ArticlePDF Available

Structural Transformation in Ecuador

Authors:

Abstract and Figures

This paper applies new techniques and metrics to analyze Ecuador's past record of and future opportunities for structural transformation. Ecuador's export dynamics and the emergence of new export activities have been the historical drivers of the country's growth, but recently Ecuador's export basket has undergone little structural transformation. The same broad sectors continue to dominate, and the overall sophistication of the export basket has actually declined in recent years. In order to consider why movement to new, more sophisticated export activities has lagged in Ecuador, we examine export connectedness and find that the country is concentrated in a peripheral part of the product space. We quantitatively scan Ecuador's efficient frontier and identify new, high-potential export activities that are nearby in the product space. This sector evaluation provides valuable information for the government to prioritize dialogue and interventions, but it is not meant to be a conclusive identification of "winners". Rather, we provide policy guidelines to facilitate the emergence of these and other new export activities, dealing with the sector-specificity of much of what the government must provide to the private sector to succeed while at the same time avoiding the well-known perils of traditional industrial policies.
Content may be subject to copyright.
Structural Transformation in Ecuador
Ricardo Hausmann
*
Center for International Development and Kennedy School of Government
Harvard University
Bailey Klinger
Center for International Development, Harvard University
Abstract
This paper applies new techniques and metrics to analyze Ecuador’s past record
of and future opportunities for structural transformation. Ecuador’s export
dynamics and the emergence of new export activities have been the historical
drivers of the country’s growth, but recently Ecuador’s export basket has
undergone little structural transformation. The same broad sectors continue to
dominate, and the overall sophistication of the export basket has actually declined
in recent years. In order to consider why movement to new, more sophisticated
export activities has lagged in Ecuador, we examine export connectedness and
find that the country is concentrated in a peripheral part of the product space. We
quantitatively scan Ecuador’s efficient frontier and identify new, high-potential
export activities that are nearby in the product space. This sector evaluation
provides valuable information for the government to prioritize dialogue and
interventions, but it is not meant to be a conclusive identification of ‘winners’.
Rather, we provide policy guidelines to facilitate the emergence of these and other
new export activities, dealing with the sector-specificity of much of what the
government must provide to the private sector to succeed while at the same time
avoiding the well-known perils of traditional industrial policies.
JEL Classification: O54, F19, O14, O33, O40
Keywords: Ecuador, structural transformation
*
Director of the Center for International Development and Professor of the Practice of Economic Development at
the Kennedy School of Government. E-mail: ricardo_hausmann@harvard.edu
Fellow at Harvard’s Center for International Development and Director of the Center’s International Finance Lab.
E-mail: bailey_klinger@hks.harvard.edu
1
1. Introduction
The purpose of this paper is to apply new techniques and metrics to analyze Ecuador’s record of
opportunities for structural transformation. Ecuador’s export dynamics and the emergence of
new export activities have been the key drivers of the country’s cycles of economic growth
during the past 70 years (Cueva, Albornoz and Avellan 2007). But we find that recently
Ecuador’s export basket has undergone little structural transformation over the past decade. The
same broad sectors continue to dominate, and the overall sophistication of the export basket has
actually declined in recent years. This is worrying, because we also show that Ecuador’s existing
export sectors have little room for growth through quality upgrading, and are typical of much
poorer countries.
In order to consider why movement to new, more sophisticated export activities has
lagged in Ecuador, the next section examines export connectedness and finds that the country is
concentrated in a peripheral part of the product space. This section also reveals greater
opportunities for the future than suggested by its current low level of export sophistication.
The final section uses these metrics of sophistication and density to scan Ecuador’s
efficient frontier and identify new, high-potential export activities. Those same metrics are used
to evaluate the government’s emerging list of priority sectors. This sector evaluation provides
valuable information for the government to prioritize dialogue and interventions, but it is not
meant to be a conclusive identification of ‘winners’. Rather, policy guidelines are provided to
facilitate the emergence of these and other activities, dealing with the sector-specificity of much
of what the government must provide to the private sector to succeed while at the same time
avoiding the well-known perils of traditional industrial policies that have been laid out. These
guidelines offer a potential way forward that would allow Ecuador to accelerate its recently
lagging structural transformation and accelerate economic growth and poverty reduction in the
country
2. Ecuador’s Export Basket
A first pass at analyzing the changes in Ecuador’s productive structure is simply to look at the
sectoral composition of the export basket. The figure below shows the composition of exports by
Leamer commodity group in 2000 and 2007.
2
Ecuador’s exports are dominated by oil, whose share has risen during the past seven
years and as of 2007 represented almost 60 percent of export earnings. The other major export
sectors are of tropical agriculture and animal (including seafood) products. These two sectors
together represent nearly the other third of Ecuador’s export earnings. Between 2000 and 2007,
these sectors each grew by 20 percent in absolute terms, but fell as a percentage of total exports
due to the even faster growth in oil exports.
Figure 1
Ecuador’s Exports by Leamer Group
(Percentage)
0 10 20 30 40 50 60 70
Petroleum
Raw Materials
Forest Prod ucts
Tropical
Agricult ure
Animal Produc ts
Cereals, etc.
Labor Intensive
Capital Intensive
Machinery
Chemical
2000
2007
Source: Authors’ calculations using UN COMTRADE.
Oftentimes, looking at export composition in dollar or percentage terms can be
misleading, as changes in world export patterns can be confounded with country-level changes.
In order to get a sense of how Ecuador’s comparative advantage has evolved over the past
decade, we can consider Revealed Comparative Advantage (RCA), which adjusts for the share of
each sector in world exports. We use the Balassa (1986) definition, where xval is the export
value of sector i in country c in year t:
3
=
i c
tic
c
tic
i
tic
tic
tic
xval
xval
xval
xval
RCA
,,
,,
,,
,,
,,
(1)
This is the ratio of the percentage of the sector in a country’s export basket to the
percentage of that sector’s total share in world exports, or alternatively, the percentage of the
country’s market share in that sector to the country’s overall market share in exports. When this
value is above 1, the country is said to have comparative advantage.
Figure 2
Revealed Comparative Advantage by Leamer Group (Index)
0 1 2 3 4 5 6 7 8 9 10
Petroleum
Raw Materials
Fores t Products
Tropical Agricul ture
Animal Products
Cereals, etc.
Labor Intens ive
Capital Intensive
Machinery
Chem ical
2000
2007
Source: Author’s calculations using UN COMTRADE.
Ecuador has comparative advantage in its three main export sectors: petroleum products,
tropical agriculture, and animal products. But some interesting differences can be noted between
figures 1 and 2. First of all, although oil export earnings grew significantly in dollar and
percentage terms, the RCA index for oil exports did not grow significantly, likely due to the
contribution of oil price increases to the growth in earnings, which were similarly enjoyed by
other oil-exporting countries. Second, compared to other countries in the world, animal products
4
and tropical agriculture are very significant sectors in Ecuador’s export basket, more so than
suggested by export shares alone, although they have been falling. Finally, although exports of
cereals are small in value terms ($350 million in 2007), Ecuador very nearly has a comparative
advantage in this sector as well (RCA index of .95). Forestry products also emerge as an
important sector when considering the RCA index rather than export earnings only.
This picture shows that Ecuador’s structure of production is concentrated in oil and
agriculture. In the past seven years, this structure has not undergone any significant changes. It
has shifted somewhat to the oil sector, which has not been growing faster than in other oil
exporting countries. Meanwhile, the other dominant sectors of tropical agriculture and animal
products have not quite kept pace with other major exporters, while cereals have enjoyed
moderate growth.
But these composition changes are difficult to interpret. The Leamer commodity group
are highly aggregated, and within each of them there are sophisticated activities paying higher
wages as well as simple commodities. How is the process of structural transformation proceeding
in Ecuador? Does the relative decline of the non-oil sectors imply that the country is not
successfully moving from simple low-wage sectors to more sophisticated high-wage sectors? Do
these broad categories mask structural transformation at the more disaggregated product level?
To consider these questions and gain a richer understanding, we must apply new methodologies
to analyze the process of structural transformation.
3. Exploring ‘Quality’: Unit Value Gaps
One dimension in which we can examine Ecuador’s export package is quality. Recent research
finds that when a country exports a new product, it tends to enter the market at a lower quality.
But this quality, as measured by unit prices, converges to the global frontier at a rate of 5 to 6
percent per annum unconditionally (Hwang 2007). That is, once a country begins to successfully
export a particular product, its quality increases to the global frontier unconditionally at a
relatively rapid pace. The implication of this finding is that countries that are currently farther
away from the global frontier in products already exported have access to a relatively rapid, and
seemingly unconditional, channel of growth.
To determine if this channel of growth is open to Ecuador, taking the gap in logs between
a country’s unit price for each (Rauch-differentiated) export sector and the world’s frontier price,
5
and the weighting of each sector by its share of the country’s total export basket, we can
calculate country-level quality gaps.
Figure 3
Unit Value Gaps, 1998-2000 Average (Percentage)
0
50
100
150
200
250
300
350
Africa
Latin Americ a &
Caribbean
South Asia
Middle East & No rth
Africa
Europ e & Central Asia
East Asia & Paciric
Ecuado r
Peru
Col ombia
Bolovi a
Brazil
Argen tina
Venezuela
Source: Author’s calculations using Hwang (2006), for only Rauch-differentiated goods. Gaps for the six regions
are median values.
This figure shows that as of the end of the 1990s, Ecuador had the lowest export-
weighted unit value gap among regional comparators. In fact, the space to upgrade quality within
existing export activities was even lower than the median value in Sub-Saharan Africa, which is
the region with the lowest unit value gaps in the world. This means that Ecuador’s non-oil
exports fetch a price per unit comparable to the highest unit prices in the world in those goods,
which suggests that the dimension of export growth through improving quality in existing sectors
does not hold much promise.
Instead, new activities will likely have to emerge. The following section examines
Ecuador’s record of transforming its structure of production towards newer, more sophisticated
sectors.
6
4. Exploring Composition: Export ‘Sophistication’
Recent research by Hausmann, Hwang, and Rodrik (2007) shows that the composition of a
country’s export basket has important implications for economic growth. Countries that have a
more ‘sophisticated’ export basket enjoy accelerated growth, while those that remain in less
sophisticated export sectors lag behind.
The authors measure this sophistication indirectly by examining the wages of countries
who are intensive exporters of each product. First, they measure the sophistication of each
product, which they call PRODY, which is the revealed comparative advantage (RCA)-weighted
GDP per capita of each country that exports the good:
(
)
( )
c
c
j
ctci
ctci
ti
Y
Xxval
Xxval
PRODY
=/
/
,,
,,
,
(2)
where xval
i,c,t
equals exports of good i by country c in year t, X
c
equals total exports by country
c, and Y
c
equals GDP per capita of country c. This is a measure of the GDP per capita of the
‘typical’ country that exports product i. Richer-country goods are more sophisticated and are
associated with higher wages.
This product-level measure of sophistication is then used to measure the sophistication of
a country’s export basket as a whole. The authors call this measure EXPY. EXPY is simply the
PRODY of each good (i) that country c exports, weighted by that good’s share in the country’s
export basket (X
c
). It represents the income level associated with a country’s overall export
package.
ti
itc
tic
tc
PRODY
X
xval
EXPY
,
,
,,
,
=
(3)
Not surprisingly, the level of income implied by a country’s export basket (
EXPY
) is
correlated with actual income. That is, rich countries produce rich-country goods, and poor
countries export poor-country goods. However, there is significant variance in this relationship.
Some countries have managed to discover products that are associated with a level of income
7
much higher than their own, such as China, India, Indonesia, and Ireland. Moreover, Hausmann,
Hwang, and Rodrik show that this variance has important consequences: countries converge to
the relative income level implied by their export basket. In essence, countries become what they
export. This means that if a country has managed to begin exporting a sophisticated export
basket relative to its income level, subsequent growth is higher.
How does Ecuador’s level of export sophistication compare to that of its neighbors? The
figure below shows the evolution of
EXPY
since 1985 for Ecuador, as well as Argentina, Brazil,
Bolivia, Colombia, Peru, and Venezuela.
Figure 4
EXPY over Time (Constant 2000 US$ PPP)
4,000
5,000
6,000
7,000
8,000
9,000
10,000
11,000
12,000
13,000
14,000
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Year
Argentina Bolivia
Brazil Colombia
Ecuador Peru
Venezuela
Source:Author’s calculations using Feenstra (2005) and UN COMTRADE.
This figure reveals interesting shifts in the composition of exports for the major South
American economies. Although Argentina began 1985 with the most sophisticated export basket
in the region, Brazil has since overtaken it, with significant shifts towards more sophisticated
‘rich-country’ type export activities (particularly since the mid-1990s). Colombia, which in 1985
8
had the least sophisticated export basket, also enjoyed rapid structural transformation since the
mid-1990s. As of 2007, Colombia had overtaken Ecuador, Bolivia, Venezuela, and Peru,
although it has trended downward in the past two years. Similarly, over the past three years,
Bolivia and Venezuela have shifted towards export sectors typical of countries with lower levels
of income.
The data show that Ecuador’s performance over the past two decades was one of
sustained but moderate growth in EXPY in the 1990s, followed by two years of decline, another
burst of upgrading from 2002 to 2005, and since then a downward track similar to Bolivia and
Venezuela. As of 2007, the country has one of the lowest levels of export sophistication in the
region.
What are the major products in Ecuador’s export basket that are contributing to this low
level of export sophistication? The table below shows the 10 sectors that are the largest share of
Ecuador’s export basket, with
PRODY
<GDP (i.e. the largest export sectors that are typical of
poorer countries).
Table 1
Top 10 ‘Unsophisticated’ Products in Ecuador’s Export Basket, 2007
Product Name
Exports
(US M)
PRODY
(PPP)
Bananas, including plantains, fresh or dried 1300 6711
Crustaceans 601 3718
Cut flowers, dried flowers for bouquets, etc, 403 3987
Cocoa beans, whole or broken, raw or roasted 190 1855
Palm oil and its fractions, not chemically modified 121 5970
Gold, unwrought, semi-manufactured, powder form 61 3689
Rice 57 5257
Dates, figs, pineapple, avocado, guava, fresh or drie 55 6059
Tobacco unmanufactured, tobacco refuse 31 2311
Coffee, coffee husks and skins and coffee substitutes 23 2747
Source: Author’s Calculation using UN COMTRADE. Products with PRODY<GDP, sorted by export share.
Ecuador’s PPP-adjusted GDP per capita in 2007 was $7035, according to the World
Bank’s world development indicators. In this table we can see that many of its exports are typical
of countries that are much poorer. Bananas (which are almost 10 percent of the export basket)
are typical of countries that have a similar, although slightly lower, GDP per capita of $6711.
Crustaceans, cut flowers, and cocoa beans on the other hand are typical of countries with half of
9
Ecuador’s level of income. If one thinks of GDP per capita as the typical wage, then these
sectors are typical of countries paying wages less than one-half of Ecuador’s.
What are the sectors pulling up Ecuador’s
EXPY
? The products contributing most to
Ecuador’s current level of export sophistication are listed below.
Table 2
Top 10 Contributors to Ecuador’s EXPY, 2007
Product Name
PRODY
(PPP)
Exports
(US M)
Contribution
to EXPY
Petroleum oils, oils from bituminous minerals, crude 13648 6930 7435
Motor vehicles for the transport of goods 13812 234 254
Motor vehicles for transport of persons (except buses 19215 104 157
Coal-tar distillation products including oils 13652 111 119
Medicaments, therapeutic, prophylactic use, in dosage 22698 44 78
Fruit, nut, edible plant parts nes, prepared/preserve 12408 79 77
Stoves, ranges/barbecues,etc, non-electric, iron/stee 14198 60 67
New pneumatic tyres, of rubber 21621 29 50
Turbo-jets, turbo-propellers/other gas turbine engine 27868 18 40
Chemical industry products nes 14316 29 33
Source: Author’s calculation using UN COMTRADE. Products with PRODY>EXPY, sorted by their total
contribution to EXPY (export share multiplied by PRODY).
Many oil-exporting countries have a higher level of GDP per capita than Ecuador. This is
due primarily to the smaller Gulf States, which have a very high oil endowment per capita. Other
‘rich country’ export products from Ecuador include small SUVs and light trucks, exported to its
neighbors under the automotive integration program. Although not significant in terms of the
export shares observed above, these vehicle exports are a significant contributor to export
sophistication. Finally, it is important to note that despite the fact that food and agricultural
goods make up many of Ecuador’s unsophisticated exports, there are also some agrifood sectors
(usually with greater value-added) which support much higher wages and are pulling up
EXPY
,
such as exports of prepared/preserved fruit and nut products, which are typical of countries with
a GDP per capita much higher than Ecuador’s.
Ecuador’s lagging level of export sophistication suggests that upgrading to new, more
sophisticated activities that pay higher wages is an important challenge for the country. But how
does this process work, and how can it be facilitated? This is taken up in the following section,
which examines the process by which new activities enter the export basket. This is a more
dynamic view that will also illustrate that although in static terms the country’s oil sector is
10
relatively more ‘sophisticated’ (high
EXPY
) and its agricultural activities unsophisticated (low
EXPY
), the latter may be valuable in terms of leading to the emergence of new activities.
5. Export Connectedness
In standard trade theory, moving to new export products (structural transformation) is a passive
consequence of changing comparative advantage based on factor accumulation. However, there
are many reasons why structural transformation may be more complicated than this picture
suggests. Several factors may create market failures such as industry-specific learning by doing
(Arrow 1962; Bardhan 1970) or industry externalities (Jaffe 1986). There may also be
technological spillovers between industries (Jaffe, Trajtenberg, and Henderson 1993).
Alternatively, the process of finding out which of the many potential products best express a
country’s changing comparative advantage may create information externalities (Hausmann and
Rodrik 2003, Klinger 2007) as those that identify the goods provide valuable information to
other potential entrepreneurs but are not compensated for their efforts.
Hausmann and Klinger (2006 and 2007) and Hidalgo et al. (2007) investigate the
determinants of the evolution of the level of sophistication of a country’s exports, and find that
these barriers are less binding when moving to ‘nearby’ products. This is based on the idea that
every product involves highly specific inputs such as knowledge, physical assets, intermediate
inputs, labor training requirements, infrastructure needs, property rights, regulatory requirements,
or other public goods. Established industries somehow have sorted out the many potential
failures involved in assuring the presence of all of these inputs, which are then available to
subsequent entrants in the industry. But firms that venture into new products will find it much
harder to secure the requisite inputs. For example, they will not find workers with experience in
the product in question or suppliers who regularly furnish that industry. Specific infrastructure
needs such as cold storage transportation systems may be non-existent, regulatory services such
as product safety and phyto-sanitary permits may be difficult to obtain, and so on.
The assets and capabilities needed to produce one good are imperfect substitutes for those
needed to produce another good, but this degree of specificity will vary. Correspondingly, the
probability that a country will develop the capability to be good at producing one good is related
to its installed capability in the production of other similar, or nearby goods for which the
11
currently existing productive capabilities can be easily adapted. The barriers preventing the
emergence of new export activities are less binding for nearby products which only require slight
adaptations of existing capacity.
This is shown by first developing a measure of distance between products. The distance
between each pair of products is measured based on the probability that countries in the world
export both. If two goods need the same capabilities, this should show up in a higher probability
of a country having comparative advantage in both. Formally, the inverse measure of distance
between goods
i
and
j
in year
t
, which is called proximity, equals
(
)
(
)
{
}
titjtjtitji
xxPxxP
,,,,,,
|,|min=
ϕ
(4)
where for any country c
>
=otherwise
RCAif
x
tci
tci
1
0
1
,,
,,
(5)
and where the conditional probability is calculated using all countries in year t. This is calculated
using disaggregated export data across a large sample of countries from the World Trade Flows
data from Feenstra et al. (2005) and UN COMTRADE.
The heterogeneity of the product space can be shown econometrically, yet it is much
more revealing to illustrate these pairwise distances graphically. Using the tools of network
analysis, we can construct an image of the product space (Hidalgo et al., 2007). Considering the
linkages as measured in the 1998-2000 period, we first create the maximum spanning tree by
taking the one strongest connection for each product that allows it to be connected to the entire
product space. This is shown below.
12
Figure 5
Maximum Spanning Tree
Source: Hidalgo et al. (2007).
The next step is to overlay this maximum spanning tree with the stronger links, and color-
code them based on their proximity. In the Figure below, each node is a product, its size
determined by its share of world trade. In these graphs, physical distances between products are
meaningless: proximity is shown by color-coding the linkages between pairs of products. A
light-blue link indicates a proximity of under .4, a beige link a proximity between .4 and .55, a
dark-blue link a proximity between .55 and .65, and a red link a proximity greater than .65
(remember, larger proximity means the products are closer together). Links below 0.55 are only
shown if they make up the maximum spanning tree, and the products are color-coded based on
their Leamer (1984) commodity group.
13
Figure 6
A Visual Representation of the Product Space
Source: Hidalgo et al. (2007).
We can immediately see from the figure above that the product space is highly
heterogeneous. There are peripheral products that are only weakly connected to other products.
There are some groupings among these peripheral goods, such as petroleum products (the large
red nodes on the left side of the network), seafood products (below petroleum products),
garments (the very dense cluster at the bottom of the network), and raw materials (the upper left
to upper periphery). Furthermore, there is a core of closely connected products in the center of
the network, mainly of machinery and other capital intensive goods.
This heterogeneous structure of the product space has important implications for
structural transformation. If a country is producing goods in a dense part of the product space,
then the process of structural transformation is much easier because the set of acquired
capabilities can be easily re-deployed to other nearby products. However, if a country is
specialized in peripheral products, then this redeployment is more challenging as there is not a
14
set of products requiring similar capabilities. The process of structural transformation can be
impeded due to a country’s orientation in this space.
In order to analyze how a country’s production is distributed in this space, and how that
structure changes over time, we can place a black square over every product in which a country
has significant exports
1
in a particular year. The figures below show Ecuador’s position and
movement within the product space in 1975, 1985, 1995, 2000, and 2006, as well as some
comparator countries in 2000.
Figure 7
Ecuador’s Location in the Product Space
Ecuador 1975
1
Taken to be when the RCA index is greater than or equal to one: when the country’s world market share in that
good is greater than its world market share in all exports, or put another way, when the good’s share of the country’s
export basket is greater than the good’s share in world exports.
15
Ecuador 1985
Ecuador 1995
16
Ecuador 2000
Source: Author’s calculations using Hidalgo et al. (2007).
Ecuador 2006
17
Figure 8
Location in the Product Space, Comparators
Argentina 2000
Brazil 2000
18
Colombia 2000
Peru 2000
Source: Author’s calculations using Hidalgo et al. (2007).
Compared to Ecuador, Argentina and Brazil have more activities in the industrial core of
the product space. Colombia and Peru do as well, but to a lesser extent. Looking over time, we
19
see that Ecuador has traditionally occupied a very peripheral part of the product space, with the
oil sector dominating. Recently, the country has diversified into other areas of the product space,
particularly aquiculture and agricultural activities. Although these are also peripheral, they are
better connected in the product space than the oil sector.
The intuition behind this is the following: to successfully exploit oil, a country needs the
natural resource endowment, a government that can provide property rights for that resource, and
a handful of investors to exploit. These productive capacities are useful for the oil sector and
potentially other extractive sectors, but not for many other activities. Agricultural activities
require institutions to support more diffuse property rights and private actors, while
agroprocessing activities require agronomists, entrepreneurs, and factories. These productive
capabilities can be used for a host of other activities, resulting in their being better connected in
the product space.
In order to evaluate how connected a particular product is for a country, the distance
between products must be combined with export data to measure how close any potential product
is to that country’s export basket as a whole. This measure, from Hausmann and Klinger (2006),
is called density: the density of current production around any good. This is the distance of good
i from country c’s export basket at time t. It is the sum of all paths leading to the product in
which the country is present, divided by the sum of all paths leading to the product. Density
varies from 0 to 1, with higher values indicating that the country has achieved comparative
advantage in many nearby products, and therefore should be more likely to export that good in
the future.
=
k
tki
k
tkctki
tci
x
density
,,
,,,,
,,
ϕ
ϕ
(6)
Hausmann and Klinger (2007) show that this measure of density is indeed highly significant in
predicting how a country’s productive structure will shift over time: countries are much more
likely to move to products that have a higher density, meaning they are closer to their current
production. This can be observed in looking at Ecuador’s map over time.
20
Using calculated densities, we can show graphically how this product space looks from
the point of view of Ecuador’s firms. Each product not currently exported with comparative
advantage has a particular distance from the country’s current export basket, measured by
density. In addition, each of these products has a level of sophistication, measured by PRODY.
We can plot each of these products according to their distance. The x-axis is the inverse of log
(density), meaning that a smaller value represents a product that is closer to the current
productive structure, and the y-axis is sophistication, with products color-coded by Leamer
commodity cluster. This is shown below for Ecuador. The horizontal line drawn is where the
PRODY of the good equals the EXPY of the country or region. Products below that line are less
sophisticated than the country’s export basket as a whole.
Figure 9
Proximity vs. Sophistication: the Efficient Frontier, 2007
-10 0 10 20 30
PRODY-EXPY (000)
1.5 2 2.5 3 3.5
Density (inverse)
Petroleum Raw Materials
Forest Tropical Ag
Animal Prods Cereals
L Intensive K Intensive
Machinery Chemicals
zero
ECU
Note: “Animal Prods” is Animal Products, “L intensive” means Labor Intensive, “Tropical Ag” is Tropical
Agriculture, and “K intensive” corresponds to Capital Intensive. As for the country name, ECU is Ecuador.
Source: Author’s calculations using UN COMTRADE.
From the point of view of adding valuable new exports to the current basket, the ideal
location on this plane is the upper-left quadrant: goods that are close and also highly
sophisticated. This figure suggests a tradeoff between proximity and export sophistication. The
products that are closest to the current export basket (and therefore further to the left) are easiest
to move toward, yet these nearest products are often not of a high level of sophistication. The
more sophisticated products are further away from the current structure of production.
21
Furthermore, there is an efficient frontier in this tradeoff. Some products are both further away
and of lower sophistication than other potential exports, while there are others that have a high
PRODY and are relatively nearby. Sophistication versus distance is an important tradeoff that we
will return to when exploring Ecuador’s opportunities for future structural transformation.
Below are equivalent figures for comparator countries. A vertical line has been inserted at
a density of 2 to aid in comparisons.
Figure 10
Proximity vs. Sophistication: the Efficient Frontier
Selected Comparators, 2007
-10 0 10 20 30
PRODY-EXPY (000)
1 1.5 2 2.5 3
Density (inverse)
Petroleum Raw Materials
Forest Tropical Ag
Animal Prods Cereals
L Intensive K Intensive
Machinery Chemicals
zero
ARG
-10 0 10 20 30
PRODY-EXPY (000)
.5 1 1.5 2 2.5
Density (inverse)
Petroleum Raw Materials
Forest Tropical Ag
Animal Prods Cereals
L Intensive K Intensive
Machinery Chemicals
zero
BRA
22
-10 0 10 20 30
PRODY-EXPY (000)
1.5 2 2.5 3 3.5 4
Density (inverse)
Petroleum Raw Materials
Forest Tropical Ag
Animal Prods Cereals
L Intensive K Intensive
Machinery Chemicals
zero
BOL
-10 0 10 20 30
PRODY-EXPY (000)
1 1.5 2 2.5
Density (inverse)
Petroleum Raw Materials
Forest Tropical Ag
Animal Prods Cereals
L Intensive K Intensive
Machinery Chemicals
zero
COL
-10 0 10 20 30
PRODY-EXPY (000)
1 1.5 2 2.5 3
Density (inverse)
Petroleum Raw Materials
Forest Tropical Ag
Animal Prods Cereals
L Intensive K Intensive
Machinery Chemicals
zero
PER
23
-10 0 10 20 30
PRODY-EXPY (000)
3 4 5 6
Density (inverse)
Petroleum Raw Materials
Forest Tropical Ag
Animal Prods Cereals
L Intensive K Intensive
Machinery Chemicals
zero
VEN
Note: “Animal Prods” is Animal Products, “L intensive” means Labor Intensive, “Tropical Ag” is Tropical
Agriculture, and “K intensive” corresponds to Capital Intensive. As for the country names, ARG is Argentina, BRA
is Brazil, BOL is Bolivia, COL is Colombia, PER is Peru, and VEN is Venezuela
Source: Author’s calculations using UN COMTRADE. Note: VEN data is for 2006.
It is clear that firms seeking to move to newer, more sophisticated export sectors in these
countries face quite widely differing option sets. Venezuela is quite isolated, while countries like
Argentina, Brazil, and Colombia have a much larger number of nearby opportunities that span
almost all potential sectors.
We can aggregate this measure of density, which is for a country around any single
product, to an overall measure of the connectedness of a country’s export basket. This country-
level measure is called ‘open forest’. A higher value indicates that the current export basket is a
part of the product space that is well connected to other new and valuable opportunities for
structural transformation. In other words, a high open forest indicates that the country is located
in a dense part of the product space. A low value of open forest indicates the country is
specialized in a sparse, unconnected part of the product space. In essence, this number
summarizes the visual analysis conducted above with the product space maps.
Open forest is calculated as follows:
( )
=
i j
tjtictjc
i
tji
tji
tc
PRODYxxforestopen
,,,,,
,,
,,
,
1_
ϕ
ϕ
(7)
24
As with export sophistication, there is a positive relationship between income and open
forest, with richer countries specialized in more connected parts of the product space. Yet, there
is variation in this relationship, and countries that have managed to move into a relatively well-
connected part of the product space given their level of development enjoy faster subsequent
structural transformation (Hausmann and Klinger 2006).
Open forest is basically a numerical summary of how well ‘connected’ a country’s export
basket is in the product space maps shown above. The evolution of open forest since 1985 is
shown below for Ecuador and some comparator countries.
Figure 11
Open Forest over Time (Constant 2000 US$ PPP)
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Year
Argentina Bolivia
Brazil Colombia
Ecuador Peru
Venezuela
Source: Author’s calculations using Feenstra (2005) and UN COMTRADE.
This figure has some similarities, but also notable differences from the evolution of
export sophistication (EXPY). But rather than measuring the static value of exports, open forest is
a measure of their ‘option value’ in terms of leading to new export activities. Thus, although
25
Argentina began 1985 with a more sophisticated export basket than that found in Brazil, Brazil’s
export activities were better connected in the product space at that time. This means that Brazil’s
export activities were more likely to themselves lead to other more sophisticated activities. In
light of this, it is then not as much of a surprise that Brazil’s export sophistication overtook
Argentina’s in the two subsequent decades. Similarly, Colombia’s export composition as of 1985
was relatively well connected, even though the activities prevalent at that time were relatively
unsophisticated. Again, in light of the Hausmann and Klinger’s (2007) result that a higher open
forest predicts faster subsequent structural transformation, it is not surprising that Colombia
enjoyed more rapid growth of EXPY.
We can see in this figure that the countries which now are suffering rather lagged export
upgrading—Ecuador, Venezuela, and Bolivia—as far back as 1985 were specialized in export
activities at the periphery of the product space. But we can also see that Ecuador is not in as bad
a relative position when considering the ‘option value’ of its current export basket as compared
to its sophistication: although it had the lowest EXPY as of 2007, Ecuador’s open forest was
higher than both Venezuela’s and Bolivia’s, suggesting that is has greater opportunities for
structural transformation moving forward. These opportunities will be explored in the final
section.
But first, we can also consider this dimension of connectedness from the point of view of
Ecuadorian firms. In terms of their connectedness in the product space, not all goods are created
equal. Some products are in a dense part of the product space, meaning that they are intensive in
capabilities that are easily deployed to a wide range of other goods. The implication is that
successfully producing these goods would create capabilities with significant value for other new
products. On the other hand, other products are located in the periphery, or in a part of the
product space where Ecuador has already achieved comparative advantage and acquired the
requisite productive capabilities. Therefore, these products have a low strategic value, because
successfully producing them would offer little in terms of future structural transformation.
The strategic value of every good not currently exported with comparative advantage can
be measured using open forest. This is done by calculating what would happen to open forest if
that good were added to the export basket. If a product is closely connected to a wide range of
other valuable products not currently exported by Ecuador, it would result in a large increase in
26
open forest, and therefore have high strategic value because it would greatly expand the
country’s option set.
Repeating the same exercise performed above on export sophistication and distance, the
distance of all products not exported with comparative advantage by Ecuador in 2007 is plotted
against their strategic value. The x-axis continues to be the inverse of log (density), meaning that
a smaller value represents a product that is closer to the current productive structure. The y-axis
is strategic value (the increase in open forest if that product were added to the export basket),
with higher values indicating greater additions to open forest, and therefore, greater strategic
value. Again, the ideal location is the upper-left quadrant: products that are nearby, meaning
easier to move to, and that have high strategic value, meaning that they themselves lead to new
and nearby opportunities for structural transformation.
Figure 12
Proximity vs. Strategic Value: the Efficient Frontier, 2007
0 5 10 15 20 25
Strategic Value (000)
1.5 2 2.5 3 3.5
Density (inverse)
Petroleum Raw Materials
Forest Tropical Ag
Animal Prods Cereals
L Intensive K Intensive
Machinery Chemicals
ECU
Note: “Animal Prods” is Animal Products, “L intensive” means Labor Intensive, “Tropical Ag” is Tropical
Agriculture, and “K intensive” corresponds to Capital Intensive. As for the country name, ECU is Ecuador.
Source: Author’s calculations using UN COMTRADE.
Just as was the case with the plots of distance versus PRODY, there is a tradeoff between
distance and strategic value. Countries are more likely to successfully move to goods that are
close to what they currently produce, because such goods require similar capabilities. Yet, such
goods may or may not have much strategic value. They may be in a sparse part of the product
27
space or may be so close that they do not imply the development of new capabilities that can be
redeployed in other directions. So moving closer is easier, but moving further may be more
valuable in terms of future structural transformation. Moreover, there is an efficient frontier in
this tradeoff, because some potential exports are both closer to the current export basket and
more strategically valuable than others.
Interestingly, in the case of Ecuador there seems to be a cluster of agriculture and animal
products making up the closest section of the efficient frontier, followed by some forestry and
labor-intensive sectors. The sectoral composition of the strategic value versus distance efficient
frontier will be explored in the following section.
Below are equivalent figures for comparator countries. A vertical line has been inserted at
a density of 2 to aid in comparisons.
Figure 13
Proximity vs. Strategic Value: the Efficient Frontier
Selected Comparators, 2007
0 5 10 15 20 25
Strategic Value (000)
1 1.5 2 2.5 3
Density (inverse)
Petroleum Raw Materials
Forest Tropical Ag
Animal Prods Cereals
L Intensive K Intensive
Machinery Chemicals
ARG
28
0 5 10 15 20
Strategic Value (000)
.5 1 1.5 2 2.5
Density (inverse)
Petroleum Raw Materials
Forest Tropical Ag
Animal Prods Cereals
L Intensive K Intensive
Machinery Chemicals
BRA
0 5 10 15 20 25
Strategic Value (000)
1.5 2 2.5 3 3.5 4
Density (inverse)
Petroleum Raw Materials
Forest Tropical Ag
Animal Prods Cereals
L Intensive K Intensive
Machinery Chemicals
BOL
0 5 10 15 20 25
Strategic Value (000)
1 1.5 2 2.5
Density (inverse)
Petroleum Raw Materials
Forest Tropical Ag
Animal Prods Cereals
L Intensive K Intensive
Machinery Chemicals
COL
29
0 5 10 15 20 25
Strategic Value (000)
1 1.5 2 2.5 3
Density (inverse)
Petroleum Raw Materials
Forest Tropical Ag
Animal Prods Cereals
L Intensive K Intensive
Machinery Chemicals
PER
0 5 10 15 20 25
Strategic Value (000)
3 4 5 6
Density (inverse)
Petroleum Raw Materials
Forest Tropical Ag
Animal Prods Cereals
L Intensive K Intensive
Machinery Chemicals
VEN
Note: “Animal Prods” is Animal Products, “L intensive” means Labor Intensive, “Tropical Ag” is Tropical
Agriculture, and “K intensive” corresponds to Capital Intensive. As for the country names, ARG is Argentina, BRA
is Brazil, BOL is Bolivia, COL is Colombia, PER is Peru, and VEN is Venezuela
Source: Author’s calculations using UN COMTRADE. Note: VEN data is for 2006.
These figures suggest very diverse efficient frontiers across countries. The efficient
frontier of Peru seems to be dominated by agriculture and related sectors, whereas Colombia and
Brazil quickly fill in with heavier industry. Moreover, the degree of the distance versus strategic
value tradeoff is quite different across countries. There is little need to jump further to reach the
highest strategic value sectors in Brazil or Argentina, whereas Colombia and Peru face a more
gradual climb to reach very well-connected activities.
The following section examines the composition of Ecuador’s efficient frontier more
closely, using these metrics to identify promising new sectors for export diversification.
30
6. Analyzing Ecuador’s Efficient Frontier
Productive activity requires different types of inputs, some of which are provided by the market
while others are provided by the government. Among the latter, some are public goods in the
sense that they are non-rivalrous and non-excludable, such as property rights, regulation,
security, and certification rules. Others do not have those characteristics but have been taken
over to a large extent by governments because of other forms of market failures, including
infrastructure, education, labor training, and certification services.
The sector specificity of these public inputs is reflected in the fact that countries have
literally hundreds of thousands of pages of economically relevant legislation and hundreds of
government agencies. Each one of these pages of legislation and each public agency have a
differential effect on different sectors.
The high number of public inputs is not unlike the plethora of privately provided inputs.
However, public inputs suffer from the fact that most of them have no price, so there is no
decentralized mechanism to reveal information. Moreover, there is no clear incentive for
governments to respond to the information, as the profit motive is not a relevant or powerful
incentive for public policy. Even if the information and incentive problems are addressed, the
government often does not have a decentralized self-organizing mechanism to mobilize
resources: these are most frequently mobilized through centralized budgetary processes.
This creates major challenges for public policy. First, how to ensure the best possible
provision of public inputs to existing activities, given the information, incentives and resource
mobilization problems mentioned above? Second, how to identify the industries that could have
existed with an alternative provision of public inputs but that do not exist precisely because of
these missing inputs?
Luckily, such efforts can be guided by the rich set of data and indicators that we have
used in this paper, which allow us to systematically scan Ecuador’s opportunity space and
evaluate which sectors should be easier for Ecuadorian firms to enter versus those that would be
more difficult, and which sectors would be worth the effort versus those without much strategic
value.
A first pass at understanding the nearby opportunities for structural transformation in
Ecuador is simply to identify those sectors that are nearest to the existing capability set in which
31
the country has not yet achieved comparative advantage. These highest-density sectors, with an
RCA of less than one, are the country’s ‘lowest-hanging fruit’ and are listed below, followed by
a listing of the nearest sectors within each Leamer commodity group.
Table 3
Ecuador’s ‘Low-Hanging Fruit’, 2007
Product Name
Exports
(US M) Density
PRODY
(PPP)
Coconuts, Brazil nuts and cashew nuts, fresh or dried 1.2 0.133 2722
Vegetable products, nes 142.5 0.122 1238
Solid cane or beet sugar and chemically pure sucrose 7302.8 0.121 4979
Vegetables nes, fresh or chilled 1232.2 0.119 6562
Fish,cured, smoked, fish meal for human consumption 1965.0 0.119 16614
Molluscs 2006.6 0.119 5902
Oil seeds and oleaginous fruits nes 10.4 0.118 2170
Gold, unwrought, semi-manufactured, powder form 61398.5 0.116 3689
Wheat or meslin flour 101.8 0.114 6580
Cotton waste, including yarn waste and garnetted stoc 0.0 0.114 6301
Cocoa shells, husks, skins and waste 50.4 0.113 1616
Leguminous vegetables, fresh or chilled 75.7 0.113 2548
Other spices 74.3 0.112 5731
Natural rubber and gums, in primary form, plates, etc 2098.5 0.112 4686
Plants, plant parts for perfumery, pharmacy, etc, 1242.2 0.112 7159
Cereal flours other than of wheat or meslin 341.4 0.111 5125
Citrus fruit, fresh or dried 945.9 0.111 11626
Fruits nes, fresh 731.5 0.110 14079
Molasses from the extraction or refining of sugar 2.6 0.110 4256
Margarine, edible animal or veg oil preparations nes 4126.8 0.110 6497
Note: Products with RCA<1 in 2007 (non-minerals), sorted by density. Source: Author’s calculations using UN
COMTRADE.
Table 4
Ecuador’s ‘Low-Hanging Fruit’ by Leamer Group, 2007
Leamer Group Product Name Exports
(US M)
Density PRODY
(PPP)
Forest Products
Ornaments of wood, jewel, cutlery caskets and cases 725.4 0.106 8420
Wood charcoal (including shell or nut charcoal) 209.2 0.104 6552
Hoopwood, split poles, pile, pickets and stakes 0.0 0.102 4105
Paper, board containers, packing items, box files, et 5801.4 0.100 9017
Wood continuously shaped along any edges 1958.8 0.098 11788
Tropical Agriculture
Coconuts, Brazil nuts and cashew nuts, fresh or dried
1.2
0.133
2722
Solid cane or beet sugar and chemically pure sucrose 7302.8 0.121 4979
Vegetables nes, fresh or chilled 1232.2 0.119 6562
Leguminous vegetables, fresh or chilled 75.7 0.113 2548
Other spices 74.3 0.112 5731
Animal Products
Vegetable products, nes
142.5
0.122
1238
Fish,cured, smoked, fish meal for human consumption 1965.0 0.119 16614
Molluscs 2006.6 0.119 5902
Plants, plant parts for perfumery, pharmacy, etc, 1242.2 0.112 7159
Vegetable material for stuffing or padding 0.0 0.108 3625
Cereals, etc.
Oil seeds and oleaginous fruits nes
10.4
0.118
2170
Wheat or meslin flour 101.8 0.114 6580
Cotton waste, including yarn waste and garnetted stoc 0.0 0.114 6301
Cocoa shells, husks, skins and waste 50.4 0.113 1616
Cereal flours other than of wheat or meslin 341.4 0.111 5125
32
Labor Intensive Documents of title (bonds etc), unused stamps etc 28.8 0.106 4718
Matches 113.3 0.105 6589
Photo-copying apparatus 3.2 0.103 6175
Womens, girls blouses & shirts, knit or crochet 175.0 0.102 14833
T-shirts, singlets and other vests, knit or crochet 4304.3 0.101 10440
Capital Intensive
Floor coverings with a base of paper or of paperboard
0.0
0.107
3538
Twine, cordage, rope and cable 277.5 0.103 11159
Sheep or lamb skin leather, without wool on 0.0 0.102 3689
Mats, screens, articles nes of plaiting materials 17.2 0.099 3539
Bovine or equine leather, no hair, not chamois, paten 4119.9 0.099 10233
Machinery
Insulated wire and cable, optical fibre cable 17387.2 0.090 8711
Floating structures nes (rafts, stages, buoys/beacons 4.0 0.089 14757
Refrigerators, freezers and heat pumps nes 10859.2 0.084 14842
Public-transport type passenger motor vehicles 11862.1 0.079 11924
Special purpose ships, vessels, nes 0.0 0.075 9686
Chemical Essential oils, resinoids and terpenic by-products 160.0 0.103 2102
Fertilizer mixtures in packs of < 10kg 572.9 0.101 10155
Paints and varnishes nes, water pigments for leather 122.3 0.092 2337
Prepared explosives, except propellant powders 0.0 0.092 10690
Hair preparations 535.4 0.091 14627
Note: Products with RCA<1 in 2007 (non-minerals), sorted by density. Top five in each Leamer group. Source:
Author’s calculations using UN COMTRADE.
These are ‘new’ in the sense that Ecuador is not currently an exporter of consequence
2
,
although there could very well be significant production for the domestic market. But although
new, these products have a very high density, meaning that most other countries in the world that
export what Ecuador exports, also export these goods. So the question is: why not Ecuador?
It could be that for some of these products, there is a very sensible reason why most
countries like Ecuador are significant exporters but Ecuador is not. But for many, ‘why not
Ecuador’ is not so clear. Ecuador has been able to achieve comparative advantage in most
products that other successful exporters of certain varieties of nuts and oilseeds have. This
suggests that many of the product-specific capabilities required for nuts and oilseeds (including
those provided by the public sector) already exist in Ecuador, yet the country has not yet become
a significant exporter of them. The data show that with no other information, one would expect
very strongly that Ecuador could be a successful exporter in these sectors. So, why not oil seeds
and nuts in Ecuador? It may be that the public sector by act or omission may be preventing that
sector from emerging, or there may be a market failure preventing it that could be corrected
through policy.
The data above are therefore useful to help guide the search for what particular inputs are
missing for new export activities to emerge in Ecuador. Since these are sector-specific, learning
what these missing inputs are can’t be done at such a high level of aggregation that the
specificity is lost. The unique needs of the oilseed industry likely will not become apparent in
2
Defined as having a RCA of 0.5 or greater, meaning that the share of that product’s export in Ecuador is greater
than half the share of that product in global trade.
33
conversations with the president of the chamber of commerce, who represents the interest of the
private sector as a whole. They also will probably not be detected by surveys such as the World
Bank’s Investment Climate Assessment or the World Economic Forum’s Global
Competitiveness Index.
Instead, sector-specificity requires this interaction to be at a much more disaggregated
sector level. The data reveal which conversations and search efforts might be prioritized: the new
activities that should be most likely to emerge in Ecuador. They can be matched to actual firms,
and interactions with these firms can reveal the particular missing inputs and constraints to
investment.
Yet when considering low-hanging fruit, we must keep in mind that the nearest sectors
may not be the best areas of focus. As suggested by Figure 9, most of Ecuador’s nearby sectors
have an extremely low PRODY, much lower than the country’s current GDP per capita, meaning
they are typical of countries much poorer than Ecuador. Many of them are also in isolated parts
of the product space, meaning they will likely generate less structural transformation in the
future than other sectors with higher strategic value. We can therefore analyze the efficient
frontier by exploring which sectors offer the best combinations of proximity, sophistication, and
strategic value while also representing large market opportunities. This is done as follows: We
consider all non-mineral products not exported with comparative advantage in 2007 that are ‘up-
market’ for Ecuador (i.e. their PRODY is greater than Ecuador’s EXPY) and are sufficiently close
to Ecuador’s current structure of production (with a density at least 1.5 standard deviations larger
than the mean). Grouping these products into sectors, we present them first in terms of their
strategic value and then in terms of their world market size. Sectors that feature prominently in
both figures are very close to the current structure of production, meaning many of the sector-
specific capabilities they require already exist in Ecuador. At the same time, these sectors are
associated with higher-wage countries, have large international markets, and are in well-
connected parts of the product space, meaning they will facilitate further structural
transformation in the future.
34
Figure 14
Unoccupied Products 1.5σ above Average Density, Ecuador 2007,
Weighted by World Trade
(
Industry’s percentage of the total of all industries meeting this criteria)
33%
10%
9%
6%
5%
5%
4%
4%
4%
4%
3%
3%
2% 2%
1% 1%
1% 1% 0%
0%
Manufacture of wearing apparel , except footwear
Manufacture of plas tic pro ducts not elsewhere classified
Goods not elsewhere classified
Manufacture of food pro ducts not elsewhere classified
Manufacture of bakery pro ducts
Agri cultural and livestock production
Sawmille, pl anin g and other wood mills
Tann eries and leather finishing
Tobac co manufactures
Manufacture of pulp , paper and paperboard
Manufacture of dair y products
Log ging
Malt liqors and malt
Manufacture of fertili zers and pes ticides
Grain mill prod ucts
Ocean and coastal fishing
Manufacture of structural metal pr oduc ts
Cor dage, rope and twine industries
Cann ing and preserving of fruits and vegetables
Spin ning, weawing and fin ishing textiles
Note: All products not exported with RCA>1 in 2007, dropped those with PRODY<EXPY, dropped minerals,
dropped those with density that is not at least 1.5 standard deviations above the mean for all non-exported products,
combined into International Standard Industrial Classification (ISIC) revision 2 sectors, weighted by 2007 world
exports of all those products in that sector meeting the above criteria. Source: UN COMTRADE.
Figure 15
Unoccupied Products 1.5σ above Average Density, Ecuador 2007,
Weighted by Strategic Value
(
Industry’s percentage of the total of all industries meeting this criteria)
22%
9%
9%
6%
6%
6%
6%
6%
3%
3%
3%
3%
3%
3%
3%
3%
2% 2% 2% 2%
Manufacture of wearing apparel , except footwear
Agri cultural and lives tock production
Manufacture of food pro ducts not elsewhere classified
Grain mill prod ucts
Manufacture of plas tic pro ducts not elsewhere classified
Sawmille, planin g and other wood mills
Cord age, rope and twine industries
Manufacture of bakery pro ducts
Manufacture of pulp , paper and paperboard
Cann ing and preserving of fruits and vegetables
Manufacture of fertilizers and pesti cides
Go ods no t elsewhere classified
Log ging
Spin ning, weawin g and finishing textiles
Manufacture of dairy products
Manufacture of structural metal pro ducts
Tobac co manufactures
Tann eries and leather finishing
Malt liqo rs and malt
Ocean and coas tal fishing
Note: All products not exported with RCA>1 in 2007, dropped those with PRODY<EXPY, dropped minerals,
dropped those with density that is not at least 1.5 standard deviations above the mean for all non-exported products,
35
combined into International Standard Industrial Classification (ISIC) revision 2 sectors, weighted by 2007 strategic
value of all those products in that sector meeting the above criteria. Source: UN COMTRADE.
The themes that emerge from this analysis are:
Manufacturing of apparel
Manufacturing of simple plastics (e.g. household and bathroom articles)
Agricultural products (non-traditional fruits) and seafood
Food products (preparations, condiments, powders, cereal)
Some forestry and mill products
These sectors are very nearby current production, and they enjoy both large global demand and
strategic value.
However, as we saw above, there is a tradeoff between strategic value and distance: the
nearest products do not involve the development of new capabilities that have many alternative
uses not yet exploited. Therefore, any attempt to increase the option value of the export package
would require movement to further away-products. We therefore repeat the analysis above,
decreasing the minimum distance from 1.5 standard deviations to 1. This gives an idea of how,
as ambition increases, the composition of the efficient frontier changes.
36
Figure 16
Unoccupied Products 1σ above Average Density, Ecuador 2007,
Weighted by World Trade
(
Industry’s percentage of the total of all industries meeting this criteria)
24%
8%
7%
7%
6%
5%
4%
4%
4%
3%
3%
3%
3%
2%
2%
2%
2%
2%
1%
1%
1%
1% 1%
1%
1%
1%
0%
0%
0%
0% 0%
0%
0%
0%
0%
0% 0%
Manufacture of wearing apparel, except footwear
Manufacture of furniture and fixtures, except primarily of metal
Manufacture of plastic products not elsewhere classified
Iron and steel basic industries
Non
-
ferrous metal basic industries
Manufacture of dairy products
Goo ds not elsewhere classified
Machi nery and equipment except electrical not elsewhere classified
Manufacture of bakery products
Manufacture of soap and cleaning preparations, perfumes, cosmetics and other toilet preparations
Manufacture of food products not elsewhere classified
Agric ultural and livestock production
Slaugh tering, preparing and preserving meat
Tobacc o manufactures
Sawmille, planing and other wood mills
Tann eries and leather finishing
Manufacture of pulp, paper and paperboard
Manufacture of prepared animal feeds
Log ging
Manufacture of structural clay products
Spin ning, weawing and finishing textiles
Malt liqo rs and malt
Manufacture of fertilizers and pesticides
Manufacture of structural metal products
Manufacture of synthetic resins, plastic materials and man
-
made fibres except glass
Grain mill products
Cann ing and preserving of fruits and vegetables
Ocean and coastal fishing
Fores try
Printi ng, publishing and allied industries
Manufacture of wooden and cane containers and small can ware
Cord age, rope and twine industries
Ship building and repairing
Win e industries
Manufacture of chemical products not elsewhere classified
Manufacture of fabricated metal products except machinery and equipement not elsewhere classified
Manufacture of motorcycles and bicycles
Note: All products not exported with RCA>1 in 2007, dropped those with PRODY<EXPY, dropped minerals,
dropped those with density that is not at least 1 standard deviation above the mean for all non-exported products,
combined into International Standard Industrial Classification (ISIC) revision 2 sectors, weighted by 2007 world
exports of all those products in that sector meeting the above criteria. Source: UN COMTRADE.
37
Figure 17
Unoccupied Products 1σ above Average Density, Ecuador 2007,
Weighted by Strategic Value
(
Industry’s percentage of the total of all industries meeting this criteria)
16%
9%
6%
4%
4%
4%
4%
4%
3%
3%
3%
3%
3%
3%
3%
3%
2%
2%
2%
2%
2%
1%
1%
1%
1%
1%
1%
1%
1% 1%
1% 1% 1%
1%
1%
1%
0%
Manufacture of wearing apparel , except footwear
Agri cultural and livestock production
Manufacture of food pro ducts not elsewhere classified
Manufacture of plasti c pro ducts not elsewhere classified
Manufacture of dairy produc ts
Spin ning, weawing and finishing textiles
Manufacture of bakery pro ducts
Non -ferrous metal basic ind ustries
Manufacture of soap and clean ing preparations, perfumes, cosmetics and other toilet preparations
Manufacture of furniture and fixtures, except primari ly of metal
Slaugh tering, preparing and preserving meat
Grain mill prod ucts
Iron and steel basic in dustries
Manufacture of structural metal pro ducts
Sawmille, plan ing and other wood mills
Cord age, rope and twine industries
Tobacc o manufactures
Cann ing and preserving of fruits and vegetables
Prin ting, publi shing and allied industries
Manufacture of wood en and cane containers and small can ware
Machi nery and equipment except electrical not elsewhere classified
Manufacture of prepa red animal feeds
Manufacture of pulp , paper and paperboard
Manufacture of structural clay produc ts
Fores try
Manufacture of synth etic resins, plastic materials and man-made fibres except glass
Manufacture of fertilizers and pesti cides
Manufacture of fabricated metal prod ucts except machi nery and equipement not elsewhere classified
Ship building and repairing
Goo ds not elsewhere classified
Log ging
Manufacture of chemic al pro ducts not elsewhere classified
Tann eries and leather finishing
Malt liqo rs and malt
Win e ind ustries
Ocean and coast al fishing
Manufacture of motor cycles and bicycles
Note: All products not exported with RCA>1 in 2007, dropped those with PRODY<EXPY, dropped minerals,
dropped those with density that is not at least 1 standard deviations above the mean for all non-exported products,
combined into International Standard Industrial Classification (ISIC) revision 2 sectors, weighted by 2007 strategic
value of all those products in that sector meeting the above criteria. Source: UN COMTRADE.
As one allows for potentially further jumps, some new themes are added to the efficient
frontier. Moving from 1.5 to 1 standard deviation above the average distance as the cutoff, the
following emerges:
Additional garment products and textiles
Furniture products (e.g. mattresses)
Dairy products
Metals and appliances (e.g. refrigerators)
Soap and cosmetics
38
These sectors are further away, and therefore likely have fewer private actors in the
economy existing at present, requiring more proactive study of either potential or foreign firms.
This allows for even further jumps leading to the emergence of sectors such as the manufacture
of drugs and medicines and other furniture and plastics manufactures.
The question is, what would it take for a vibrant and internationally competitive dairy or
cosmetics industry to emerge in Ecuador? What types of investments in training and education
would be required? What type of intellectual property rights regime would be needed? What is
the cost-benefit of such investments? Asking such sector-specific questions is not picking
winners, and the answers should draw on the relevant private sector actors, either local or
international.
7. Ministry Priorities
The Ministry for the Coordination of Production, Competitiveness and Commercialization has
itself been analyzing the economy of Ecuador to identify high-potential export sectors. This
process, while using quantitative data, was likely done in a more qualitative and comprehensive
way than the analysis conducted above. Such an approach has many benefits. It is not limited to
sectors appearing in international trade data and therefore allows for a consideration of service
sectors. And importantly, it allows for a much wider set of information to be incorporated in the
analysis, such as projections of the future global market growth for each sector, and national and
regional context.
However the downside of such an approach is that it does not allow for the systematic
consideration of all
3
potential sectors at a disaggregated level, and therefore might overlook
some high-potential opportunities, or bet against convincing empirical evidence. Therefore, just
as the high-potential sectors identified above require strong second looks incorporating wider
information and country context, the priority sectors identified by the Ministry can also be given
a second look using the product space data.
The Ministry has identified 15 strategic sectors in its plan: flowers, processed fruits and
vegetables, aquiculture, fish, forestry, metal products, tourism, logistics and transport, biofuels,
software and consulting, textiles, leather and footwear, ceramics, construction, and artisan
3
That is, all non-service export sectors that appear in international trade data.
39
products. Of these sectors, eight can be evaluated using export data, both at an aggregate level
and at various sub-sectors
4
.
First, we identify these strategic sectors in the tradeoffs between distance and
sophistication and distance versus strategic value, as done above for the country as a whole.
Those sectors that are targeted in the strategy are highlighted in red.
Figure 18
Proximity vs. Sophistication: Ministry’s Priority Sectors, 2007
0 10000 20000 30000 4 0000
PRODY
2 2.5 3 3.5 4
Density (inverse)
Source: Author’s calculations using UN COMTRADE.
Figure 19
Proximity vs. Strategic Value: Ministry’s Priority Sectors, 2007
0 5 10 15 20 25
Strategic Value (000)
2 2.5 3 3.5 4
Density (inverse)
Source: Author’s calculations using UN COMTRADE.
4
The product codes for this analysis were graciously provided by David Molina of the Ministry for the Coordination
of Production, Competitiveness and Commercialization.
40
Overall, the targeted sectors seem to be concentrated near the efficient frontier,
particularly on the tradeoff between distance and strategic value. That is, the targeted sectors are
either nearby, or if far away, at least have a high strategic value. Yet it is also noticeable from
this figure that a large number of sectors fall under the Ministry’s prioritization. Of the 1267
customs lines appearing in the 4-digit harmonized system, 619 fall within one (or more) priority
sectors.
The strategy is therefore not very finely focused at the sectoral level, at least at this point.
This may be a problem, since if you are targeting almost all sectors, it is not easy to learn the
sector-specific inputs and constraints as you don’t know where to look first. On the other hand,
such a broad focus is not necessarily a bad thing, as it depends on the policy interventions
employed and their ability to drill down to sector-specific requirements. Specific initiatives and
policies will either have to be subjected to further filtering of these ‘priority’ sectors, or even
better, this drilling down will have to emerge through interaction with, and some self-
organization of, the relevant private sector actors.
We can look at each priority sector individually (some of them have been disaggregated
slightly) and compare them in terms of these three variables: density (how ‘nearby’ is the sector:
have other countries similar to Ecuador been successful in it, or is it a bet against the
international experience), PRODY (how ‘sophisticated’ is the product: is it typical of countries
with higher or lower wages), and strategic value (does this sector lead to other, as of yet
unexploited opportunities). The results are shown below.
Table 5
Priority Sectors
Sector
Average
Density Average PRODY
Average
Strategic
Value
Flores 0.108 5319 10783
Metalmecánica 0.058 18945 16923
automotriz y transporte 0.059 17429 16849
biocombustibles 0.095 7095 11442
frutas y vegetales 0.097 9424 12033
linea blanca 0.066 18116 18771
pesca y acuicultura 0.119 10509 10789
silviculture y madera 0.078 15999 15424
textiles 0.074 13130 14601
not targeted 0.065 16035 14889
Source: Author’s calculations using UN COMTRADE.
41
The very nearby strategic sectors in the Ministry’s plan are flowers, fish and aquiculture,
biofuels and fruits and vegetables (although it should be noted that the products identified as
biofuels are also exported as simple grains: one can’t differentiate between the two applications
in this data). Not surprisingly, these nearby sectors have a relatively low strategic value, with
fruits and vegetables being the highest. But there are noticeable differences in the degree of
sophistication, with flowers having nearly half the PRODY of the aquiculture and fruits and
vegetables sectors.
The sectors of intermediate distance are forestry, textiles, and appliances. These sectors
are somewhat further away compared to the nearby sectors mentioned above, meaning that the
country may not currently possess all of the necessary productive capabilities for them to
emerge. However, they remain closer than those sectors excluded from the plan, and they have a
relatively high strategic value (particularly the appliances subsector). They are also all typical of
countries that are richer than Ecuador.
Finally, the metal-mechanic and automotive sectors are the most distant sectors included
in the strategy. These sectors are typical of countries with radically different productive
structures than Ecuador, and the empirical evidence suggests that they are very unlikely to
emerge in the export basket in the near future. They are in the category of ‘strategic bets’, as they
would require significant leaps in the product space to reach. They both have relatively high
strategic value and sophistication. Yet, it is interesting to note that the appliances sub-sector has
a similar PRODY and even higher strategic value, while being much closer to the current
structure of production.
The Appendix contains these same variables for the strategic sectors, disaggregated by
sub-sector, which allows for a finer analysis. This may be important for those sectors which
include a large number of diverse products, as within the broad sector there may be a subset of
highly valuable strategic sectors that are being washed out on average by others that are inside
the efficient frontier. This illustrates the value of the product space data in terms of drilling down
to a highly disaggregated level.
Although the devil is in the details in terms of how these sectors are actually supported, it
does seem that on average the plan identifies sectors that are either nearby, or if further away, are
at least worth the effort in terms of having a higher strategic value. Yet it also seems that the
42
prioritization could be further rationalized. The metal-mechanic and automotive sectors are of
comparative sophistication and strategic value to appliances, yet are much further away, meaning
that jumps to these products will be more difficult and would likely require greater coordination.
Most importantly, the widely varying distances of the priority sectors can indicate what kind of
policy approach is more or less appropriate for each sector. This is discussed in the final section.
8. Policy Implications
Appropriate policy approaches to facilitating structural transformation depend on how far away
the relevant sector is from the current structure of production. Facilitating jumps to nearby
products is likely to be very different from bringing about the emergence of ‘strategic bets’ that
are far away in the product space.
The capabilities for nearby sectors, such as non-traditional fruits, aquiculture, and some
forestry sectors, will already exist to some degree in the country. There are probably private
sector actors considering these sectors, already producing for the local market, or in some cases
already exporting. There are already counterparts in the private sector, and therefore to facilitate
jumps the government needs a way to dialogue with them to learn the publically-provided sector-
specific inputs that are missing. In order to identify sector-specific constraints, the dialogue must
occur at a much more disaggregated level, and therefore have the necessary bandwidth to deal
with that complexity (Hausmann 2008).
Organizing such a private-public dialogue at lower levels of aggregation is difficult, as
there are hundreds of thousands of different business interests and limited government time and
attention and it is not obvious what the right way of organizing the issues may be. Moreover, the
country’s productive structure and the structure of the product space are both changing over
time. Therefore, this dialogue process should have the ability to bring in new sectors of the
economy as new opportunities for structural transformation emerge.
Hausmann, Rodrik, and Sabel (2008) offer some specific policy proposals to achieve
such a dialogue and overcome the three problems mentioned above: the information, incentive,
and resource mobilization problems. We can identify some general design principles for these or
any other policy initiative to promote public-private dialogue that can identify and act on sector-
specific constraints and opportunities. Based on Hausmann and Rodrik (2006) and Hausmann,
Rodrik, and Sabel (2008), these principals are:
43
Let the private sector self-organize and coalesce around common requirements rather
than placing them in pre-determined buckets, and allow new interests to engage the
public sector rather than limiting it to those identified as high-potential at some given
date. Although these lists of high-potential sectors can help prioritize discussions as
well as decide on the allocation of scarce resources once these are identified, they
shouldn’t be taken as a final determination on where to focus efforts.
The process should be transparent. This dialogue, particularly the requests from the
private sector, should be public in order to limit rent-seeking and increase legitimacy
of this endeavor vis-à-vis the rest of society so as to make sure that policy goals are in
the public interest.
Interventions should be focused on identifying and providing public inputs that
increase a sector’s productivity or allow it to come into being. Their effect should be
to increase productivity, not subsidize low productivity. This is critical: in the past,
some have argued that the low productivity in certain sectors should be subsidized
because the sectors have some type of special spillovers to other sectors. Here, we
aren’t talking about subsidies to compensate for low returns. We are talking about
investments in required public inputs that increase productivity and allow private
returns to be realized.
The private sector should be willing to invest its own funds in these sectors so that the
investment passes a market test. Co-financing is a good signaling mechanism that
there is real demand for the requisite input.
Interventions should have clear criteria for success (to identify losers), accountability
(to let losers go as early as possible), and sunset clauses (to ensure that no financial
commitments are open-ended).
But while creating this high-bandwidth public-private dialogue will help overcome
barriers to the emergence of nearby activities (as well as growth in existing sectors), it will likely
not be sufficient for those high-potential sectors that are further away in the product space.
Moving to more distant export activities is difficult. These long jumps do not occur with much
regularity. While nearby activities require the same or similar capabilities to those already
existing in the country, distant export activities have capability requirements that are very
different. Firms that wish to jump to these new activities will face many missing capabilities, and
44
the wider range of these capabilities would have to appear simultaneously to make such jumps
feasible.
In addition, it may not be as easy to learn what particular capabilities are missing. With
nearby sectors, there are already firms in similar activities present in the economy. For many of
the ‘low-hanging fruit’ sectors, there are already small amounts of exports from Ecuador, and
there is most likely production for the domestic market as well. This means that there are
existing firms in the country that can be engaged to learn what is missing. They are the
counterparts for the dialogue discussed above. But for very distant activities, it is not as easy to
find a counterpart, and more of a process of search, promotion (including actively seeking
foreign direct investment), and evaluation is necessary.
Some general policy proposals to facilitate the search for distant opportunities and larger
leaps in the product space are also provided in Hausmann, Rodrik, and Sabel (2008). The authors
suggest either a ‘venture fund’ or a re-focusing of development banks on facilitating longer
jumps. Such a body would have an open window that encourages investors to come with
business plans for such activities and should identify what aspects of the business environment
are problematic or missing for the industry to be viable. Financial support is granted in part to
encourage the private sector to develop such plans and to reveal this publicly valuable
information to the venture fund. The venture fund should act as an information revelation
mechanism of the space of opportunities and obstacles and to prepare policy solutions to the
obstacles identified. It should be evaluated not in terms of the amount of money they lend, but
instead on the amount of investment it triggers by helping to fix the provision of public inputs,
even if these investments are financed privately.
For some industries dominated by large international firms, this can be learned by
engaging those international firms directly, encouraging them to invest in the country, and
having them identify the problems that would limit their productivity. There could also be
domestic firms in related industries whose problems may be indicative of those of the industries
further afield. This process of learning the particular constraints to further-away sectors as well
as cost/benefit analysis of the investments that they would require to emerge could also be
subcontracted to management consulting firms.
The result would be the identification of interested parties willing to invest their own
funds and conduct feasibility studies for a variety of potential strategic bets, identifying those
45
sector-specific capabilities that are missing and making proposals for policy reforms and public
investments that would be required to allow these new activities to succeed, along with an
attempt at cost-benefit analysis. The venture fund would be willing to partially co-finance these
projects if requested by the private sector.
Another way to facilitate the search for new activities is to build a new industrial zone
with its own management team. The zone would solve some easy to identify constraints such as
power, water supply, transportation infrastructure for goods and workers, and access to
regulatory and certification services. Beyond this, the management team will have to promote the
use of the industrial zone by attracting new investors. These will have specific concerns about
operating in the country, given its public inputs or other missing capabilities. The management
team should have the capacity to analyze these missing inputs, explore ways to circumvent them,
and inform the government of problems, solutions, and costs in order to assess whether
addressing these problems is warranted in light of the potential new investments that it would
crowd in.
Here again, the idea is that the industrial zone, like the venture fund, is really in the core
business of exploring the space of opportunities and obstacles and identifying solutions that
would trigger new activities. Every opportunity must be taken to design solutions that are as
general as possible in order to have the widest possible effect on new activities beyond the
investor who helped identify the obstacle.
These institutions are designed in this open-architecture search mode in order to avoid the
well-known failures in directed industrial policies of the past that created white elephants rather
than structural transformation. To this end, the guidelines for facilitating nearby jumps apply
equally to such institutions, particularly the focus on productivity-enhancing investments and
providing sector-specific public goods rather than subsidizing low productivity.
9. Conclusion
In this paper we have examined a host of new metrics to analyze structural transformation in
Ecuador. These metrics have shown that Ecuador’s export basket has not changed significantly
over the past decade, and that within those existing products there is little room to grow by
improving quality. Moreover, we have seen that existing export sectors are typical of countries
46
much poorer than Ecuador, and that country has not been adding new, more sophisticated
products to its export package at the same rate as some of its comparators. However, while the
current export basket is highly unsophisticated, it is not as poorly connected in the product space
as other countries in the region, suggesting that there are opportunities for structural
transformation moving forward.
We have used the data to identify new ‘high-potential’ sectors that represent attractive
tradeoffs between proximity, sophistication, and strategic value. The same data have been used
to analyze some of the strategic sectors already identified by the government, in order to help
refine that list.
But these resulting lists of sectors are not meant to be an identification of spillover-rich
‘winners’ that are worthy of subsidies and support. Instead, they are meant to be a guide to what
should be a constant process of the government searching for the sector-specific public inputs it
must provide in order for structural transformation to occur.
There are many inputs that the government must provide to the production process, and
these may be highly specific and broadly unknown to the authorities. Absent these specific
public inputs, private returns would be very low, but with them, private returns would be very
high. The task for the public sector is to figure out what specific
infrastructure/regulation/institutions it should be providing to the private sector so that these new
activities can emerge and structural transformation can occur. It would be best to learn these
needs from those same entrepreneurs that have or would enter those sectors, and some policy
guidelines for such a dialogue have been laid out. But with limited bandwidth, a cacophony of
requests, and the desire for movements to more distant sectors in the product space, the
government faces a significant challenge. The lists of sectors therefore can be used as a pointer
for where to look first. The public sector can take a closer look at what it is doing by acts of
omission or commission to prevent these particular sectors from emerging, and identify the
public inputs needed to realize high returns and spur private investment.
Just as we must be careful to avoid the mistakes of industrial policy in the past, the high
degree of specificity of public goods and institutions cannot be ignored just because it is
“delicate to suggest sector-specific” policies (Cueva, Albornoz, and Avellan 2007, p. 77)
5
. The
5
One could argue that the ‘non-sector-specific’ policies mentioned therein (human capital development, research
and development spending, and promoting foreign investment) are themselves highly sector specific when one must
47
policy guidelines above are geared to dealing with this specificity while navigating the perils of
traditional industrial policies. These offer a potential way forward that would allow Ecuador to
accelerate its recently lagging structural transformation and accelerate economic growth and
poverty reduction in the country.
consider actual implementation. Supporting primary education versus technical training for secondary school
graduates versus public university education will favor some sectors over others. Does research and development
spending mean university laboratories, corporate R&D in IT, or commercialization assistance for smaller-scale
agricultural producers? What foreign investors will be engaged, and in what order? These ‘horizontal’ policies could
benefit from recognition of their specificity, and some prioritization in light of it.
48
References
Arrow, K. 1962. “The Economic Implications of Learning by Doing.” Review of Economic
Studies 29(3):155-173.
Balassa, B. 1986 “Comparative Advantage in Manufactured Goods: a Reappraisal.” The Review
of Economics and Statistics 68(2): 315-19.
Bardhan, P. 1970. Economic Growth, Development, and Foreign Trade. New York: Wiley-
Interscience.
Cueva, S., V. Albornoz and L. Avellan. 2007. “Ecuador – Binding Constraints to Growth.” Inter-
American Development Bank. October 2007.
Feenstra, R., R. Lipsey, H. Deng, A. Ma, and H. Mo. 2005. “World Trade Flows: 1962–2000.”
Working Paper #11040. Cambridge, MA: National Bureau of Economic Research.
Hausmann, R., J. Hwang, and D. Rodrik. 2006. “What You Export Matters.” Working Paper
#11905. Cambridge, MA: National Bureau of Economic Research.
Hausmann, R., and B. Klinger. 2006. “Structural Transformation and Patterns of Comparative
Advantage in the Product Space.” Harvard University Center for International
Development Working Paper #128.
_____. 2007. “The Structure of the Product Space and the Evolution of Comparative
Advantage.” Harvard University Center for International Development Working Paper
#146 (extended and revised version of #128).
Hausmann, R., and D. Rodrik. 2003. “Economic Development as Self-discovery.” Journal of
Development Economics, 72:603–633.
_____. 2006. “Doomed to Choose: Industrial Policy as a Predicament.” Center for International
Development Blue Sky Conference Paper, September.
Hausmann, R., D. Rodrik and C. Sabel. 2008. “Reconfiguring Industrial Policy: A Framework
with an Application to South Africa.” HKS Faculty Research Working Paper
Series RWP08-031.
Hidalgo, C., B. Klinger, A. Barabasi and R. Hausmann. 2007. “The Product Space Conditions
the Development of Nations.” Science Magazine 317(5837): 482-487.
Hwang, J. 2006. “Introduction of New Goods, Convergence and Growth.” Department of
Economics, Harvard University. Mimeographed paper.
49
Jaffe, A. 1986. “Technological Opportunity and Spillovers of R&D: Evidence from Firm’s
Patents, Profits, and Market Value.” American Economic Review 76(5):984–1001.
Jaffe, A., M. Trajtenberg and R. Henderson. 1993. “Geographic Localization of Knowledge
Spillovers as Evidenced by Patent Citations.” Quarterly Journal of Economics 108(3):
577-98.
Leamer, Edward E. 1984. Sources of Comparative Advantage: Theory and Evidence. Cambridge
MA: The MIT Press.
50
Appendix
Sector Sub-group Average Density Average PRODY
Average
Strategic
Value
Flores 0.108 5319 10783
Metalmecánica Equipo electrónico y eléctrico 0.056 20951 17553
Línea Blanca 0.066 18116 18771
Manufacturas de metales 0.058 19305 17419
Materias Primas 0.063 14952 13440
automotriz y transporte Automotriz 0.061 16211 17330
Metalmecánica 0.059 17863 16677
biocombustibles Biocombustible 0.084 7402 17410
Oleaginosas y cereales varios 0.096 7061 10779
frutas y vegetales Aceites 0.090 9972 11312
Azúcares 0.121 4979 9706
Bebidas alcohólicas 0.077 13127 13267
Cacao en grano 0.172 1855 4770
Café en grano 0.136 2747 9725
Coco 0.097 3455 6426
Confituras y mermeladas 0.088 5849 13614
Congelado 0.103 9395 14620
Cortezas 0.097 6545 10453
Fresco 0.099 12443 13619
Fresco o refrigerado 0.094 9805 13371
Fresco o seco 0.118 7742 10005
Harina 0.095 11931 15854
Harinas 0.082 8199 12392
Jugos 0.120 7652 12100
Maní 0.103 2078 10173
NCP 0.084 12234 9948
Preparaciones de cacao 0.107 7493 12657
Preparaciones de café 0.106 9464 15117
Preparaciones y conservas 0.092 12104 13480
Raíces 0.117 14525 6569
Residuos 0.086 8497 10954
Salsas 0.095 11931 15854
Seco 0.094 7750 14099
Semillas 0.091 10800 12213
Torta 0.087 3578 8322
Vinagre 0.073 18856 13183
linea blanca Cocinas y estufas 0.089 14099 16245
Máquinas para lavar ropa 0.057 18566 19666
Máquinas, aparatos y artefactos mecánicos y partes 0.050 21536 20138
Refrigeradores 0.084 14842 17667
pesca y acuicultura Aceites 0.113 21333 9406
Congelado 0.132 7351 10088
Fresco o refrigerado 0.130 7943 9942
Harina 0.127 16130 8389
Peces vivos 0.095 3633 13204
Preparaciones y conservas 0.099 11863 13045
Seco 0.119 16614 10876
silviculture y madera Agroindustria 0.082 14692 14011
Manufacturas de madera 0.082 13590 14690
Muebles 0.078 14446 17615
Papel y cartón 0.073 18650 16510
Pasta de madera 0.057 13104 19050
Prefabricados 0.083 14966 17391
Primario 0.092 10316 13442
textiles Confección 0.086 11260 14021
textil 0.068 13966 14860
not targeted 0.065 16035 14889
... The PSM indicators are defined as follows: PRODY: PRODY is a product-specific measure that illustrates the sophistication of each product [41]. It is the RCA-weighted GDP per capita (PPP) of all countries that export the product [42]. Developed countries export sophisticated goods with higher PRODY values and developing countries export less sophisticated products with lower values. ...
... Products with higher strategic value are advantageous to future structural transformation. These products are rich in complex and sophisticated capabilities, which can easily be used in the production of other unexploited goods [42]. The Strategic Value indicator can be calculated using Equation (6). ...
Article
Asian liquefied natural gas (LNG)-exporting countries have large natural gas reserves with a combined LNG export share value of 58% globally. Nevertheless, their global share in petrochemical exports is merely 2.2%. Among the selected countries, Indonesia and Malaysia have exploited 39% and 17% of the products in their petrochemical sectors, but the UAE, Myanmar, Oman, Qatar, and Brunei have exploited only 2%–10%. The significant potential for these countries with regard to exploiting these petrochemical products to scale up their export diversification, in turn leads to sustainable economic growth. The primary reason for their low global share is insufficient knowledge concerning their production capacity, as well as the sector’s feasibility and potential with respect to their capabilities. This study thoroughly investigates the export diversification potential and opportunities for Asian LNG-exporting countries by exploring the nexus between LNG and petrochemicals using a product space model (PSM). The results indicate that production of unexploited petrochemicals is imminent in Indonesia and Malaysia, while the UAE, Myanmar, and Oman have moderate opportunities for exploration. These findings can assist policymakers in formulating realistic policies in accordance with their country’s capabilities. It will also assist entrepreneurs in identifying potential sectors for establishing new enterprises.
... For example, if a country produces product A, given that it can produce product B or not, this method will show how closely related the products are to each other and if they share inputs in terms of the productive knowledge that is involved in the production of these products [71]. Moreover, various international organizations, such as the United Nations, the World Bank, and OECD, have used the PSM approach in their reports to appraise the potential of different countries and help them explore new unexploited exportable products for forthcoming successful industrialization and sustainable economic growth, which validates its application elsewhere [73][74][75][76]. ...
... the per capita income of the countries that export a given product [72]. It also reflects a given product's income potential by showing its complexity and sophistication level [73]. Usually, developed countries export highly sophisticated products with complex capabilities that have high income potential, and high PRODY values. ...
Article
Hydrogen is considered a potential game changer for world energy systems and a solution to climate change concerns, as it generates zero waste and it is suited for power generation and transportation. Despite its several advantages, there are significant technical challenges in deploying a stable hydrogen economy including improving its process efficiencies, lowering production costs, maintaining cost-effective transmission and distribution, and exploiting inexpensive and sustainable feedstocks. In this context, a detailed study was conducted to analyze the production sources, technologies, storage and transport systems, and global potential exportable feedstocks to produce hydrogen. A comprehensive analysis of current hydrogen production technologies with their energy efficiencies and hydrogen selling prices was reported in this study. Various hydrogen production technologies with their capital investments and CO2 emissions were also presented. Potential feedstocks for hydrogen production were identified and analyzed through a product space model, which characterizes a network of global exportable products based on their similarities and productive knowledge. It was established that the hydrogen production feedstocks and sources currently used are primarily available in six countries: the United States of America, France, Russia, Sweden, the Netherlands, and Spain. Broadly, the results revealed that the United States of America and Russia shared the highest hydrogen feedstock exports, indicating a higher probability of hydrogen production in these countries. Except for Russia, all the studied countries fell in the most desired quadrant, indicating that they can move in all product space directions to exploit unexplored hydrogen feedstocks for better sustainable economic growth.
... Strategic Value: Strategic value is a country-specific indicator, which measures unexploited products potential contribution to the open forest. High strategic products are embedded with complex capabilities that can be used in the production of other unexploited goods (Hausmann and Klinger, 2010). Strategic value is calculated using the following equation. ...
Article
Full-text available
Successful structural transformation leads to sustainable economic development, which warrants better standard of living for citizenry. It is particularly important to analyze how to achieve structural transformation through export diversification. Structural transformation is mainly determined by successful industrialization that does not take off on its own. Rather it requires committed leadership, systematic and strong coordination mechanism. In the case of Pakistan, structural transformation has not been very impressive and faced with many challenges. In this regard, product space analysis was conducted to thoroughly analyze Pakistan's historic export performance, future opportunities, and provision of systematic pathways by prioritizing various unexploited sectors. It was found that Pakistan's current orientation in product space is of peripheral nature and Pakistan's economy has a medium level of diversity with an average sophistication level. Based on our PSM structural transformation policy calculation for Pakistan, parsimonious industrial policy is suggested to tap the unexploited products for future structural transformation and various sectors were prioritized, which will pave the road to industrial upgradation and ultimately lead to sustainable economic development. This study is greatly helpful for government policy makers to formulate feasible industrial policies in accordance with the productive capability of Pakistan, for potential investors and entrepreneurs to venture into potentially viable business opportunities.
... As in the original contribution by Hidalgo et al (2007), they use trade specialisationmeasured by revealed comparative advantage -as a proxy of production specialisation and analyse its evolution across the PS over time. Hausmann and Klinger (2010) and Hidalgo (2012) show how the export baskets of Ecuador and a pool of African countries (Kenya, Mozambique, Rwanda, Tanzania and Zambia) mostly consist in peripheral products 13 and highlight a rather strong persistence of the position of these countries over the PS through time. Some studies have focused on the nexus between centrality in the PS and trade diversification. ...
Article
A country’s specialization evolves over time in a dynamic process, with shifts in comparative advantages, resulting in new products being added to the country’s export basket. According to the renowned Product Space (PS) framework (Hausmann and Klinger, 2007; Hidalgo et al., 2007), this dynamic process is characterized by strong path dependence, as a country’s current production capabilities (technologies, production factors, institutions, etc.) determine what a country produces today, but also limits what it can produce tomorrow. We use a novel methodology to explore whether the patterns of specialization of a large sample of countries for the period 1995–2015 correspond to the predictions of the PS framework. Despite finding evidence of path dependence, our analysis also finds that a significant number of new products later added to countries’ export baskets were unrelated to their initial specialization pattern. We shed light on the determinants of these path-dependent changes in countries’ export baskets and show that economic growth is weaker in countries with a higher degree of path dependence.
... This data, denotes Ecuador's effort in terms of innovation being made in the country, although its low starting point, compared to the other countries in the region, still formed a situation of delay. This is mainly evident in the contraction of more than five percentage points in high-tech exports, in the last ten years [95]. ...
Article
Full-text available
Abstract This investigation seeks to explore the importance of agglomeration mechanisms in the location decisions of new manufacturing firms in Ecuador, based on sector and canton level data for the 2000–2010 period. A model is proposed to explore the relative importance of agglomeration mechanisms in location decisions of new manufacturing companies, a regression is performed using instrumental variables, the econometric estimation is developed, and an identification strategy is proposed. The results of the empirical analysis show that the learning mechanism, and history, have a positive and significant impact on the creation of new firms. An increase of 1% in the transfer of knowledge in the industries and cantons of the country is correlated with the increase in the location of new firms in the order of 9.2%. In turn, history has a positive and significant effect on the creation of new firms, in industries and cantons characterized by a past industrial environment. Even when the learning mechanism and history are controlled by provinces, sectors, and cantons, they continue to be the most important determinants of the location of new firms. This evidence could be attributed to the public investment in Ecuadorian industry in recent years. In this sense, the contribution of this work is found in the empirical distinction of the mechanism that favors or inhibits the location decisions of new companies. The analysis was replicated for a three-digit sectorial disaggregation level, to verify whether the agglomeration mechanisms operate differently on a different industrial scale. The results suggested that there were no differences to be considered. When the analysis was done excluding the cantons of Quito, Guayaquil, and Cuenca, given their high representation in terms of the birth of industries and employment, the results were consistent with those previously mentioned. However, it is so only with respect to history, which in this case accounts for 38.8% of the birth of firms; whereas, matching accounts for an order of 38.9% in the period of analysis. This result is explained in the context of the country’s industrial policy. View Full-Text Keywords: agglomeration; location; agglomeration mechanism; agglomeration economies; new firms; cantons of Ecuador
Article
The development of polymeric membranes from polymers such as polystyrene (PS), polyvinylchloride (PVC), and their associated family has brought great momentum to the environmental remediation universe, mainly due to their surprisingly diverse and multi-purpose nature. Their usage has surged 20 times in the last half-century and is likely to double again in the coming 20 years. As a result, the polymeric materials economy and commercialization of research become increasingly important as a possible option for a country to boost prosperity while decreasing its reliance on limited raw resources and mitigating negative externalities. This transformation demands a systematic strategy, which involves progress beyond improving the existing models and building new avenues for collaboration. In this work, a sophisticated system, i.e., product space model (PSM), has been presented, explicitly appraising the opportunity space for United Kingdom, Italy, Poland, India, Canada, Indonesia, Brazil, Saudi Arabia, Russia and Colombia for their potential future industrialization and commercialization of polymeric membranes for environmental remediation. The results revealed that UK, Italy, Poland and India are at advantageous positions owing to their close proximity of (distance<2) and their placement in Parsimonious policy, which is the most desired quadrant of Policy Map of PSM, Canada and Indonesia have medium level opportunities, while Russia and Saudi Arabia have opportunities with more challenges to fully exploit the unexploited polymers products in terms of membranes for environmental remediation and prove favorable for export diversification, sustainable economic growth, and commercialization.
Article
Full-text available
El objetivo de este trabajo es analizar el patrón de especialización y la posición competitiva de las exportaciones de Ecuador dentro la Unión Europea durante el periodo 2000-2017, con el fin de conocer si las exportaciones han incrementado su contenido tecnológico, elevando así los niveles de generación de valor en el país. Para ello, se emplean el Índice de Especialización Normalizado de las Exportaciones, el Saldo Comercial Relativo, el Índice de Contribución al Saldo y el Índice de Posición de Mercado.
Conference Paper
The aim of this paper is to analyze Ecuador´s competitive position with the European Union (EU-28) during 2000-2015 from a technological intensity perspective. To accomplish this objective this paper studies Ecuadorian exports specialization to the EU-28 using the Specialization of Exports Index. Then this research determines Ecuador´s competitive position in the European Union from a technological intensity perspective using three indicators, that combine exports and imports data: the Relative Trade Balance Index, the Contribution to the Trade Balance Index and the Market Position Index. The results allow us to affirm that Ecuador's competitive position in the European Union market has not experienced any technological intensity improvement during the studied period.
Article
Full-text available
This paper has been formed in the framework of the “economic complexity approach” which has a cruel role in identifying policies that accelerate current structural transformation at Turkish NUTS 2 regions. This approach influence a large scale of development plans due to the fact that its variables are very useful to determine the similarities or differences of production structure among the regions. Thus, Turkish NUTS 2 regions are being classified by means of the economic complexity approach. This paper aims to specify different policy implications among the regions. To do this, we make a concrete connection via economic complexity variables between current output structure and future economic growth of regions. In this paper initially, cluster analysis is used to categorize different regions according to economic complexity. Then some policies that aim to accelerate structural transformation in manufacturing industries are proposed. After all, ECI values of the regions are being discussed. Preliminary results suggest that, TR10 (İstanbul), TR 42 (Kocaeli, Sakarya, Düzce, Bolu, Yalova) TR 51 (Ankara) and TR 31 (İzmir) are the most developed regions which departed from other regions of the country.
Article
Full-text available
We compare the geographic location of patent citations with that of the cited patents, as evidence of the extent to which knowledge spillovers are geographically localized. We find that citations to domestic patents are more likely to be domestic, and more likely to come from the same state and SMSA as the cited patents, compared with a “control frequency” reflecting the pre-existing concentration of related research activity. These effects are particularly significant at the local (SMSA) level. Localization fades over time, but only very slowly. There is no evidence that more “basic” inventions diffuse more rapidly than others.
Article
Full-text available
Economies grow by upgrading the products they produce and export. The technology, capital, institutions, and skills needed to make newer products are more easily adapted from some products than from others. Here, we study this network of relatedness between products, or "product space," finding that more-sophisticated products are located in a densely connected core whereas less-sophisticated products occupy a less-connected periphery. Empirically, countries move through the product space by developing goods close to those they currently produce. Most countries can reach the core only by traversing empirically infrequent distances, which may help explain why poor countries have trouble developing more competitive exports and fail to converge to the income levels of rich countries.
Article
Demonstrates that technical change is attributable to experience. The cumulative production of capital goods is used as the index of experience. New capital goods are assumed to completely embody technical change. The assumption is made that the model will be operating in an environment of full employment although reference is made throughout to the case of capital shortage. The implications of this model on wage earners are discussed, and profits and investments are examined. The rate of return is determined by the expected rate of increase in wages, current labor costs per unit output, and the physical lifetime of the investment. Learning is an act of investment that benefits future investors. Further analysis shows that the socially optimal ratio of gross investment to output is higher than the competitive level. (SRD)
Article
In the presence of uncertainty about what a country can be good at producing, there can be great social value to discovering costs of domestic activities because such discoveries can be easily imitated. We develop a general-equilibrium framework for a small open economy to clarify the analytical and normative issues. We highlight two failures of the laissez-faire outcome: there is too little investment and entrepreneurship ex ante, and too much production diversification ex post. Optimal policy consists of counteracting these distortions: to encourage investments in the modern sector ex ante, but to rationalize production ex post. We provide some informal evidence on the building blocks of our model.
Article
This is the first book to present a clear empirical picture of the international exchange of goods and of the resources that account for the exchanges that occur. It fully articulates the Heckscher-Ohlin theory of international comparative advantage, in which a country's factor endowments (land, labor, capital) play a crucial role in determining trade patterns. The theory is carefully link to the book's analysis. Using tables, graphs, and econometric data summaries, Learner describes the patterns of trade and the patterns of resource supplies of fifty-nine countries and explains these trade patterns in terms of the abundance of eleven resources. His study should create a standard by which other data analyses will be judged in the future.Edward E. Learner is Professor of Economics at the University of California at Los Angeles.
Article
The paper provides a text for the Hechscher-Ohlin theory by simultaneously introducing trade flows, factor intensities, and factor endowments in the framework of a multi-country and multi-product model. The findings show that differences in physical and human capital endowments explain a substantial part of intercountry differences in the pattern of trade in manufactured goods. It is further shown that the pattern of specialization is also affected by the extent of trade orientation, the concentration of the export structure, and foreign direct investment.
Article
When local cost discovery generates knowledge spillovers, specialization patterns become partly indeterminate and the mix of goods that a country produces may have important implications for economic growth. We demonstrate this proposition formally and adduce some empirical support for it. We construct an index of the “income level of a country’s exports,” document its properties, and show that it predicts subsequent economic growth. Copyright Springer Science+Business Media, LLC 2007
Article
he main purpose of industrial policy is to speed up the process of structural change towards higher productivity activities. This paper builds on our earlier writings to present an overall design for the conduct of industrial policy in a low- to middle-income country. It is stimulated by the specific problems faced by South Africa and by our discussions with business and government officials in that country. We present specific recommendations for the South African government in the penultimate section of the paper.
Article
In this paper we examine the product space and its consequences for the process of structural transformation. We argue that the assets and capabilities needed to produce one good are imperfect substitutes for those needed to produce other goods, but the degree of asset specificity varies widely. Given this, the speed of structural transformation will depend on the density of the product space near the area where each country has developed its comparative advantage. While this space is traditionally assumed to be smooth and continuous, we find that in fact it is very heterogeneous, with some areas being very dense and others quite sparse. We develop a measure of revealed proximity between products using comparative advantage in order to map this space, and then show that its heterogeneity is not without consequence. The speed at which countries can transform their productive structure and upgrade their exports depends on having a path to nearby goods that are increasingly of higher value. [Jointly published as Center for International Development Working Paper No. 128 and KSG Faculty Research Working Paper Series RWP06-041.]