“Latin American Export Specialization and Growth: an inquiry into the nature of productcompetition between different exporters”
September 2003
Jorge Chami Batista
Instituto de Economia
Federal University of Rio de Janeiro1
1.

Introduction

The main objectives of this paper are to examine the patterns of export specialization of Latin
American countries and try to see if they are related to the export performance of these countries.
It is assumed that better export performance will foster economic growth. Given Latin America
general specialization in resource-based products, the paper focus on the question as to whether or
not it is possible for a country specialized in resource-based products to have high rates of export
and growth.
There are many ways of classifying products and hence defining patterns of specialization. The
conventional practice has been to apply some measure of technology intensity to define countries’
patterns of specialization. However, very few resource-based products are actually classified as
high-tech products. In this paper an attempt is made to classify resource-based products,
according to the role played by prices in the mechanism through which countries compete in
specific international product markets and, eventually, gain or loose market shares in thesemarkets.
The remainder of this paper is organized as follows. Section two reviews some possible
theoretical effects of trade integration on specialization, technological progress and economic
growth. It reviews OECD’s classification of high-tech products and describes the methodology fora new taxonomy of traded goods based on their degree of differentiation. It also discusses somepolicy implications. Section three analyses the development of world trade by groups of products,
classified according to their degree of manufacturing and technology intensity. The distribution ofthese groups of products by countries is also analysed and related to the countries’ export
performance. The performance of Latin American countries’ exports to the US is examined at theend of section three, using a constant market share analysis. Section four applies the newtaxonomy to a group of fifty-one resource-based products, excluding energy-related goods, and to
twelve machines used in the production of resource-based goods. The dynamics of each group of
product is then used to analyse the performance of some Latin American countries’ exports to the
US. The fifth section sums up the conclusions of the paper.
2.

Specialisation, technological progress and growth

Theoretical framework
The principal theoretical reference of this paper is to be found in the literature that attempts tointegrate trade and growth theories. A large number of dynamic models have been developed inthis literature. Grossman and Helpman (1991) sum up some of these models and work out theeffects of integration on innovation and growth in a two-country general equilibrium framework. I
review some of their results here.

1

I would like to thank Thiago Cherques for his research assistance and Alex Alves for his help in organizing some ofthe database used in this research. Financial support from the Cooperacion Andina de Fomento is gratefully
acknowledged. The usual disclaimer applies.
1Economic growth in the long run is the result of technological progress. In traditional neoclassicalgrowth models, technological progress is assumed to be exogenous. This would be an adequate
assumption “if advances in industrial know-how followed automatically from fundamental
discoveries and if basic research was guided mostly by nonmarket forces”2.
One way of making technological progress endogenous is by assuming that market forces canallocate resources to R&D, generating innovation and growth. In Grossman and Helpman’s(G&H) models, new technologies are endogenous and result from the intentional actions of
economic agents that perceive profit opportunities. Firms allocate resources to R&D when they
expect a return. These returns come most often in the form of economic rents in product markets
operating in imperfect competition. Therefore, monopoly profits provide the basis for economic
growth in these models.
Innovation successes materialize into two types of new products or inputs: those that are imperfect
substitutes of the existing ones; and those that are perfect substitutes of the existing ones. With thefirst type of new product or input, the economy expands horizontally, as new products are added
to the existing ones. With the second type, the economy expands vertically, as new and better
quality products or inputs make the existing ones obsolete. The model of expanding variety
captures the first type of innovation, while the rising product quality model (quality ladders)
captures the second type. In the real world, of course, economies can expand horizontally andvertically at the same time as both types of products coexist.
G&H build on these models by adding three sectors: the traditional sector (where no innovation
takes place), the high-tech industry (where innovation is applied), and the R&D sector (where
innovation is created). The R&D sector is assumed to be the most intense in human capital,
whereas the traditional sector is the least intense. Given this set up and assuming thattechnological spillovers are global, the country’s size, human capital endowment, and stock of
accumulated knowledge all contribute to the country’s competitiveness in research.
Research successes create export opportunities to the extent that innovators learn how to produce
goods that are better, different or cheaper than those of their competitors abroad. In the long run,the country’s pattern of specialization and economic growth are both the result of thiscompetitiveness in research. When technological spillovers are national, the initial conditions,
historically determined, become crucial for the countries’ pattern of specialization and growth in
the long term.
Therefore, one possible outcome is that countries that are small and/or poorly endowed withhuman capital and have a relatively small stock of country-specific knowledge would tend to
specialize in traditional and non-innovative sectors, export low-tech products, and grow more
slowly. On the other hand, large economies, well endowed with human capital and with relatively
large stocks of country-specific knowledge would tend to specialize in innovative sectors, export
high-tech products and experience high rates of innovation and growth.
Empirical Questions
A large number of empirical questions can possibly be derived from the above theoreticalframework. If indeed countries that are more competitive in research are expected to grow fasterand export high-tech products, one should empirically find a positive relationship between rates of
economic growth and specialization in high-tech sectors and goods across countries. However, inorder to address this empirical question, it is necessary to be able to define, measure, and identify
high-tech sectors and products in a meaningful and practical way.

2

Grossman and Helpman (1991), p.334.
2

A theoretical and conceptual discussion on how to define and measure technology could take us
well beyond the scope of this work. It here suffices to say that a high-technology industry is theone producing technology (better, different or cheaper products or inputs) or using it intensively.
They are also expected to be “those expanding most strongly in international trade and theirdynamism helps to improve performance in other sectors (spillover)”3.
A number of factors may be used to measure the technological level of a sector. Hatzichronoglou(1997) lists the following: R&D intensity, scientific and technical personnel, technology
embodied in patents, licenses and know-how, strategic technical co-operation between companies,
the rapid obsolescence of the knowledge available, quick turnover of equipment, etc4.
However, one should bear in mind that researchers are restricted by the data that are available forthe existing international classifications of sectors and products. To the best of my knowledge, themain systematic effort to classify sectors and products according to their technological content has
been pursued by OECD 5 . It is largely recognized that there is no perfect way to identify thetechnology content of an industry or product, measure it and determine the cut-off points between
different categories.
OECD’s classification of sectors by technology content applies the concept of R&D direct andindirect intensity. The former is measured by the ratio of R&D expenditure to output or value
added by industry and tries to capture the industry effort for producing technology. The lattermeasures R&D expenditure embodied in intermediates and capital goods purchased by theindustry, through the use of input-output matrices, and it is an attempt to capture technology
diffusion or how intensively technology is used in a particular industry. The sum of direct andindirect R&D intensity is then calculated to rank manufacturing industries into four groups: hightechnology, medium-high-technology, medium-low-technology and low technology.
In order to identify high-tech products, OECD calculates R&D expenditure over total sales byproduct at the 5-digit level of the Standard International Trade Classification (SITC), Revision 3.
Indirect R&D intensity is not applied at this level of disaggregation to define high-tech products.
In principle, high-tech products do not have to belong to a high-tech industry. Hence, a true
proportion of high technology of an industry could then be calculated, excluding all non-high-tech
products from it. However, at this level of aggregation, many products manufactured by mediumand-low-technology sectors, but with high R&D expenditure over total sales, could not justifiably
be considered high-tech and were excluded from the OECD list on the basis of expert opinion. As
a result, OECD only publishes a list of high-tech products, which are largely consistent with theindustries classified as high-tech6, though it includes some products manufactured by mediumhigh technology industries.
It is worth noting that sectors can be more technology intensive in one country, but less so in
another. That is why OECD classifies the technology content of each manufacturing industry on
the basis of a weighted average of a large number of their member countries. On the other hand,
the technology content of products is regarded as independent of the country where they are
manufactured.
Hatzichronoglou (1997) recognizes some of the main limitations of the method applied by OECD
to classify sectors and list the group of high-tech products by their technology content. First, R&D
intensity is a very important characteristic of high technology, but it is not the only one. Second,3

Hatzichronoglou (1997), p. 4.
Ibid, p.8.
5
See Mani (2000) and Hatzichronoglou (1997) for a brief history of OECD’s and others’ efforts.
6
The concordance between SITC Rev.3 (product classification) and ISIC Rev.2 (sectoral classification) gives the listof products by sectors classified by their R&D intensity.
43

R&D intensity measurements are biased against the sectors and periods in which turnover or
production increase more rapidly than R&D expenditure on account of strong demand in growth
or exceptionally vigorous marketing. They are also biased because all research in each sector is
attributed to the principal activity of the firms making up the sector. High-tech products cannot be
selected exclusively by quantitative methods unless a relatively high level of aggregation isadopted. Resorting to expert opinion helps to mitigate this problem, but the results cannot readily
be reproduced in their entirety by other panel of experts. Since the choice is not based exclusively
on quantitative measurements, it is difficult to classify products in increasing or decreasing order.
Finally, the data are not comparable with other industrial data, as information published by otheragencies on, for example, value added, employment and gross fixed capital formation are notavailable at product level.
The lack of sufficiently disaggregated data is another limitation, forcing the method to be applied
to industries and products defined at still high levels of aggregation. A firm that has a high levelof R&D expenditure to sales may produce a product, whose final assembly may consist of simple
operations that can be located in any cheap labour country. In this case, although the product isvery likely classified as high-tech according to OECD’s classification, it should not have been
considered a high-tech product 7 . Therefore, “countries with low technological capabilities canappear technologically advanced, giving a misleading picture of industrial performance. This
problem is not possible to solve by refining available data on MVA and exports”8.
A new taxonomy for traded goods
Given the limitations discussed above of OECD’s methodology to classify high-tech products andthe fact that OECD applies it only to manufactured goods, a new taxonomy is here proposed tosupplement OECD’s list of high-tech products. The idea is to classify traded goods, especiallyresource-based products, be manufactures or non-manufactures, as homogeneous, differentiated,and highly differentiated products.
Products that follow the law of one price (LOP) are regarded as homogeneous products. This law
states that homogeneous products must be traded at the same price, regardless of where they are
sold, as long as prices are expressed in the same currency and taking due account of transfer costs9.
Any price difference should be rapidly eliminated by commodity arbitrage. Countries would,
therefore, specialise either as exporters or importers of these products and would not discriminate
between domestic and exporting markets.
Formally, a strict version of LOP may be expressed as10:
Pi*/ Pj* = 1,

(1)where Pi* and Pj* are the domestic prices paid in a given market for the same good (or perfect
substitute goods) imported from countries i and j, respectively. These are cif (cost, insurance andfreight) prices plus import duties, so they may be written as:
Pi* = (Pi/Ei) (1+ti),(2)

where Pi is the cif export price expressed in country i’s currency, Ei is the exchange rate relating
the value of country i’s currency to one unit of the market currency and ti is the ad valorem importtariff (plus any non-tariff ad valorem equivalent) for country i.
7This is one reason why indirect R&D intensity ought not to be applied to define a high-tech product, as opposed to ahigh-tech industry, even if it could be measured at this high level of disaggregation.
8
UNIDO (2002), box 2.1, p.30.
9That includes transportation costs, tariff and non-tariff barriers.
10
See Chami Batista and Silveira (2003).
4

A weaker version of LOP would allow a price difference (premium), but no variations in relative
prices:d(Pi*/ Pj*)/dt = 0(3).

The presumption behind the law of one price is that suppliers are price takers in perfectcompetitive markets. The intersection between global demand and supply curves determines theworld equilibrium price, which should vary according to the location of delivery, but relative
prices from different exporting countries must remain constant at each location.
On the other hand, differentiated good (DG) models assume that a commodity produced by one
country is an imperfect substitute in demand for the “same” commodity produced by anothercountry. Following Armington’s (1969) convention we here refer to these commodities as goods
and to the good produced by a particular country as a product. It is assumed that changes in theprice of a product will change both relative prices and relative quantities demanded by the market.
Formally, DG models often assume that11:
Qi*/ Qj*= F (Pi*/ Pj*),

where F’<0, or (4)d(Qi*/ Qj*)/(Qi*/ Qj*)= f [d(Pi*/ Pj*)/(Pi*/ Pj*)], where f’<0.

(5).

Assuming that the long-run price elasticity of substitution is constant, it follows that:d(Qi*/ Qj*)/(Qi*/ Qj*)= V d(Pi*/ Pj*)/(Pi*/ Pj*), where V is Armington’s long-run elasticity of
substitution between two products.
Thus, in order to classify resource-based products as homogeneous (LOP), differentiated (DIF)
and highly differentiated (HIGH-DIF) products, time series of monthly US import prices from1996 to 2003 by products and country of origin will be tested econometrically. When the timeseries of relative import price from a pair of countries, exporting a particular product to the US, isfound by ADF test (Augmented Dickey-Fuler test) to be stationary, the product is classified as ahomogeneous product, since it follows the law of one price (LOP).
If the test finds the series of relative import price to be non-stationary, the product is considereddifferentiated (DIF). In this case, the time series of relative quantities of the same product and pairof countries is also investigated. If it happens to be non-stationary, a number of Johansen
cointegration tests are run, in order to look for a cointegration equation between the two nonstationary series. If a cointegration equation is found and the long run elasticity of relative
quantities with respect to relative prices is negative, the product is simply classified as
differentiated (DIF). This means that countries expand their export volume relatively tocompetitors by cutting their relative prices.
If the long run price elasticity of substitution is found to be positive or if no long run relationshipis found between relative prices and quantities, the product is classified as highly differentiated(HIGH-DIF). This means that international competition in these products is not predominantly
based on price differences.
11

Following Armington (1969) again, we make the independence assumption; i.e., marginal rates of substitution
between any two products of the same kind must be independent of the quantities of the products of all other kinds;and quantity index functions, relating the quantity of a good to the quantities of its products, must be linear andhomogeneous.
5

Economic growth and the new taxonomy
What is the relation between this product classification and economic growth? The idea is that thereturns from new technologies come most often in the form of economic rents in product markets
operating in imperfect competition, as it has been seen in theoretical models. These monopoly
profits provide the impetus for economic growth in these models. If a product follows the law of
one price, it means that firms are price takers operating in perfect competition. Therefore, firms
will not get economic rents in the long run. It is presumed that productivity gains in the
production of homogeneous products are rapidly diffused among competitors.
Exporters of differentiated products, on the other hand, operate in imperfectly competitive productmarkets. Firms have some degree of monopoly power and are price makers. In order todifferentiate their products or to maintain their products different from those of their competitors,
exporters need to innovate and, presumably, get returns in the form of economic rents.
However, to the extent that exporters can gain market share through price cuts, the degree ofdifferentiation may be regarded as relatively small. Hence, when competition is quite insensitive
to price changes, products may be regarded as highly differentiated. The rate of innovation is
likely to be greater among exporters of these products. In point of fact, although Carlin et al.
(2001) found that R&D expenditures have not been able to help relative unit labour costs to
explain the export performance of OECD countries over the period between 1970 and 1992, theyfound evidence that the sensitivity of export performance to these costs tends to be smaller inhigh-tech industries.
Moreover, as it is well known, exporters of a differentiated product may well prefer to maintaintheir export price relatively stable and thus change their margin of profits when unit costs change,
as it happens when, for example, the exchange rate changes. When changes in exchange rates are
fully transmitted to relative prices, exporters are said to have passed through completely thechange in cost. When exporters absorb at least part of the exchange rate changes into their profit
margins to keep their destination price stable, they are said to be pricing-to-market12.
In this literature, Yang (1998) finds empirical evidence that exporters of highly differentiatedproducts do not price to market as much as exporters in other industries. This is true when productdifferentiation is measured by the ratio of non-production workers to total employees, the ratio of
scientists and engineers to total employees, or by an intra-industry trade index. However, this is
not true when product differentiation is measured by advertising intensity. This appears to imply
that profit rates are greater for exporters of highly differentiated products, which can be regarded
as having a high technology intensity, at least when differentiation is measured by the ratio of
scientists and engineers to total employees.
Therefore, countries specialized in homogeneous products are unlikely to catch up with thosespecialized in differentiated products. Specialization in differentiated and highly differentiatedproducts is likely to require competitiveness in research, resulting in higher rates of innovationand growth in the countries exporting them.
Policy implications
If trade integration induces relative specialization in stagnant industries, diminishing R&D
activities in the economy of a country, it is very tempting to propose government policies torelocate resources favouring high-technology industries, products and R&D activities. But what
are these stagnant/non-innovative industries? What are the reasons behind the reduction in R&D

12

See Krugman (1987).
6

activities? Is it simply because of economic integration? Is it related to the pattern ofspecialization?
G&H have argued that,“ when technological spillovers are local, by strengthening the incentives
for private research, the government of a technologically lagging country can ‘level the playing
field’, then a nation that would otherwise specialize in traditional manufacturing can betransformed into an exporter of high-technology goods” 13 . However, when technologicalspillovers are global, they argue that R&D incentives may turn an exporter of high-tech goods into
an importer, as more human capital goes to R&D.
Faced with a highly uneven industrial development, in which a few developing countries haveshown spectacular and sustained successes and a large number of other countries haveexperienced dismal and prolonged failures, it is only natural to ask if these disparities will correct
themselves over time. It has been argued that they will not. “Structural drivers of industrial
development are slow, difficult and expensive to change, and the new global setting only raises
their importance. Some of the drivers can improve only through greater reliance on market forces.
But most need strong policy support” 14 . This may be correct, but which policies should be
implemented?
Market forces do not operate in a vacuum. They require rules, regulations, institutions as well as
good policies. It may well be that slow growth in some countries is the result of lack ofinstitutions conducive to physical and human capital accumulation and to providing rewards toinnovative effort. These institutions “include the rule of law in general (as opposed to bureaucraticwhim), security of private property, business contract law, a functional mechanism for domestic
payments (i.e., a working banking system), intellectual property rights, and a minimization of
government corruption”15. Bad policy management leading to inflation or political instability may
also be at the root of slow growth. There is no reason to blame trade, as the cause of stagnation,nor, a priori, consider industrial policies as an essential part of the strategy to promote growth.
Theoretical models yield ambiguous predictions about the welfare effects of economic integration
as well as policies such as R&D subsidies or strengthening intellectual property protection. “At
the heart of this ambiguity is the tradeoff between competition in pricing (which increases social
welfare by cheapening old goods, but reduces the incentive to invent new ones) and temporarymonopoly in new innovations (which promotes incentives by insuring the rewards of invention to
the monopolist, but also prevents useful dissemination of the innovation)”16.
3.

Dynamism and structure of world trade

World Imports: 1987-2000
In order to examine the dynamism and structure of world trade, world import data 17 werecollected from 1987 to 2000. Table (1) breaks down world imports in different groups of products,
according to their resource and technology intensity 18 . Given our concern with the relative
performance of resource-based products, world imports have been divided into two groups:
resource-based products and non-resource based products. Resource-based products are, in turn,
13

Grossman and Helpman (1991), p.339.
UNIDO (2002), p.28.
15
USITC (1997), p.2-13.
16
USITC (1997), p.2-10.
17
United Nations database, in which products are classified according to the Standard International Trade
Classification (SITC), Revision 3, and disaggregated at 3 and 5-digit levels have been used throughout this section.
Whenever possible, world imports are used as a measure of world trade, as import data are generally regarded as moreaccurate than export data.
18
See Appendix A (1) for the list of products in each category.
14

7

broken down into primary goods (excluding crude petroleum), agro-industrial goods, energyrelated goods, low-tech manufactures and high-tech manufactures. High-tech products areclassified according to the ratio of R&D expenditure to total sales19. Non-resource based products
are split into low and high-tech manufactures.
The figures in Table (1) reveal that trade of resource-based products tended to expand much more
slowly than trade of non-resource-based products in all examined periods20. Primary products had
the worse performance. Trade of agro-industrial products grew above the resource-based averagefor the whole period, but was below that average in most recent periods. And even trade ofresource-based manufactures, the best performing in the group of resource-based products,
expanded below total world imports. The trade growth-rate of energy-related products reflected
the dynamism of crude petroleum, being equal to the average of resource-based products for thewhole period, but growing faster in most recent periods. Within non-resource-based products,
trade of high-tech manufactures turned up to be the most dynamic of all categories. On the other
hand, within resource-based products, trade of high-tech manufactures was the slowest of all
categories.
Note that, excluding energy-related products and ignoring the distinction between low-tech andhigh-tech, Table (1) shows that trade has tended to be more dynamic for groups of products thatare less related to natural resources, as we move from primary products to agro-industrial,
resource-based manufactures and non-resource-based manufactures. However, it is worth
mentioning that, as far as low-tech manufactures are concerned, the rate of growth of resourcebased manufactures has been higher than that of non-resource-based manufactures.
Table (2) shows the shares of each category in world imports from 1987 to 2000. It reveals that
resource-based products have accounted for a much smaller share in world imports than nonresource-based products and the gap has broadened in the period. Within resource-based products,
the weight of primary goods has decreased significantly and now accounts for about the same
share as crude petroleum. In fact, the products that go through some kind of industrial processing,which roughly exclude primary products and crude petroleum, account for almost 88% of total
world imports. It is worth noting that trade in manufactures21 has also gone up from 70% of world
imports in 1987 to 80% in 200022. High-tech-resource-based manufactures account for a tiny anddecreasing share of world imports. On the other hand, trade in high-tech-non-resource-based
products has doubled its share in world imports between 1993 and 2000 and now accounts foralmost one fifth of the total. Nevertheless, low-tech-non-resource-based manufactures, thoughdeclining, still account for almost half of world imports, while low-tech-resource-based
manufactures maintains a firm 9% share of world imports.
Looking now at the composition of world exports23 by groups of products and countries24 in Table(3), it can be seen that developed countries, except for crude petroleum, hold a much higher
proportion of world trade than the other countries together. But the group of non-developed

19

Expert opinion also helped to classify these products. See OECD classification in Hatzichronoglou (1997).
Growth-rates were calculated on the basis of the coefficient of a line adjusted by OLS to the trade data inlogarithmic form. Considering only the initial and end years would tend to distort the rates of expansion.
21
SITC from 5 to 9.
22
Manufactures accounted for approximately 55% of total world trade in 1980. Many (2001) makes the sameobservation.
23
SITC, Rev.3, 3 to 5-digit level.
24
The list of developed countries is taken from the United Nations, which basically includes the US, Canada, thecountries of Western Europe, Japan, Australia, New Zealand, Israel and South Africa. All other countries are nondeveloped countries, including developing economies as well as economies in transition. Appendix A (2) shows thelist of countries included in tables (3) and (4).
20

8countries reveals comparative advantage25 in non-manufactured-resource-based products. On theother hand, the group of developed countries reveals comparative advantage in resource-based
manufactures and in non-resource low-tech manufactures. It is interesting to see that developed
countries do not any longer reveal comparative advantage in non-resource high-tech manufactures,
due to the quite spectacular rise in the share of a few Asian countries in world exports of theseproducts26. Note that Latin America’s revealed comparative advantages remain well establishedwithin the groups of non-manufactured-resource-based products.
Table (4) shows that although resource-based products remain more important in the export
structure of non-developed countries than in the group of developed countries, non-resourcebased-high-tech manufactures have a slightly greater weight in the export structure of nondeveloped countries than of developed countries. Again, countries of emerging Asia 27 arebasically responsible for that. In point of fact, non-resource-based-high-tech manufactures accountfor very high shares of total exports for countries like the Philippines (38.5%), Singapore (33.3%),
Malaysia (29.7%) and the Republic of Korea (20.5%). It is not clear to what extent these countries
have developed the technological capability to produce these goods or if they are merely
assembling them28.
Looking at the composition of exports of Latin American countries in more detail in Table (5), it
can be seen that Mexico accounts for almost half of Latin American exports and has an export
structure that is quite different from the other countries of the region. Mexico reveals comparativeadvantage only in crude petroleum, among the resource-based product groups, and in low-tech
manufactures, among the non-resource-based product groups. Without Mexico, Latin America’s
revealed comparative advantages are well defined within the resource-based products. Brazil isthe main country responsible for Latin American comparative advantage in resource-based-hightech manufactures and, together with Chile, for comparative advantage in resource-based-lowtech manufactures. Costa Rica is the only Latin American country to reveal comparativeadvantage in non-resource based high-tech manufactures.
Table (6) shows quite clearly that, without Mexico, resource-based products still account foralmost two-thirds of Latin American exports29. On the other hand, non-resource-based products
account for 83% of Mexico’s exports. Given its weight in world exports, non-resource-based-lowtech manufactures constitute the main group of Latin America’s exports, particularly of Mexico.
But primary products, crude petroleum, agro-industrial products, and resource-based-low-tech
manufactures also account for significant shares of LA’s exports30.
When the composition and dynamics of world imports in the period from 1996 to 2000 are
examined at a more disaggregated level (5-digit level SITC Rev.3) some new features of world
trade emerge. World imports show a very high and growing level of concentration, as the Top 20
products31 by value account for 27% of total imports of 3112 products32, rising from 25% in 1996
25

Revealed comparative advantage is measured here as the ratio of the world share of a country’s exports in aparticular group of product and the world share of this country’s (or group of countries) total export.
26
UNIDO, (2002).
27
That is South Asia, including China.
28
Mani (2001) has termed this as the statistical artifact debate and has argued that in the cases of Korea and,increasingly, Singapore there seems to be local capability to design, manufacture and export high tech items. Thailand
and Philippines appear to be at the other end with very low (relatively speaking) capability, while Malaysia issomewhere in the middle.
29
Including Mexico, resource-based products would account for only 43% of Latin America’s total exports. See
Table (4).
30
Although the data set, the period of analysis and the breakdown by countries and groups of products were
somewhat different the general picture here is quite similar to the one shown in Lall (2000).
31
See Appendix A (3) for a complete list of the Top 20 products.
32
Excluding sector 9.
9to 30% in 2000. Among these Top 20 products, Graph (1) shows that non-resource-based-lowtech products account for the largest share of these imports, but non-resource-based-high-tech
products and energy-related products also have significant shares. Therefore, excluding crude
petroleum and other energy-related goods, there are no resource-based goods among the Top 20
products and high-tech products are proportionally much better represented at the top than in total
world trade. The Top 100 products account for 45% of world imports, rising from 43% in 1996 to
49% in 2000. Again, Graph (2) shows that non-resource low-tech manufactures and non-resourcehigh-tech manufactures account for most of the top 100’s import value. Energy-related goods
have a significant share, but resource-based-low-tech manufactures, agro-industrial and primary
products account for a very small proportion of the total.
GRAPH (1): TOP 20 Products in World Exports(SITC Rev.3, 5-digit level)

Non-resourcehigh-tech
34%

Energy related25%

Non-resource
low-tech41%

GRAPH (2): TOP 100 Products in World Exports
resourcebased low-tech
manufactures
6%(SITC Rev.3, 5-digit level)
primary agro-industrial
products
products
3%
2%

non-resource
low-techmanufactures45%

energy-related
goods
17%

non-resourcehigh-tech
manufactures
27%

Among the Top 20 products, four belong to the computers and office machines industry, four tothe motor vehicle industry, three to the petroleum industry, three to electronics andtelecommunication industries, two to the aerospace industry and each of the following industry
has one product: pharmaceuticals, non-electrical machine, articles of apparel and clothing
accessories and footwear.
As the rising level of concentration already indicates, products at the top have risen faster thanworld trade average in the period. The Top 20 expanded at 8.2% per year from 1996 to 2000,
10

while the Top 100 expanded at 6.9% per year, compared to the growth-rate of 4.2% per year of
total world imports. Dividing the products from top down into four equal parts in import value,
Graph (3) clearly shows that the dynamism rises sharply as we move from the bottom to the top of
the list. This is largely due to some very important non-resource-high-tech manufactures that areat the top of the list and are quite dynamic.
GRAPH (3): Growth-rates of Top to Bottom Products:
1996-2000(SITC Rev.3, 5-digit level)
10.00%

8.37%

8.00%
6.00%

4.41%
2.40%

4.00%

0.04%

2.00%
0.00%

TOP 0-25%

TOP 25-50%

TOP 50-75%

TOP 75-100%

Examining the group of high-tech manufactures 33 more closely, it can be seen that they are
extremely concentrated in a few products, since the Top 2034 out of 221 products account for
about two-thirds of total exports of high-tech manufactures in the period from 1996 to 200035.
These Top 20 products tend to have the highest rates of growth and are, therefore, responsible forthe dynamism of the non-resource-based-high-tech group of products. In other words, the nonweighted average of the rates of growth for the products of this group is much smaller (3.3%) than
the weighted average (9.5%), reflecting the fact that trade for the majority of high-tech products
was not dynamic at all in the period – the rate of growth of world imports of 124 high-tech
manufactures was indeed below total world imports in the period and 74 high-tech manufactures
actually showed negative growth-rates. Apparently, this point has not been noticed in theliterature, as it tends to analyse the performance of the group of high-tech products as whole.
Non-resource-based-high-tech exports are very concentrated in two industries: electronics &telecommunications and computers & office machines. As shown in Graph (4), together theyaccount for over two thirds of world exports of high-tech products. Mani (2000) shows thatexports of high-tech products from developing countries are even more concentrated on these two
industries than those of developed countries, reaching 87% of the total for the former group and
57% for the latter36.

33

SITC, Rev.3, 5-digit level.
See Appendix A (4) for the list of these Top 20 high-tech products.
35
Concentration has increased from 49.6% in 1993 to 69.8% in 2000.
36
Data are as of 1997.
34

11

GRAPH (4): High-Tech Products by Sector: 1996/2000(SITC Rev.3, 5-digit level)
Non-Electrical Machinery
3%
Electrical Machinery
3%
Chemistry
3%

Scientific Instruments
9%

Aerospace
11%
Computers - Office
Machines
28%

Armaments
1%
Pharmacy
3%

Electronics Telecommunications
39%

The concentration of exports of high-tech products by countries is also extremely high. Table (7)shows that the Top 5 largest exporters accounted for 54% of total high-tech exports in the period
from 1996 to 2000, the Top 10 for 75% and the Top 25 for 96%37. Mexico appears as the 13thlargest exporter. The group of developed countries accounts for 73% of world exports of hightech products, whereas non-developed countries are responsible for 26,8%. Emerging countries ofAsia alone take 22.4% of these exports, as many of them are quite noticeable among the Top 25
countries, while Latin America has only 2.9% of this market. Within Latin America, Table (8)shows that Mexico leads with 78% of the region’s exports of high-tech products. Brazil comes insecond with 12,5% of these exports and Costa Rica is in third with a surprising 4.6% share.
In contrast with high-tech manufactures, resource-based products 38 , excluding energy-related
products, are much less concentrated both by products and countries. The Top 20 products
account for 21% of the exports of 1133 products in the period between 1996 and 2000. Table (9)shows that the Top 5, Top 10 and Top 25 countries account for 38%, 55% and 83% of theseexports, respectively. It is interesting to note that the Top 9 countries belong to the group of
developed countries. China is the tenth and Brazil the thirteenth. There are only two nondeveloped countries among the Top 15 countries and eight among the Top 25 countries. Out of
1125 resource-based products at the 5-digit level for which the rates of growth can be calculated,
only 216 turn up to be dynamic (growth-rate higher than 4.2% per year) in the period between
1996 and 2000 and they account for only 21% of total exports of resource-based products, or amere 4% of total world imports in the period.
Graph (5) shows that the growth-rates of exports in the period between 1990 and 2000 were
negatively related to the share of resource-based products in total exports of the main countries in
Latin America and in East Asia in the period between 1996 and 2000. Mexico and Costa Rica, thetwo best export performers in Latin America in the period, have the lowest shares in the region of
resource-based products in their exports. Brazil’s export performance was disappointing,considering that it has a much lower share of resource-based products than countries that
outperformed it, like Argentina and Chile.

37

This distribution by countries is very similar to that of 1998 presented by UNIDO (2002), p.31, though Taiwan was
included in that database there but not in here.
38
SITC, Rev.3, 5-digit level.
12

GRAPH (5): Growth-rates of Exports 1990/2000
20.0%
15.0%
10.0%

MexicoPilippines

Costa Rica

China
Malaysia
Korea
ThailandSingapore

5.0%
0.0%
0.0%

20.0%

40.0%

Indonesia
Brazil60.0%

Argentina

Chile
Colombia
Venezuela

80.0%

100.0%

Share of resource-based products 1996/2000

US Imports 1989-2002
As with world imports, US imports were broken down into the same groups of products. In Table(10), it is possible to see that US import dynamism in the period between 1989 and 2002 was very
similar to that observed for world imports from 1987 to 2000. The rate of growth of imports ofresource-based products was lower than that of non-resource-based products. Non-resourcebased-high-tech manufactures were the most dynamic group, while resource-based-high-tech
manufactures was the least dynamic of all groups. Dynamism rises as we move from primary toenergy-related goods, and then to agro-industrial and manufactured products.
However, for the two most recent periods, from 1993 to 2002 and from 1996 to 2002, the rates ofgrowth of resource-based products have been higher than those of non-resource-based products.
This has been partly due to the rise in US import of energy-related goods, especially crude
petroleum, but also because of the firm expansion of imports of resource-based-low-tech
manufactures, while the rate of growth of non-resource-based-high-tech manufactures declined
sharply. In point of fact, the growth-rate of imports of resource-based (low-tech) manufactures hasremained higher than non-resource-based manufactures both in 1989/2002 as well as in the mostrecent periods. On the other hand, imports of primary goods, agro-industrial products andresource-based-high-tech manufactures have maintained a slow rate of growth throughout these
periods.
The structure of US imports, showed in Table (11), is also very similar to that of world imports,
though it tends to have a higher proportion of non-resource-based products than world imports, as
the US is well endowed with natural resources. Indeed, non-resource based products rose fromslightly over 70% at the beginning of the 1990s to over 76% in the mid-1990s, largely due to therise in high-tech imports, but fell again to 71% in 2001/2002, basically because of a fall in nonresource-low-tech products, though imports of high-tech products also fell in the last two years.
Trade performance of Latin American countries in the US market: 1996/2002
In this section, a constant market share analysis is applied to US imports from Latin Americancountries. The constant market share (CMS) model accounts explicitly for the effects of importdemand, product composition, and competitiveness on the change in export revenues from aparticular country in a given market. The model can be expressed as follows:

13

[2]

¦ X X
i


t

t 1

i

i

exportvariation

 r¦ X i { ¦ ri  r
i
i

t 1

demandeffectproducteffectXt 1i

 ¦( X  X i  ri X i )
i
i

t

t 1

t 1

competitivenesseffectwhere:
is the value of the focus country’s exports of product i;Xi
is the growth rate of US imports between the periods t and t-1; andris the growth rate of US imports of product i between the periods t and t-1.
ri

The model breaks down into two basic effects the difference between the increase in the value ofa country’s exports over a given period and the increase that would be required in order for thatcountry to maintain its share in the market (the demand effect). A positive difference means thatthe country has increased its share in the market, while a negative difference indicates a reductionin that share. The first effect (measured by the first term on the right-hand side of the identity
above) –identified as the product effect, i.e., the composition of exported merchandise– calculates
to what extent market share gains (losses) can be attributed to the concentration of exports ingoods for which demand is growing more rapidly (or slowly) in relative terms. The second effect(measured by the second term on the right-hand side of the identity) –identified as thecompetitiveness effect– is calculated as the residual and estimates to what extent factors other thanthe product effect can explain market share gains or losses in the market39.
Latin-American countries gained market share in US imports in 2002 compared with 1996 andthis gain was equivalent to US$22 billion or 10.6% of their exports to the US in 2002.
Competitiveness and product composition effects were both positive in this period, but the former
accounted for 80% of the total gain. However, as Table 12(a) reveals, the group of non-resourcebased products was responsible for this positive effect, whereas the group of resource-based
commodities negatively contributed to the Latin America (LA) gain in terms of bothcompetitiveness and product composition effects40.
It is interesting to note that, within the group of non-resource-based manufactures, low-tech goods
accounted for the largest part of the gains. But this was largely due to the product composition
effect, shown in Table 12(c), since non-resource high-tech manufactures accounted for the largest
part of the competitiveness effect, shown in Table 12 (b). In other words, LA has substantially
improved its competitiveness in non-resource-based manufactures in the period, especially inhigh-tech goods, but the relatively slow growth of these high-tech US imports in 1996 to 2002
meant that the group of low-tech goods allowed larger gains in absolute value41.
On the other hand, LA’s losses in primary and energy-related goods were the result of lack of bothcompetitiveness and dynamism in these commodities. LA’s gain in agro-industrial products was
the result of competitiveness gains, since the product composition effect for this group ofcommodities was negative. However, LA’s gains in resource-based manufactures resulted frompositive competitiveness and product composition effects, except for resource-based-high-tech
manufactures, which revealed low dynamism. Note that for Latin America, in general, the lack ofdynamism of imports of resource-based products accounted for over 80% of the region’s loss inthis sector.
39

See Leamer and Stern (1970) for a detailed analysis of the constant market share model and Chami Batista andAzevedo (2001) for a recent application of this model to US imports.
40
See Table 12, parts (b) and (c) for competitiveness and product composition effects, respectively. The formulae forthe competitiveness effect remains the same as exports are broken down into groups such as resource and nonresource-based products. But the left-hand side of equation (2) should be calculated as:
t
t1
t1
] , where M is total US imports. The product effect can then be calculated¦ [X i  M . X i y Mti

as a residual.
41
Note that, as a proportion of exports, gains in non-resource high-tech goods were much larger.
14However, Table (12) also shows that there are major differences among the countries that makeup Latin America. On the one hand, Mexico, Honduras, Brazil, Guatemala, Chile, and Costa Rica
were the big winners in the US import market 42 . On the other, the Domenican Republic,
Venezuela, and Colombia were big losers in the same market, while Argentina experienced asmall loss of market share in US total imports.
It should be noted that the countries that had the largest gains in absolute value, as well as inproportion to their exports to the US, also had large gains in non-resource-based manufactures.
Mexico, Honduras, Guatemala, and Costa Rica only gained, because of their gains in nonresource-based manufactures, as they all experienced losses of market share in resource-based
products in the period. Honduras and Guatemala’s gains of market shares in non-resource
manufactures came, by and large, from low-tech products, whereas Costa Rica’s gains came
exclusively from high-tech products.
Mexico, the largest winner43, had substantial gains in both low-tech and high-tech non-resource
based manufactures, but benefited enormously from a large and positive product composition
effect in low-tech exports, and to a much lesser degree in high-tech products. Indeed, most Latin
American countries benefited from large and positive product composition effects in their exports
of non-resource-based manufactures, especially of low-tech products. However, Brazil, Argentina,
Guatemala and Venezuela had negative product composition effects in non-resource-based
manufactures, indicating that being an exporter of this group of products provides no guaranteethat markets will be dynamic. In the case of Argentina, the slow growth of US imports of resource
as well as non-resource-based goods exported by this country more than offset Argentina’s
competitiveness gains in both types of goods.
In sharp contrast to Latin America in general, Brazil and Chile gained market share in resourcebased products. In the case of Brazil, this gain came from energy-related goods, resource-based
manufactures and agro-industrial products. Brazil had positive competitiveness effects in allgroups of resource-based products. Therefore, the loss in primary commodities was due to thenegative product composition effect in this item. On the other hand, Chile’s gains in resourcebased products came from all items, but resource-based-low-tech manufactures. The gain inprimary commodities is quite extraordinary, since Chile was the sole country amongst the largest
exporters from LA to gain in this group of commodities. Yet more surprisingly, this gain inprimary products came about as a result of a positive product composition effect, as Chile lostcompetitiveness in these commodities in the period. This suggests that countries may specialize inresource-based products, or even primary commodities, for which markets can be quite dynamic.
4.

Resource-based products by degree of differentiation

In this section, fifty-one resource-based products and thirteen machines that produce resourcebased products have been tested and classified, according to the methodology described in section
244. Monthly US import data by product and by country of origin from 1996 to the end of 2002
and, in some cases, to July 2003 were used for the tests and classification of products. The lack ofcontinuous series of monthly US import data has prevented the test and classification of a numberof other products.
Most products were defined at HS 10-digit level, though in some cases HS 8-digit level, HS 6digit level and SITC 5-digit level were thought to be more appropriate. As seen in section 2, the42These countries were put in descending order, according to their gains as a proportion of their exports.
Mexico’s gains were actually larger than those of LA as a group.
44
The econometric results of these tests are available at the reader’s request.
43

15

level of aggregation is extremely important for testing the role of price in the mechanism throughwhich countries gain market share in a particular product. If the level of aggregation is too high, it
may include products that compete in different regimes45. In other words, it may include a few
products that compete following the law of one price and others that may, for example, have a low
price elasticity of substitution. The result is an average between an infinite elasticity of
substitution and a near zero elasticity. Though this average is a mathematical impossibility, it isquite possible that a figure between zero and infinite may be found for the elasticity of
substitution. Some might even apply this elasticity in some simulations of free trade agreements.
But, in point of fact, any other arbitrary number could be equally used, probably changing entirely
the results of the simulation. If the level of aggregation is too low, it might miss important productsubstitutes that might as well distort the analysis of the role of prices in competition.
US imports of the products actually classified amounted to US$296 billion from 1996 to 2002.
This is equivalent to 36.5% of US imports of resource-based products, excluding energy-related
products, in the period. Considering the products converted to SITC 5-digit level, the classified
sample would be equivalent to 19% of world imports of resource-based products in 1996 to 2000.
In fact, the weight of each product in both world imports and US imports was a relevant variable
in selecting the sample.
Table (13) shows the distribution of the sample by the degree of industrial processing andtechnology intensity. It can be seen that the sample has a smaller share of primary andmanufactured products and a bigger share in agro-industrial goods than total US imports ofresource-based products. But the difference does not appear to be significant to distort the overall
analysis, though it can in principle distort the trade pattern of specific countries.
Out of the fifty-one products, twenty-one (41.2%) were classified as LOP, fifteen (29.4%) as DIF
and fifteen (29.4%) as HIGH-DIF. However, according to their import value, LOP products
accounted for US$190.1 billion, DIF products for US$70.5 billion and HIGH-DIF for US$35.2
billion. GRAPH (6) shows the share of each group in the total. Therefore, most resource-based
products, excluding energy-related products, appear to be homogeneous products that follow thelaw of one price.
In the machinery sample, there were no LOP products, only two were found to be DIF, while theother eleven were classified as HIGH-DIF. But the two DIF machines accounted for one third(US$1.3 billion) of total imports (US$4.0 billion), while the other eleven products responded for
two-thirds (US$2.7 billion).
GRAPH (6): Groups of Products by the Degree ofDifferentiation
HIGH-DIF
12%
DIF
24%
LOP
64%

45

It may also capture product composition effects, which are not expected to be negatively related to relative prices,rather than competitiveness effects, which may be negatively related to relative prices of competing exporters.
16

Table (14) shows that, within resource-based products and excluding Mexico, Latin Americancountries reveal some comparative advantage in LOP products, but so do developed countries andEast Asia. Mexico, on the other hand, reveals comparative advantage in HIGH-DIF products,
while the “other” countries reveal comparative advantage in DIF products.
An entirely different picture emerges from the distribution of US imports of machinery amongst
the exporting countries46. Table (15) shows that developed countries account for over 90% of total
imports from the US, while Latin America takes less than 5% and East Asia slightly more than2%. Germany alone accounts for 34% of these US imports, and together with the United Kingdom,
Italy, Japan and Canada, they account for 75% of these US imports. Brazil is responsible for88.5% of Latin America’s exports, whereas Mexico takes 7.1%.
Among Latin American countries, Table (16) shows that Mexico is the Top first country in all
categories of products, holding a very large share in HIGH-DIF products. Brazil is second in LOP
products, but loses this position to Chile in DIF, HIGH-DIF and in the total of resource-based
products. As Chile, Argentina also improves its position as we move from LOP to DIF and HIGHDIF products. On the other hand, Peru, worsens its position as we move from LOP to DIF and
HIGH-DIF products.
Table (17) shows that LOP products revealed the lowest dynamism between 1996 and 2002,
expanding at 3.5% per year in the period. The vast majority of products showed a negative rate of
growth in the period, but the high rate of growth of imports of diamonds and its significant weight
among the LOP products helped to raise the overall expansion of this group of commodities47.
Nevertheless, the rate of growth of LOP products was much smaller than that of US imports ofresource-based products (excluding energy-related goods).
On the other hand, the dynamism of DIF and HIGH-DIF products was much greater than that of
LOP products48. In fact DIF products turned up to be more dynamic than HIGH-DIF products inthe period, also surpassing the overall growth-rate of US imports of all types of manufactures(resource and non-resource-based, low-tech and high-tech manufactures)49.
Examining now the structure of exports by groups of countries in Table (18), it can be seen that
LOP products account for a very large proportion of exports from Latin American countries,
excluding Mexico. This proportion is much larger than the world average. Mexico, on the other
hand, has a relative low proportion of LOP products in its exports of resource-based products, as
HIGH-DIF products account for more than one third of these exports, and DIF products for over
20%. Chile also has a better distribution of its exports of resource-based products among LOP,DIF and HIGH-DIF products. The shares of DIF and HIGH-DIF products in Argentina’s
resource-based exports are quite significant, though the largest part of these exports is classified
as LOP products. Most other countries, including Brazil, Peru, Colombia, Ecuador, Guatemala,Venezuela, Costa Rica and Honduras, just to mention some of the largest exporters, have a verylarge proportion of their exports concentrated on LOP products.
In order to see whether or not the new taxonomy adds anything to explaining the export
performance of resource-based products from Latin American countries to the US in the period
between 1996 and 2002, I have re-applied the constant market share model just to the US imports
46

See Appendix A (5) for the detailed description of the machinery included in the sample.
See Appendix A (6) for the rates of growth and total imports of each LOP product.
48
See Table (17) and Appendix A (7) and A (8) for the rates of growth and total imports of each DIF and HIGH-DIF
product.
49
See Table (10) for the growth-rates of US imports.
47

17

of resource-based products (excluding energy related goods) and to the sample of products
classified by the degree of differentiation. The results are in Tables (19) and (20).
At the bottom end of both tables, it is possible to observe that Latin America lost market share in
total resource-based products (excluding energy-related goods) in the US and in the sample in the
period in 2002 compared to 1996. In point of fact, only Chile and Venezuela gained market sharein the US in the period among the major exporters of LA. However, in the sample, Argentina and
Mexico gained, whereas Venezuela lost market share. The competitiveness effects are also quite
different when the results from US imports of resource-based products in Table (19) are comparedto the results from the sample in Table (20). In the former, LA lost competitiveness, whereas in
the latter it gained.
As a result of these discrepancies, Mexico and Venezuela have negative product composition
effects in the sample when, in fact, these effects were positive for the whole range of resourcebased products exported to the US. Nevertheless, except for these two countries, the sign of the
product composition effects are the same for the whole set of resource-based products and for the
sample, including the positive sign of Chile. And it is in the product composition effects that one
ought to focus when examining the distribution of resource-based products by the degree ofdifferentiation, since the competitiveness effects have to do with changes in each exporter country
rather than with the characteristics of each product, especially the dynamics of product markets.
Table (20) show the product composition effects in the sample. It can observed that they are
negative in LOP products for all countries. For LA as a group, the negative product effect is solely
due to the LOP products, as DIF and HIGH-DIF products contribute positively to the productcomposition effect. The same pattern occurs for Argentina, Chile, Costa Rica, Domenican
Republic, Mexico and Venezuela. Furthermore, for all countries that have negative DIF productcomposition effects, the negative contribution of LOP products is always much greater. Finally,
the HIGH-DIF product composition effects of all countries are positive. In other words, thedynamism of the markets for HIGH-DIF products contributed positively to the expansion of
resource-based exports of all countries examined.
Therefore, the break down of the product composition effects by groups of products classified
according to their degree of differentiation seems to capture the different market dynamism
effects within the resource-based products. It should be noted that the same is not true for thegroups of products classified according to their degree of industrial processing (primary, agroindustrial and manufactures) and technology intensity (low and high-tech manufactures).
In point of fact, there is a significant number of low-tech manufactured resource-based products in
the group of LOP products. They account for 48% of the total imports of the group. Agroindustrial products account for 28%, while primary products account for 24%. There is even ahigh-tech manufactured product among the LOP group, though it accounts for only 0.2% of thetotal imports of the group. Therefore, the characteristics of the LOP products in this much
enlarged sample reinforce the conclusion drawn in a previous study50 that, contrary to a generalview that associates the law of one price to primary goods, the sample of products that were here
found to follow this law consists of products that go through some basic industrial processing,even when they are classified as agricultural or primary good. In fact, products that are extracted
from nature tend to be different according to their location. Therefore, some degree of basic
industrial processing is necessary to standardize the products and turn them into homogeneous
goods, wherever their production is located.

50

See Chami Batista and Silveira (2003).
18

Products like aluminium ores, unworked diamonds, a variety of fish and fruits were found to be
DIF or HIGH-DIF products. Indeed, products classified as primary commodities account for 16%
of imports of HIGH-DIF products and 7% of DIF products. Agro-industrial products account forover half of imports of HIGH-DIF products and over one third of DIF products 51 . Low-tech
manufactures account for over half of DIF products, but less than one third of HIGH-DIF products.
Just one high-tech product appears in the DIF group and another in the HIGH-DIF group, andthey account for only 1% and 2% of their imports, respectively.
A Note on the methodology for the new taxonomy
In every classification of traded goods there is some degree of arbitrariness, this is one is no
exception. The methodology used in this paper to classify traded products according to theirdegree of differentiation also faces a number of difficulties and limitations. Perhaps the firstdifficulty is to choose the level of aggregation of products. The best I could do was to start at themost possible disaggregated level. In the case of LOP products, price time series of individual
countries would typically be non-stationary, as one would expect, though in some cases a few
outliers had to be withdrawn for them to be non-stationary. Then, relative prices would have to bestationary for the product to be classified as LOP52. In most cases, including those in which theweaker version of the law of one price applied, it was possible to see graphically that individual
prices moved together over time53.
When at the highest level of disaggregation, there was not enough data to apply the tests or whensome changes in the definition of products occurred in the period at this level, I have moved down
to the next level of disaggregation. But although a product found to be LOP at a lower level ofdisaggregation could include products that were not LOP at a higher level of disaggregation, I was
happy to ignore these non-LOP products as they were clearly dominated by those following thelaw of one price.
Differentiated (DIF) products require a more complicated analysis. For a product to be classified
as DIF, the first condition is that the time series of relative prices between each pair of country has
to be non-stationary. The times series of relative quantities between the same pair of countries
also has to be non-stationary. Finally, the time series of relative quantities must cointegrate withthe time series of relative prices with a negative coefficient for at least one pair of major exporters.
When no cointegration with a negative coefficient was found, products were categorised as
HIGH-DIF54. Typically, however, the time series of prices of HIGH-DIF products were stationary
for individual countries. Although, rigorously, the ADF test requires that each time series is nonstationary, relative prices would often be stationary as well55. In this case, when individual andrelative prices were stationary and relative quantities were either stationary or non-stationary,
products were classified as HIGH-DIF56. The same would happen if relative prices were nonstationary and relative quantities were stationary, since it was assumed that there could be no51

The shares of agro-industrial products in DIF and HIGH-DIF imports are likely to be overestimated as they wereover represented in the sample. The opposite is true for primary and manufactured goods.
52
Given the low power of the test, critical values at 5% level were applied throughout the classification process todetermine that a time series was stationary.
53
Some of these graphs are shown in Appendix A (9).
54
In the case of products classified as HIGH-DIF, it is very often possible to see graphically that exporters tend to
gain market share, though with rising relative prices, or at least without having to lower their relative prices.
55
An even more complicated econometric problem would arise when time series of prices or quantities seemed to beseasonal. In these cases, as long as relative prices and quantities did not seem to be seasonal, tests were carried out
normally. When relative prices or quantities seemed to be seasonal, the product was not classified.
56
In a few instances negative relationships between stationary relative quantities and prices have been found throughOLS regressions. However, these relationships were regarded as short term and, therefore, do not characterize a DIF
product.
19

long-run relationship between them. In some cases, individual and relative prices and quantities
were all non-stationary, but no cointegration with the right sign was found.
Given that the classification depends on the analysis of pair of countries, it is possible that thesame product has different classification for different pairs of countries. Especially when aproduct is classified as DIF, which in our criterion requires that one right sign cointegration isfound at least for one pair of major exporters, it is possible that for all other pairs of countries the
product would behave as a HIGH_DIF product57. A more rare event, but also possible is a productthat behaves as LOP for some pairs of countries and as DIF or HIGH-DIF for others58. Here again
I have used the weight of the countries in the market to decide how to classify the product. These
sorts of hybrid products can be found in the real world, given that what I am calling a tradedproduct here is determined by the existing harmonized (HS) and standard international trade(SITC) systems of classification of traded goods. The methodology applied in this work is,therefore, able to capture the complexity of this reality.
By choosing very high levels of disaggregation, one has to sacrifice the coverage of one’sclassification. The sample here examined covers more than one third of US imports, but less than
one fourth of world imports of resource-based products, excluding energy-related goods.
One fundamental limitation of the data is that they refer to imports by countries rather than byfirms. Intra-firm trade may cause prices to behave in a manner that would not occur otherwise.
Hence, products may be classified differently in different markets because of the presence of
intra-firm trade59 in one market but not in another. The same can happen if domestic suppliers(US suppliers in our case) offer a product that has very different elasticities of substitution withregard to the different exporters to the domestic market. Finally, time may change thecharacteristics of a product. Because this methodology does not consider a point in time, but aperiod of time, it is quite possible that the product being analysed is in the process of moving fromone classification to another, blurring somewhat the result.
5.

Conclusions

The countries of East Asia, including China, and Mexico, that have raised their ranks and become
top exporters in the world in the past fifteen years, have all benefited enormously from market
share gains in the very dynamic groups of non-resource-based manufactured goods, especially inhigh-tech products. On the other hand, countries that have gained market share in resource-based
goods, particularly in primary commodities or even agro-industrial products, have generally
suffered from the slow expansion of world imports.
Generally speaking, exporter countries with a low share of resource-based products in totalexports tended to perform better in the last decade than those with high shares. Within Latin
57

This was the case, for example, of unworked diamonds, spirits, baker’s wares and diamonds weighing less than 0.5
carat each. It should be recognized that the distinction between DIF and HIGH-DIF products can be very difficult insome cases.
58
This was the case of, for example, frozen shrimps and coffee, nesoi, not roasted, not decaffeinated. The prices ofshrimp from Thailand, Ecuador and Indonesia follow the law of one price, but Chinese shrimps do not. Likewise,
prices of coffee from Colombia, Guatemala and Indonesia follow the law of one price, but Brazil’s prices are a
determining factor in Brazil’s changes in market share as compared to Colombia and Indonesia. In the case of nickel
powders and flakes, Canada is the main supplier to the US and its price is not related to the prices of the other smaller
suppliers: Australia, Finland and Russia. However, the prices of these three smaller suppliers seem to follow the lawof one price, though there are insufficient data for a proper ADF test. See Appendix A (10), Graph (A-10.1).
59
Long-term contracts between importers and exporters may have similar effects on prices as intra-firm trade.
Products such as aluminium ores, for example, are known to be traded within firms or under long-term contracts. The
stability of Canadian prices of unrefined copper, as opposed to the prices of Chile and Mexico, suggests either thepresence of intra-firm trade or long-term contracts. See Appendix A (10), Graph (A-10.2).
20

America, Mexico and Costa Rica with low shares of resource-based exports performed relatively
well, while Brazil, Colombia and Venezuela, with high shares of resource-based exports, did not
do very well.
However, this paper has shown that within each category of resource-based and non-resourcebased goods, dynamism varies quite considerably. Indeed, the dynamism of the high-tech group isvery much concentrated in few products, whereas the vast majority of high-tech products have notbeen dynamic at all. Furthermore, considering the expansion of US imports as a basis, the rate of
growth of imports of high-tech products has substantially declined in recent years, especially after
2000. Still taking the expansion of US imports until 2002 as a basis, it has been seen that thegrowth-rate of resource-based-low-tech manufactures has been higher than that of non-resourcebased-low-tech manufactures.
Examining the export performance of Latin American countries in the US in 2002 compared to
1996, it could be seen that as a group they had significant gains in market share. Nevertheless,these gains were largely the result of competitiveness gains, while the product composition effect
was relatively small. These gains also came from the group of non-resource-based exports,
whereas the group of resource-based commodities negatively contributed to Latin America’s
competitiveness and product composition gains. The lack of dynamism of imports of resourcebased products accounted for over 80% of Latin America’s losses in this group of commodities.
Latin American countries that had the largest gains in the US market also had large gains in nonresource-based manufactures. Some of them like, for example, Mexico, Honduras, Guatemala andCosta Rica only gained because of their gains in non-resource-based manufactures, as they all
experienced losses of market share in resource-based products in the period.
Most Latin American countries benefited from large and positive product composition effects in
non-resource-based manufactures, especially in low-tech products. But Brazil, Argentina,
Guatemala and Venezuela, for example, had negative product composition effects in their groupof manufactured exports. The Domenican Republic, Chile and Guatemala, for example, had
negative product effects even in their exports of non-resource-high-tech manufactures. This
proves that being an exporter of non-resource-based manufactures, or even of non-resource-basedhigh-tech manufactures, generally dynamic groups of products, provide no guarantee that thecountry’s specific export specialization in a subset of these groups of products is dynamic.
In sharp contrast with Latin America in general, Brazil and Chile have recently gained market
share in resource-based products in the US market. Chile’s gains in this group of products resulted
from positive competitiveness as well as product composition effects. More especially, Chile’s
product composition effect was positive for its exports of primary commodities. This proves,again, that even within a generally no dynamic group of commodities, a country may specialize ina subset of dynamic products.
In point of fact, the new taxonomy presented in this paper, that classifies resource-based products(excluding energy-related goods) according to their degree of differentiation, has shown thatexports of differentiated and highly differentiated products tend to be much more dynamic thanthat of homogeneous products that follow the law of one price.
Moreover, the new taxonomy, despite the relatively small size of the sample of products actuallyclassified, seems to be able to explain why countries like Chile, for example, have their exports ofprimary goods benefiting from positive product composition effects. And the reason is that mostof Chile’s exports of primary and agro-industrial products are classified as differentiated or highly
differentiated products. On the other hand, Brazil’s exports of primary and agro-industrial
21

products are classified as homogeneous goods following the law of one price. Hence, Brazil’s
product composition effects in these products tend to be negative.
The greater dynamism of differentiated and highly differentiated products does not imply thatgovernments ought to be implementing policies designed to promote these exports in particular.
However, there may be a case for both the public and private sectors to try to identify the causes
why exporters in some countries have failed to increase the shares of differentiated and highly
differentiated products in their total exports.
Bibliography
Armington, P. S. (1969). “A Theory of Demand for Products Distinguished by Place ofProduction”. In: IMF Staff Papers, vol. 16, March.
Carlin, W., A. Glyn and J. Van Reenen (2001). “Export Market Performance of OECD Countries:
an empirical examination of the role of cost competitiveness”. The Economic Journal, 111,
p.128-162, January, Oxford.
Chami Batista, J and J.P. Azevedo (2002) “El TLC y las pérdidas de mercado de Brasil en losEstados Unidos: 1992-2001”, Revista de la Cepal, No.78, December.
Chami Batista, J. and G.B. da Silveira (2003) “International Competition: is there a place for thelaw of one price yet?” Discussion Paper, Instituto de Economia/UFRJ, Rio de Janeiro.
Grossman G. & E. Helpman (1991). “Innovation and Growth in the Global Economy”, The MIT
Press, Cambridge.
Hatzichronoglou, T. (1997). “Revision of the High-Technology Sector and Product
Classification”, STI Working Papers, OECD/GD (97)216, Paris.
Krugman P., 1987 Pricing to Market When the Exchange Rate Changes, In: Ardnt, S. W. andRichardson, J.D. (Eds.), Real_Financial Linkages Among Open Economies, Cambridge, MA:
MIT Press, pp. 49-70.
Lall, S (2000). “The Technological Structure and Performance of Developing Country
Manufactured Exports, 1985-1998”, Oxford Development Studies 28(3):337-369.
Leamer, E. and Stern (1970). “Quantitative International Economics”. Aldine Publishing Co.,
Chicago.
Mani, S. (2000). “Exports of High Technology Products from Developing Countries: Is it eal or a
Statistical Artifact”, Discussion Paper Series, Institute for New Technologies, The United
Nations University.
UNIDO (2002). “Industrial Development Report 2002/2003 Competing through Innovation andLearning”, United Nations Industrial Development Organization, Vienna.
USITC (1997), “The Dynamic Effects of Trade Liberalization: An Empirical Analysis”,
Investigation No. 332-375, Publication 3069, US International Trade Commission, October
1997, Washington D.C..
Yang, J., 1998. Pricing-to-market in US imports and exports: a time series and cross-sessional
study, The Quarterly Review of Economics and Finance 38, No. 4, pp. 843-861.

22

TABLE (1): Annual growth-rates of world imports
CATEGORIES

YEAR

1987/2000 1993/2000 1996/2000

RESOURCE-BASED PRODUCTS

5.0%

4.9%

2.5%

PRIMARY
ENERGY-RELATEDCRUDE PETROLEUM
COAL,COKE & OIL DERIVATIVESGAS & ELECTRIC CURRENT
AGRO-INDUSTRIAL
MANUFACTURES
LOW-TECH MANUFACTURES
HIGH-TECH MANUFACTURES

3.3%
5.0%
5.0%
3.7%
8.5%
5.2%
6.4%
N.A.
N.A.

2.3%
6.7%
7.2%
5.0%
8.8%
2.9%
6.2%
6.3%
1.9%

-2.4%
6.4%
7.7%
4.2%
5.5%
-0.5%
3.7%
3.8%
-3.6%

NON-RESOURCE-BASED PRODUCTS
LOW-TECH MANUFACTURES
HIGH-TECH MANUFACTURES

8.0%
N.A.
N.A.

7.1%
4.6%
17.2%

4.8%
3.2%
9.7%

UNALLOCATED

11.0%

9.2%

7.2%

TOTAL

7.1%

6.5%

4.2%

SOURCE: BASED ON UNCTAD DATA (SITC Rev.3, 3 to 5-digit levels)

23

TABLE (2): STRUCTURE OF WORLD IMPORTS
CATEGORIESYEARRESOURCE-BASEDPRIMARY
ENERGY-RELATEDCRUDE PETROLEUM
COAL,COKE & OIL DERIVATIVESGAS & ELECTRIC CURRENT
AGRO-INDUSTRIAL
LOW-TECH MANUFACTURES
HIGH-TECH MANUFACTURESNON-RESOURCE-BASEDLOW-TECH MANUFACTURES
HIGH-TECH MANUFACTURES
UNALLOCATEDTOTAL

1987 1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

37%

36%

35%

34%

32%

31%

30%

30%

31%

30%

27%

27%

29%

10.4% 10.7% 10.6% 9.5% 9.2%
11.1% 8.8% 9.5% 10.6% 10.0%
6.0% 4.7% 5.2% 5.9% 5.5%
3.9% 3.2% 3.4% 3.6% 3.3%
1.2% 0.9% 0.9% 1.0% 1.2%
5.4% 5.5% 5.4% 5.3% 5.1%

8.9%
9.1%
5.1%
3.0%
1.0%
5.2%

9.6% 10.1% 10.1%

9.6%

9.4%

9.1%

8.4%
8.9%
4.9%
3.0%
1.1%
5.2%
8.8%
0.3%

8.4%
7.9%
4.2%
2.6%
1.1%
5.2%
9.0%
0.3%

8.3%
7.5%
4.0%
2.4%
1.0%
5.3%
9.4%
0.3%

8.1%
8.6%
4.7%
2.7%
1.2%
4.9%
8.9%
0.2%

7.7%
8.5%
4.6%
2.6%
1.2%
4.7%
8.9%
0.2%

7.4%
6.4%
3.2%
2.1%
1.1%
4.6%
8.9%
0.2%

6.8% 6.2%
7.3% 10.3%
4.0% 6.0%
2.3% 2.9%
1.1% 1.4%
4.5% 4.0%
8.9% 8.7%
0.2% 0.2%

61%

63%

62%

63%

64%

65%

2%

2%

2%

2%

3%

3%

35%

66% 66% 66% 66% 67% 69% 69% 67%
56.5% 56.3% 55.5% 51.5% 51.4% 52.7% 51.6% 48.9%
61.4% 62.6% 62.1% 62.6% 63.8% 64.8%
9.1% 9.9% 10.6% 14.6% 15.3% 16.3% 17.4% 18.1%
3%

3%

3%

3%

4%

4%

4%

4%

100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

SOURCE: BASED ON UNCTAD DATA (SITC, Rev.3, 3 to 5-digit levels).

24

TABLE (3): Composition of exports by groups of products and countries - 1996/2000
NonLatin Emerging
Other Economies
Developed
developed
America
Asia
Asia in Transition
Countries
Groups of products and countries
countries

AfricaTOTAL

Resource-based products

67.0%

33.0%

10.0%

11.1%

7.2%

2.8%

1.9%

100.0%

Primary
Crude petroleum
Other energy-related products
Agro-industrial
Low-tech manufactures
High-tech manufactures

68.0%
29.0%
57.7%
73.3%
79.7%
77.6%

32.0%
71.0%
42.3%
26.7%
20.3%
22.4%

15.0%
19.8%
6.8%
8.2%
5.1%
3.5%

9.2%
6.7%
18.8%
13.0%
9.9%
14.9%

4.0%
38.4%
8.2%
1.4%
1.5%
3.2%

2.2%
1.5%
3.2%
3.3%
3.3%
0.8%

1.5%
4.5%
5.3%
0.8%
0.5%
0.0%

100.0%
100.0%
100.0%
100.0%
100.0%
100.0%

Non-resource-based products
Low-tech manufactures
High-tech manufactures

75.3%
75.8%
73.4%

24.7%
24.2%
26.6%

3.9%
4.1%
2.9%

17.0%
15.6%
22.6%

1.5%
1.8%
0.2%

2.1%
2.4%
0.9%

0.3%
0.3%
0.1%

100.0%
100.0%
100.0%

Unallocated

86.9%

13.1%

2.4%

9.1%

0.8%

0.8%

0.1%

100.0%

TOTAL

73.8%

26.2%

5.2%

15.5%

2.7%

2.2%

0.6%

100.0%SOURCE: BASED ON UNCTAD DATA (SITC Rev.3, 3 to 5-digit levels)

25

TABLE (4): Structure of exports by groups of products and countries 1996/2000
NonDeveloped
Latin Emerging
developed
Countries
America
Asia
Groups of products and countries
countries

Other Economies in
Asia
Transition

AfricaTOTAL

Resource-based products

20.3%

28.2%

43.0%

16.2%

59.1%

28.3%

68.2%

22.4%

Primary
Crude petroleum
Other energy-related products
Agro-industrial
Low-tech manufactures
High-tech manufactures

5.2%
1.0%
2.3%
4.0%
7.6%
0.2%

6.9%
6.9%
4.7%
4.1%
5.5%
0.2%

16.1%
9.6%
3.8%
6.4%
6.9%
0.1%

3.4%
1.1%
3.6%
3.4%
4.5%
0.2%

8.3%
35.9%
8.7%
2.1%
3.9%
0.2%

5.6%
1.8%
4.3%
6.1%
10.5%
0.1%

13.9%
18.6%
25.1%
5.0%
5.6%
0.0%

5.6%
2.5%
2.9%
4.1%
7.1%
0.2%

Non-resource-based products
Low-tech manufactures
High-tech manufactures

76.5%
61.7%
14.9%

70.5%
55.3%
15.2%

55.8%
47.4%
8.4%

82.3%
60.4%
21.8%

40.1%
39.0%
1.1%

70.8%
64.8%
6.0%

31.4%
30.1%
1.3%

74.9%
60.0%
15.0%

Unallocated

3.1%

1.3%

1.2%

1.6%

0.8%

0.9%

0.4%

2.6%

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%TOTAL

SOURCE: BASED ON UNCTAD DATA (SITC Rev.3, 3 to 5-digit levels)

26

TABLE (5): World share of Latin American exports by groups of products and countries - 1996/2000
Latin
America
BrazilMexico Argentina Venezuela
Chile Colombia
without
Groups of products and countries
MexicoCosta
Rica

Resource-based products

8.18%

2.01%

1.85%

1.33%

1.56%

1.05%

0.63%

0.17%

Primary
Crude petroleum
Other energy-related products
Agro-industrial
Low-tech manufactures
High-tech manufactures

13.17%
12.97%
6.28%
7.05%
4.05%
2.91%

4.03%
0.03%
0.26%
2.80%
1.39%
2.29%

1.83%
6.83%
0.47%
1.18%
1.06%
0.60%

2.83%
1.39%
0.65%
1.60%
0.28%
0.37%

0.18%
8.22%
3.50%
0.12%
0.32%
0.04%

1.72%
0.00%
0.05%
1.07%
1.33%
0.01%

0.95%
1.90%
0.79%
0.21%
0.11%
0.02%

0.50%
0.00%
0.01%
0.16%
0.04%
0.01%

Non-resource-based products
Low-tech manufactures
High-tech manufactures

1.15%
1.28%
0.63%

0.60%
0.66%
0.34%

2.73%
2.84%
2.31%

0.18%
0.21%
0.06%

0.05%
0.06%
0.01%

0.04%
0.05%
0.01%

0.08%
0.09%
0.03%

0.08%
0.07%
0.14%

Unallocated

2.18%

0.79%

0.18%

0.17%

0.04%

0.41%

0.04%

0.01%

TOTAL

2.75%

0.92%

2.47%

0.44%

0.39%

0.28%

0.20%

0.10%

Grey areas represent products in which countries or groups of countries have revealed comparative advantages.
SOURCE: BASED ON UNCTAD DATA (SITC Rev.3, 3 to 5-digit levels)

27

TABLE (6): Structure of exports by groups of products and countries of Latin America - 1996/2000
Latin
America
BrazilMexico Argentina Venezuela
Chile Colombia
without
Groups of products and countries
MexicoCosta
Rica

Resource-based products

66.5%

49.0%

16.8%

68.2%

89.8%

85.7%

70.6%

38.2%

Primary
Crude petroleum
Other energy-related products
Agro-industrial
Low-tech manufactures
High-tech manufactures

26.9%
12.0%
6.7%
10.4%
10.4%
0.2%

24.6%
0.1%
0.8%
12.4%
10.6%
0.4%

4.2%
7.0%
0.6%
1.9%
3.0%
0.0%

36.3%
8.1%
4.3%
14.8%
4.5%
0.2%

2.6%
53.8%
26.3%
1.2%
5.8%
0.0%

35.1%
0.0%
0.5%
15.9%
34.2%
0.0%

26.7%
24.1%
11.5%
4.3%
4.0%
0.0%

28.2%
0.0%
0.4%
6.7%
2.9%
0.0%

Non-resource-based products
Low-tech manufactures
High-tech manufactures

31.4%
28.0%
3.4%

48.8%
43.2%
5.6%

83.0%
69.0%
14.0%

30.8%
28.7%
2.0%

9.9%
9.5%
0.3%

10.3%
9.9%
0.4%

28.9%
26.4%
2.4%

61.6%
41.4%
20.3%

Unallocated

2.1%

2.3%

0.2%

1.1%

0.3%

3.9%

0.5%

0.2%

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%TOTAL

SOURCE: BASED ON UNCTAD DATA (SITC Rev.3, 3 to 5-digit levels)

28

TABLE (7):Top 25 exporters of high-tech products -1996 to 2000
Countries
USA,PR,USVIJAPANGERMANY
UNTD KINGDOMSingapore
FRANCENETHERLANDS
Korea, Republic of
Malaysia
China
IRELAND
CANADA
MexicoPhilippinesITALY
SWITZ_LIECHT
Thailand
SWEDENFINLANDBELGIUM-LUX
SPAIN
DENMARKAUSTRIABELGIUM
China, Hong Kong SAR
Total for TOP 25(US$ 000)
870,591,403
550,369,448
358,969,653
327,498,824
306,705,544
261,693,935
189,354,287
185,562,200
174,673,528
132,274,455
121,377,895
119,964,964
102,085,937
92,808,691
89,417,272
72,230,394
69,428,663
66,973,433
39,218,218
33,175,479
31,768,101
29,523,776
28,928,569
28,181,603
25,385,688

Share
19.4%
12.3%
8.0%
7.3%
6.8%
5.8%
4.2%
4.1%
3.9%
3.0%
2.7%
2.7%
2.3%
2.1%
2.0%
1.6%
1.5%
1.5%
0.9%
0.7%
0.7%
0.7%
0.6%
0.6%
0.6%

4,308,161,960

96.1%

Cummulative
shares
19.4%
31.7%
39.7%
47.0%
53.9%
59.7%
63.9%
68.1%
71.9%
74.9%
77.6%
80.3%
82.6%
84.6%
86.6%
88.2%
89.8%
91.3%
92.2%
92.9%
93.6%
94.3%
94.9%
95.5%
96.1%

WORLD TOTAL
4,482,922,090 100.0%
Taiwan is not included, but thirty-two other countries were added
to the list of Appendix A(2). Developed countries are in capital letters.
SOURCE: Based on UNCTAD data

29

TABLE (8): Latin American exports of high-tech products - 1996 to
2000
Countries(US$ 000)

Share

MexicoBrazil
Costa Rica
Argentina
Colombia
Bolivia
Venezuela
Chile
GuatemalaPeru
El Salvador
Ecuador
Uruguay
Barbados
Nicaragua
Others102,085,937
16,380,544
6,002,782
2,811,697
1,451,584
678,580
382,772
333,396
320,196236,730
195,693
108,689
107,230
91,88673,333
124,780

77.7%
12.5%
4.6%
2.1%
1.1%
0.5%
0.3%
0.3%
0.2%
0.2%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%

Total

131,385,829

100.0%SOURCE: Based on UNCTAD data

30

TABLE (9):Top 25 exporters of resource-based products* -1996 to
2000(US$ 000)

Share

Cummulative
shares606,245,589
403,933,396
338,477,183
324,014,244
263,460,940
233,389,019
186,092,360
156,579,747
148,326,047
145,439,473
144,786,896
141,985,455
130,468,313
104,245,540
100,265,297
84,740,105
81,952,277
80,754,648
78,585,016
78,419,396
75,843,997
72,102,666
69,096,133
68,698,438
67,114,112

12%
8%
7%
6%
5%
5%
4%
3%
3%
3%
3%
3%
3%
2%
2%
2%
2%
2%
2%
2%
1%
1%
1%
1%
1%

12%
20%
27%
33%
38%
43%
47%
50%
53%
55%
58%
61%
64%
66%
68%
69%
71%
73%
74%
76%
77%
79%
80%
81%
83%

TOP 25 countries

4,185,016,287

83%WORLD TOTAL

5,064,443,333

100%

Countries
United States
Germany
FranceCanadaNetherlands
United Kingdom
Italy
Japan
Belgium-Luxembourg
China
Spain
Australia
BrazilBelgium
IrelandIndonesia
Switzerland
SwedenThailandFinlandDenmarkArgentina
Chile
Malaysia
Korea, Republic of* It excludes energy-related goods.
SOURCE: Based on UNCTAD data.

31

TABLE (10): ANNUAL GROWTH-RATES OF US IMPORTS
CATEGORIES

YEAR

1989/02 1993/02 1996/02

RESOURCE BASED

6.6%

7.9%

7.4%

PRIMARY
ENERGYCRUDE PETROLEUM
COAL,COKE&OIL DERIVATIVESGAS AND ELECTRIC CURRENT
AGRO-INDUSTRIAL
MANUFACTURES
LOW-TECH MANUFACTURES
HIGH-TECH-MANUFACTURES

4.1%
5.6%
1.6%
9.1%
16.7%
6.9%
8.5%
8.6%
3.2%

3.7%
8.6%
3.0%
15.4%
18.2%
6.8%
9.4%
9.6%
0.0%

1.4%
10.0%
6.7%
11.9%
17.0%
5.5%
8.1%
8.3%
-4.2%

NON-RESOURCE-BASED

8.2%

7.5%

6.7%

LOW-TECH-MANUFACTURES
HIGH-TECH-MANUFACTURES

7.6%
10.0%

7.3%
8.1%

7.1%
5.6%

UNALLOCATED

23.5%

26.8%

10.5%

TOTAL

8.2%

8.2%

7.0%

SOURCE: USITC

32

TABLE (11): STRUCTURE OF US IMPORTS
CATEGORIES

YEAR

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

RESOURCE BASED

28.9% 29.9% 27.8% 26.5% 25.2% 24.0% 23.9% 24.7% 24.0% 21.6% 21.8% 24.6% 24.7% 24.1%

PRIMARY
ENERGYCRUDE PETROLEUM
COAL,COKE&OIL DERIVATIVESGAS AND ELECTRIC CURRENT
AGRO-INDUSTRIAL
MANUFACTURES
LOW-TECH MANUFACTURES
HIGH-TECH-MANUFACTURES

5.2% 5.0%
5.1%
5.0%
11.2% 13.2% 11.3% 10.4%
7.6% 9.0%
7.8%
7.3%
2.9% 3.4%
2.7%
2.2%
0.7% 0.8%
0.8%
0.8%
3.5% 3.3%
3.2%
3.2%
9.0% 8.4%
8.2%
7.9%
8.9% 8.2%
8.0%
7.7%
0.2% 0.2%
0.2%
0.2%

NON-RESOURCE-BASED

71.1% 70.1% 72.2% 73.5% 74.8% 76.0% 76.1% 71.6% 72.2% 74.2% 73.5% 71.1% 70.8% 71.4%

LOW-TECH-MANUFACTURES
HIGH-TECH-MANUFACTURES

56.3% 55.3% 55.7% 56.1% 56.8% 57.1% 55.5% 52.0% 52.5% 54.5% 53.6% 50.7% 52.2% 53.7%
14.8% 14.8% 16.5% 17.4% 18.0% 18.9% 20.6% 19.6% 19.8% 19.6% 20.0% 20.4% 18.6% 17.7%

UNALLOCATED

1.0%

TOTAL

1.0%

1.2%

1.2%

4.6%
9.6%
6.7%
2.0%
0.9%
3.3%
7.7%
7.5%
0.2%

1.1%

4.3%
8.6%
5.9%
1.7%
1.0%
3.2%
7.9%
7.7%
0.2%

1.0%

4.3%
8.0%
5.7%
1.4%
0.9%
3.1%
8.5%
8.3%
0.2%

1.0%

4.2%
9.4%
5.7%
2.5%
1.2%
3.1%
8.0%
7.9%
0.2%

3.6%

4.2%
8.4%
4.5%
2.6%
1.4%
3.1%
8.2%
8.1%
0.2%

3.7%

4.0%
6.0%
2.8%
2.0%
1.2%
3.0%
8.5%
8.4%
0.2%

4.3%

3.6%
3.2%
3.2%
6.6% 10.0% 10.0%
3.1%
4.7%
4.4%
2.3%
3.4%
3.2%
1.3%
1.9%
2.4%
3.0%
2.7%
2.9%
8.6%
8.7%
8.7%
8.5%
8.6%
8.6%
0.1%
0.1%
0.1%

4.6%

4.3%

4.5%

3.2%
9.4%
4.7%
2.9%
1.8%
3.0%
8.5%
8.4%
0.1%

4.5%

100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

SOURCE: USITC

33

Table (12): Gains and Losses of Latin American Countries in the US - 1996/2002(a) Total Effectin US$

Latin America

MexicoBrazilGuatemalaArgentina

Chile

Resource-based-7,601,855,899 -1,087,490,842

633,852,995-278,517,987-103,801,939

237,890,393

primary
agro
energyManufactures
low-techhigh-tech-5,474,570,276 -1,725,452,486
604,114,883
952,402,297-3,550,661,167 -255,336,165
819,260,662-59,104,487
786,007,744-72,777,475
33,252,917
13,672,988-747,299,711
36,244,023
904,057,075
440,851,608
416,313,605
24,538,003-203,879,068-125,590,075
63,639,669-12,688,513-12,692,472
3,959-336,385,039-101,795,173
138,974,279
195,403,993
187,548,194
7,855,799

106,108,789
116,909,456
46,906,800-32,034,653-32,388,973
354,320

27,831,474,490 25,156,111,412 1,823,625,410
low-tech manufactures 15,672,100,656 16,021,283,814 -772,797,103
high-tech manufactures 12,159,373,834 9,134,827,597 2,596,422,513
Unallocated1,864,941,446 1,514,295,139 247,042,935

652,289,313
638,924,768
13,364,545
19,984,117

71,334,977
67,908,266
3,426,712
12,540,854

128,632,089
131,205,393-2,573,304-28,705,826

TOTAL

393,755,442-19,926,107

337,816,656

Colombia Domenican Rep

Venezuela

OtherCountries

Non-Resource-Based

22,094,560,037 25,582,915,709 2,704,521,340
Honduras

Costa Rica

Resource-based-231,725,402-172,477,524-969,411,890-274,185,917 -4,019,228,342 -1,336,759,444

primary
agro
energyManufactures
low-techhigh-tech-225,057,444
630,928
0-7,298,886-7,298,886
0-145,799,592-54,952,144
8,679,593
19,594,619
19,594,619
0-711,407,754
1,256,412-386,196,267
126,935,718
126,935,718
0-108,702,832 -153,283,261 -1,223,411,878-140,545,505-16,143,153-64,302,183-969,017 -4,084,258,265
13,841,131-23,968,562 234,456,337-62,886,514-23,992,540 239,113,031-54,347,078
23,978-4,656,694-8,539,436

Non-Resource-Basedlow-tech manufactureshigh-tech manufactures
Unallocated692,024,564
691,313,568
710,996
117,505,798

346,346,310-178,059,046
524,405,356
58,666,434-63,228,259-55,888,652-7,339,607-53,354,377-920,466,530-889,833,139-30,633,392-45,417,263

TOTAL

577,804,960

232,535,220 -1,085,994,526

in US$-201,574,297-202,185,558
611,261
33,023,971

146,379,502
220,228,345-73,848,843-10,640,335-1,240,069,710 -4,187,778,669 -1,201,020,27834

LatinAmerica

MexicoBrazilGuatemalaArgentina

Chile

Resource-based-11.8%

-4.8%

11.2%

-27.4%

-4.1%

6.7%

primary
agro
energyManufactures
low-techhigh-tech-37.7%
9.3%-10.5%
8.4%
8.1%
29.5%

-39.3%
32.4%
-2.2%
-1.6%
-2.0%
36.5%

-52.5%
3.1%
77.7%
23.3%
22.7%
39.1%

-27.9%-131.6%38.2%-51.8%-51.8%
100.0%-71.4%-30.8%
9.7%
60.2%
59.4%
89.1%

5.3%
18.3%
64.7%
-3.8%
-3.8%
55.8%

Non-Resource-Based

20.5%

23.5%

17.6%

31.1%

8.4%

33.5%

low-tech manufactureshigh-tech manufactures
Unallocated

14.2%
48.2%
21.1%

18.7%
42.9%
26.0%

-10.7%
81.9%
31.6%

30.7%
83.9%
45.4%

8.3%
11.5%
12.1%

35.2%
-22.7%-14.7%

TOTAL

10.6%

18.8%

16.1%

12.5%

-0.6%

8.2%

Honduras

Costa Rica

Colombia Domenican Rep

Venezuela

OtherCountries

Resource-based-44.5%-15.1%-22.0%-49.4%-28.6%-15.9%

primary
agro
energyManufactures
low-techhigh-tech-54.8%
0.7%
0.0%-44.2%-44.2%
0.0%-14.7%-97.0%
100.0%
22.4%
22.4%
0.0%

-67.9%
0.9%-13.6%
34.1%
34.1%
0.0%

-60.5%-39.4%-892.9%-133.4%-133.7%100.0%-26.3%-53.3%-33.7%
17.9%
18.3%-6387.5%-52.8%-10.2%
0.3%
-4.8%
-4.1%
-298.3%

Non-Resource-Basedlow-tech manufactureshigh-tech manufactures
Unallocated

24.7%
24.7%
78.6%
74.8%

16.8%
-12.2%
86.9%
38.6%

-7.0%
-6.3%
-83.8%-11.3%-25.3%-24.7%-95.1%-35.6%-24.6%-24.7%
20.0%
15.9%

3.3%
5.0%
-129.2%
-1.4%

TOTAL

16.6%

6.9%

-18.8%-28.7%-27.8%-8.8%

as % of exports in 2002

as % of exports in 2002

35

Table (12): continuation(b) Competitiveness Effectin US$

Latin America

MexicoBrazilGuatemalaArgentina

Chile

Resource-based-1,422,742,152

50,948,833 1,595,866,373

231,194,582

432,537,899

184,952,549

primary
agro
energyManufactures
low-techhigh-tech-1,158,077,686 -841,643,383
894,935,914
508,569,713-1,408,190,585 1,153,610,976
248,590,205 -769,588,473
182,303,767 -793,271,939
66,286,437
23,683,466

186,482,879
279,385,919
855,436,050
274,561,524
237,205,724
37,355,801

168,761,276-11,268,313
82,728,717-9,027,098-9,031,057
3,959-26,649,939-17,994,990
283,211,931
193,970,896
185,600,148
8,370,749-16,719,582
100,763,271
43,489,493
57,419,367
57,207,686
211,681

Non-Resource-Basedlow-tech manufactureshigh-tech manufactures
Unallocated

18,267,337,114 16,108,016,428 2,486,790,540
8,064,758,725 8,713,317,001
20,523,008
10,202,578,390 7,394,699,427 2,466,267,532
900,772,915
469,665,750
324,038,012

711,958,965
697,869,844
14,089,121
10,418,474

210,817,646
210,140,028
677,618-8,049,927

85,927,702
84,262,807
1,664,894
25,937,105

TOTAL

17,745,367,877 16,628,631,011 4,406,694,925

953,572,021

635,305,617

296,817,356

Colombia Domenican Rep

Venezuela

OtherCountries

Honduras

Costa Rica

Resource-based-137,278,970-48,986,600-219,020,661-31,952,361 -3,242,298,780-238,705,015

primary
agro
energyManufactures
low-techhigh-tech-131,078,601
6,917,092
0-13,117,461-13,117,461
0-41,186,915-22,756,327
8,679,593
6,277,049
6,277,049
0

20,086,247
27,860,291-343,696,399
76,729,199
76,729,199
0-12,531,057-21,625,359-214,721-33,299,306-1,318,430 -3,343,488,224-17,888,154
156,114,109-17,912,132
158,895,719
23,978-2,781,611-441,973,252
56,973,284-146,844,293
293,139,246
293,720,831-581,585

Non-Resource-Basedlow-tech manufactureshigh-tech manufactures
Unallocated288,023,864
287,330,884
692,980
107,812,992

109,168,658-402,242,776
511,411,434
46,504,773-112,631,637-104,200,375-8,431,262-68,875,990-1,144,810,144-1,125,092,932-19,717,212-32,852,480-166,235,577-166,203,573-32,0034,462,445-309,689,330-150,945,193-158,744,137
21,711,761

TOTAL

258,557,886

106,686,830-400,528,288-1,209,614,986 -3,404,071,912-526,682,584

in US$

36

Table (12):continuation
as % of exports in 2002

Latin
America

MexicoBrazilGuatemalaArgentina

Chile

Resource-based

-2.2%

0.2%

28.3%

22.7%

16.9%

5.2%

primary
agro
energyManufactures
low-techhigh-tech

-8.0%
13.8%
-4.2%
2.5%
1.9%
58.9%

-19.2%
17.3%
9.8%-21.4%-22.3%
63.2%

13.1%
24.1%
73.6%
14.5%
13.0%
59.6%

23.1%
-11.8%
49.7%-36.8%-36.8%
100.0%

-5.7%
-5.4%
19.8%
59.8%
58.8%
94.9%

-0.8%
15.7%
60.0%
6.8%
6.8%
33.4%

Non-Resource-Basedlow-tech manufactureshigh-tech manufactures
Unallocated

13.5%
7.3%
40.5%
10.2%

15.0%
10.1%
34.7%
8.1%

23.9%
0.3%
77.7%
41.5%

34.0%
33.5%
88.4%
23.7%

24.7%
25.5%
2.3%
-7.8%

22.4%
22.6%
14.7%
13.3%

TOTAL

8.5%

12.3%

26.2%

30.2%

18.1%

7.2%

Honduras

Costa Rica

Resource-based-26.3%

-4.3%

-5.0%

-5.8%

-23.1%-2.8%

primary
agro
energyManufactures
low-techhigh-tech-31.9%
7.4%
0.0%-79.5%-79.5%
0.0%-4.2%-40.2%
100.0%
7.2%
7.2%
0.0%

1.9%
20.7%
-12.1%
20.6%
20.6%
0.0%

-7.0%
-0.1%
-1214.8%-99.6%-99.8%
100.0%-3.7%-109.9%-27.6%
11.9%
12.2%-3815.5%-19.1%
9.1%
-3.5%
22.3%
22.4%
-20.3%

Non-Resource-Based

10.3%

5.3%

-12.5%-31.5%-20.2%-6.9%

low-tech manufactureshigh-tech manufactures
Unallocated

10.3%
76.6%
68.6%

-27.5%
84.8%
30.6%-11.7%-96.3%-14.5%-31.2%-61.2%-25.7%-20.3%
-1.0%
2.1%

-3.4%
-277.7%
2.8%

TOTAL

7.4%

3.2%

-6.9%

-28.0%-22.6%-3.8%

as % of exports in 2002

Colombia Domenican Rep

Venezuela Other Countries

37

Table (12):continuation(c) Product Composition EffectLatin America
in US$

MexicoBrazilGuatemalaArgentina

Resource-based-6,179,113,747 -1,138,439,675-962,013,377-509,712,569-536,339,838

52,937,844

primary
agro
energyManufactures
low-techhigh-tech-4,316,492,590 -883,809,103-290,821,031
443,832,584-2,142,470,582 -1,408,947,141
570,670,457
710,483,986
603,703,977
720,494,463-33,033,520-10,010,478-933,782,590-243,141,896
48,621,025
166,290,084
179,107,881-12,817,798-372,640,344-114,321,762-19,089,049-3,661,414-3,661,414
0-309,735,100-83,800,182-144,237,652
1,433,097
1,948,047-514,950

122,828,371
16,146,185
3,417,307-89,454,019-89,596,658
142,639

Non-Resource-Based

9,564,137,376 9,048,094,983-663,165,131-59,669,652-139,482,668

42,704,388

low-tech manufactures 7,607,341,931 7,307,966,813
high-tech manufactures 1,956,795,444 1,740,128,171
Unallocated964,168,531 1,044,629,389-793,320,111
130,154,981-76,995,077-58,945,076-724,575
9,565,642-142,231,762
2,749,094
20,590,781

46,942,586-4,238,199-54,642,931

TOTAL

4,349,192,160 8,954,284,697 -1,702,173,585-559,816,578-655,231,724

40,999,300

in US$

Honduras

Costa Rica

Colombia

Domenican Rep Venezuela

Chile

Other
Countries

Resource-based-94,446,432-123,490,924-750,391,229-242,233,555-776,929,562 -1,098,054,429

primary
agro
energyManufactures
low-techhigh-tech-93,978,843-6,286,165
0
5,818,576
5,818,576
0-104,612,677-32,195,817
0
13,317,570
13,317,570
0-731,494,001-26,603,879-42,499,868
50,206,519
50,206,519
0-96,171,775-140,330,784
349,412-6,080,409-6,080,409
0-131,657,902
17,156,153-740,770,041
78,342,228
80,217,312-1,875,083-781,438,626-121,275,468
160,685,424-356,025,760-348,067,909-7,957,851

Non-Resource-Based404,000,700

237,177,652

49,403,378

224,343,614-35,338,721

456,068,831

low-tech manufactureshigh-tech manufactures
Unallocated403,982,684
18,016
9,692,805

224,183,730
12,993,922
12,161,661

48,311,723
1,091,655
15,521,613

235,259,793-10,916,179-12,564,783-35,981,985
643,264
28,561,526

371,173,537
84,895,294-32,352,096

TOTAL

319,247,073

125,848,389-685,466,238-30,454,724-783,706,757-674,337,694

38

LatinAmerica

MexicoBrazilGuatemalaArgentina

Chile

Resource-based

-9.6%

-5.0%

-17.1%-50.1%-21.0%

1.5%

primary
agro
energyManufactures
low-techhigh-tech-29.7%
-4.5%
-6.3%
5.8%
6.2%
-29.3%-20.1%
15.1%-11.9%
19.8%
20.3%-26.7%-65.6%-21.0%
4.2%
8.8%
9.8%
-20.5%-51.0%-119.8%-11.5%-14.9%-14.9%
0.0%-65.7%-25.3%-10.1%
0.4%
0.6%
-5.8%

6.2%
2.5%
4.7%
-10.6%-10.6%
22.5%

Non-Resource-Based

7.1%

8.4%

-6.4%

-2.8%

-16.4%

11.1%

low-tech manufactureshigh-tech manufactures
Unallocated

6.9%
7.8%
10.9%

8.5%
8.2%
17.9%

-11.0%
4.1%
-9.9%

-2.8%
-4.5%
21.7%

-17.3%
9.2%
19.9%

12.6%
-37.4%-28.0%

TOTAL

2.1%

6.6%

-10.1%-17.7%-18.6%

1.0%

Honduras

Costa Rica

Colombia Domenican Rep

Venezuela

OtherCountries

Resource-based-18.1%-10.8%-17.1%-43.7%-5.5%-13.0%

primary
agro
energyManufactures
low-techhigh-tech-22.9%
-6.7%
0.0%
35.3%
35.3%
0.0%

-10.5%-56.8%
0.0%
15.3%
15.3%
0.0%

-69.9%-19.8%
-1.5%
13.5%
13.5%
0.0%

-53.5%-39.3%
322.0%-33.8%-33.9%
0.0%-22.6%
56.6%
-6.1%
6.0%
6.1%
-2572.0%-33.8%-19.3%
3.9%-27.1%-26.5%-278.0%Non-Resource-Based

14.4%
14.4%
2.0%
6.2%

11.5%
15.3%
2.2%
8.0%

5.5%
5.4%
12.5%
3.3%

6.2%
6.5%
-33.9%
-9.8%

-4.3%
-4.4%
21.0%
13.7%

10.1%
8.4%
148.5%-4.2%

as % of exports in 2002

as % of exports in 2002

low-tech manufactureshigh-tech manufactures
Unallocated39TABLE (13): COMPOSITION OF THE SAMPLE AND OF US IMPORTS
OF RESOURCE-BASED PRODUCTS FROM 1996 TO 2002
SAMPLEPRIMARY
AGRO-INDUSTRY
MANUFACTURES
LOW-TECH MANUF.
HIGH-TECH MANUF.

US IMPORTS
19%
24%
32%
20%
49%
57%
48%
56%
1%
1%

TOTAL

100%

100%

TABLE (14): DISTRIBUTION BY GROUPS OF COUNTRIES OF RESOURCE-BASEDPRODUCTS CLASSIFIED BY DEGREE OF DIFFERENTIATIONSHARE IN
SUBTOTALSHARE IN
TOTAL

15.8%
4.0%
66.3%
5.4%
8.5%
100.0%

10.2%
2.6%
42.9%
3.5%
5.5%
64.7%

9.6%
5.6%
59.8%
5.2%
19.8%
100.0%

2.3%
1.3%
14.2%
1.2%
4.7%
23.8%

12.0%
19.0%
62.3%
1.6%
5.1%
100.0%

1.4%
2.2%
7.2%
0.2%
0.6%
11.5%

LATIN AMERICA WITHOUT MEXICOMEXICODEVELOPED COUNTRIESEAST ASIA
OTHERS

13.9%
6.1%
64.3%
4.9%
10.8%

13.9%
6.1%
64.3%
4.9%
10.8%

TOTAL

100.0%

100.0%LOP
LATIN AMERICA WITHOUT MEXICOMEXICODEVELOPED COUNTRIESEAST ASIA
OTHERSSUBTOTALDIF
LATIN AMERICA WITHOUT MEXICOMEXICODEVELOPED COUNTRIESEAST ASIA
OTHERSSUBTOTALHIGH DIF
LATIN AMERICA WITHOUT MEXICOMEXICODEVELOPED COUNTRIESEAST ASIA
OTHERSSUBTOTALTOTAL

40TABLE (15): Distribution of US Imports of Machinery* by Groups ofCountries
LATIN AMERICA
EXCLUDING MEXICODEVELOPED COUNTRIESEAST ASIA
OTHERS

4.9%
4.5%
92.3%
2.3%
0.5%

TOTAL

100.0%*Machinery for the production of resource-based products
SOURCE: BASED ON USITC DATA.

41

TABLE (16): TOP 10 COUNTRIES IN LATIN AMERICA BY GROUP OF RESOURCE-BASEDPRODUCTSCOUNTRIESLOPCOUNTRIESDIF

MexicoBrazilChile
Peru
Colombia
EcuadorGuatemalaVenezuela
Honduras
Costa Rica
Others

4.0%
3.9%
2.0%
2.0%
1.7%
1.5%
1.0%
0.8%
0.5%
0.5%
1.8%

MexicoChile
BrazilColombia
Jamaica
Argentina
Costa Rica
GuyanaPeru
GuatemalaOthers

5.6%
4.8%
1.6%
0.8%
0.6%
0.4%
0.3%
0.2%
0.2%
0.2%
0.7%

TOTAL

100.0%

COUNTRIES HIGH-DIF
MexicoChile
BrazilArgentina
Dominican Rep
Colombia
Jamaica
Uruguay
EcuadorVenezuela
Others100.0%

19.0%
8.8%
0.9%
0.8%
0.4%
0.3%
0.2%
0.1%
0.1%
0.1%
0.3%

COUNTRIES TOTAL
MexicoChile
BrazilPeru
Colombia
EcuadorGuatemalaVenezuela
Argentina
Costa Rica
Others100.0%

6.1%
3.4%
3.0%
1.3%
1.3%
1.0%
0.7%
0.6%
0.5%
0.4%
1.6%
100.0%TABLE (17): ANNUAL RATES OF GROWTH OF LOP, DIF AND HIGH-DIF PRODUCTS
FROM 1996 TO 2002*
LOP PRODUCTS
3.5%
DIF PRODUCTS
8.9%
HIGH-DIF PRODUCTS
8.0%*growth-rates were calculated by OLS regressing annual import data in logarithmic form on
years from 1996 to 2002.
TABLE (18): STRUCTURE OF RESOURCE-BASED PRODUCTS BY THE DEGREE OF
DIFFERENTIATIONWORLD
DEVELOPED COUNTRIESEAST ASIA
LATIN AMERICAMEXICOLATIN AMERICA EXCL. MEXICO
ARGENTINA
BRAZILCHILE
COLOMBIACOSTA RICADOMENICAN REP
GUATEMALAHONDURASVENEZUELAOTHERS LATIN AMERICAOTHERSLOP

DIF

HIGH-DIF

TOTAL

65%
67%
71%
64%
43%
74%
61%
84%
38%
84%
80%
47%
95%
99%
82%
90%
51%

24%
22%
25%
18%
22%
16%
19%
12%
33%
14%
19%
3%
5%
1%
17%
8%
44%

12%
11%
4%
18%
36%
10%
21%
4%
29%
2%
1%
50%
0%
0%
1%
2%
5%

100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%

42

TABLE (19): MARKET SHARE ANALYSIS OF US IMPORTS OF RESOUREC-BASED PRODUCTS
FROM LATIN AMERICAN COUNTRIES* - 2002/1996
PRODUCT EFFECTS
COUNTRIES
Argentina
BrazilChile
Colombia
Costa Rica
Dominican Rep
GuatemalaHonduras
MexicoVenezuela
OthersAL

PRIMARY

AGRO-277,574,328-847,292,724
198,107,220-661,457,313-59,308,005-84,688,701-335,438,698-68,654,997-640,255,592-102,362,416-640,510,728-3,519,436,281-66,570,791-198,401,088
36,991,906-21,293,099-27,750,362-120,516,063-105,517,878-2,575,633
523,128,004
19,005,339-93,713,513-57,213,178

LOW-TECH HIGH-TECH
7,069,776
221,466,821-55,438,929
46,373,290
14,290,218-4,428,672-2,179,712
6,766,435
864,658,886
121,900,505-294,590,449
925,888,170

TOTAL-476,678-11,298,789
153,804
00
0
00-9,061,436-1,686,714-7,503,733-29,873,547-337,552,021-835,525,780
179,814,001-636,377,122-72,768,149-209,633,436-443,136,288-64,464,196
738,469,862
36,856,714-1,036,318,423-2,680,634,837

LOW-TECH HIGH-TECHTOTALCOMPETITIVENESS EFFECTS
COUNTRIES
Argentina
BrazilChile
Colombia
Costa Rica
Dominican Rep
GuatemalaHonduras
MexicoVenezuela
OthersAL

PRIMARY

AGRO-26,649,939
186,482,879-16,719,582
20,086,247-41,186,915-12,531,057
168,761,276-131,078,601-841,643,383-21,625,359-441,973,252-1,158,077,686-17,994,990
279,385,919
100,763,271
27,860,291-22,756,327-214,721-11,268,313
6,917,092
508,569,713-33,299,306
56,973,284
894,935,914

PRIMARY

AGRO-304,224,267-660,809,845
181,387,638-641,371,066-100,494,920-97,219,758-166,677,422-199,733,598-1,481,898,975-123,987,775-1,082,483,980-4,677,513,967-84,565,781
80,984,832
137,755,177
6,567,193-50,506,689-120,730,784-116,786,191
4,341,459
1,031,697,717-14,293,967-36,740,229
837,722,736

184,183,321
209,436,767
57,392,621
53,570,050
6,101,506-16,700,652-9,009,024-13,094,381-778,939,946
159,811,556
298,642,797
151,394,615

8,370,749
37,355,801
211,6810023,9783,959
023,683,466-2,781,611-581,585
66,286,437

147,909,141
712,661,366
141,647,992
101,516,588-57,841,736-29,422,452
148487897.8-137,255,890-1,088,330,151
102,105,280-86,938,756-45,460,719

LOW-TECH HIGH-TECH

TOTAL

TOTAL EFFECTS
COUNTRIES
Argentina
BrazilChile
Colombia
Costa Rica
Dominican Rep
GuatemalaHonduras
MexicoVenezuela
OthersAL

191,253,097
430,903,588
1,953,693
99,943,340
20,391,724-21,129,324-11,188,736-6,327,947
85,718,940
281,712,062
4,052,348
1,077,282,785

7,894,071
26,057,011
365,4850023,9783,959
014,622,030-4,468,325-8,085,319
36,412,890-189,642,880-122,864,414
321,461,993-534,860,534-130,609,885-239,055,888-294,648,390-201,720,086-349,860,288
138,961,995-1,123,257,180-2,726,095,556*The analysis includes all countries, though only the results of Latin American countries are shown.

43

TABLE (20): MARKET SHARE ANALYSIS OF RESOURCE-BASEDPRODUCTS BY THE DEGREE OF DIFFERENTIATION - 2002/1996*
PRODUCT EFFECTS
COUNTRY

LOP

DIF

Argentina
BrazilChile
Colombia
Costa Rica
Dominican Rep
GuatemalaHonduras
MexicoVenezuela
Other CountriesLA-82,760,686
6,113,133-344,882,176 -18,576,628-50,118,333 260,954,811-357,352,880 -48,024,695-7,591,129
3,617,109-34,219,090
336,757-217,509,669-4,802,828-26,864,470-277,464-427,569,035 125,144,270-116,101,351 21,922,577-298,716,988 -67,217,070-1,963,685,806 279,189,972

HIGH-DIF

TOTAL

5,849,534-70,833,436
11,335,921-352,384,257
175,351,907
386,067,639
3,551,787-401,968,713
236,779-3,771,153
25,871,211-8,022,354
206,429-222,193,435
143,121-27,034,761
263,997,324-38,787,999
5,591,781-88,663,017
9,280,444-356,956,052
501,416,240 -1,184,547,538

COMPETITIVENESS EFFECTS
COUNTRY

LOP

Argentina
BrazilChile
Colombia
Costa Rica
Dominican Rep
GuatemalaHonduras
MexicoVenezuela
Other Countries
LA

DIF

90,289,898 24,325,625
299,120,918 -85,014,398
223,022,508 -208,820,082
25,423,470 14,624,145-21,199,901 14,224,542-8,586,431
1,274,406-4,619,853
6,953,269-41,165,384-180,193-543,196,884 87,183,049
3,313,466 -61,132,159
32,845,311 -24,971,521
55,247,117 -231,533,316

HIGH-DIF
43,453,803
39,047,182-31,983,859
309,917-506,933-17,134,695
327,227
517,395
502,520,074
1,620,668
30,734,542
568,905,321

TOTAL
158,069,325
253,153,701-17,781,432
40,357,532-7,482,292-24,446,720
2,660,643-40,828,182
46,506,239-56,198,025
38,610,951
392,621,739

TOTAL EFFECTS
COUNTRY

LOP

DIF

HIGH-DIF

TOTAL

Argentina
7,529,212 30,438,758
49,303,336
87,235,889
Brazil-45,761,258 -103,591,027
50,383,103-99,230,555
Chile
172,904,176 52,134,729 143,368,049
368,286,207
Colombia-331,929,411 -33,400,549
3,861,704-361,611,182
Costa Rica
-28,791,031 17,841,650-270,154-11,253,445
Dominican Rep-42,805,521
1,611,163
8,736,517-32,469,074
Guatemala-222,129,522
2,150,442
533,657-219,532,791
Honduras-68,029,854-457,656
660,516-67,862,943
Mexico-970,765,919 212,327,319 766,517,398
7,718,239
Venezuela-112,787,885 -39,209,581
7,212,449-144,861,042
Other Countries-265,869,059 -92,188,590
40,014,986-318,345,101
LA-1,908,436,072 47,656,656 1,070,321,560-791,925,799*The analysis includes all countries, though only the results of Latin Americancountries are shown.

44

APPENDIX A (1): SITC products classified by the degree of manufacturing andtechnology intensity.
(1) PRIMARY GOODS: SITC 0,1,2 and 4, excluding those products classified as agroindustrial.
(2) AGRO-INDUSTRIAL GOODS: SITC 01 (excl. 011), 023, 024, 025, 035, 037, 046,
047, 048, 056, 058, 06, 073, 098, 1 (excl. 121), 232 (OR 233 rev.2), 247, 248, 25, 264,
265, 269 and 4.
(3) ENERGY-RELATED GOODS: SITC 3.
(4) RESOURCE-BASED MANUFACTURES: SITC 51 (excl. 512 and 513), 52 (excl.
525 or 524 rev.2), 53 (excl. 533), 551, 592, 62, 63, 641, 66 (excl. 665 and 666) and 68.
(5) RESOURCE-BASED-HIGH-TECH MANUFACTURES: SITC 52222, 52223,
52229, 53111, 53112, 53113, 53114, 53115, 53116, 53117, 53119, 53121, 53122.
(6) RESOURCE-BASED-LOW-TECH MANUFACTURES: (4) – (5).
(7) NON-RESOURCE-BASED MANUFACTURES: SITC 5, 6, 7, 8 – (4).
(8) NON-RESOURCE-BASED-HIGH-TECH: SEE HATZICHRONOGLOU (1997).
(9) NON-RESOURCE-BASED-LOW-TECH MANUFACTURES: (7) –(8).
(10) UNALLOCATED: SITC 9.

45

APPENDIX A (2): Non-developed countries
LATIN AMERICA
Argentina
Barbados
BelizeBolivia
BrazilChile
Colombia
Costa Rica
EcuadorEl Salvador
GrenadaGuatemalaHonduras
MexicoNicaragua
PanamaParaguay
Peru
Saint Lucia
Saint Vincent and the Grenadines
Trinidad and Tobago
Uruguay
Venezuela
EAST ASIA
China
China, Hong Kong SAR
China, Macao SAR
Indonesia
Korea, Republic of
Malaysia
Philippines
Singapore
Thailand
OTHER ASIA
Bangladesh
CyprusIndia
JordanKuwaitNepal
Oman
Pakistan
Syrian Arab RepublicTurkeyECONOMIES IN TRANSITION
Croatia
Czech Republic

Estonia
Hungary
Kazakhstan
LatviaLithuania
Moldova, Republic of
PolandRomania
Slovakia
Slovenia
AFRICA
Algeria
Egypt
Kenya
Madagascar
Morocco
Mauritius
Saudi Arabia
SenegalTunisia
Zimbabwe

46

APPENDIX A (3): TOP 20 PRODUCTS TRADED IN THE WORLD FROM 1996 TO 2000
PRODUCT DESCRIPTION
Motor vehicles for the transport of persons (other than public transport), nes.
Petroleum oils and oils from betuminous minerals, crude
Digital monolithic units
Parts,auto data.proc mch
tPetroleum oils and oils from betuminous minerals (other than crude), preparations…
Other parts,motor vehl.nes
Storage units, whether or not presented with the rest of the system for data processing
Input or output units whether or not presented with the rest of a system
Goods vehicles, nes
Medicaments,nes,for retail
Airplanes, other aircraft (other than helicopters), unladen weight exceeding 15,000 KG
Digital processing units whether or not presented with the rest of the system
Transmssn,receptn appart
Jerseys, pullovers, cardigans, waistcoats and similar articles, knitted or crocheted.
Part,TV,telecom,etc.eqpt
Natural gas, in the gaseous state
Other parts,vehicle bodies
Footwear nes,leather sole
Other parts,aeroplanes etc
Mch with indiv funct nes

SITC NO. Trade Value in US$ from 1996 to 2000
78120
1,327,265,845
33300
1,218,572,047
77641
714,798,824
75997
555,174,708
33400
415,107,452
78439
324,065,900
75270
269,911,263
75260
247,578,786
78219
228,738,981
54293
208,328,374
79240
184,192,732
75230
183,090,240
76432
172,651,963
84530
134,889,761
76493
132,400,233
34320
125,513,638
78432
124,796,14185148
124,037,948
79295
120,150,065
72849
113,429,998
6,924,694,899

47

Appendix A (4): TOP 20 HIGH-TECH PRODUCTS: 1996-2000
01 Monolithic integrated circuits, digital (Eletronics - Telecomunications)
02 Parts&accessories of automatic data processg machines&units thereof (Computers - Office Machines)
03 Aircraft nes of an unladen weight exceeding 15,000 kg (Aerospace)
04 Storage units, whether or not presented with the rest of a system (Computers - Office Machines)
05 Input o output units,whether o not presentd w the rest of a system etc (Computers - Office Machines)
06 Transmission apparatus,for radioteleph incorporatg reception apparatus (Eletronics - Telecomunications)
07 Digital process units whether/not presentd w the rest of a system etc (Computers - Office Machines)
08 Monolithic integrated circuits, nes (Eletronics - Telecomunications)09 Parts of electrical apparatus for line telephone or line telegraphy (Electronics - Telecomunications)
10 Digital auto data process mach cntg in same housg a CPU input&output (Computers - Office Machines)
11 Electronic integrated circuits and microassemblies, nes (Eletronics - Telecomunications)
12 Parts of turbo-jets or turbo-propellers (Aerospace)
13 Recorded media for sound or other similarly recorded phenomena, nes (Eletronics - Telecomunications)
14 Apparatus, for carrier-current line systems, nes (Eletronics - Telecomunications)
15 Video recording or reproducing apparatus (Eletronics - Telecomunications)
16 Printed circuits (Eletronics - Telecomunications)
17 Aircraft nes of an unladen weight > 2,000 kg but not exceedg 15,000 kg (Aerospace)
18 Turbo-jets (Aerospace)
19 Other machines, having individual functions (Electrical Machinery)
20 Hybrid integrated circuits (Eletronics - Telecomunications)48

APPENDIX (5): MACHINERY USED IN THE PRODUCTION OF RESOURCE-BASED PRODUCTS
Product description
SELF-PROPELLED SCRAPERS
TAMPING MACHINES AND ROAD ROLLERS, VIBRATORY, NEW
BORING OR SINKING MACHINERY, SELF-PROPELLED
MVING, GRDING, LVLING, SCRPING, EXCVTING, EXTRCTING MACHINERYTAMPING OR COMPACTING MACHINERY, NOT SELF-PROPELLEDCOMBINE HARVESTER-THRESHERS, SELF-PROPELLEDPRESSES AND SIMILAR MCH, USED IN THE MANUFACTURE OF BEVERAGESMCH FOR CLNING, SRTING OR GRDING SEED, GRAIN OR DRIED LEGS VEGSMCH USED IN MILLING IND OR WORK CEREALS OR DRIED LEGS VEGS.
BAKERY MACHINERYMACHINERY FOR THE MANUFACTURE OF MACARONI OR SIMILAR PRODUCTS
MCH FOR INDUST PREPARATION OR MANUF OF FOOD OR DRINKMCH FOR THE EXTRCTN OR PREPRTN OF ANIMAL OR FIXED VEG FATS OR OILSTOTAL MACHINERY

classification

share

US Imports ($)*

HS 84293000
HS 8429400020
HS 8430410000
HS 8430505000
HS 8430610000
HS 8433510010
HS 8435100000
HS 8437100000
HS 8437800090
HS 8438100010
HS 8438100090
HS 8438800000
HS 8479200000

4.1%
18.5%
6.7%
19.2%
3.8%
10.1%
2.2%
1.4%
1.0%
13.3%
5.0%
14.2%
0.6%

164151192
740760041
266848301
768789749
151133054
405403287
88991417
56075315
39055974
531581944
201117171
567926611
25019365

100.0%

4006853421* total from 1996 to 2002.

49

APPENDIX (6): RESOURCE-BASED PRODUCTS CLASSIFIED AS LOP PRODUCTS - 1996/2002
Product description
classification growth-rate* shareboneless meat
HS 0201305000
25.2%
2.1%
haddock fillets, nesoi, frozen
HS 0304203062
3.9%
0.2%
shrimps cooked in shell or uncooked, dried, salted or in brine, frozen
HS 03061300
3.8%
9.8%
crab cooked in shell or uncooked, dried, salted or in brine, frozen
HS 03061440
25.2%
1.3%
coffee,arabica,not roasted,not decaff
HS 0901110010-11.7%
4.4%
coffee,nesoi,not roasted,not decaff
HS 0901110090-19.4%
3.5%
coffee,nesoi,roasted,not decaff.
HS 0901210060
10.0%
0.1%
coffee,roasted,decaff.,nesoi
HS 0901220060-18.8%
0.0%
apple juice
HS 200970
-6.3%
1.0%
iron ore agglomerated pellets
HS 2601120030-12.2%
1.1%
silicon less than 99% pureHS 2804695000-11.7%
0.2%
coniferous wood sawn or chipped lengthwise of a thickness exceeding 6mm
HS 44071000-1.2% 24.4%
pine (pinus SPP.) standard wood molding
HS 4409104000
12.3%
1.3%
chemical woodpulp, sulfate or soda, other than dissolving grade, nonconiferous, bleached HS 4703290040
7.5%
2.2%
diamonds nonindustrial (cut, faceted, set or mounted) weighing 0.5 carat and over eachHS 7102390050
13.7% 23.6%
refined copper cathodes and sections of cathodes
HS 7403110000
4.6%
5.0%
nickel, unwrought, not alloyed
HS 7502100000
-2.7%
2.8%
unwrought aluminum not alloyed nesoi
HS 7601106000
1.8%
6.8%
zinc, unwrought, not alloyed, containing by weight 99.99% or more zinc
HS 7901110000
-7.8%
2.5%
tin, unwrought, not alloyed
HS 8001100000
-3.2%
0.8%
paper and paperboard, uncoated, nesoi, in rolls or sheets
SITC 64129
5.6%
7.0%
Total LOP
3.5% 100.0%* growth-rates were calculated by regressing import values in logarithmic form on years from 1996 to 2002.
** total from 1996 to 2002.

US Imports ($)**
4,070,676,698
317,572,457
18,586,178,395
2,430,339,030
8,354,938,447
6,614,868,647
262,722,987
66,662,482
1,839,202,404
2,184,045,799
368,678,271
46,352,336,452
2,443,244,098
4,121,867,053
44,892,166,093
9,452,870,173
5,322,314,780
12,952,062,189
4,687,793,356
1,496,074,147
13,249,136,998
190,065,750,956

50

APPENDIX (7): RESOURCE-BASED PRODUCTS CLASSIFIED AS DIF PRODUCTS
Product description
classification growth-rate*
farmed Atlantic salmon
HS 0304104093
16.9%
salmonidae frozen
HS 0304206006
34.4%
fish fillets
HS 0305306080
4.8%
coffee,not roasted,decaffeinated
HS 0901120000-10.3%
cigarettes
HS 2402208000
33.5%
aluminum ores and concentrates not calcine bauxite
HS 2606000090-13.2%
silicon: containing by weight not less than 99.99% of silicon
HS 2804610000-4.1%
new pneumatic radial tires of rubber used on motor cars
HS 40111010
6.6%
doors and frames of wood
HS 44182080
16.9%
diamonds nonindustrial (cut,faceted,not set or mounted) weighing less than 0.5 carat eachHS 7102390010
0.3%
unrefined copper
HS 7402000000
11.3%
aluminum plates sheets a strip rect inc sq alloy not clad with a thickness of 6.3mm or less, nesoi
HS 7606123090
10.2%
bakers' wares,n.e.s.,communion wfrs,empty cachets for pharmactcl use,sealing wfrs,rice,paper,etc
SITC 04849
10.5%
wine or fresh grapes (other than sparkling wine); grape fermentation by the addition of alcohol
SITC 11217
10.2%
Spirits and distilled alcoholic beverages
SITC 11249
12.9%
Total DIF
8.9%
* growth-rates were calculated by regressing import values in logarithmic form on years from 1996 to 2002.
** total from 1996 to 2002.

share US Imports ($)**
2.3%
1,654,867,083
0.5%
332,822,778
0.3%
210,097,575
2.5%
1,775,742,089
1.2%
832,524,295
2.0%
1,403,308,033
0.9%
617,517,786
17.3% 12,223,581,853
3.0%
2,140,306,211
25.2% 17,733,355,246
4.0%
2,793,875,959
6.9%
4,849,197,312
5.8%
4,077,612,151
17.1% 12,076,710,332
11.0%
7,776,148,996
100.0% 70,497,667,699

51

APPENDIX (8): RESOURCE-BASED PRODUCTS CLASSIFIED AS HIGH-DIF PRODUCTS
Product description
classification growth-rate*
beer made from malt (including ale, stout and porter)(SITC) 11230
11.0%
sugar confectionary (including white chocolate), not containing cocoa, nes(SITC) 6229
10.3%
smoked salmon
HS 03054100
15.5%
crabmeat frozen
HS 03061420
12.2%
cheeseHS 040690
6.4%
avocados
HS 08044000
28.5%
grapesHS 080610
10.0%
fresh apples
HS 08081000
2.3%
blueberries
HS 0810400028
27.6%
coffee,roasted,not decaff.,in retail containers 2kg or lessHS 0901210030
5.4%
coffee,roasted,decaff.,in retail containers 2kg or lessHS 0901220030-3.7%
silicon containing by weight less than 99.99% but not less than 99% of silicon
HS 2804691000
7.7%
diamond except industrial,unworked or simply sawn, cleaved or brutd
HS 7102310000-3.0%
unwrought nickel alloys
HS 7502200000
4.7%
nickel powders
HS 7504000010-4.8%
Total HIGH-DIF
8.0%* growth-rates were calculated by regressing import values in logarithmic form on years from 1996 to 2002.
** total from 1996 to 2002.

share US Imports ($)**
41.3% 14,536,278,236
12.3%
4,313,167,246
0.4%
127,920,501
0.3%
114,076,604
12.0%
4,226,724,132
1.5%
514,859,322
10.1%
3,538,694,009
1.9%
667,811,681
0.5%
168,983,463
1.9%
667,523,316
0.3%
113,534,548
1.9%
653,755,604
13.0%
4,557,847,367
0.8%
273,719,072
1.9%
682,896,793
100.0% 35,157,791,894

52

APPENDIX A (9): GRAPHS OF SOME LOP PRODUCTS

GRAPH (1): Prices of unwrought not alloyed tin (in log)

GRAPH (2): Prices of unwrought not alloyed nickel (in log)

2.6

2.0

2.4
2.2
US$/Kilograms

US$/metric tons

1.8

1.6

2.0
1.8
1.6

1.4

1.4
1.2

1.2
96

97

98

Bolivia

99

00

Brazil

01

China

96

02
Indonesia

97

0.4

0.8

-0.2

00

Canada01

02

NorwayRussiaGRAPH (4): Prices of unwrought not alloyed aluminium

1.0

US$/Kilograms

US$/Kilograms

GRAPH (3): Prices of unwrought not alloyed zinc (in log)

0.6

0.0

99

Australia

Peru

0.2

98

0.6

0.4

0.2

-0.4

0.0
96

97

98

99

Canada00

01

Mexico

02

96

Peru

97

98

Canada99
Russia00

01

02

Other Countries

GRAPH (6): Prices of arabica coffee, not roasted, not decaff.

GRAPH (5): Prices of pine standard wood molding
0.0

2.0

-0.2

1.5

-0.4

1.0

0.5

-0.6

0.0

-0.8
-1.0
96

97

98
BRAZIL

99

00
CHILE

01

02
MEXICO

-0.5
1996

1997

1998

1999

BRAZILCOLOMBIA

2000

2001

GUATEMALA
MEXICO53

APPENDIX A (10): GRAPH (A-10.1) AND (A-10.2)

Graph (A-10.1): Prices of nickel powders and flakes (in log)
2.8

US$/kilograms

2.4
2.0
1.6
1.2
0.8
96

97

98

99

00

Australia
Canada01

02

03

Finland
RussiaGRAPH (A-10.2): Prices of unrefined copper (in log)
1.4

US$/Kilograms

1.2
1.0
0.8
0.6
0.4
0.2
96

97

98
Canada

99

00
Chile

01

02
Mexico54