WORLD TRADE
ORGANIZATIONWT/COMTD/SE/W/5
23 October 2002
(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 theSecretariat 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 thoserequests.
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 particularrelevance 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 relationshipbetween 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 wo uld 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.WT/COMTD/SE/W/5
Page 2
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 beconsidered 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 mostfrequently 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 suggest ed using the share in global trade as an
indicator of economic size in the context of WTO issues.  The relationship between thisvariable 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 aseparate 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.WT/COMTD/SE/W/5
Page 3
Small economies and trade:  high reliance on imports and lack of export diversification
• "Smallness" is likely to limit an economy’s possi bilities 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 withthose 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 confirmsthat 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 on es.  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 atwork.  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.WT/COMTD/SE/W/5
Page 4
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 betweeneconomic 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 wo uld 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.  Aneconomy's share in global trade is likely to refl ect 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 Appendi x 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.
                                                     
2The European Communities have been excluded from our analysis, as its member countries are
included separately.WT/COMTD/SE/W/5
Page 5
12. Although GDP per capita income ma y 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 rela tively 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 ch ances are high, for instance, that the country with
the tenth largest population also has the tenth larg est GDP.  Figure 1 refl ects this relationship
graphically.
Figure 1:  Correlation between WTO Members' position in the
population ranking and their position in the GDP ranking
020406080100120140160
0 50 100 150
Population ranks
14. The relationship between land area and population size turns out to be less strong that the one
between GDP and population.  The normal correlat ion 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 areaof 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 toavoid 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
                                                     
3 The coefficient takes values between 0 and –1 if the relationship is negative.
4 The correlation coefficient measures the closeness of the relationship between the value of one
variable and the value of another variable. The rank corre lation coefficient instead measures the closeness of the
relationship between two sets of rankings – that is, between rankings of one variable and the rankings of theother variable.WT/COMTD/SE/W/5
Page 6
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 foreconomic 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 thanthat 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
020406080100120140160
0 20 40 60 80 100 120 140 160
Population ranking
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.
6 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.WT/COMTD/SE/W/5
Page 7
Figure 3:  Correlation between WTO Members' position
in the GDP ranking and their position
in the ranking of share in WTO trade
020406080100120140160
0 50 100 150 Rank 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 keyeconomic 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 havean 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
21. The literature has identified several factors that affect the size of transport costs:9
• 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).
9 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 a20 per cent increase in transport costs.WT/COMTD/SE/W/5
Page 8
• 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 wh ether 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 load ed on to a carrier for transport.  The CIF (cost-
insurance-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 th e 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 thelarge 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.WT/COMTD/SE/W/5
Page 9
Figure 4:  Transport costs for WTO Members
Average CIF/FOB ratios for landlocked Members, islands and other Members
0.173
0.139
0.103
0.000.020.040.060.080.100.120.140.160.180.20
LANDLOCKED ISLANDS OTHERS
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 withpopulation 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).  Thesepopulation 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.WT/COMTD/SE/W/5
Page 10
Figure 5:  Number of islands, landlocked and other WTO Members
in different population groups and average CIF-FOB ratios in percentages
05101520253035
<1.5 1.51- 5 5.1- 10 10.1-25 >25Population range
Other
Islands
Landlocked
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 thresholdshave been set in order to create groups of similar sizes.WT/COMTD/SE/W/5
Page 11
Figure 6:  CIF-FOB ratios grouped accord ing to WTO Members share in WTO trade
00.020.040.060.080.10.120.140.160.180.2
<0.015 0.0151- 0.05 0.051- 0.2 0.21- 1 >1
Share of WTO TradeCIF-FOB ratios
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).
15 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 productscharacterized by relatively high transport costs.
17 That is,  products for which unit costs fall when they are produced on a larger scale.WT/COMTD/SE/W/5
Page 12
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 Motor vehicles 11 Non metalic mineral products
2 Other vehicles 12 Metal goods
3 Chemicals 13 Rubber and plastic products 4 Man-made fibres 14 Drink and tobacco
5 Metals 15 Food
6 Office machinery 16 Other manufacturing industries7 Mechanical engineering 17 Textiles
8 Electrical engineering 18 Timber and furniture
9 Instrument engineering 19 Footwear and clothing
10 Paper printing and publishing 20 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 ofdiversification.  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 ofgoods 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 m eeting 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).WT/COMTD/SE/W/5
Page 13
Figure 7:  Small economies' reliance on trade
Based on data for all WTO Members presented in Table 5 Appendix
010203040506070
0 - 1.5 1.51 - 5 5.1 - 10 10.1 - 25 25.1 +
Population (millions)
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 s econd 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 fact ors also seem to affect countries' level of export
diversification.  Petrol producers tend to be highly dependent on exportation of petroleum andpetroleum 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.WT/COMTD/SE/W/5
Page 14
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
02468101214161820
0-1,5 1.51-5 5.1-10 10.1-25 25+
population (millions)
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 si ze.  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 capturesboth 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 largeWT/COMTD/SE/W/5
Page 15
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 andLuxemburg) 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
3.724.474.565.82
5.06
01234567
0-1.5 1.51-5 5.1-10 10.1-25 >25.1
Population (millions)
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.
21 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 (2 000) study quoted in paragraph 37, the analysis of
which is restricted to developing economies. When in cluding 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.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 al so 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 wholepopulation 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
0100020003000400050006000700080009000
<1.5 1.51-5 5.1-10 10.1-25 >25
Population range
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 relationshipbetween 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 diff erence 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).  OnlyChina 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.
26 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.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 wouldbe 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
0200040006000800010000120001400016000
<0.015 0.0151- 0.05 0.051- 0.2 0.21- 1 >1
Share of WTO TradeGDP per capita
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 therelevant 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 thatcharacterizes 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 bebeneficial 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 start-
up 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.
                                                     
30 Australia, Japan and New Zealand have been excluded.
31 See Rama (1999) and Bacchetta and Jansen (2002).
32 See Bacchetta and Jansen (2002).1.91
0.41
0.340.471.53
00.511.522.5
Population in millions
21 Countries, 0 - 1.5 million
14 Countries, 1.5 - 5 million
18 Countries, 5.1 - 10 million
20 Countries, 10 - 25 million
20 Countries, 25 million plusWT/COMTD/SE/W/5
Page 19
VI. CONCLUSIONS
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 relatesto 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 orservices.
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 Membersinstead 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 domesticeconomic 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 a nd R. Read (1998), "A Comparison of the Economic
Performance of Different Micro-states, and Be tween 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.WT/COMTD/SE/W/5
Page 21DATA APPENDIX
Table1:  Population Data of WTO Members ranked according to population size (in 2000)
Members 0-1.5 Members 1.51-5 Members 5.1-10 Members 10.1-25 Members 25.1>
1Liechtenstein 32,000 31Botswana 1,602,000 58Georgia 5,024,000 84Portugal  10,008,000 113Peru       25,661,000
2St. Kitts and Nevis 41,000 32Namibia 1,757,000 59Sierra Leone * 5,031,000 85Hungary  10,022,000 114Morocco       28,705,000
3Antigua and Barbuda 68,000 33Kuwait 1,984,400 60Nicaragua 5,071,000 86Zambia *  10,089,000 115Kenya       30,092,000
4Dominica 73,000 34Slovenia 1,988,000 61Papua New Guinea 5,130,000 87Belgium  10,252,000 116Canada       30,750,000
5Grenada 98,000 35Lesotho * 2,035,000 62Finland 5,177,000 88Czech Republic  10,273,300 117Tanzania *       33,696,000
6St. Vincent and the Grenadines 115,000 36Latvia 2,372,000 63Denmark 5,336,000 89Malawi *  10,311,000 118Argentina       37,032,000
7St. Lucia 156,000 37Oman 2,395,000 64Slovak Republic 5,401,790 90Greece  10,560,000 119Poland       38,650,000
8Belize 240,000 38Mongolia 2,398,000 65Paraguay 5,496,000 91Niger *  10,832,000 120Spain       39,465,000
9Barbados 267,000 39Jamaica 2,633,000 66Israel 6,233,210 92Mali *  10,840,000 121Colombia       42,299,300
10Maldives * 276,000 40Mauritania * 2,665,000 67Benin * 6,272,000 93Cuba  11,188,000 122South Africa       42,800,992
11Iceland 281,000 41Panama 2,856,000 68El Salvador 6,276,000 94Burkina Faso *  11,274,000 123Korea, Rep.       47,275,000
12Brunei 338,000 42United Arab Emirates 2,905,080 69Honduras 6,417,000 95Guatemala  11,385,300 124Myanmar *       47,749,000
13Malta 390,000 43Congo, Rep. 3,018,000 70Hong Kong, China 6,797,000 96Zimbabwe  12,627,000 125Congo, Dem. Rep.*
14Suriname 417,000 44Uruguay 3,337,000 71Burundi * 6,807,000 97Ecuador  12,646,000 126Italy       57,690,000
15Macao, China 438,000 45Albania 3,411,000 72Switzerland 7,180,000 98Angola *  13,134,000 127France       58,892,000
16Luxembourg 438,400 46Lithuania 3,695,000 73Guinea * 7,415,000 99Cameroon  14,876,000 128United Kingdom       59,738,900
17Solomon Islands * 447,000 47Central African Rep.* 3,717,000 74Chad * 7,694,000 100Chile  15,211,300 129Thailand       60,728,000
18Qatar 584,890 48Ireland 3,794,000 75Haiti * 7,959,000 101Madagascar *  15,523,000 130Egypt, Arab Rep.       63,976,000
19Djibouti * 632,000 49Costa Rica 3,811,000 76Austria 8,110,240 102Netherlands  15,919,000 131Turkey       65,293,000
20Bahrain 691,000 50New Zealand 3,830,800 77Bulgaria 8,166,960 103Cote d'Ivoire  16,013,000 132Philippines       75,580,000
21Cyprus 757,000 51Singapore 4,018,000 78Bolivia 8,328,700 104Mozambique*  17,691,000 133Germany       82,150,000
22Guyana 761,000 52Moldova 4,282,000 79Dominican Rep. 8,373,000 105Australia  19,182,000 134Mexico       97,966,000
23Fiji 811,900 53Croatia 4,380,000 80Rwanda * 8,508,000 106Ghana  19,306,000 135Japan     126,870,000
24Swaziland 1,045,000 54Norway 4,491,000 81Sweden 8,869,000 107Sri Lanka  19,359,000 136Nigeria     126,910,000
25Mauritius 1,186,140 55Togo * 4,527,000 82Senegal 9,530,000 108Uganda *  22,210,000 137Bangladesh *     131,050,000
26Guinea-Bissau * 1,199,000 56Jordan 4,886,810 83Tunisia 9,563,500 109Taipei, Chinese  22,400,000 138Pakistan     138,080,000
27Gabon 1,230,000 57Kyrgyz Republic 4,915,000 110Romania  22,435,000 139Brazil     170,406,000
28Trinidad and Tobago 1,301,000 111Malaysia  23,270,000 140Indonesia     210,420,992
29Gambia, The * 1,303,000 112Venezuela, RB  24,170,000 141United States     281,550,016
30Estonia 1,369,000 142India  1,015,923,008
143China 1,262,460,032
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.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
Rank MembersGDP, PPP
(Millions, current
international $)Land area
(sq km)Share in WTO
Trade
1Liechtenstein 1,216 160 N.A
2St. Kitts and Nevis 513 360 0.003
3Antigua and Barbuda 717 440 0.007
4Dominica 268 750 0.002
5Grenada 743 340 0.003
6St. Vincent and the Grenadines 639 390 0.003
7St. Lucia 890 610 0.006
8Belize 1,346 22,800 0.006
9Barbados 4,137 430 0.020
10 Maldives * 1,238 300 0.006
11 Iceland 8,312 100,250 0.045
12 Brunei 1,707 5,270 0.038
13 Malta 6,736 320 0.049
14 Suriname 1,584 156,000 0.007
15 Macao, China 7,967 24 0.054
16 Luxembourg 21,947 2,586 N.A
17 Solomon Islands * 737 27,990 0.003
18 Qatar 12,628 11,000 0.083
19 Djibouti * 513 23,180 0.002
20 Bahrain 10,053 690 0.069
21 Cyprus 15,764 9,240 0.061
22 Guyana 3,016 196,850 0.012
23 Fiji 3,790 18,270 0.015
24 Swaziland 4,694 17,200 0.016
25 Mauritius 11,882 2,030 0.039
26 Guinea-Bissau * 905 28,120 0.001
27 Gabon 7,672 257,670 0.033
28 Trinidad and Tobago 11,663 5,130 0.047
29 Gambia, The * 2,149 10,000 0.003
30 Estonia 13,780 42,270 0.064
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.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
Rank MembersGDP, PPP
(Millions, current
international $)Land area
(sq km)Share in WTO
Trade
31 Botswana 11,508 566730 0.039
32 Namibia 11,300 823290 0.027
33 Kuwait 31,351 17820 0.192
34 Slovenia 34,526 20120 0.161
35 Lesotho * 4,133 30350 0.008
36 Latvia 16,711 62050 0.049
37 Oman 19,748 212460 0.105
38 Mongolia 4,277 1566500 0.009
39 Jamaica 9,582 10830 0.055
40 Mauritania * 4,468 1025220 0.006
41 Panama 17,137 74430 0.115
42 United Arab Emirates 48,855 83600 0.538
43 Congo, Rep. 2,196 341500 0.021
44 Uruguay 30,150 175020 0.058
45 Albania 11,959 27400 0.012
46 Lithuania 26,257 64800 0.077
47 Central African Rep.* 4,355 622980 0.003
48 Ireland 113,311 68890 1.205
49 Costa Rica 32,963 51060 0.114
50 New Zealand 76,884 267990 0.244
51 Singapore 93,846 610 1.980
52 Moldova 9,032 32910 0.012
53 Croatia 35,441 55920 0.134
54 Norway 134,362 306830 0.823
55 Togo * 6,526 54390 0.008
56 Jordan 19,380 88930 0.063
57 Kyrgyz Republic 13,324 191800 0.010
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.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
Rank MembersGDP, PPP
(Millions, current
international $)Land area
(sq km)Share in WTO
Trade
58 Georgia 13,386 69700 0.013
59 Sierra Leone* 2,467 71620 0.003
60 Nicaragua 11,999 121400 0.020
61 Papua New Guinea 11,698 452860 0.030
62 Finland 129,405 304590 0.644
63 Denmark 147,417 42430 0.920
64 Slovak Republic 60,735 48080 0.199
65 Paraguay 24,325 397300 0.052
66 Israel 125,481 20620 0.570
67 Benin * 6,212 110620 0.010
68 El Salvador 28,226 20720 0.059
69 Honduras 15,743 111890 0.039
70 Hong Kong, China 170,964 1098 3.164
71 Burundi * 4,021 25680 0.001
72 Switzerland 206,561 39550 1.641
73 Guinea * 14,696 245720 0.012
74 Chad * 6,704 1259200 0.006
75 Haiti * 11,677 27560 0.011
76 Austria 217,075 82730 1.378
77 Bulgaria 46,630 110550 0.094
78 Bolivia 20,190 1084380 0.025
79 Dominican Republic 50,515 48380 0.128
80 Rwanda * 8,024 24670 0.003
81 Sweden 215,312 411620 1.441
82 Senegal 14,386 192530 0.023
83 Tunisia 60,849 155360 0.127
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.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
Rank MembersGDP, PPP
(Millions, current
international $)Land area
(sq km)Share in WTO
Trade
84 Portugal 173,033 91,500 0.572
85 Hungary 124,431 92,340 0.418
86 Zambia * 7,868 743,390 0.019
87 Belgiumb278,629 32,820 2.838
88 Czech Republic 143,734 77,280 0.501
89 Malawi * 6,336 94,080 0.008
90 Greece 174,252 128,900 0.416
91 Niger * 8,079 1,266,700 0.005
92 Mali * 8,640 1,220,190 0.010
93 Cuba 24,099 109,820 0.069
94 Burkina Faso * 11,005 273,600 0.005
95 Guatemala 43,501 108,430 0.063
96 Zimbabwe 33,270 386,850 0.043
97 Ecuador 40,506 276,840 0.077
98 Angola * 28,726 1,246,700 0.073
99 Cameroon 25,334 465,400 0.033
100 Chile 143,242 748,800 0.291
101 Madagascar * 13,041 581,540 0.015
102 Netherlands 408,436 33,880 3.493
103 Cote d'Ivoire 26,100 318,000 0.064
104 Mozambique* 15,101 784,090 0.014
105 Australia 492,850 7,682,300 1.147
106 Ghana 37,913 227,540 0.043
107 Sri Lanka 68,330 64,630 0.094
108 Uganda * 26,839 197,100 0.019
109 Taipei, Chinese 313,900 36,000 2.039
110 Romania 144,098 230,340 0.168
111 Malaysia 211,019 328,550 1.276
112 Venezuela, RB 140,036 882,050 0.317
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.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
Rank MembersGDP, PPP
(Millions, current
international $)Land area
(sq km)Share in WTO
Trade
113 Peru 123,157 1280000 0.127
114 Morocco 101,798 446300 0.157
115 Kenya 30,752 569140 0.044
116 Canada 856,090 9220970 3.958
117 Tanzania * 17,606 883590 0.024
118 Argentina 458,344 2736690 0.465
119 Poland 349,838 304420 0.697
120 Spain 768,454 499440 2.430
121 Colombia 264,267 1038700 0.212
122 South Africa 402,380 1221040 0.482
123 Korea, Rep. 821,652 98730 2.373
124 Myanmar * 348,484 657550 0.032
125 Congo, Dem. Rep.* 36,877 2267050 0.024
126 Italy 1,363,003 294110 4.150
127 France 1,426,548 550100 5.291
128 United Kingdom 1,404,385 240880 5.742
129 Thailand 388,792 510890 0.954
130 Egypt, Arab Rep. 232,539 995450 0.260
131 Turkey 455,336 769630 0.762
132 Philippines 300,136 298170 0.556
133 Germany 2,062,239 356680 8.951
134 Mexico 883,974 1908690 2.276
135 Japan 3,394,373 364500 6.381
136 Nigeria 113,663 910770 0.185
137 Bangladesh * 209,928 130170 0.106
138 Pakistan 266,159 770880 0.153
139 Brazil 1,299,353 8456510 0.929
140 Indonesia 640,345 1811570 0.776
141 United States 9,612,680 9158960 15.954
142 India 2,395,376 2973190 0.853
143 China 5,019,395 9327420 3.166
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 .WT/COMTD/SE/W/5
Page 27Table 3:  Transport Costs described by CIF-FOB input ratios sorted by population groups
Members LL I CIF/FOB LL I CIF/FOB LL I CIF/FOB LL I CIF/FOB 25.1> LL I CIF/FOB
1Liechtenstein X 31Botswana X 17.60% 58Georgia 84 Portugal 10.30% 113 Peru 20.00%
2St. Kitts and Nevis X 32Namibia 3.17% 59Sierra Leone * 13.61% 85 Hungary X 0.09% 114 Morocco 9.89%
3Antigua and Barbuda X 10.00% 33Kuwait 15.07% 60Nicaragua 12.35% 86 Zambia * X 2.63% 115 Kenya 16.30%
4Dominica X 34Slovenia 61Papua New
GuineaX 15.00% 87 Belgium 3.10% 116 Canada 2.50%
5Grenada X 11.70% 35Lesotho * X 3.70% 62Finland 4.50% 88 Czech Republic X 3.93% 117 Tanzania * 17.60%
6St. Vincent and the
GrenadinesX 10.00% 36Latvia 63Denmark 3.86% 89 Malawi * X 66.67% 118 Argentina 9.10%
7St. Lucia X 10.00% 37Oman 12.00% 64Slovak
RepublicX5 . 0 0 % 90 Greece 13.00% 119 Poland 17.28%
8Belize 10.00% 38Mongolia X 65Paraguay X 13.89% 91 Niger * X 17.30% 120 Spain 6.00%
9Barbados X 10.00% 39Jamaica X 13.90% 66Israel 8.00% 92 Mali * X 42.90% 121 Colombia 9.01%
10 Maldives * X 17.67% 40Mauritania * 13.00% 67Benin * 20.50% 93 Cuba X 122 South Africa 8.06%
11 Iceland X 10.00% 41Panama 14.40% 68El Salvador 11.00% 94 Burkina Faso * X 28.20% 123 Korea, Rep. 5.60%
12 Brunei 42United Arab
Emirates10.00% 69Honduras 20.01% 95 Guatemala 12.90% 124 Myanmar * 10.00%
13 Malta X 11.10% 43Congo, Rep. 22.90% 70Hong Kong,
China96 Zimbabwe X 15.00% 125 Congo, Dem. Rep. *
14 Suriname 12.30% 44Uruguay 4.80% 71Burundi * X 15.00% 97 Ecuador 13.36% 126 Italy 6.90%
15 Macao, China X 45Albania 72Switzerland X 1.00% 98 Angola * 127 France 3.71%
16 Luxembourg X 46Lithuania 73Guinea * 99 Cameroon 10.00% 128 United Kingdom 4.39%
17 Solomon Islands * X 20.00% 47Central
AfricanRep.*X8 . 9 0 % 74Chad * X 35.00% 100 Chile 9.48% 129 Thailand 10.80%
18 Qatar 12.00% 48Ireland 5.00% 75Haiti * X 15.00% 101 Madagascar * X 20.50% 130 Egypt, Arab Rep. 11.10%
19 Djibouti * 49Costa Rica 10.70% 76Austria X 4.70% 102 Netherlands 5.60% 131 Turkey 5.70%
20 Bahrain X 11.00% 50New Zealand 9.29% 77Bulgaria 103 Cote d'Ivoire 24.40% 132 Philippines 7.00%
21 Cyprus X 10.20% 51Singapore 6.00% 78Bolivia X 8.48% 104 Mozambique* 12.03% 133 Germany 2.53%
22 Guyana 10.00% 52Moldova X 79Dominican
Rep.X 15.00% 105 Australia 8.92% 134 Mexico 4.70%
23 Fiji X 33.84% 53Croatia 80Rwanda * X 43.60% 106 Ghana 6.77% 135 Japan 9.00%
24 Swaziland X 1.40% 54Norway 3.67% 81Sweden 2.30% 107 Sri Lanka 11.10% 136 Nigeria 10.70%
25 Mauritius X 10.00% 55Togo * 16.40% 82Senegal 14.40% 108 Uganda * X 11.00% 137 Bangladesh * 10.58%
26 Guinea-Bissau * 15.00% 56Jordan 12.40% 83Tunisia 7.20% 109 Taipei, Chinese X 138 Pakistan 9.50%
27 Gabon 21.10% 57Kyrgyz
RepublicX 110 Romania 7.51% 139 Brazil 8.93%
28 Trinidad and Tobago X 11.10% 111 Malaysia 10.50% 140 Indonesia 12.00%
29 Gambia, The * 17.78% 112 Venezuela, RB 11.00% 141 United States 4.27%
30 Estonia 8.28% 142 India 11.70%
143 China 9.00%
Average CIF/FOB by
group12.80% 10.68% 13.15% 14.55% 9.13%
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.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 manycases estimated by IMF staff based on incomplete information.  For most countries, theyshow 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 nowstopped publishing IMF ratios.  This is why we use data for 1990 in our calculations, as hasbeen done in Radelet and Rodrik (1998) and Limão and Venables (1999).  CIF/FOB ratioscan also be calculated using data from the US Bureau of Census, but those data would onlyreflect 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 accordingto the product trade, CIF/FOB ratios for exports differ significantly from the CIF/FOB ratiofor imports in the case of small countries.  It is the latter that gives a more appropriatemeasure 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 ofcountries on a systematic basis.  Limão and Venables (1999) were able to use such a datasetand found that results from their analysis using CIF/FOB ratios were quite consistent withthose obtained when from the shipping cost data.
Note 2:  The Commonwealth Secretariat/W orld Bank Joint Task Force (2000) recently
published the following CIF/FOB ratios for a number of countries:  Kiribati 26%, Comoros24%, São Tomé 23%, Tonga 22%, Vanuatu 20%, Gabon 20%, The Gambia 20%, SolomonIslands 20%, Equatorial Guinea 19%, Trinidad & Tobago 18%, and Seychelles 18%.  Someof 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    Food and live animals 7.62%
00  Live Animals 2.34%
01  Meat and Meat Preparations 4.54%
02  Dairy Products and Birds' Eggs 4.86%
03  Fish, Crustaceans, Molluscs etc and preparations thereof 5.28%
04  Cereals and cereal preparations 7.50%
05  Vegetables and fruit 14.45%
06  Sugars, sugar preparations and honey 6.87%
07  Coffee, tea, cocoa, spices, and manufactures thereof 5.18%
08  Feeding stuff for animals (not including unmilled cereals) 6.76%
09  Miscellaneous edible products and preparations 5.35%
1   Beverages and tobacco 3.00%
11  Beverages 2.91%
12  Tobacco and tobacco manufactures 3.77%
2   Crude materials, in edible, except fuels 7.89%
21  Hides, skins and furskins, raw 1.97%
22  Oil seeds and oleaginous fruits 8.38%
23  Crude rubber (including synthetic and reclaimed) 9.33%
24  Cork and Wood 5.62%
25  Pulp and waste paper 4.55%
26  Textile fibers (other than wool tops and othe combo wool) and their wastes ( not
manufactured into yarn or fabric)8.79%
27  Crude fertilizers, other than those of division 56, and crude minerals (excluding
coal, petroleum and precious stones)23.44%
28  Metalliferous ores and metal scrap 7.84%
29  Crude animal and vegetable materials 10.29%
3   Mineral fuels, lubrican ts and related materials 4.96%
32  Coal, coke and briquettes 15.29%
33  Petroleum, petroleum products and related materials 5.43%
34  Gas, natural and manufactured 1.72%
35  Electric current 0.00%
4   Animal and vegetable oils, fats and waxes 6.46%
41  Animal oils and fats 5.00%
42  Fixed vegetables fats and oils, crude, refined or fractionated 6.61%
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.s5.91%
5   Chemicals and related products, n.e.s. 2.88%
51  Organic chemicals 2.31%
52  Inorganic chemicals 5.92%
53  Dyeing, tanning and colouring materials 3.27%
54  Medical and pharmaceutical products 0.86%
55  Essential oils and resinoids and perfume materials; toilet, polishing and cleaning
preparations3.40%
56  Fertilizers (other than those in group 272) 6.84%
57  Plastics in primary forms 4.22%
58  Plastics in non-primary forms 4.55%WT/COMTD/SE/W/5
Page 30
CIF/FOB Ratios
Average 1999-2001
59  Chemical materials and products, n.e.s. 3.79%
6   Manufactured goods classified chiefly by material 5.10%
61  Leather, leather manufactures, n.e.s and dressed furskins 3.97%
62  Rubber manufactures, n.e.s. 5.62%
63  Cork and wood manufactures (excluding furniture) 6.42%
64  Paper, paperboard and articles of pa per pulp, of paper or of paperboard 4.69%
65  Textile yarn, fabrics, made-up articles, n.e.s., and related products 5.13%
66  Non-metallic mineral manufactures, n.e.s. 5.57%
67  Iron and steel 8.09%
68  Non-ferrous metals 1.70%
69  Manufactures of metals, n.e.s. 5.24%
7   Machinery and transport equipment 1.90%
71  Power generating machinery and equipment 1.55%
72  Machinery specialized for particular industries 2.82%
73  Metal working machinery 3.02%
74  General industrial machinery and equipment, n.e.s. and machine parts, n.e.s 3.04%
75  Office machines and automatic data processing machines 2.02%
76  Telecommunications and sound recording and reproducing apparatus and
equipment1.78%
77  Electrical machinery, apparatus and app liances, n.e.s. and electrical parts thereof
(including non-electrical count erparts n.e.s. of electrical  household type equipment1.82%
78  Road Vehicles (including  air-cushion vehicles) 1.74%
79  Other transport equipment 0.78%
8   Miscellaneous manu factured articles 4.67%
81  Prefabricated buildings; sanitary plumbing, heating and lighting fixtures and
fittings, n.e.s.8.22%
82  Furniture and parts thereof; bedding, mattresses, mattress supports, cushions and
similar stuffed furnishings8.37%
83  Travel goods, handbags and similar containers 8.01%
84  Articles of apparel and clothing accessories 4.18%
85  Footwear 5.15%
87  Professional, scientific and controlling instruments and apparatus, n.e.s. 1.56%
88  Photographic apparatus, equipment and supplies and optical goods, n.e.s;
watches and clocks2.27%
89  Miscellaneous manufactured articles, n.e.s. 5.05%
9   Commodities and transactions not classified elsewhere in SITC 0.99%
93  Special Transactions and commodities not classified according to kind 1.45%
95  Coin including gold 0.48%
96  Coin (other than gold coin) not being legal tender 1.71%
97  Gold, non-monetary (excluding gold, ores and concentrates) 0.08%
98  Estimate Of Low Valued Import Transactions 0.00%
Source:  US Bureau of Census. 1-Digit and 2-Digit  SITC classification, Rev.3.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
GDPVolatility of Export
Earnings
1Liechtenstein N.A N.A
2St. Kitts and Nevis 60.97 10.527
3Antigua and Barbuda 80.68 15.261
4Dominica 61.01 17.010
5Grenada 62.42 11.505
6St. Vincent and the Grenadines 60.40 13.440
7St. Lucia 64.24 11.053
8Belize 55.15 7.322
9Barbados 55.20 8.276
10 Maldives * 98.89 8.870
11 Iceland 39.38 9.858
12 Brunei N.A 15.307
13 Malta N.A 13.297
14 Suriname 21.93 20.832
15 Macao, China 60.04 10.493
16 Luxembourg 106.51 N.A
17 Solomon Islands * N.A 15.216
18 Qatar N.A 21.894
19 Djibouti * N.A 12.720
20 Bahrain N.A 14.435
21 Cyprus N.A 11.710
22 Guyana 102.90 11.570
23 Fiji 56.96 11.676
24 Swaziland 84.83 19.935
25 Mauritius 65.83 15.520
26 Guinea-Bissau * 30.25 44.878
27 Gabon 46.12 20.920
28 Trinidad and Tobago 57.09 14.139
29 Gambia, The * 54.53 17.724
30 Estonia 94.13 20.442
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 bytwo.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
GDPVolatility of Export
Earnings
31 Botswana 39.92 22.708
32 Namibia 54.42 7.523
33 Kuwait N.A 77.700
34 Slovenia 61.46 8.755
35 Lesotho * 63.32 21.807
36 Latvia 60.22 11.858
37 Oman N.A 22.439
38 Mongolia N.A 16.995
39 Jamaica 53.76 9.418
40 Mauritania * 42.72 13.082
41 Panama 40.43 10.269
42 United Arab Emirates N.A 17.746
43 Congo, Rep. 71.09 15.896
44 Uruguay 21.19 10.391
45 Albania 24.35 38.401
46 Lithuania 74.58 12.840
47 Central African Rep.* N.A 8.333
48 Ireland 85.46 14.805
49 Costa Rica 47.62 10.948
50 New Zealand 31.79 8.604
51 Singapore N.A 13.603
52 Moldova 86.57 13.327
53 Croatia 48.00 6.044
54 Norway 37.93 10.588
55 Togo * 40.31 16.049
56 Jordan 57.09 8.740
57 Kyrgyz Republic 29.24 10.452
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 (goodsand 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
GDPVolatility of Export
Earnings
58 Georgia 45.903 9.685
59 *Sierra Leone 20.319 18.976
60 Nicaragua 60.555 17.275
61 Papua New Guinea 49.865 14.442
62 Finland 36.786 12.099
63 Denmark 36.025 11.104
64 Slovak Republic 69.650 15.370
65 Paraguay 31.470 22.294
66 Israel 41.431 8.570
67 Benin * 24.948 33.258
68 El Salvador 34.347 15.681
69 Honduras 44.595 9.211
70 Hong Kong, China 153.416 11.017
71 Burundi * 22.092 31.854
72 Switzerland 37.617 9.680
73 Guinea * 21.805 10.788
74 Chad * 22.650 28.666
75 Haiti * 24.070 49.068
76 Austria 44.395 10.565
77 Bulgaria 55.181 16.113
78 Bolivia 25.717 12.566
79 Dominican Republic 34.039 32.666
80 Rwanda * 17.670 24.428
81 Sweden 43.785 11.005
82 Senegal 35.790 11.799
83 Tunisia 44.184 11.150
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 (goodsand 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
GDPVolatility of Export
Earnings
84 Portugal 37.658 11.771
85 Hungary 54.194 16.642
86 Zambia * 40.912 15.032
87 Belgium 71.606 10.965
88 Czech Republic 70.586 17.326
89 Malawi * 42.559 15.961
90 Greece 25.605 10.750
91 Niger * 20.457 17.914
92 Mali * 32.870 13.577
93 Cuba N.A 16.452
94 Burkina Faso * 22.954 18.635
95 Guatemala 24.696 10.053
96 Zimbabwe 43.662 9.375
97 Ecuador 27.926 10.527
98 Angola * N.A 24.308
99 Cameroon 28.838 12.188
100 Chile 32.017 13.323
101 Madagascar * 29.589 12.183
102 Netherlands 59.505 9.710
103 Cote d'Ivoire 40.067 12.311
104 Mozambique* 27.879 16.452
105 Australia 21.561 9.469
106 Ghana 46.060 18.839
107 Sri Lanka 43.032 10.351
108 Uganda * 19.528 28.270
109 Taipei, Chinese N.A 11.587
110 Romania 39.389 16.829
111 Malaysia 92.031 12.568
112 Venezuela, RB 27.767 21.025
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 (goodsand 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
GDPVolatility of Export
Earnings
113 Peru 16.499 11.649
114 Morocco 31.890 10.061
115 Kenya 31.402 10.450
116 Canada 42.317 6.123
117 Tanzania * 19.707 16.217
118 Argentina 11.908 13.435
119 Poland 31.604 15.151
120 Spain 28.190 8.785
121 Colombia 18.443 14.819
122 South Africa 24.027 9.211
123 Korea, Rep. 36.802 12.291
124 Myanmar * N.A 28.867
125 Congo, Dem. Rep.* 27.536 23.187
126 Italy 27.058 10.123
127 France 24.878 9.549
128 United Kingdom 32.782 8.314
129 Thailand 42.703 13.178
130 Egypt, Arab Rep. 22.821 11.095
131 Turkey 28.644 17.317
132 Philippines 42.407 12.492
133 Germany 28.516 10.678
134 Mexico 39.730 11.135
135 Japan 9.125 8.894
136 Nigeria 46.378 31.193
137 Bangladesh * 14.992 11.857
138 Pakistan 16.831 9.472
139 Brazil 9.487 11.260
140 Indonesia 27.892 13.216
141 United States 13.653 7.452
142 India 13.430 6.752
143 China 16.458 10.709
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 bytwo.
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 (goodsand 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 First commodity or s ervices Second commodity or service
1Liechtenstein n.a.**  n.a.**
2St. Kitts and Nevis n.a.**  n.a.**
3Antigua and Barbuda n.a.**  n.a.**
4Dominica 32.4 (travel)  23.5 (other services)
5Grenada** 45.7 (travel)  20.6 (other services)
6St. Vincent and the
Grenadines**45.3 (travel)  17.9 (other services)
7St. Lucia 72.9 (travel)  10.9 (fruits, nuts, fresh, dried)
8Belize 31.8 (travel)  17.1 (sugar and honey)
9Barbados 55.2 (travel)  21.6 (other services)
10 Maldives * 72.1 (travel)  8.4 (fish fresh, chilled, frozen)
11 Iceland 25.3 (fish fresh, chilled and frozen)  15.5 (transport)
12 Brunei n.a.**  n.a.**
13 Malta 34.9 (transistors, valves, etc.)  21.54 (travel)
14 Suriname** 48.1 (base metal, ores, conc nes)  11.43 (transport)
15 Macao, China** 51.7 (travel)  17.1 (outer garments knit non-elastic)
16 Luxembourg n.a.**  n.a.**
17 Solomon Islands * 26.6 (other wood ro ugh, squared)  24.9 (fish, fresh, chilled frozen)
18 Qatar 59.8 (crude petroleum)  15.0 (gas, n.a.**
19 Djibouti * n.a.**  n.a.**
20 Bahrain n.a.**  n.a.**
21 Cyprus 47.5 (travel)  15,6 (other services)
22 Guyana** 19.4 (sugar and honey)  17.2 (gold, non-monetary nes)
23 Fiji 27.2 (travel)  14.9 (transport)
24 Swaziland n.a.**  n.a.**
25 Mauritius 20.0 (travel)  13.3 (sugar and honey)
26 Guinea-Bissau * n.a.**  n.a.**
27 Gabon n.a.**  n.a.**
28 Trinidad and Tobago 25.4 (petroleum products
unrefined) 9.8 (crude petroleum)
29 Gambia, The * 37.4 (travel)  32.2 (vegetable etc fresh, simply
preserved)
30 Estonia 17.4 (transport)  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 s ervices Second commodity or service
31 Botswana n.a.** n.a.**
32 Namibia n.a.** n.a.**
33 Kuwait 40.7 (crude petroleum) 24.5 (petroleum products, refined)
34 Slovenia 9.4 (travel) 8.3 (passenger motor vehicles, exc. bus)
35 Lesotho * n.a.** n.a.**
36 Latvia 24.0 (transport) 13.2 (wood, shaped, rail sleepers)
37 Oman 67.3 (crude petroleum) 5.0 (passenger motor vehicle, exc. bus)
38 Mongolia 41.8 (base metals, ores, conc nes) 13.0 (wool (exc. tops), animal hair)
39 Jamaica 36.4 (travel) 23.9 (base metals, ores, conc nes)
40 Mauritania * 37.9 (iron ore and concentrates) 24.7 (fish, fresh, chilled and frozen)
41 Panama 20.1 (fruit, nuts, fresh, dried) 13.5 (shellfish, fresh, frozen)
42 United Arab Emirates 44.4 (crude petroleum) 7.4 (gas, natural and manufactured)
43 Congo, Rep. 60.0 (crude petroleum) 7.3 (other services)
44 Uruguay 17.7 (travel) 10.9 (meet fresh, chilled and frozen)
45 Albania 28.4 (travel) 10.8 (leather, etc., manufactures)
46 Lithuania 11.0 (travel) 9.4 (petroleum products, refined)
47 Central African Rep.* n.a.** n.a.**
48 Ireland 12.9 (other services) 11.1 (automatic data processing equip.)
49 Costa Rica 21.5 (office, adp machy parts, access) 12.9 (travel)
50 New Zealand 12.3 (travel) 9.7 (meat, fresh, chilled frozen)
51 Singapore 15.9 (transistors, valves, etc) 15.0 (automatic data processing equip.)
52 Moldova 24.5 (alcoholic beverages) 9.8 (transport)
53 Croatia 31.3 (travel) 8.9 (ships, boats etc.)
54 Norway 25.8 (crude petroleum) 14.7 (transport)
55 Togo * 29.6 (fertilizers, crude) 28.3 (cotton)
56 Jordan 23.1 (travel) 17.4 (other services)
57 Kyrgyz Republic 21.2 (special transactions) 10.5 (tobacco, unmanufactured, refuse)
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 s ervices Second commodity or service
58 Georgia 25.2 (travel) 20.4 (transport)
59 *Sierra Leone n.a.** n.a.**
60 Nicaragua 20.6 (coffee and substitutes) 14.0 (travel)
61 Papua New Guinea 30.6 (prec. metal ores, waste nes) 16.7 (other fixed vegetable oils)
62 Finland 17.2 (paper and paperboard ) 13.6 (telecom equip, parts, access)
63 Denmark 11.7 (transport) 7.8 (other services)
64 Slovak Republic 11.7(passenger motor vehicles, etc bus) 6.9 (other services)
65 Paraguay 36.2 (seeds for soft fixed oils) 11.5 (other services)
66 Israel 20.4 (pearl, prec, semi-prec stones) 13.5 (other services)
67 Benin * n.a.** n.a.**
68 El Salvador 19.1 (coffee and substitutes) 6.9 (travel)
69 Honduras 32.9 (coffee and substitutes) 10.8 (fruits, nuts, fresh, dried)
70 Hong Kong, China 12.0 (outer garments knit non-elastic) 9.2 (women's outwear non-knit)
71 Burundi * 77.5 (coffee and substitutes) 6.67 (tea and mate)
72 Switzerland 11.5 (other services) 10.5 (medicalm, pharmaceutical prd)
73 Guinea * n.a.** n.a.**
74 Chad * n.a.** n.a.**
75 Haiti * 23.3 (travel) 16.2 (women's outwear non-knit)
76 Austria 15.4 (other services) 11.9 (travel)
77 Bulgaria 16.2 (travel) 8.3 (transport)
78 Bolivia 12.9 (aircraft etc.) 10.4 (base metal ores, conc nes)
79 Dominican Republic 53.1 (special transactions) 30.3 (travel)
80 Rwanda * n.a.** n.a.**
81 Sweden 10.9 (telecom equip, part s, access) 9.4 (other services)
82 Senegal 14.6 (inorg chem elmnt, oxides, etc.) 12.8 (travel)
83 Tunisia 20.5 (travel) 12.7 (men's outweat non-knit)
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 First commodity or s ervices Second commodity or service
84 Portugal 15.7 (travel) 7.8 (passenger motor vehicles exc. bus)
85 Hungary 12.8 (travel) 8.7 (intern combust piston engines)
86 Zambia * n.a.** n.a.**
87 Belgium n.a.** n.a.**
88 Czech Republic 10.2 (travel) 6.7 (other services)
89 Malawi * 58.6 (tobacco unmanufactured,
refuse)8.6 (tea and mate)
90 Greece 30.9 (travel) 23.8 (other services)
91 Niger * 51.3 (uranium, thorium ores, conc) 11.2 (vegtb etc fresh, simply prsrvd)
92 Mali * 75.7 (cotton) 4.8 (gold, non-monetary nes)
93 Cuba n.a.** n.a.**
94 Burkina Faso * 51.8 (cotton) 9.1 (travel)
95 Guatemala 18.7 (coffee and substitutes) 9.8 (travel)
96 Zimbabwe 22.9 (tobacco unmanufactured,
refuse)11.8 (travel)
97 Ecuador 21.3 (crude petroleum) 20.8 (fruit, nuts, fresh, dried)
98 Angola * n.a.** n.a.**
99 Cameroon 27.1 (crude petroleum) 9.8 (other wood ro ugh squared)
100 Chile 22.0 (copper) 9.2 (base metal ores, conc nes)
101 Madagascar * 14.7 (other services) 11.5 (travel)
102 Netherlands 9.6 (other services) 8.5 (transport)
103 Cote d'Ivoire 33.3 (cocoa) 10.9 (petroleum products, refined)
104 Mozambique* n.a.** n.a.**
105 Australia 10.6 (travel) 8.5 (coal, lignite and peat)
106 Ghana 32.3 (cocoa) 11.8 (travel)
107 Sri Lanka 14.9 (women's outwear non-knit) 11.5 (tea and mate)
108 Uganda * 41.8 (coffee and substitutes) 21.4 (travel)
109 Taipei, Chinese n.a.** n.a.**
110 Romania 7.9 (women's outwear non-knit) 6.4 (men's outweat non-knit)
111 Malaysia 17.2 (transistors, valves etc) 9.3 (office, adp machy parts acces)
112 Venezuela, RB 60.8 (crude petroleum) 10.4 (petroleum products, refined)
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 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 s ervices Second commodity or service
113 Peru 14.6 (gold, non-monetary nes) 11.6 (travel)
114 Morocco 18.4 (travel) 8.3 (women's outwear non-knit)
115 Kenya 20.9 (tea and mate) 13.3 (transport)
116 Canada 12.2 (passenger motor vehicles exc. bus) 6.5 (other services)
117 Tanzania * 37.7 (travel) 8.4 (fruits, nuts, fresh, dried)
118 Argentina 10.2 (travel) 8.4 (fixed vegetable oils)
119 Poland 9.1 (travel) 7.8 (other services)
120 Spain 19.0 (travel) 10.1 (passenger motor vehicles exc. bus)
121 Colombia 20.9 (crude petroleum) 13.3 (coffee and substitutes)
122 South Africa 7.9 (travel) 7.6 (pearl, prec, semi-prec stones)
123 Korea, Rep. 12.6 (transistors, valves etc.) 6.6 (transport)
124 Myanmar * 18.0 (other services) 11.7 (other wood ro ugh, squared)
125 Congo, Dem. Rep.* n.a.** n.a.**
126 Italy 9.7 (travel) 7.6 (other services)
127 France 8.4 (other services) 7.9 (travel)
128 United Kingdom 17.5 (other services) 6.1 (travel)
129 Thailand 9.6 (travel) 8.5 (office, adp machy parts)
130 Egypt, Arab Rep. 23.9 (travel) 20.7 (other services)
131 Turkey 20.6 (other services) 12.3 (travel)
132 Philippines 23.3 (special transactions) 22.0 (transistors, valves, etc)
133 Germany 9.8 (passenger motor vehicles exc. bus) 6.9 (other services)
134 Mexico 8.4 (passenger motor vehicles exc. bus) 5.5 (travel)
135 Japan 11.3 (passenger motor vehicles exc. bus) 7.9 (other services)
136 Nigeria 90.6 (crude petroleum) 6.6 (other services)
137 Bangladesh * 22.5 (men's outwear non-knit) 18.1 (under garments non-knit)
138 Pakistan 12.1 (cotton fabrics, woven) 11.7 (textiles articles nes)
139 Brazil 7.1 (other services) 5.3 (iron ore and concentrates)
140 Indonesia 8.2 (special transactions) 7.8 (travel)
141 United States 12.0 (other services) 9.4 (travel)
142 India 16.0 (other services) 12.3 (pearl, prec, semi-prec stones)
143 China 6.2 (travel) 4.4 (other services)
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
GDPAverage % growth rate of GDP
Per CapitaGDP per capita
1Liechtenstein N.A N.A $ 38,000
2St. Kitts and Nevis 4.12 5.18 $ 12,510
3Antigua and Barbuda 3.64 4.42 $ 10,541
4Dominica N.A N.A $ 3,671
5Grenada 3.14 3.96 $ 7,580
6St. Vincent and the
Grenadines3.36 3.69 $ 5,555
7St. Lucia 8.25 3.60 $ 5,703
8Belize 4.64 2.74 $ 5,606
9Barbados 4.09 1.23 $ 15,494
10 Maldives * 3.63 5.99 $ 4,485
11 Iceland 3.03 1.79 $ 29,581
12 Brunei 5.41 -3.04 $ 5,049
13 Malta 1.60 4.11 $ 17,273
14 Suriname 8.14 0.12 $ 3,799
15 Macao, China 4.64 2.30 $ 18,190
16 Luxembourg 2.91 4.04 $ 50,061
17 Solomon Islands * 6.86 0.27 $ 1,648
18 Qatar N.A N.A $ 21,590
19 Djibouti * 3.02 -4.61 $ 812
20 Bahrain 5.99 -0.10 $ 14,548
21 Cyprus 2.58 4.16 $ 20,824
22 Guyana 5.52 0.88 $ 3,963
23 Fiji 5.57 0.12 $ 4,668
24 Swaziland 5.74 2.00 $ 4,492
25 Mauritius 4.06 3.86 $ 10,017
26 Guinea-Bissau * 9.43 0.36 $ 755
27 Gabon 6.15 -0.60 $ 6,237
28 Trinidad and Tobago 4.34 0.80 $ 8,964
29 Gambia, The * 2.69 0.10 $ 1,649
30 Estonia 7.00 0.73 $ 10,066
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.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
GDPAverage % growth rate of GDP
Per CapitaGDP per capita
31 Botswana 3.00 4.58 $ 7,184
32 Namibia 2.86 -0.09 $ 6,431
33 Kuwait 13.72 -2.26 $ 15,799
34 Slovenia 4.92 2.01 $ 17,367
35 Lesotho * 4.17 1.79 $ 2,031
36 Latvia 9.48 0.33 $ 7,045
37 Oman 5.24 3.20 $ 8,246
38 Mongolia 4.89 0.64 $ 1,783
39 Jamaica 3.60 0.34 $ 3,639
40 Mauritania * 2.40 0.16 $ 1,677
41 Panama 4.96 0.98 $ 6,000
42 United Arab Emirates 8.79 -3.15 $ 16,817
43 Congo, Rep. 7.69 0.48 $ 728
44 Uruguay 5.17 1.14 $ 9,035
45 Albania 8.63 0.33 $ 3,506
46 Lithuania 9.82 -0.83 $ 7,106
47 Central African Rep.* 4.70 -1.22 $ 1,172
48 Ireland 3.38 4.71 $ 29,866
49 Costa Rica 3.97 1.09 $ 8,650
50 New Zealand 2.08 1.19 $ 20,070
51 Singapore 3.31 5.02 $ 23,356
52 Moldova 10.62 -3.52 $ 2,109
53 Croatia 10.34 -0.03 $ 8,091
54 Norway 1.80 2.52 $ 29,918
55 Togo * 6.70 -0.95 $ 1,442
56 Jordan 6.40 0.29 $ 3,966
57 Kyrgyz Republic 9.45 -2.08 $ 2,711
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
GDPAverage % growth rate of GDP
Per CapitaGDP per capita
58 Georgia 16.72 -5.07 $ 2,664
59 *Sierra Leone 6.84 -2.85 $ 490
60 Nicaragua 4.16 -1.58 $ 2,366
61 Papua New Guinea 5.68 0.20 $ 2,280
62 Finland 3.18 2.40 $ 24,996
63 Denmark 1.74 1.63 $ 27,627
64 Slovak Republic 5.52 0.83 $ 11,243
65 Paraguay 3.80 0.10 $ 4,426
66 Israel 1.82 2.08 $ 20,131
67 Benin * 3.43 0.88 $ 990
68 El Salvador 4.83 -0.11 $ 4,497
69 Honduras 2.60 -0.25 $ 2,453
70 Hong Kong, China 4.37 4.22 $ 25,153
71 Burundi * 4.98 -1.02 $ 591
72 Switzerland 1.91 1.02 $ 28,769
73 Guinea * 1.46 1.31 $ 1,982
74 Chad * 8.10 0.92 $ 871
75 Haiti * 4.33 -2.01 $ 1,467
76 Austria 1.14 1.95 $ 26,765
77 Bulgaria 5.55 0.77 $ 5,710
78 Bolivia 2.91 -0.40 $ 2,424
79 Dominican Republic 3.64 2.35 $ 6,033
80 Rwanda * 12.25 -0.28 $ 943
81 Sweden 1.97 1.63 $ 24,277
82 Senegal 4.09 0.25 $ 1,510
83 Tunisia 2.81 2.22 $ 6,363
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
GDPAverage % growth rate of GDP
Per CapitaGDP per capita
84 Portugal 2.39 2.85 $ 17,290
85 Hungary 3.86 1.28 $ 12,416
86 Zambia * 3.76 -1.82 $ 780
87 Belgium 1.53 2.00 $ 27,178
88 Czech Republic 4.66 0.18 $ 13,991
89 Malawi * 5.82 0.26 $ 615
90 Greece 2.21 0.98 $ 16,501
91 Niger * 5.47 -2.34 $ 746
92 Mali * 4.49 -0.50 $ 797
93 Cuba N.A 3.89 $ 2,154
94 Burkina Faso * 3.63 1.58 $ 976
95 Guatemala 2.38 -0.03 $ 3,821
96 Zimbabwe 5.34 0.69 $ 2,635
97 Ecuador 3.71 -0.23 $ 3,203
98 Angola * 7.25 -1.31 $ 2,187
99 Cameroon 5.93 -0.40 $ 1,703
100 Chile 4.99 3.81 $ 9,417
101 Madagascar * 3.25 -1.61 $ 840
102 Netherlands 1.48 1.83 $ 25,657
103 Côte d'Ivoire 4.19 -2.25 $ 1,630
104 Mozambique* 7.71 1.18 $ 854
105 Australia 2.09 2.04 $ 25,693
106 Ghana 3.59 0.21 $ 1,964
107 Sri Lanka 1.36 3.32 $ 3,530
108 Uganda * 3.19 2.23 $ 1,208
109 Taipei, Chinese N.A N.A N.A
110 Romania 5.25 -0.71 $ 6,423
111 Malaysia 4.18 3.89 $ 9,068
112 Venezuela, RB 4.71 -1.15 $ 5,794
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
GDPAverage % growth rate of GDP
Per CapitaGDP per capita
113 Peru 6.60 -0.149 $ 4,799
114 Morocco 5.32 1.187 $ 3,546
115 Kenya 1.97 -0.056 $ 1,022
116 Canada 2.38 1.556 $ 27,840
117 Tanzania * 1.86 0.449 $ 523
118 Argentina 5.97 0.381 $ 12,377
119 Poland 4.08 3.589 $ 9,051
120 Spain 1.84 2.416 $ 19,472
121 Colombia 2.18 1.084 $ 6,248
122 South Africa 3.46 -0.339 $ 9,401
123 Korea, Rep. 4.31 5.814 $ 17,380
124 Myanmar * 5.00 2.183 $ 7,298
125 Congo, Dem. Rep.* 5.18 -4.578 $ 724
126 Italy 1.20 1.885 $ 23,626
127 France 1.14 1.646 $ 24,223
128 United Kingdom 2.01 1.948 $ 23,509
129 Thailand 4.84 4.737 $ 6,402
130 Egypt, Arab Rep. 2.06 2.852 $ 3,635
131 Turkey 4.19 2.199 $ 6,974
132 Philippines 3.76 0.162 $ 3,971
133 Germany 1.25 1.647 $ 25,103
134 Mexico 3.97 1.106 $ 9,023
135 Japan 1.78 2.327 $ 26,755
136 Nigeria 5.18 -0.833 $ 896
137 Bangladesh * 1.78 2.400 $ 1,602
138 Pakistan 2.05 2.689 $ 1,928
139 Brazil 3.79 0.773 $ 7,625
140 Indonesia 4.80 3.721 $ 3,043
141 United States 2.00 1.986 $ 34,142
142 India 1.96 3.658 $ 2,358
143 China 3.12 8.154 $ 3,976
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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