
Guinea-Bissau *
Gabon
Average CIF/FOB by
group
31
32
33
34
LL I
CIF/FOB
Botswana
Namibia
Kuwait
Slovenia
X
17.60%
3.17%
15.07%
X
X
X
X
10.00%
X
X
11.70%
10.00%
35 Lesotho *
36 Latvia
X
10.00%
37 Oman
X
X
X
10.00%
10.00%
17.67%
10.00%
38
39
40
41
42
X
11.10%
12.30%
X
X
X
20.00%
12.00%
X
X
X
X
X
X
12.00%
Mongolia
X
Jamaica
Mauritania *
Panama
United Arab
Emirates
43 Congo, Rep.
44
45
46
47
11.00%
10.20%
10.00%
48
49
50
51
52
33.84%
1.40%
10.00%
15.00%
21.10%
53
54
55
56
57
Uruguay
Albania
Lithuania
Central
X
African
Rep.*
Ireland
Costa Rica
New Zealand
Singapore
Moldova
X
Croatia
Norway
Togo *
Jordan
Kyrgyz
Republic
3.70%
X
13.90%
13.00%
14.40%
10.00%
22.90%
4.80%
8.90%
5.00%
10.70%
9.29%
6.00%
3.67%
16.40%
12.40%
LL I
Georgia
Sierra Leone *
Nicaragua
Papua New
Guinea
62 Finland
63 Denmark
64 Slovak
Republic
65 Paraguay
66 Israel
67 Benin *
68 El Salvador
69 Honduras
70 Hong Kong,
China
71 Burundi *
72 Switzerland
73 Guinea *
74 Chad *
75
76
77
78
79
80
81
82
83
Haiti *
Austria
Bulgaria
Bolivia
Dominican
Rep.
Rwanda *
Sweden
Senegal
Tunisia
CIF/FOB
4.50%
3.86%
88 Czech Republic
89 Malawi *
X
5.00%
90 Greece
X
13.89%
8.00%
20.50%
11.00%
20.01%
X
13.61%
12.35%
15.00%
84
85
86
87
91
92
93
94
95
Niger *
Mali *
Cuba
Burkina Faso *
Guatemala
96 Zimbabwe
X
X
15.00%
1.00%
X
35.00%
X
X
X
X
X
15.00%
4.70%
8.48%
15.00%
43.60%
2.30%
14.40%
7.20%
X
11.10%
17.78%
8.28%
12.80%
LL I
Portugal
Hungary
Zambia *
Belgium
58
59
60
61
97
98
99
100
Ecuador
Angola *
Cameroon
Chile
101
102
103
104
105
Madagascar *
Netherlands
Cote d'Ivoire
Mozambique*
Australia
106
107
108
109
110
Ghana
Sri Lanka
Uganda *
Taipei, Chinese
Romania
111 Malaysia
112 Venezuela, RB
10.68%
Data are for the year 2000, from 2002 World Development Indicators Statistics.
* indicates Least Developed Countries as defined by the UN.
LL stands for Land-Locked as defined by CIA World Fact Book country report.
I stands for Island as defined by CIA World Fact Book country report.
13.15%
CIF/FOB
25.1>
LL I
CIF/FOB
X
X
10.30%
0.09%
2.63%
3.10%
113
114
115
116
Peru
Morocco
Kenya
Canada
20.00%
9.89%
16.30%
2.50%
X
X
3.93%
66.67%
117
118
Tanzania *
Argentina
17.60%
9.10%
13.00%
119
Poland
17.28%
17.30%
42.90%
Spain
Colombia
South Africa
Korea, Rep.
Myanmar *
6.00%
9.01%
8.06%
5.60%
10.00%
X
X
X
28.20%
12.90%
120
121
122
123
124
X
15.00%
125
Congo, Dem. Rep. *
13.36%
10.00%
9.48%
126
127
128
129
Italy
France
United Kingdom
Thailand
6.90%
3.71%
4.39%
10.80%
20.50%
5.60%
24.40%
12.03%
8.92%
130
131
132
133
134
Egypt, Arab Rep.
Turkey
Philippines
Germany
Mexico
11.10%
5.70%
7.00%
2.53%
4.70%
6.77%
11.10%
11.00%
135
136
137
138
139
Japan
Nigeria
Bangladesh *
Pakistan
Brazil
9.00%
10.70%
10.58%
9.50%
8.93%
140
141
142
143
Indonesia
United States
India
China
12.00%
4.27%
11.70%
9.00%
9.13%
X
X
X
X
7.51%
10.50%
11.00%
14.55%
WT/COMTD/SE/W/5
Page 27
28 Trinidad and Tobago
29 Gambia, The *
30 Estonia
CIF/FOB
WT/COMTD/SE/W/5
Page 28
Notes for Table 3:
Note 1: Data have been taken from the IFS Yearbook, 1995, published by the IMF. These
figures are not a perfectly accurate measure of actual CIF/FOB ratios, since they are in many
cases estimated by IMF staff based on incomplete information. For most countries, they
show little variance over time, indicating that IMF staff retain a constant CIF/FOB
conversion factor once it is established for a country, and revise it only infrequently. Indeed
for many countries the ratios have not been updated after 1990 and the IMF has by now
stopped publishing IMF ratios. This is why we use data for 1990 in our calculations, as has
been done in Radelet and Rodrik (1998) and Limão and Venables (1999). CIF/FOB ratios
can also be calculated using data from the US Bureau of Census, but those data would only
reflect exports by WTO Members to the US. It has been pointed out in the main text that the
exports of small economies tend to be heavily concentrated in a few commodities, whereas
the same countries import a broad range of goods. Given that transport costs differ according
to the product trade, CIF/FOB ratios for exports differ significantly from the CIF/FOB ratio
for imports in the case of small countries. It is the latter that gives a more appropriate
measure for the transport costs due to geographic characteristics as it is not biased by a
particular product mix. More direct shipping cost data – e.g. from transport companies- is
generally proprietary information and therefore hard to assemble for a large number of
countries on a systematic basis. Limão and Venables (1999) were able to use such a dataset
and found that results from their analysis using CIF/FOB ratios were quite consistent with
those obtained when from the shipping cost data.
Note 2: The Commonwealth Secretariat/World Bank Joint Task Force (2000) recently
published the following CIF/FOB ratios for a number of countries: Kiribati 26%, Comoros
24%, São Tomé 23%, Tonga 22%, Vanuatu 20%, Gabon 20%, The Gambia 20%, Solomon
Islands 20%, Equatorial Guinea 19%, Trinidad & Tobago 18%, and Seychelles 18%. Some
of these numbers differ significantly from the data in Table….The Secretariat order the
publication holding the data on which these figures are based, but is currently still awaiting
response.
WT/COMTD/SE/W/5
Page 29
Table 4: CIF-FOB Ratios by Product Group
CIF/FOB Ratios
Average 1999-2001
0
00
01
02
03
04
05
06
07
08
09
Food and live animals
Live Animals
Meat and Meat Preparations
Dairy Products and Birds' Eggs
Fish, Crustaceans, Molluscs etc and preparations thereof
Cereals and cereal preparations
Vegetables and fruit
Sugars, sugar preparations and honey
Coffee, tea, cocoa, spices, and manufactures thereof
Feeding stuff for animals (not including unmilled cereals)
Miscellaneous edible products and preparations
7.62%
2.34%
4.54%
4.86%
5.28%
7.50%
14.45%
6.87%
5.18%
6.76%
5.35%
1 Beverages and tobacco
11 Beverages
12 Tobacco and tobacco manufactures
3.00%
2.91%
3.77%
2 Crude materials, inedible, except fuels
21 Hides, skins and furskins, raw
22 Oil seeds and oleaginous fruits
23 Crude rubber (including synthetic and reclaimed)
24 Cork and Wood
25 Pulp and waste paper
26 Textile fibers (other than wool tops and othe combo wool) and their wastes ( not
manufactured into yarn or fabric)
27 Crude fertilizers, other than those of division 56, and crude minerals (excluding
coal, petroleum and precious stones)
28 Metalliferous ores and metal scrap
29 Crude animal and vegetable materials
7.89%
1.97%
8.38%
9.33%
5.62%
4.55%
8.79%
3 Mineral fuels, lubricants and related materials
32 Coal, coke and briquettes
33 Petroleum, petroleum products and related materials
34 Gas, natural and manufactured
35 Electric current
23.44%
7.84%
10.29%
4.96%
15.29%
5.43%
1.72%
0.00%
4 Animal and vegetable oils, fats and waxes
41 Animal oils and fats
42 Fixed vegetables fats and oils, crude, refined or fractionated
43 Animal or vegetable fats and oils, processed; waxes of animal or vegetable
origin; inedible mixtures or preparations of animal or vegetable fats and oils, n.e.s
6.46%
5.00%
6.61%
5.91%
5 Chemicals and related products, n.e.s.
51 Organic chemicals
52 Inorganic chemicals
53 Dyeing, tanning and colouring materials
54 Medical and pharmaceutical products
55 Essential oils and resinoids and perfume materials; toilet, polishing and cleaning
preparations
56 Fertilizers (other than those in group 272)
57 Plastics in primary forms
58 Plastics in non-primary forms
2.88%
2.31%
5.92%
3.27%
0.86%
3.40%
6.84%
4.22%
4.55%
WT/COMTD/SE/W/5
Page 30
59 Chemical materials and products, n.e.s.
CIF/FOB Ratios
Average 1999-2001
3.79%
6 Manufactured goods classified chiefly by material
61 Leather, leather manufactures, n.e.s and dressed furskins
62 Rubber manufactures, n.e.s.
63 Cork and wood manufactures (excluding furniture)
64 Paper, paperboard and articles of paper pulp, of paper or of paperboard
65 Textile yarn, fabrics, made-up articles, n.e.s., and related products
66 Non-metallic mineral manufactures, n.e.s.
67 Iron and steel
68 Non-ferrous metals
69 Manufactures of metals, n.e.s.
5.10%
3.97%
5.62%
6.42%
4.69%
5.13%
5.57%
8.09%
1.70%
5.24%
7 Machinery and transport equipment
71 Power generating machinery and equipment
72 Machinery specialized for particular industries
73 Metal working machinery
74 General industrial machinery and equipment, n.e.s. and machine parts, n.e.s
75 Office machines and automatic data processing machines
76 Telecommunications and sound recording and reproducing apparatus and
equipment
77 Electrical machinery, apparatus and appliances, n.e.s. and electrical parts thereof
(including non-electrical counterparts n.e.s. of electrical household type equipment
78 Road Vehicles (including air-cushion vehicles)
79 Other transport equipment
1.90%
1.55%
2.82%
3.02%
3.04%
2.02%
1.78%
8 Miscellaneous manufactured articles
81 Prefabricated buildings; sanitary plumbing, heating and lighting fixtures and
fittings, n.e.s.
82 Furniture and parts thereof; bedding, mattresses, mattress supports, cushions and
similar stuffed furnishings
83 Travel goods, handbags and similar containers
84 Articles of apparel and clothing accessories
85 Footwear
87 Professional, scientific and controlling instruments and apparatus, n.e.s.
88 Photographic apparatus, equipment and supplies and optical goods, n.e.s;
watches and clocks
89 Miscellaneous manufactured articles, n.e.s.
4.67%
8.22%
9 Commodities and transactions not classified elsewhere in SITC
93 Special Transactions and commodities not classified according to kind
95 Coin including gold
96 Coin (other than gold coin) not being legal tender
97 Gold, non-monetary (excluding gold, ores and concentrates)
98 Estimate Of Low Valued Import Transactions
0.99%
1.45%
0.48%
1.71%
0.08%
0.00%
Source: US Bureau of Census. 1-Digit and 2-Digit SITC classification, Rev.3.
1.82%
1.74%
0.78%
8.37%
8.01%
4.18%
5.15%
1.56%
2.27%
5.05%
WT/COMTD/SE/W/5
Page 31
Table 5: Openness to Trade and Volatility of Export Earnings
A. Members with Population < 1.5 million
Rank
Members
% Share of Trade in
GDP
Volatility of Export
Earnings
N.A
N.A
1 Liechtenstein
60.97
10.527
2 St. Kitts and Nevis
80.68
15.261
3 Antigua and Barbuda
61.01
17.010
4 Dominica
62.42
11.505
5 Grenada
60.40
13.440
6 St. Vincent and the Grenadines
64.24
11.053
7 St. Lucia
Belize
55.15
7.322
8
55.20
8.276
9 Barbados
98.89
8.870
10 Maldives *
39.38
9.858
11 Iceland
N.A
15.307
12 Brunei
N.A
13.297
13 Malta
21.93
20.832
14 Suriname
60.04
10.493
15 Macao, China
106.51
N.A
16 Luxembourg
N.A
15.216
17 Solomon Islands *
N.A
21.894
18 Qatar
N.A
12.720
19 Djibouti *
N.A
14.435
20 Bahrain
N.A
11.710
21 Cyprus
102.90
11.570
22 Guyana
56.96
11.676
23 Fiji
84.83
19.935
24 Swaziland
Mauritius
65.83
15.520
25
30.25
44.878
26 Guinea-Bissau *
46.12
20.920
27 Gabon
57.09
14.139
28 Trinidad and Tobago
54.53
17.724
29 Gambia, The *
94.13
20.442
30 Estonia
Source: World Development Indicators 2002 and WTO Secretariat.
* refers to Least Developed Countries as defined by the UN.
The share of trade in GDP is computed as the sum of imports and exports of goods and services divided by
two.
The average of the years 1997-1999 is taken unless noted otherwise and constant 1995 US$ have been used.
Volatility of export earnings was computed as standard deviation of the growth rates of export earnings (goods
and services, current prices). Data for the years 1981-2001 were used.
There was no available data for Luxembourg alone for volatility of export earnings.
Data for volatility of export earnings was grouped with Belgium.
See Belgium category
(Belgium + Luxembourg).
The formula of volatility of export earnings for Guinea-Bissau was calculated with data starting at 1983.
WT/COMTD/SE/W/5
Page 32
Table 5: Openness to Trade and Volatility of Export Earnings
B: Members with Population 1.51 - 5 million
Rank
Members
% Share of Trade in
GDP
Volatility of Export
Earnings
39.92
22.708
31 Botswana
54.42
7.523
32 Namibia
N.A
77.700
33 Kuwait
61.46
8.755
34 Slovenia
63.32
21.807
35 Lesotho *
60.22
11.858
36 Latvia
N.A
22.439
37 Oman
N.A
16.995
38 Mongolia
53.76
9.418
39 Jamaica
Mauritania
*
42.72
13.082
40
40.43
10.269
41 Panama
N.A
17.746
42 United Arab Emirates
71.09
15.896
43 Congo, Rep.
21.19
10.391
44 Uruguay
24.35
38.401
45 Albania
74.58
12.840
46 Lithuania
N.A
8.333
47 Central African Rep.*
85.46
14.805
48 Ireland
47.62
10.948
49 Costa Rica
31.79
8.604
50 New Zealand
N.A
13.603
51 Singapore
86.57
13.327
52 Moldova
48.00
6.044
53 Croatia
37.93
10.588
54 Norway
40.31
16.049
55 Togo *
Jordan
57.09
8.740
56
29.24
10.452
57 Kyrgyz Republic
Source: World Development Indicators 2002 and WTO Secretariat.
* refers to Least Developed Countries as defined by the UN.
The share of trade in GDP is computed as the sum of imports and exports of goods and services divided by
two.
The average of the years 1997-1999 is taken unless noted otherwise and constant 1995 US$ have been used.
Volatility of export earnings was computed as standard deviation of the growth rates of export earnings (goods
and services, current prices). Data for the years 1981-2001 were used.
For Slovenia and Croatia data for volatility of export earnings are from 1993-2001.
WT/COMTD/SE/W/5
Page 33
Table 5: Openness to Trade and Volatility of Export Earnings
C: Members with Population 5.1-10 million
Rank
Members
% Share of Trade in
GDP
Volatility of Export
Earnings
45.903
9.685
58 Georgia
20.319
18.976
59 *Sierra Leone
60.555
17.275
60 Nicaragua
49.865
14.442
61 Papua New Guinea
36.786
12.099
62 Finland
36.025
11.104
63 Denmark
69.650
15.370
64 Slovak Republic
31.470
22.294
65 Paraguay
Israel
41.431
8.570
66
24.948
33.258
67 Benin *
34.347
15.681
68 El Salvador
44.595
9.211
69 Honduras
153.416
11.017
70 Hong Kong, China
22.092
31.854
71 Burundi *
37.617
9.680
72 Switzerland
21.805
10.788
73 Guinea *
22.650
28.666
74 Chad *
24.070
49.068
75 Haiti *
44.395
10.565
76 Austria
55.181
16.113
77 Bulgaria
25.717
12.566
78 Bolivia
34.039
32.666
79 Dominican Republic
17.670
24.428
80 Rwanda *
43.785
11.005
81 Sweden
Senegal
35.790
11.799
82
44.184
11.150
83 Tunisia
Source: World Development Indicators 2002 and WTO Secretariat.
* refers to Least Developed Countries as defined by the UN.
The share of trade in GDP is computed as the sum of imports and exports of goods and services divided by
two.
The average of the years 1997-1999 is taken unless noted otherwise and constant 1995 US$ have been used.
Volatility of export earnings was computed as standard deviation of the growth rates of export earnings (goods
and services, current prices). Data for the years 1981-2001 were used.
For the Slovak Republic, data for volatility of export earnings was from 1994-2001.
WT/COMTD/SE/W/5
Page 34
Table 5: Openness to Trade and Volatility of Export Earnings
D: Members with Population 10.1 - 25 million
Rank
Members
% Share of Trade in
GDP
Volatility of Export
Earnings
37.658
11.771
84 Portugal
54.194
16.642
85 Hungary
40.912
15.032
86 Zambia *
71.606
10.965
87 Belgium
70.586
17.326
88 Czech Republic
42.559
15.961
89 Malawi *
25.605
10.750
90 Greece
20.457
17.914
91 Niger *
Mali
*
32.870
13.577
92
N.A
16.452
93 Cuba
22.954
18.635
94 Burkina Faso *
24.696
10.053
95 Guatemala
43.662
9.375
96 Zimbabwe
27.926
10.527
97 Ecuador
N.A
24.308
98 Angola *
28.838
12.188
99 Cameroon
32.017
13.323
100 Chile
29.589
12.183
101 Madagascar *
59.505
9.710
102 Netherlands
40.067
12.311
103 Cote d'Ivoire
27.879
16.452
104 Mozambique*
21.561
9.469
105 Australia
46.060
18.839
106 Ghana
43.032
10.351
107 Sri Lanka
Uganda
*
19.528
28.270
108
N.A
11.587
109 Taipei, Chinese
39.389
16.829
110 Romania
92.031
12.568
111 Malaysia
27.767
21.025
112 Venezuela, RB
Source: World Development Indicators 2002 and WTO Secretariat.
* refers to Least Developed Countries as defined by the UN.
The share of trade in GDP is computed as the sum of imports and exports of goods and services divided by
two.
The average of the years 1997-1999 is taken unless noted otherwise and constant 1995 US$ have been used.
Volatility of export earnings was computed as standard deviation of the growth rates of export earnings (goods
and services, current prices). Data for the years 1981-2001 were used.
Belgium's share of WTO Trade is the share of Belgium + Luxembourg.
Czech Republic data for volatility of export earnings are from 1994-2001.
WT/COMTD/SE/W/5
Page 35
Table 5: Openness to Trade and Volatility of Export Earnings
E: Members with Population > 25 million
Rank
Members
% Share of Trade in
GDP
Volatility of Export
Earnings
16.499
11.649
113 Peru
31.890
10.061
114 Morocco
31.402
10.450
115 Kenya
42.317
6.123
116 Canada
19.707
16.217
117 Tanzania *
11.908
13.435
118 Argentina
31.604
15.151
119 Poland
28.190
8.785
120 Spain
Colombia
18.443
14.819
121
24.027
9.211
122 South Africa
36.802
12.291
123 Korea, Rep.
N.A
28.867
124 Myanmar *
27.536
23.187
125 Congo, Dem. Rep.*
27.058
10.123
126 Italy
24.878
9.549
127 France
32.782
8.314
128 United Kingdom
42.703
13.178
129 Thailand
22.821
11.095
130 Egypt, Arab Rep.
28.644
17.317
131 Turkey
42.407
12.492
132 Philippines
28.516
10.678
133 Germany
39.730
11.135
134 Mexico
9.125
8.894
135 Japan
46.378
31.193
136 Nigeria
14.992
11.857
137 Bangladesh *
16.831
9.472
138 Pakistan
9.487
11.260
139 Brazil
27.892
13.216
140 Indonesia
13.653
7.452
141 United States
13.430
6.752
142 India
16.458
10.709
143 China
Source: World Development Indicators 2002 and WTO Secretariat.
* refers to Least Developed Countries as defined by the UN.
The share of trade in GDP is computed as the sum of imports and exports of goods and services divided by
two.
The average of the years 1997-1999 is taken unless noted otherwise and constant 1995 US$ have been used.
Volatility of export earnings was computed as standard deviation of the growth rates of export earnings (goods
and services, current prices). Data for the years 1981-2001 were used.
For China, years prior to 1983 were not taken into account for the computation of volatility of export earnings.
WT/COMTD/SE/W/5
Page 36
Table 6: Concentration of export commodities and services for WTO Members
(as a percentage of exports of goods and services, averages 1998 and 1999)
A. Members with Population <1.5 million
Rank
Members
Second commodity or service
n.a.**
n.a.**
n.a.**
32.4 (travel)
45.7 (travel)
45.3 (travel)
n.a.**
n.a.**
n.a.**
23.5 (other services)
20.6 (other services)
17.9 (other services)
10.9 (fruits, nuts, fresh, dried)
17.1 (sugar and honey)
21.6 (other services)
8.4 (fish fresh, chilled, frozen)
15.5 (transport)
n.a.**
21.54 (travel)
11.43 (transport)
17.1 (outer garments knit non-elastic)
n.a.**
24.9 (fish, fresh, chilled frozen)
15.0 (gas, n.a.**
n.a.**
n.a.**
15,6 (other services)
17.2 (gold, non-monetary nes)
14.9 (transport)
n.a.**
13.3 (sugar and honey)
n.a.**
n.a.**
9.8 (crude petroleum)
29 Gambia, The *
72.9 (travel)
31.8 (travel)
55.2 (travel)
72.1 (travel)
25.3 (fish fresh, chilled and frozen)
n.a.**
34.9 (transistors, valves, etc.)
48.1 (base metal, ores, conc nes)
51.7 (travel)
n.a.**
26.6 (other wood rough, squared)
59.8 (crude petroleum)
n.a.**
n.a.**
47.5 (travel)
19.4 (sugar and honey)
27.2 (travel)
n.a.**
20.0 (travel)
n.a.**
n.a.**
25.4
(petroleum
products
unrefined)
37.4 (travel)
30 Estonia
17.4 (transport)
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
Liechtenstein
St. Kitts and Nevis
Antigua and Barbuda
Dominica
Grenada**
St. Vincent and the
Grenadines**
St. Lucia
Belize
Barbados
Maldives *
Iceland
Brunei
Malta
Suriname**
Macao, China**
Luxembourg
Solomon Islands *
Qatar
Djibouti *
Bahrain
Cyprus
Guyana**
Fiji
Swaziland
Mauritius
Guinea-Bissau *
Gabon
Trinidad and Tobago
First commodity or services
32.2 (vegetable etc fresh, simply
preserved)
13.4 (travel)
Source: UNCTAD 2001 and WTO Secretariat.
* Least-developed countries as defined by the UN.
** no data available on concentration of commodity exports in commodity trade from UNCTAD (2001).
WT/COMTD/SE/W/5
Page 37
Table 6: Concentration of export commodities and services for WTO Members
(as a percentage of exports of goods and services, averages 1998 and 1999)
B. Members with Population 1.5 - 5 million
Rank
Members
First commodity or services
Second commodity or service
n.a.**
n.a.**
31 Botswana
n.a.**
n.a.**
32 Namibia
40.7 (crude petroleum)
24.5 (petroleum products, refined)
33 Kuwait
9.4 (travel)
8.3 (passenger motor vehicles, exc. bus)
34 Slovenia
n.a.**
n.a.**
35 Lesotho *
24.0 (transport)
13.2 (wood, shaped, rail sleepers)
36 Latvia
67.3 (crude petroleum)
5.0 (passenger motor vehicle, exc. bus)
37 Oman
41.8 (base metals, ores, conc nes)
13.0 (wool (exc. tops), animal hair)
38 Mongolia
36.4 (travel)
23.9 (base metals, ores, conc nes)
39 Jamaica
Mauritania
*
37.9
(iron
ore
and
concentrates)
24.7 (fish, fresh, chilled and frozen)
40
20.1 (fruit, nuts, fresh, dried)
13.5 (shellfish, fresh, frozen)
41 Panama
44.4 (crude petroleum)
7.4 (gas, natural and manufactured)
42 United Arab Emirates
60.0 (crude petroleum)
7.3 (other services)
43 Congo, Rep.
17.7 (travel)
10.9 (meet fresh, chilled and frozen)
44 Uruguay
28.4 (travel)
10.8 (leather, etc., manufactures)
45 Albania
11.0 (travel)
9.4 (petroleum products, refined)
46 Lithuania
n.a.**
n.a.**
47 Central African Rep.*
12.9 (other services)
11.1 (automatic data processing equip.)
48 Ireland
21.5 (office, adp machy parts, access) 12.9 (travel)
49 Costa Rica
12.3 (travel)
9.7 (meat, fresh, chilled frozen)
50 New Zealand
15.9 (transistors, valves, etc)
15.0 (automatic data processing equip.)
51 Singapore
24.5 (alcoholic beverages)
9.8 (transport)
52 Moldova
31.3 (travel)
8.9 (ships, boats etc.)
53 Croatia
25.8 (crude petroleum)
14.7 (transport)
54 Norway
29.6 (fertilizers, crude)
28.3 (cotton)
55 Togo *
Jordan
23.1
(travel)
17.4 (other services)
56
21.2 (special transactions)
10.5 (tobacco, unmanufactured, refuse)
57 Kyrgyz Republic
Source: UNCTAD 2001 and WTO Secretariat.
* Least-developed countries as defined by the UN.
** no data available on concentration of commodity exports in commodity trade from UNCTAD (2001).
WT/COMTD/SE/W/5
Page 38
Table 6: Concentration of export commodities and services for WTO Members.
(as a percentage of exports of goods and services, averages 1998 and 1999)
C. Members with Population 5.1-10 million
Rank
Members
First commodity or services
Second commodity or service
25.2 (travel)
20.4 (transport)
58 Georgia
n.a.**
n.a.**
59 *Sierra Leone
20.6 (coffee and substitutes)
14.0 (travel)
60 Nicaragua
30.6 (prec. metal ores, waste nes)
16.7 (other fixed vegetable oils)
61 Papua New Guinea
17.2 (paper and paperboard)
13.6 (telecom equip, parts, access)
62 Finland
11.7 (transport)
7.8 (other services)
63 Denmark
11.7(passenger motor vehicles, etc bus)
6.9 (other services)
64 Slovak Republic
36.2 (seeds for soft fixed oils)
11.5 (other services)
65 Paraguay
Israel
20.4
(pearl,
prec,
semi-prec
stones)
13.5 (other services)
66
n.a.**
n.a.**
67 Benin *
19.1 (coffee and substitutes)
6.9 (travel)
68 El Salvador
32.9 (coffee and substitutes)
10.8 (fruits, nuts, fresh, dried)
69 Honduras
12.0 (outer garments knit non-elastic)
9.2 (women's outwear non-knit)
70 Hong Kong, China
77.5 (coffee and substitutes)
6.67 (tea and mate)
71 Burundi *
11.5 (other services)
10.5 (medicalm, pharmaceutical prd)
72 Switzerland
n.a.**
n.a.**
73 Guinea *
n.a.**
n.a.**
74 Chad *
23.3 (travel)
16.2 (women's outwear non-knit)
75 Haiti *
15.4 (other services)
11.9 (travel)
76 Austria
16.2 (travel)
8.3 (transport)
77 Bulgaria
12.9 (aircraft etc.)
10.4 (base metal ores, conc nes)
78 Bolivia
30.3 (travel)
79 Dominican Republic 53.1 (special transactions)
n.a.**
n.a.**
80 Rwanda *
10.9 (telecom equip, parts, access)
9.4 (other services)
81 Sweden
Senegal
14.6
(inorg
chem
elmnt,
oxides,
etc.)
12.8 (travel)
82
20.5 (travel)
12.7 (men's outweat non-knit)
83 Tunisia
Source: UNCTAD 2001 and WTO Secretariat.
* Least-developed countries as defined by the UN.
** no data available on concentration of commodity exports in commodity trade from UNCTAD (2001).
WT/COMTD/SE/W/5
Page 39
Table 6: Concentration of export commodities and services for WTO Members
(as a percentage of exports of goods and services, averages 1998 and 1999)
D. Members with Population 10.1-25 million
Rank
Members
Second commodity or service
15.7 (travel)
7.8 (passenger motor vehicles exc. bus)
12.8 (travel)
8.7 (intern combust piston engines)
n.a.**
n.a.**
n.a.**
n.a.**
10.2 (travel)
6.7 (other services)
58.6 (tobacco unmanufactured,
8.6 (tea and mate)
refuse)
30.9 (travel)
23.8 (other services)
90 Greece
51.3 (uranium, thorium ores, conc) 11.2 (vegtb etc fresh, simply prsrvd)
91 Niger *
75.7 (cotton)
4.8 (gold, non-monetary nes)
92 Mali *
n.a.**
n.a.**
93 Cuba
Burkina
Faso
*
51.8
(cotton)
9.1 (travel)
94
18.7 (coffee and substitutes)
9.8 (travel)
95 Guatemala
22.9 (tobacco unmanufactured,
11.8 (travel)
96 Zimbabwe
refuse)
21.3 (crude petroleum)
20.8 (fruit, nuts, fresh, dried)
97 Ecuador
n.a.**
n.a.**
98 Angola *
27.1 (crude petroleum)
9.8 (other wood rough squared)
99 Cameroon
22.0 (copper)
9.2 (base metal ores, conc nes)
100 Chile
14.7 (other services)
11.5 (travel)
101 Madagascar *
9.6 (other services)
8.5 (transport)
102 Netherlands
33.3 (cocoa)
10.9 (petroleum products, refined)
103 Cote d'Ivoire
n.a.**
n.a.**
104 Mozambique*
10.6 (travel)
8.5 (coal, lignite and peat)
105 Australia
32.3 (cocoa)
11.8 (travel)
106 Ghana
14.9 (women's outwear non-knit)
11.5 (tea and mate)
107 Sri Lanka
41.8 (coffee and substitutes)
21.4 (travel)
108 Uganda *
n.a.**
n.a.**
109 Taipei, Chinese
7.9 (women's outwear non-knit)
6.4 (men's outweat non-knit)
110 Romania
17.2 (transistors, valves etc)
9.3 (office, adp machy parts acces)
111 Malaysia
Venezuela,
RB
60.8
(crude
petroleum)
10.4 (petroleum products, refined)
112
Source: UNCTAD 2001 and WTO Secretariat.
* Least-developed countries as defined by the UN.
** no data available on concentration of commodity exports in commodity trade from UNCTAD (2001).
84
85
86
87
88
89
Portugal
Hungary
Zambia *
Belgium
Czech Republic
Malawi *
First commodity or services
WT/COMTD/SE/W/5
Page 40
Table 6: Concentration of export commodities and services for WTO Members
(as a percentage of exports of goods and services, averages 1998 and 1999)
E. Members with Population 25.1+ million
Rank
Members
First commodity or services
Second commodity or service
14.6 (gold, non-monetary nes)
11.6 (travel)
113 Peru
18.4 (travel)
8.3 (women's outwear non-knit)
114 Morocco
20.9 (tea and mate)
13.3 (transport)
115 Kenya
12.2 (passenger motor vehicles exc. bus) 6.5 (other services)
116 Canada
37.7 (travel)
8.4 (fruits, nuts, fresh, dried)
117 Tanzania *
10.2 (travel)
8.4 (fixed vegetable oils)
118 Argentina
9.1 (travel)
7.8 (other services)
119 Poland
19.0 (travel)
10.1 (passenger motor vehicles exc. bus)
120 Spain
20.9 (crude petroleum)
13.3 (coffee and substitutes)
121 Colombia
7.9 (travel)
7.6 (pearl, prec, semi-prec stones)
122 South Africa
12.6 (transistors, valves etc.)
6.6 (transport)
123 Korea, Rep.
18.0 (other services)
11.7 (other wood rough, squared)
124 Myanmar *
n.a.**
125 Congo, Dem. Rep.* n.a.**
Italy
9.7
(travel)
7.6 (other services)
126
8.4 (other services)
7.9 (travel)
127 France
17.5 (other services)
6.1 (travel)
128 United Kingdom
9.6 (travel)
8.5 (office, adp machy parts)
129 Thailand
23.9 (travel)
20.7 (other services)
130 Egypt, Arab Rep.
20.6 (other services)
12.3 (travel)
131 Turkey
23.3 (special transactions)
22.0 (transistors, valves, etc)
132 Philippines
9.8 (passenger motor vehicles exc. bus) 6.9 (other services)
133 Germany
8.4 (passenger motor vehicles exc. bus) 5.5 (travel)
134 Mexico
11.3 (passenger motor vehicles exc. bus) 7.9 (other services)
135 Japan
90.6 (crude petroleum)
6.6 (other services)
136 Nigeria
22.5 (men's outwear non-knit)
18.1 (under garments non-knit)
137 Bangladesh *
12.1 (cotton fabrics, woven)
11.7 (textiles articles nes)
138 Pakistan
7.1 (other services)
5.3 (iron ore and concentrates)
139 Brazil
8.2 (special transactions)
7.8 (travel)
140 Indonesia
12.0 (other services)
9.4 (travel)
141 United States
India
16.0
(other
services)
12.3 (pearl, prec, semi-prec stones)
142
6.2 (travel)
4.4 (other services)
143 China
Source: UNCTAD 2001 and WTO Secretariat.
* Least-developed countries as defined by the UN.
** no data available on concentration of commodity exports in commodity trade from UNCTAD (2001).
WT/COMTD/SE/W/5
Page 41
Table 7: Volatility of GDP, Average Growth Rate of GDP and GDP per capita
A. Members with Population <1.5 million
Rank
Members
Volatility of
GDP
Average % growth rate of GDP
Per Capita
GDP per capita
Liechtenstein
N.A
N.A
St. Kitts and Nevis
4.12
5.18
Antigua and Barbuda
3.64
4.42
Dominica
N.A
N.A
Grenada
3.14
3.96
St. Vincent and the
3.36
3.69
Grenadines
8.25
3.60
7 St. Lucia
4.64
2.74
8 Belize
4.09
1.23
9 Barbados
3.63
5.99
10 Maldives *
Iceland
3.03
1.79
11
5.41
-3.04
12 Brunei
1.60
4.11
13 Malta
8.14
0.12
14 Suriname
4.64
2.30
15 Macao, China
2.91
4.04
16 Luxembourg
6.86
0.27
17 Solomon Islands *
N.A
N.A
18 Qatar
3.02
-4.61
19 Djibouti *
5.99
-0.10
20 Bahrain
2.58
4.16
21 Cyprus
5.52
0.88
22 Guyana
5.57
0.12
23 Fiji
5.74
2.00
24 Swaziland
4.06
3.86
25 Mauritius
9.43
0.36
26 Guinea-Bissau *
Gabon
6.15
-0.60
27
4.34
0.80
28 Trinidad and Tobago
2.69
0.10
29 Gambia, The *
7.00
0.73
30 Estonia
Source: World Development Indicators 2002 and WTO Secretariat.
* refers to Least Developed Countries as defined by the UN.
Volatility of GDP is computed as the standard deviation of per capita growth rates 1980-2000.
Average growth rate of GDP was computed for 1980-2000.
GDP per capita refers to the year 2000 and is expressed in PPP current international $.
GDP data for Qatar and Djibouti are for years 1998 from WDI 2002.
GDP data for Liechtenstein are from the TPR of Switzerland and Liechtenstein 2000.
1
2
3
4
5
6
$ 38,000
$ 12,510
$ 10,541
$ 3,671
$ 7,580
$ 5,555
$ 5,703
$ 5,606
$ 15,494
$ 4,485
$ 29,581
$ 5,049
$ 17,273
$ 3,799
$ 18,190
$ 50,061
$ 1,648
$ 21,590
$ 812
$ 14,548
$ 20,824
$ 3,963
$ 4,668
$ 4,492
$ 10,017
$ 755
$ 6,237
$ 8,964
$ 1,649
$ 10,066
WT/COMTD/SE/W/5
Page 42
Table 7: Volatility of GDP, Average Growth Rate of GDP and GDP per capita
B. Members with Population 1.51 – 5 million
Rank
Members
Volatility of
Average % growth rate of GDP
GDP per capita
GDP
Per Capita
3.00
4.58
$ 7,184
31 Botswana
2.86
-0.09
$ 6,431
32 Namibia
13.72
-2.26
$ 15,799
33 Kuwait
4.92
2.01
$ 17,367
34 Slovenia
4.17
1.79
$ 2,031
35 Lesotho *
9.48
0.33
$ 7,045
36 Latvia
5.24
3.20
$ 8,246
37 Oman
4.89
0.64
$ 1,783
38 Mongolia
Jamaica
3.60
0.34
$ 3,639
39
2.40
0.16
$ 1,677
40 Mauritania *
4.96
0.98
$ 6,000
41 Panama
8.79
-3.15
$ 16,817
42 United Arab Emirates
7.69
0.48
$ 728
43 Congo, Rep.
5.17
1.14
$ 9,035
44 Uruguay
8.63
0.33
$ 3,506
45 Albania
9.82
-0.83
$ 7,106
46 Lithuania
4.70
-1.22
$ 1,172
47 Central African Rep.*
3.38
4.71
$ 29,866
48 Ireland
3.97
1.09
$ 8,650
49 Costa Rica
2.08
1.19
$ 20,070
50 New Zealand
3.31
5.02
$ 23,356
51 Singapore
10.62
-3.52
$ 2,109
52 Moldova
10.34
-0.03
$ 8,091
53 Croatia
1.80
2.52
$ 29,918
54 Norway
6.70
-0.95
$ 1,442
55 Togo *
6.40
0.29
$ 3,966
56 Jordan
9.45
-2.08
$ 2,711
57 Kyrgyz Republic
Source: World Development Indicators 2002 and WTO Secretariat.
* refers to Least Developed Countries as defined by the UN.
Volatility of GDP is computed as the standard deviation of per capita growth rates 1980-2000.
Average growth rate of GDP was computed for 1980-2000.
GDP per capita refers to the year 2000 and is expressed in PPP (current international $).
GDP data for Oman is from WDI 2002 for 1998.
GDP date for United Arab Emirates are from the WDI 2002 for 1998.
WT/COMTD/SE/W/5
Page 43
Table 7: Volatility of GDP, Average Growth Rate of GDP and GDP per capita
C. Members with Population 5.1 – 10 million
Rank
Members
Volatility of
Average % growth rate of GDP
GDP per capita
GDP
Per Capita
16.72
-5.07
$ 2,664
58 Georgia
6.84
-2.85
$ 490
59 *Sierra Leone
4.16
-1.58
$ 2,366
60 Nicaragua
5.68
0.20
$ 2,280
61 Papua New Guinea
3.18
2.40
$ 24,996
62 Finland
1.74
1.63
$ 27,627
63 Denmark
5.52
0.83
$ 11,243
64 Slovak Republic
3.80
0.10
$ 4,426
65 Paraguay
1.82
2.08
$ 20,131
66 Israel
3.43
0.88
$ 990
67 Benin *
El
Salvador
4.83
-0.11
$
4,497
68
2.60
-0.25
$ 2,453
69 Honduras
4.37
4.22
$ 25,153
70 Hong Kong, China
4.98
-1.02
$ 591
71 Burundi *
1.91
1.02
$ 28,769
72 Switzerland
1.46
1.31
$ 1,982
73 Guinea *
8.10
0.92
$ 871
74 Chad *
4.33
-2.01
$ 1,467
75 Haiti *
1.14
1.95
$ 26,765
76 Austria
5.55
0.77
$ 5,710
77 Bulgaria
2.91
-0.40
$ 2,424
78 Bolivia
3.64
2.35
$ 6,033
79 Dominican Republic
12.25
-0.28
$ 943
80 Rwanda *
1.97
1.63
$ 24,277
81 Sweden
4.09
0.25
$ 1,510
82 Senegal
2.81
2.22
$ 6,363
83 Tunisia
Source: World Development Indicators 2002 and WTO Secretariat.
* refers to Least Developed Countries as defined by the UN.
Volatility of GDP is computed as the standard deviation of per capita growth rates 1980-2000.
Average growth rate of GDP was computed for 1980-2000.
GDP per capita refers to the year 2000 and is expressed in PPP (current international $).
WT/COMTD/SE/W/5
Page 44
Table 7: Volatility of GDP, Average Growth Rate of GDP and GDP per capita
D. Members with Population 10.1 - 25 million
Rank
Members
Volatility of
Average % growth rate of GDP
GDP per capita
GDP
Per Capita
2.39
2.85
$ 17,290
84 Portugal
3.86
1.28
$ 12,416
85 Hungary
3.76
-1.82
$ 780
86 Zambia *
1.53
2.00
$ 27,178
87 Belgium
4.66
0.18
$ 13,991
88 Czech Republic
5.82
0.26
$ 615
89 Malawi *
2.21
0.98
$ 16,501
90 Greece
5.47
-2.34
$ 746
91 Niger *
Mali
*
4.49
-0.50
$ 797
92
N.A
3.89
$ 2,154
93 Cuba
3.63
1.58
$ 976
94 Burkina Faso *
2.38
-0.03
$ 3,821
95 Guatemala
5.34
0.69
$ 2,635
96 Zimbabwe
3.71
-0.23
$ 3,203
97 Ecuador
7.25
-1.31
$ 2,187
98 Angola *
5.93
-0.40
$ 1,703
99 Cameroon
4.99
3.81
$ 9,417
100 Chile
3.25
-1.61
$ 840
101 Madagascar *
1.48
1.83
$ 25,657
102 Netherlands
4.19
-2.25
$ 1,630
103 Côte d'Ivoire
7.71
1.18
$ 854
104 Mozambique*
2.09
2.04
$ 25,693
105 Australia
3.59
0.21
$ 1,964
106 Ghana
1.36
3.32
$ 3,530
107 Sri Lanka
Uganda
*
3.19
2.23
$ 1,208
108
N.A
N.A
N.A
109 Taipei, Chinese
5.25
-0.71
$ 6,423
110 Romania
4.18
3.89
$ 9,068
111 Malaysia
4.71
-1.15
$ 5,794
112 Venezuela, RB
Source: World Development Indicators 2002 and WTO Secretariat.
* refers to Least Developed Countries as defined by the UN.
Volatility of GDP is computed as the standard deviation of per capita growth rates 1980-2000.
Average growth rate of GDP was computed for 1980-2000.
GDP per capita refers to the year 2000 and is expressed in PPP (current international $).
WT/COMTD/SE/W/5
Page 45
Table 7: Volatility of GDP, Average Growth Rate of GDP and GDP per capita
E. Members with Population > 25 million
Rank
Members
Volatility of
Average % growth rate of GDP
GDP per capita
GDP
Per Capita
6.60
-0.149
$ 4,799
113 Peru
5.32
1.187
$ 3,546
114 Morocco
1.97
-0.056
$ 1,022
115 Kenya
2.38
1.556
$ 27,840
116 Canada
1.86
0.449
$ 523
117 Tanzania *
5.97
0.381
$ 12,377
118 Argentina
4.08
3.589
$ 9,051
119 Poland
1.84
2.416
$ 19,472
120 Spain
2.18
1.084
$ 6,248
121 Colombia
3.46
-0.339
$ 9,401
122 South Africa
4.31
5.814
$ 17,380
123 Korea, Rep.
5.00
2.183
$ 7,298
124 Myanmar *
5.18
-4.578
$ 724
125 Congo, Dem. Rep.*
1.20
1.885
$ 23,626
126 Italy
1.14
1.646
$ 24,223
127 France
2.01
1.948
$ 23,509
128 United Kingdom
4.84
4.737
$ 6,402
129 Thailand
2.06
2.852
$ 3,635
130 Egypt, Arab Rep.
4.19
2.199
$ 6,974
131 Turkey
3.76
0.162
$ 3,971
132 Philippines
1.25
1.647
$ 25,103
133 Germany
3.97
1.106
$ 9,023
134 Mexico
1.78
2.327
$ 26,755
135 Japan
5.18
-0.833
$ 896
136 Nigeria
Bangladesh
*
1.78
2.400
$
1,602
137
2.05
2.689
$ 1,928
138 Pakistan
3.79
0.773
$ 7,625
139 Brazil
4.80
3.721
$ 3,043
140 Indonesia
2.00
1.986
$ 34,142
141 United States
1.96
3.658
$ 2,358
142 India
3.12
8.154
$ 3,976
143 China
Source: World Development Indicators 2002 and WTO Secretariat.
* refers to Least Developed Countries as defined by the UN.
Volatility of GDP is computed as the standard deviation of per capita growth rates 1980-2000.
Average growth rate of GDP was computed for 1980-2000.
GDP per capita refers to the year 2000 and is expressed in PPP (current international $).
GDP data for Myanmar is from WDI for 1995.
GDP data for Congo Dem. Rep. is from WDI for1998.
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