Heterogeneity of Armington Elasticities
Across Countries
Master Thesis
Agriculture and Rural Policy Group
Wageningen University
Author: Harmen Mijnen
Supervisor: Koos Gardebroek
Examiner: Jack Peerlings
May 2013
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Acknowledgements
First of all I would like to thank Koos Gardebroek for his guidance and judgement during the
process of writing this thesis. His contributions and knowledge on the field of econometrics
were especially important for the eventual conclusion of this thesis. Furthermore his laissez
faire approach and constructive critique provided a very pleasant working experience.
I would also like to thank the LEI and specifically Geert Woltjer for giving me the opportunity
to work on the topic of CGE trade models during my internship at LEI. It was during this
internship that I was inspired to choose Armingtons as the topic of my research. Finally, I
would like to thank my examiner Jack Peelings for his useful contributions.
Wageningen, 23 may 2013
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Abstract
In an Armington trade setting the Armington elasticity is the substitution elasticity between the
foreign and domestic variety of a product. Armington substitution elasticities are important
parameters in CGE trade models, however little is known about what determines their size.
Therefore, many CGE models take Armington elasticities to be the same for each country, even
though in reality Armington elasticities are likely to vary across countries. This thesis aimed to find
the determinants of heterogeneity of Armington elasticities across countries. To achieve this, in a
first stage, over 300 Armington elasticities where estimated for 29 very disaggregate products in 14
EU countries. Armington elasticities were found to be heterogeneous across countries and the
average of the Armington elasticity was found to be close to unity, which is in accordance with other
empiric literature. Using a linear regression approach similar to that of Blonigen and Wilson (1999),
the country characteristic determinants of Armington elasticities were estimated. Different
regression methods where used to regress the Armington elasticities found in the first stage on 10
possible economic and social country characteristic determinants of Armington elasticities. It was
found that the size of a country’s Armington elasticity depends positively on imports from outside
the EU( far away) and negatively on GDP/capita and the level of urbanisation of a country. This study
also confirmed results found in literature that intermediate goods have higher Armington elasticities
than final goods.
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Contents
1. Introduction
1.1 Introduction.......................................................................................................................8
1.2 Problem statement............................................................................................................9
1.3 Research questions..........................................................................................................10
1.4 Thesis overview...............................................................................................................10
2. Literature Review
2.1 International trade models..............................................................................................12
2.2 Computable General Equilibrium models........................................................................12
2.3 Armington elasticities......................................................................................................13
2.4 The size of Armington elasticities....................................................................................14
2.5 Determinants of Armington elasticities...........................................................................15
2.6 Country specific determinants of Armington elasticities.................................................16
3. Method
3.1 Empirical model...............................................................................................................22
3.2 Country characteristic determinants...............................................................................23
3.3 Regression methods........................................................................................................25
4. Data
4.1 Databases........................................................................................................................28
4.2 Data selection..................................................................................................................30
4.3 Outliers............................................................................................................................32
4.4 Data on country characteristics.......................................................................................34
5. Results of Armington Regressions
5.1 Results Armington elasticity estimations.........................................................................36
5.2 Results of Armington elasticities per country..................................................................37
5.3 Results of per country panel data Armington regressions..............................................38
5.4 Adjusting for Belgium......................................................................................................39
5.5 Armington elasticities per product..................................................................................40
6. Results of Country Characteristic Regressions
6.1 Results of country characteristic regressions..................................................................42
6.2 Alternative estimation methods for country characteristic regressions.........................43
6.3 Panel data approaches on country and good characteristics..........................................46
6.4 Overview of results on country characteristic determinants..........................................48
7. Conclusion and Discussion
7.1 Conclusion.......................................................................................................................50
7.2 Discussion........................................................................................................................51
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Bibliography.............................................................................................................................54
Appendix 1...............................................................................................................................56
Appendix 2...............................................................................................................................66
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1. Introduction
1.1 Introduction
In economics, computable general equilibrium (CGE) models are often used to analyse different
policy initiatives. For example, models used to predict and explain international trade flows and their
welfare implications commonly take the form of CGE models (Bchir, Decreux et al. 2002).
Computable General Equilibrium models are founded on microeconomic theory and incorporate data
derived from the real economy (Breuss 1999). CGE models simultaneously specify the behaviour of
supply, demand and prices for a number of interacting markets. The prices of factors and goods are
equilibrated to provide the general (competitive) equilibrium for these markets. CGE models can be
used to estimate trade effects for a wide variety of issues such as: trade policy changes, resource
allocations within countries, custom union issues, various trade negotiations and North-South trade
related questions (Shoven and Whalley 1984). Considering their widespread use in the analysis of
trade policies, the importance of CGE models can hardly be understated. However, CGE models are
very complex and almost impossible for non-initiates to fully comprehend. Policy makers therefore
have to rely heavily on modellers’ ability to provide reliable models for use in the decision making
process. This thesis makes an effort to improve our knowledge on one important parameter of CGE
models, the Armington elasticity.
CGE models on international trade commonly use the so-called ‘Armington assumption’ on trade.
The defining property of the Armington assumption is that goods are considered homogeneous
except for their country of origin. Because goods are differentiated according to their country of
origin, it implies consumers value goods differently according to where the good was produced. This
assumption has the important effect that it makes it possible to have two-way (or inter-industry)
trade of a good between countries. A model that uses the Armington assumption ‘includes intraindustry trade with the intention to reflect actual international trade patterns more accurately ‘
(Armington 1969). This is important as two-way trade is witnessed frequently in reality and therefore
should be allowed in any realistic trade model. Besides providing desirable two-way trade patterns,
Armington models also have been found to generate more realistic trade responses to price changes
than models of homogeneous products. Because of these properties the Armington assumption has
become a standard assumption for international CGE models (Zhang 2006).
In CGE modeling, policy changes are converted into price effects. These price effects determine how
the policy affects various variables of interest such as output, trade flows and economic welfare. In
CGE models the behavioral relationships between policy changes, price effects and economic effects
are largely represented by elasticities. The behavioral relationship associated with the Armington
assumption is the relationship between the demand of the foreign and domestic ‘goods variety’
considering their relative prices. This relationship is represented by the Armington substitution
elasticity. The Armington elasticity, or ‘Armington’, determines the substitution in demand between
foreign and domestic good varieties. This means that goods with a high Armington elasticity are
goods for which consumers will substitute relatively easily between domestic and foreign varieties
given a relative change in domestic and foreign prices. Conversely, goods with a low Armington
elasticity will see consumers stick to their preferred variety more firmly. In this case consumers
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strongly distinguish between the domestic and the foreign good varieties and are less willing to
substitute between the two.
The Armington elasticity is an important elasticity in CGE trade models because it plays a central role
in determining the trade outcomes of price changes. Patterns of trade can have important effects on
countries’ income levels, employment and welfare distribution. As a result the outcomes of CGE
trade models are sensitive to Armington elasticities (Mc Daniel and Balistreri 2003). Because of this,
the choices CGE modelers make on the size and variation of the Armington elasticities can greatly
affect model’s outcomes.
In an effort to estimate the size of Armington elasticities, authors of empirical studies have
attempted to estimate Armingtons for both individual countries and sectors. Most of these studies
have found Armington elasticities close to one (Reinert and Roland-Holst 1992). Interestingly, CGE
modellers generally use Armingtons that are substantially larger than those found by empirical
studies and in the words of Welsch (2008) are often based on ‘guestimations’. Mc Daniel and
Balistreri (2003) state that much of the controversy surrounding Armington elasticity estimates
arrives from structural differences between econometric models used to measure Armington’s size
and the CGE models used to evaluate policy.
1.2 Problem statement
Apart from just estimating the size of Armington elasticities, there have also been a few studies
which have attempted to estimate the determinants of their size. These empirical studies found
Armington elasticities to vary according to the properties of the good and the sector. Particularly the
degree of homogeneity of a good is found to affect a good’s substitutability. Intermediate goods, for
example, are generally more homogeneous than consumer goods and thus have higher Armington
elasticities. Also, the level of FDI (foreign direct investment) within a sector increases homogeneity
and therefore also the Armington elasticities. High levels of advertising on the other hand, are
associated with more differentiable goods and lower Armington elasticities (Blonigen and Wilson
1999). However, on the whole, knowledge about the determinants of these Armingtons remains very
limited and more research is needed (Mc Daniel and Balistreri 2003). Furthermore, most research on
the size of Armingtons has been done for a single country, and then mostly on the United States
(Welsch 2008). This causes biased estimates if the U.S. has properties which cause its Armingtons to
differ from those of other countries.
Recapitulating, Armington elasticities are important behavioural parameters in CGE trade models,
determining quantitative, and qualitative, results that policy makers use. However, there is a gap
between the values found in econometric studies and the values used by CGE modellers. Also, little is
known about the determinants of Armington elasticities and their variation across countries. As a
result, many CGE models simply assume Armingtons to be the same for each country. This is an
unlikely assumption, as Armingtons elasticities are based on preferences and it is likely that
preferences vary between countries (Welsch 2008). However, because most empirical studies on
Armington elasticities focus on a single country, the variation between countries has thus far been
left almost unexplored. Zhang and Verikios (2006) name a lack of readily comparable cross-country
data as one of the main contributing factors for the lack of research in this area.
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Knowledge about the absolute differences in Armingtons between different countries can be of
practical importance to CGE modelers because they could then directly apply them in their models.
However, given the number of countries in the world this would be an unfeasible effort. Especially
considering that Armingtons might change over time, thus requiring constant updating of the
estimates. Besides, CGE modelers do not generally apply empirical results on Armingtons in their
models in the first place. Therefore it may be more useful to know of certain country properties (or
characteristics) determining the Armington elasticities of a country. This way, the Armington
elasticities of each country can be easily derived from their individual characteristics.
1.3 Research Questions
This thesis aims to take the principles of Lopez and Pagoulatos (2002) and Blonigen and Wilson
(1999) and apply them on the aforementioned country characteristics determinants of Armington
elasticities, in order to explain cross-country heterogeneity of Armington elasticities. To achieve this,
two main research questions need answering:
1. What are the Armington elasticities for goods in different countries?
2. What are the country characteristics determining heterogeneity of Armington elasticities
across countries?
The first research question is answered by estimating the Armington elasticities of 30 highly
disaggregate goods for 14 EU countries. For the second question, a number of country characteristic
determinants are specified. These include economic determinants such as a country’s level of trade,
level of wealth and quantity of FDI, but also social determinants like multiculturalism and national
pride. In order to analyze the country characteristic determinants of Armington elasticities, in the
second stage these determinants are regressed on the Armington elasticities obtained in step one.
1.4 Thesis overview
Chapter 2 contains background information and an overview of relevant literature and previous
results of studies into the determinants of Armington elasticities. Chapter 3 includes the method
used for estimating the Armingtons and their determinants. In chapter 4 an overview is given of the
data that was used. Chapter 5 reports the results of the Armingtons, while chapter 6 reports the
results of the regressions on the country characteristic determinants. Chapter 7 contains conclusions
and discussion.
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2. Literature Review
This thesis deals with the Armington elasticity, an important parameter in general equilibrium trade
models. Therefore, this chapter provides a short introduction on trade modelling and discusses
relevant literature on Armington elasticities.
2.1 International trade models
The question is, why do countries trade with each other? In international trade theory there are a
number of theories explaining international trade. One of the best known theories is that of
comparative advantage. A country has a comparative advantage in the production of a good if the
opportunity cost of producing that good in terms of other goods is lower in that country than in
another country. The comparative advantage of an industry depends not only on its productivity
relative to the foreign industry, but also on the domestic wage rate compared to the foreign wage
rate. A country’s wage rate, in turn, depends on the relative productivity of its other industries
(Krugman and Obstfeld 2006). Trade is beneficial for both countries if they export the good in which
they have a comparative advantage. The Ricardian model bases international trade solely on
comparative advantage, or similarly on productivity of labour.
Another influential model is the Heckscher-Ohlin theory on trade. This theory states that countries
tend to export goods that are intensive in the factors which they have in abundance. This model is
well suited to explain for example North-South trade in terms of high skilled and low-skilled labour.
The high skill abundant north exports skill-intensive goods while the low skilled south exports labourintensive goods to the North. The Heckscher-Ohlin theory assumes equal technologies across
countries and complete factor price equalisation in the absence of trade barriers. Trade provides
gains for both countries but within countries the holders of relatively scarce factors lose and those
with relatively abundant factors win. According to Krugman and Obstfeld (2006), while empirical
evidence on the Heckscher-Ohlin model is mixed, it remains useful to analyse effects of trade on
income distribution.
2.2 Computable General Equilibrium models
While the previous two theories can provide some insights into international trade they lack the
complexity necessary to analyse and predict the exact nature of international trade flows. To this end
Computable General Equilibrium (CGE) models have been constructed. CGE models are commonly
used to analyse a variety of policy initiatives. They are, for example, the type of model most
commonly used in the analysis of welfare effects of trade liberalization policies (Bchir, Decreux et al.
2002). It is important to note that while it is clear that CGE models should incorporate a number of
markets, there is less unity on what the essential components and markets should be (Shoven and
Whalley 1984). It is however generally accepted that a general equilibrium model is a model where
all markets clear in equilibrium.
The purpose of CGE models is to convert Walrasian general equilibrium structures for an economy
into realistic models of actual economies (Shoven and Whalley 1984). General equilibrium models
are founded on microeconomic theory and incorporate data derived from the real economy (Breuss
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1999). They simultaneously specify the behaviour of supply, demand and prices for a number of
interacting markets, where agents are usually price takers. The prices of factors and goods are
equilibrated to provide the general (competitive) equilibrium. For research on international trade,
CGE-models are often used to estimate effects of trade policy changes, custom union issues, various
trade negotiations and North-South related trade questions (Shoven and Whalley 1984).
In the past, many CGE models were largely static. This means that the effects of trade policy are
immediate and only lead to a one-time shift in an economy’s reallocation of resources. Although this
improves the economy’s efficiency it is limited in its potential for continued growth (Breuss 1999). In
reality, adapting to a trade policy shock is neither immediate nor is it costless. It is therefore useful to
take into account the dynamic effects of policy changes. Particularly, changes in trade may be
accompanied by changes in technology because of trade related technology spillovers and learning
by doing effects. These effects may cause policy changes to have bigger economic impacts than
suggested by the primary trade response only (Keller 2004). Many recent CGE models include these
types of dynamics in one form or another.
2.3 Armington elasticities
Standard trade theory is involved only with inter-industry trade. A country or region either exports a
good, imports a good or is absent of trade for the good. In any case, it does not both export and
import the same good. The reason for this is the assumption of perfect substitutability between
goods produced by each country, making two-way trade redundant. This means that two-way trade
(or intra-industry trade) is ignored in standard trade theory despite the fact that it is witnessed
frequently in the real world. To obtain more realistic results in trade modelling and make intraindustry trade possible Paul Armington (1968) came up with his famous assumption which states that
firms in each region produce a unique variety of a particular good. The number of varieties is
therefore equal to the number of regions. In other words, goods produced in different countries are
imperfects substitutes in demand (Armington 1969). These unique varieties can be exported to and
from trading countries, making intra-industry trade possible. Because of this the Armington model is
better suited to reflect actual international trade patterns and has become standard in international
computable general equilibrium models.
Key to the Armington assumption is the Armington substitution elasticity. Actually, there are two
types of Armington elasticities. When people refer to ‘the’ Armington elasticity, usually what they
mean is the elasticity of substitution between domestic/home goods and imported/foreign goods.
This elasticity determines the demand response of one good variety (e.g. foreign) to a change in
relative prices of the other good variety (e.g. domestic). The substitution elasticity between domestic
and foreign goods is also known as the ‘macro’ Armington elasticity ((Feenstra, Obstfeld et al. 2010)).
The second type of Armington elasticity is called the ‘micro’ Armington elasticity. The micro
Armington elasticity is the substitution elasticity between two different foreign suppliers of the same
good. In other words, it is the substitution elasticity between ‘foreign nation one’ and ‘foreign nation
two’. Both Armingtons are present in CGE models, although in empirical research the focus is
predominantly on the macro elasticity. This because of the greater difficulties and data requirements
involved in estimating micro elasticities (Mc Daniel and Balistreri 2003).
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Besides the inclusion of the inter-industry trade effect there are also other reasons why CGE
modellers favour the use of the Armington elasticity. First of all models with Armington specifications
yield smaller trade and output effects than models with either homogeneous goods or models with
firm-level product differentiation (Francois, McDonald et al. 1995). CGE modellers view these smaller
responses as more realistic in international trade policy modelling (Gallaway, McDaniel et al. 2003).
Another reason is that standard inter-industry modelling leads to countries reaching unrealistic levels
of specialisation because of homogeneous products and production possibility frontiers that are close
to linear (Shoven and Whalley 1984). Finally, an advantage of using the Armington specialisation is
that import and export-demand elasticities can be easily defined (Shoven and Whalley 1984).
Generally, the macro Armington trade elasticity is derived from a two-stage budgeting process. In an
economy the aggregate representative consumer has a well-behaved utility function defined over
composite goods (C), which contains an imported goods variety (M) and a domestic goods variety (D).
In the first stage, the representative consumer allocates his total expenditure to different products.
The Armington model typically specifies a constant elasticity of substitution (CES) function over
domestic and foreign goods (Blonigen and Wilson 1999). It is only in the second stage that the
representative consumer allocates expenditure within each product over foreign and domestic. As a
third step the micro Armington elasticity determines from which foreign country imports occur if
imports are present.
2.4 The size of Armington elasticities
Because it governs the strength of the relative demand response to relative international prices, the
size of the Armington elasticity is important in order to understand many features of the global
economy. Knowledge of the size of the Armingtons is important for CGE policy modelling because the
degree to which a policy change will affect for example a country’s balance of trade, level of income,
and employment all depend on the size of the Armingtons used in the model. (Feenstra, Obstfeld et
al. 2010).
In the mid 80’s and early 90’s there was a relatively large number of studies trying to estimate the
size of Armington elasticities. These studies used linear regression methods to estimate macro
Armingtons for various industries, mostly for the U.S.. Studies from this time include that of Lachler
(1985) who found Armingtons between 0.8 and 4.9 and Reinert and Shiells (1991) who obtained
Armingtons between 0.14 and 1.98. Generally, these studies found values for the macro Armington
elasticity of around unity. More recently there have been a few studies involving more complex
estimation models such as Hummels (1999). These studies sometimes find values that are
significantly higher than those found in older studies. However, only a small number of these studies
have been performed and their results are far from consistent. Therefore it is not (yet) possible to
draw any definitive conclusion from this. Table 1.1 provides a summary of the Armington elasticities
obtained by previous studies.
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Table 1.1 Empirical studies on Armington elasticities
Authors
Period
Imports
from
Importing
country
Armington
Elasticities
Macro/
Micro
Shiells and
Reinert (1993)
1980–1988
(quarterly)
U.S.
0.10 - 1.49
Macro
Feenstra
(1994)
1964–1987
Mexico,
Canada,
World
All U.S.
partners
U.S.
1.3 - 3.0
Macro
Blonigen and
Wilson (1999)
1980–1988
(quarterly)
All U.S.
partners
U.S.
0.81: std. dev. 0.63
Macro
Feenstra et al.
(2010)
1992-2007
Flores and
Cassoni
(2010)
Gallaway and
Mc Daniel
(2003)
1989-2001
(quarterly)
All Uruguay
Partners
Uruguay
Macro
and
Micro
Macro
1989–1995
(annual)
All U.S.
partners
U.S.
Gibson (2003)
1970-2001
(annual)
All S.A.
partners
South Africa
macro elasticity:
around unity
median micro: 4.42
long-run elasticities
vary in a range of 0.5
to 4.3
average short-run:
0.95.
average long-run:
1.55
long-run: 0.6-2.7
short run: 0.44-2.7
Hummels
(1999)
1994
Macro
mid-1970s
through to late
1980s
1960-1981
U.S., New
Zealand, 5
S. American
countries
Philippines
average: 4.8, 5.6,
and 6.9
Kapuscinsky
and Warr
(1999)
Lachler (1985)
U.S., New
Zealand, 5
S. American
countries
All trade
partners
0.2 – 4.0
Macro
Germany
0.8 - 4.9
Macro
Nemeth(2011) 1995–2005
All trade
partners
EU-25
EU-25
avg. micro: 2.6
avg. macro: 1.2
Reinert and
Holst (1992)
1980-1988
(quarterly)
All Trade
Partners
U.S.
0.14 - 3.49
Macro
and
Micro
Macro
Reinert and
Shiells (1991)
1980-88
(quarterly)
All trade
Partners
U.S.
0.14 - 1.98
Macro
Saito (2004)
1970 and 1990
(annual)
14 OECD
countries
14 OECD
countries
macro: 0.94 - 3.54
micro: 0.24 - 1.39
Macro
and
Micro
Macro
Macro
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Sauquet
(2011)
1995 to 2002
All trade
partners
France
0.51 - 0.92
Macro
Shiells et al.
(1986)
1962-1978
All trade
partners
U.S.
0.45 - 6.5
Welsch (2006)
1970–1997
(annual)
All trade
partners
France
0.21 - 0.84
Macro
Welsch (2008)
1979–1990
All trade
partners
France,
Germany, Italy,
U.K.
avg. DE: 0.87
avg. FR: 0.99
avg. IT: 0.77
avg. U.K.: 0.05
Macro
The Armington elasticities used in CGE models generally differ substantially from the Armingtons
found empirically. In GTAP for example, average macro elasticities are set at 3.1, which is
substantially higher than the Armingtons found in most empirical studies. Gallaway, McDaniel et al.
(2003) explains that CGE modellers are aware of the empirical results but that they believe them to
be too low and therefore consciously choose to neglect them. The reason modellers think empirical
results are too low is that if they use these low values, their models display unrealistically low levels
of trade. Whether this neglect of empirical evidence is justified remains a matter for debate. Some
authors argue that differences in Armington elasticities stem from the differences in model
specifications between CGE modelling practices and the econometric approaches used to obtain
estimates for the Armington elasticities (Mc Daniel and Balistreri 2003)
Empirical studies unambiguously show that micro Armingtons are higher than macro Armington
elasticities (Németh, Szabó et al. 2011). This implies that it is easier to substitute between two
foreign goods than it is to substitute between a domestic and a foreign good. Intuitively this makes
sense if we account for a home-bias factor which makes consumers favour home products over
foreign products. These results are generally accepted in CGE modelling practices as well. In GTAP for
example, all micro-elasticities are twice that of the corresponding macro elasticity.
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2.5 Determinants of Armington elasticities
In an Armington setting consumers view foreign goods as imperfect substitutes for domestic goods.
In order for consumers to be able to differentiate between goods, as is assumed by the Armington
model, consumers have to value foreign and domestic goods differently. How do they do this?
Theoretically there are two possible explanations. First of all, the substitution elasticity may depend
on actual characteristics of the product or industry, i.e. foreign and domestic goods are clearly
distinguishably different. Secondly, it may be partly due to a home-bias factor independent of the
product’s actual properties. At the core of the latter kind of differentiation is that there is some
intrinsic value given to the label ‘home’. This kind of ‘primitive’ consumer preference can be
compared to differentiating between Hertog-Jan and Grolsch beer, where consumers state
preferences despite being unable to differentiate in a blind test (Blonigen and Wilson 1999). Political
and social variables may strengthen home bias and increase product differentiation between home
and foreign products (e.g. ‘buy American’ campaigns). As the word ‘home-bias’ implies, consumers
are assumed prefer home over foreign, although it is certainly possible theoretically that consumers
would prefer foreign over home goods.
McCallum (1995) found border effects on trade for US-Canada trade beyond those to be expected
from trade barriers alone. Using a gravity approach Wolf (2000) found a home bias between U.S.
states despite controlling for transport costs through a distance variable. Since there are no trade
barriers between U.S. states or extra transaction costs associated with importing or exporting, this
provides strong evidence for home bias, even on a regional level.
Of course the other reason for heterogeneity of products is that differences between products do
not only exist in the minds of consumers but also in reality, i.e. products are in fact heterogeneous
across countries. In some cases this is obvious. German cars for example are generally of higher
quality than Chinese cars. Therefore German and Chinese cars differ from each other according to
the country where they were produced. We know that cars are relatively heterogonous products, as
there are a lot of different types and qualities. This makes it easier to differentiate between these
products according to their country of origin than is the case, for example, for a ton of steel, because
steel is a far more homogeneous good. Studies like Lachler (1985) and Zhang and Verikios (2006)
have found that heterogeneous goods generally have lower Armington elasticities than
homogeneous goods. Thus, heterogeneous goods are harder to substitute across countries.
Corresponding to this, final goods (like cars), which are more heterogeneous, are found to have
lower Armingtons than intermediate goods (like steel) (Saito 2004).
In an Armington setting, there might be another reason why domestic and imported commodities
are treated as imperfect substitutes, namely commodity aggregation. In the real world, most
industries produce many commodities. In a CGE model these commodities have to be aggregated to
represent the total outputs per industry type. As a result, individual commodities disappear in these
models and, instead, we end up with industry composites of many different commodities. These
industry composites do not necessarily have the same set of commodities across countries.
Therefore, even if individual commodities produced in different countries are perfect substitutes, the
industry composites of these commodities in different countries still might not be the same.
Commodity aggregation can therefore create imperfect substitution between composite goods
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produced in different countries (Zhang and Verikios 2006). Indeed, one frequently encountered
finding on Armington elasticities is that disaggregate empirical studies yield higher Armingtons than
more aggregated approaches (Mc Daniel and Balistreri 2003). This indicates that the way goods are
aggregated matters for the obtained Armington elasticities. Mc Daniel and Balistreri (2003) also
report in their literature review that long-run estimates for Armington elasticities are higher than
short-run estimates.
We have already mentioned heterogeneity as one property of a good which affects its Armington
elasticity. The question is, are there others? Unfortunately, literature on the determinants of
Armingtons is sparse, as also concluded by Mc Daniel and Balistreri (2003). To the knowledge of the
author only two studies have made serious attempts to empirically estimate determinants of
Armingtons. The first of these studies is that of Blonigen and Wilson (1999), who, by using a varying
coefficients model, estimate Armington elasticities between U.S. domestic and foreign goods across
over 100 industrial sectors from 1980 until 1988. They examine the role of product, industry political,
and ‘home-bias’ factors as determinants of the variation in Armington elasticities. The results
strongly indicate that foreign-owned affiliates positively affect Armington elasticities, while there is
also some support that entry barriers and union presence have an effect. Their results also show a
significant home-bias effect.
Another study which takes into account some determinants of Armington elasticities is that of Lopez
and Pagoulatos (2002), who provide estimates of elasticities of substitution between domestic and
imported goods (macro Armington) for 40 4-digit SIC food manufacturing industries. The paper
focusses on the U.S. food sector and finds a large variation in Armington elasticities. Lopez estimates
the determinants of Armingtons using a GLS approach with a covariance matrix that takes into
account the errors of estimation. It is found that a high percentage of intermediate consumption
compared to final consumption increases the Armington elasticities, as would be expected.
Furthermore, a high level of advertising within a sector also decreases the Armington elasticity. This
makes sense if advertising increases differentiability within a sector.
2.6 Country specific determinants of Armington elasticities
The results of the two studies listed above and additional literature on four possible country
characteristic determinants of Armington elasticities are briefly summarised below.
The effect of country wealth on Armington elasticities
Zhang and Verikios (2006) use an alternative ‘database approach’ to estimate the Armington
elasticities. In their study they also look at Armingtons of developed vs. developing countries. Zhang
and Verikios (2006) find that Armingtons of developing countries are low compared to those of
developed nations. This can be attributed to a relatively low connection to world markets for
developing countries. A better connection to world markets makes products look more like each
other, and/or make their consumers less critical towards foreign products. Either way, these results
indicate that consumers from developing nations are less price sensitive when substituting between
foreign and domestic goods.
19
Blonigen and Wilson (1999) find that sectors in an industrial country with a larger percentage of
industry imports from a developing country tend to have lower macro Armington elasticities. This
result suggests that for an industrialized country such as the United States, import goods from
developing countries may be much more different from the domestic goods than would be true for
imports from similarly industrialized countries. Therefore, on average, domestic purchasers are less
likely to substitute their domestic good for a foreign good from a developing country. It is important
to note that this result says nothing on the absolute values of Armingtons for either wealthy or less
wealthy countries in general, only that a difference in income between countries decreases the
Armington elasticity.
The effect of openness to trade on Armington elasticities
Limited empirical evidence exists on the effects of trade policies on Armington substitution
elasticities. However, Welsch (2006) does provide some theory on this. At the empirical level,
Armington elasticities are usually recovered from estimates of the price responsiveness of trade.
However, actual price responsiveness depends not only on the perceived difference of imported and
domestic goods, but also on the existence and strictness of trade barriers. This is why estimated
Armington elasticities reflect not only incomplete substitutability due to differences in (perceived)
product characteristics, but also de facto incomplete substitutability due to trade barriers. As a
result, the primary effect entails that estimated Armington elasticities are negatively correlated to
trade barriers (Welsch 2006).
Welsch (2006) also defines a secondary effect. He claims that intra-industry specialisation likely
develops as a result of trade liberalization. Therefore, relaxation of trade barriers has an indirect (or
secondary) effect on the Armington elasticities, namely that it induces domestic and imported
product groups to become more different from each other over time. Therefore, while the primary
effect is an immediate consequence of trade liberalization and may become effective even in the
short term, the secondary effect is likely to gain momentum only with some delay. It can therefore
be conjectured that in the wake of trade liberalization Armington elasticities first rise and then
decline, implying a hump-shaped pattern over time. Overall, the empirical results found by Welsch
(2006) were supportive of such a hump shaped pattern.
Welsch (2006) finds that for France, for most of the product groups considered, the Armington
elasticity had a peak value in the 1980s and declined thereafter. While the average elasticity amounts
to 0.84 in 1976–91, it declined almost continuously to 0.21 in 1982–97. This may, or may not, be
related to the establishment of the EU free trade zone.
Blonigen and Wilson (1999) find some indication that barriers to entry lower the elasticity of
substitution between the domestic and the foreign good. Thus, trade liberalisation would increase
the substitutability between goods, indicating the primary effect as defined by Welsch (2006) is
dominant. Therefore, entry barriers may represent some kind of home bias beyond the price effect,
thus making substitution of domestic with foreign products more difficult. Lopez and Pagoulatos
(2002) mention import quotas as an important determinant of Armington elasticities but fail to
provide any evidence for this claim.
A final trade related finding is made by Zhang and Verikios (2006), who find that countries which are
very import dependent also have relatively lower Armington elasticities. These countries are usually
20
small countries which tend to have highly specialised industries and are totally dependent on imports
for a wide range of commodities which are not domestically available. Because many commodities
are simply not available domestically foreign-domestic substitution becomes difficult, if not
impossible.
The effect of FDI on Armington elasticities
Lopez and Pagoulatos (2002) find that the presence of FDI in a sector increases the Armington
elasticity and thus makes goods more internationally substitutable. The presence of foreign-owned
firms within the domestic territory tends to blur the distinction between foreign and domestic goods.
Also, foreign affiliates tend to favour the use of foreign produced inputs, thus resulting in an importbias instead of the usual home-bias. Blonigen and Wilson (1999) find a similar relation between
foreign involvement in the industry and the substitution elasticity. They find strong evidence that,
everything else equal, a higher degree of foreign ownership in the industry's industrial customers
(i.e., the sectors down-stream of the industry) leads to a larger elasticity of substitution between the
home and the foreign good. Their results strongly indicate that the presence of foreign-owned
affiliates is positively correlated with the Armington elasticity.
21
22
3. Method
3.1 Empirical model
This thesis broadly follows the approach of Blonigen and Wilson (1999) to estimate the determinants
of Armington elasticities. The main difference being that country characteristics rather than product
characteristics are investigated as determinants of the size of Armington elasticities. Like Blonigen
and Wilson (1999) a two-tier estimation procedure will be applied to arrive at the determinants of
the Armingtons. The first tier exists of the estimation of Armington elasticities for 14 countries and
29 goods. The second tier consists of estimating the country characteristic determinants by
regressing country characteristics on the Armington elasticities obtained in the first tier.
Because micro Armington elasticities require data on bilateral trade, which is not readily available,
this thesis will only estimate macro Armington elasticities. Remember that the macro Armington
elasticity is the Armington substitution elasticity between foreign and domestic goods regardless of
the origin of the foreign good (Feenstra, Obstfeld et al. 2010). In the first tier estimation of
Armington elasticities we follow the linear regression method of Reinert and Roland-Holst (1992).
In a world with J countries and G different goods, each country produces a different variety of each
good g ϵ {1, … , 𝐺} , the set of varieties produced to be determined inside the model.
To model demand for a home and import good in a particular industry, Armington (1969) specifies a
CES functional form for C, consumption, yielding:
[
(1)
Where
(
1
)
is the quantity of import and
(
)
]
is the quantity of the domestic consumption of good g.
is the total consumption of good g in country j, the aggregate of M and D. β is a parameter in the
demand function weighing the foreign relative to the home good.
From this the first order conditions, for each good g and for each country j, can be derived, solving
the combination of the imported and domestically produced good:
(2)
[(
Taking logs of equation 2 yields:
)(
)]
23
(3)
(
)
(
)
Where
and
are domestic and import prices respectively. This equation relates the rates of
substitution and relative prices and allows the Armington elasticity σ to be estimated for each
product in each country. In equation (3)
(
) is a constant which holds no practical relevance.
Therefore the Armington regression equation can also be written as:
(
(4)
)+ ϵ
Where α is an intercept and ϵ a normally distributed error term. If the two goods are not perfect
substitutes σ will take some finite number. The lower the value of σ the less substitution there is
between the foreign and domestic good.
3.2 Country characteristic determinants
The second step is similar to the procedure of Blonigen and Wilson (1999) and Lopez and Pagoulatos
(2002). This step regresses the Armington elasticity σgc (for good g in country c) on the country
characteristics Xgc. The following linear function of a set of regressors, Xgc is estimated:
(5)
σgc = Xgc γ
The question is, what are the relevant country characteristic determinants (Xgc) of the Armington
elasticity? One could, theoretically, come up with a wide array of possible country characteristics as
determinants of the Armington elasticity. However, we restrict ourselves to two broad categories of
determinants; economic determinants and social determinants. The economic determinants are:
GDP/capita, total imports, total exports, imports and exports of the goods specific to each
Armington, the level of foreign direct investment and finally the ratio of trade from outside the EU to
inside the EU. The social determinants are multiculturalism, urbanisation and national pride.
Most of the economic determinants have to do with trade. It is not uncommon for an economic
variable to be dependent on trade. For example, in endogenous growth theory, Coe and Helpman
(1995) proposed a link between foreign trade and economic growth. Similarly, this thesis proposes a
link between trade and the Armington elasticity. Theoretically trade could impact the Armington
elasticity in a number of ways. First of all, the very existence of trade, particularly imports, may point
to the fact that consumers are probably already willing to substitute home and foreign goods. This
would imply that imports are positively related to the Armington elasticity. Second, the existence of
imports may make consumers more familiar with foreign products and therefore more willing to
substitute between foreign and domestic goods, thus increasing the Armington elasticity. Finally,
trade (including exports) may also say something in general about the openness of an economy and
its connectedness to the world economy. If an economy is more open and connected to the world,
chances are that its people will also be more open to foreign commodities. Enough reasons,
therefore, to expect a positive correlation between trade and the size of the Armington elasticity.
24
New trade theory commonly defines trade in terms of trade shares. Trade shares are the sum of a
country’s imports and exports divided by its GDP. In our analyses we split trade into imports and
exports to estimate the separate effects of imports and exports on the Armington elasticity.
Another economic determinant included in the present analysis is wealth. Because Armington
elasticities depend on preferences of people, and preferences depend on the circumstances people
live in, it is expected that people’s wealth (represented as GDP/capita) influences consumer choices
between foreign and domestic goods (i.e. the Armington elasticity). Whether a high level of wealth is
positively or negatively correlated with the Armington elasticity is difficult to assess a priori. It stands
to reason however that wealth has a lot to do with people’s preferences and therefore also with the
Armington elasticity.
Also included as determinants of the Armington elasticities are the trade shares of the specific good
for which the Armington was calculated. We will call these the ‘good specific trade shares’. This
means that for each Armington elasticity there exists a unique good specific trade share. While the
total value of foreign trade in an economy may say something about the openness of that economy
as a whole, trade in a specific good may be an indicator for the substitutability of that particular
good. After all, it is very possible that while a country has a very strong overall home-bias, this might
be less the case for a particular good wherein already a lot of trade exists. Similar to the case of total
trade values, cause and effect are not directly identifiable. Trade in a good may either cause the
Armington to be higher, or vice versa, a higher Armington may cause more trade. Again, imports
rather than exports are likely to have the largest impact on the Armington elasticity.
Another possibility is that the origin of countries’ trade also affects the Armington elasticity. Since we
only have macro Armingtons however, we cannot account for the origin of an individual good’s
trade. Therefore, we focus on the origins of total trade as a determinant of the Armington. There are
two reasons why Armington elasticities might be related to the origin of a country’s total trade. First
of all, a country importing large quantities from far away might be more connected to world markets
than a country only trading with its neighbours. Secondly, more trade with distant nations may point
to a greater acceptance of consumers for products from different cultures. A greater acceptance of
foreign goods and a better connection to world markets point to a higher Armington elasticity. In
order to capture this effect the ratio of intra-EU trade to extra-EU trade is included as an explanatory
variable. Trade inside the EU is thus considered trade with ‘neighbours’ (i.e. nations with similar
cultures), whereas trade outside the EU is considered more ‘exotic’ and therefore likely to contribute
to higher Armington elasticities.
The final economic variable is foreign direct investment or FDI. In fact FDI was also included as a
determinant by Blonigen and Wilson (1999) and Lopez and Pagoulatos (2002) in their studies on the
determinants of Armington elasticities. In these studies FDI constituted the share of FDI in a certain
sector. Here, FDI is defined as the foreign investment stock within a country divided by the size of its
economy, the GDP. FDI is proposed to influence the Armington elasticity because inward FDI may
support the distribution of foreign products in the home country, making it easier for consumers to
substitute between foreign and domestic products. Also, FDI potentially results in domestic products
becoming more similar to foreign products, thus increasing their substitutability.
Besides the economic variables there are also a number of social variables which could be considered
possible determinants of Armington elasticities. Differences between countries’ social settings are
25
likely to be responsible for at least some part of the variation of Armington elasticities across
countries. Social determinants included in this analysis are multiculturalism, urbanisation and
national pride. A high level of multiculturalism might impact a country’s Armington elasticities
because multiculturalism might result in consumers being more familiar with foreign products.
Multiculturalism is defined as the fraction of foreign born people within a country’s population. The
level of urbanisation is expected to have a similar effect because more urbanised areas are likely to
have more contact with world markets and other cultures and therefore have higher Armington
elasticities. Finally, national pride could say something about the preference of all things foreign and
domestic, including goods. Therefore, countries with a high level of national pride are expected to
have lower Armington elasticities.
3.3 Regression methods
First tier regression methods
For the regression of equation (4) the statistical software package Stata 10 was used. The first
regression method is a simple OLS regression for 14 countries and 29 products. This procedure
consists of a separate OLS regression for each product for each country. Therefore a total of 14*29 =
406 regressions were performed. The results of these regressions are available in appendix 1.
Because the observations for each individual estimation of an Armington elasticity are in the form of
a time series, there is a possibility of autocorrelation. With autocorrelation (or serial correlation) the
error terms of ε are related to the error term εt-1. Therefore, the estimated standard errors are
biased. Blonigen and Wilson (1999) use a Cochrane-Orcutt procedure to correct for this problem, an
approach that is replicated here. A disadvantage of using the Cochrane-Orcutt procedure is the loss
of one observation per Armington regression and again one observation for each gap present in the
time-series. Considering the time series are fairly limited and sometimes incomplete this might be a
substantial drawback. The Cochrane-Orcutt method was chosen over a regression with Newey-West
standard errors because the gaps in the time series made the latter infeasible. 406 Cochrane-Orcutt
regressions were performed, the results of which are available in appendix 1.
For the second tier regression of country characteristic determinants of Armingtons the Armingtons
per county and good are used. However, it is also interesting to compare the Armingtons of countries
irrespective of the type of good directly. One way to obtain the total Armington elasticity for a
country is by simply taking the average of all the individual product Armingtons. However the total
Armington elasticity can also be estimated by running a regression for the country as a whole. Two
separate regressions were done for each county. First, an OLS regression was performed for all the
country’s observations simultaneously. Second, random effects panel data regressions with products
as the panel variable were run for each country. Hausman tests were performed for each regression.
Out of interest and to compare our results to Blonigen and Wilson (1999) and Zhang (2006), the
regressions of total Armingtons per country were also performed per product. Again OLS and panel
data regressions were run. The OLS for each good irrespective of the country of origin and the
random effects model panel data approach with countries as the panel variable.
26
Second tier regression methods
In the second tier regression the country characteristic determinants of the Armington elasticity are
regressed on the Armington elasticities obtained in the first tier (eq. 5). As such, the determinants
can be regressed on either the OLS or the C-O Armington elasticities. It is not definitely clear which of
these two estimation methods is the most accurate. While the Cochrane-Orcutt method adjusts for
autocorrelation, compared to OLS it also reduces the total amount of observations by 27% from 4789
to 3488, as well as reducing the number of estimated Armingtons from 344 to 325. Furthermore,
only about half of the estimated Armingtons showed significant autocorrelation in the first place,
thus reducing the need for a correction. Because it is not obvious which estimation method is the
best, the country characteristics are regressed separately on both OLS and C-O Armingtons. The
regression equations looks as follows:
(6)
𝐺
Where GDPcap is the average GDP per capita over the time period 1995-201. ImpEUintra and
ImpEUextra are the average imports from inside the EU and outside the EU respectively. Impgood
and Expgood are the average imports and exports of the good for which the Armington
was
estimated, also known as the good specific trade shares. InwFDI represents the average inward FDI
stock. The variable MigrEUextra serves as a proxy for multiculturalism and represents the percentage
of a population born in a country outside the EU. Natpride is a measure of national pride obtained
from Smith and Kim (2006) and Finalgood is a dummy variable for goods considered final goods, as
defined in section Appendix 2. is a constant with no practical interpretation.
Because the second-stage dependent variable σgc is estimated in the first stage, we have an estimate
of the variation of each dependent variable (Blonigen & Wilson, 1999). This gives rise to the problem
that the error term does not have constant variance across observations. This problem is also known
as heteroscedasticity. Testing for heteroscedasticity using a Breusch-Pagan test statistic confirms that
heteroscedasticity is indeed a problem. The p-value for the Breusch-Pagan test on the OLS regression
is 0.004 and for the Cochrane-Orcutt regression 0.000. While the results of OLS with
heteroscedasticity should yield unbiased estimates of the coefficients of the independent variables, it
leads to inefficiency of the estimates (Dougherty 2011). To solve this problem we apply a robust
regression approach with heteroscedasticity consistent standard errors using the Huber-White
sandwich estimators. These robust standard errors can deal with minor problems with
heteroscedasticity and normality (Chen, Ender et al. 2003) and their use has become standard in
many econometric applications (Verbeek 2000). With the robust standard error regression the point
estimates of the coefficients are exactly the same as in ordinary OLS. Only the standard errors are
changed to take into account heteroscedasticity and a lack of normality (Chen, Ender et al. 2003)
Looking at the Variance Inflation Factor (VIF) multicollinearity does not appear to be a problem for
either OLS (mean VIF= 1.41) or C-O (mean VIF= 1.43) regressions. In both cases the highest VIF is 2.03
for imports from inside the EU, which is well below the value of 10 which is commonly used as a
highest boundary (Dougherty 2011). The low multicollinearity values are actually no coincidence as
the explanatory variables where selected with a low multicollinearity in mind. This in order to
increase the explanatory power of the regressions. In particular, the variable exports was excluded
27
from the regression because of its strong correlation with imports. For the same reason outward FDI
was also discarded as an explanatory variable.
Besides a regression with White standard errors, two other regression methods are also performed
to correct for heteroscedasticity. The first is an OLS using robust standard errors clustered over the
different product types. This assumes that standard errors are independent across goods but not
within each good. Clustering across countries is meaningless because for most variables there is no
variation within a country. The second method is using GLS Gaussian regression.
Finally in order to check the robustness of the results to outliers, influential observations and nonnormality, a robust regression using iteratively reweighted least squares is performed. This approach
assigns weights to each observation on the basis of goodness of fit (Chen, Ender et al. 2003).
Extremely deviant cases are given low weights or are even removed completely.
Extra second tier estimations
A number of extra estimations were performed. These include country characteristic regressions on
the results of the first tier panel data approaches per country, as well as per good. In the per country
case these estimations provide additional evidence for the relation of country specific characteristics
to the Armington elasticity. In the case of the Armingtons per good they are regressed on a number
of ‘good characteristic’ determinants. These determinants are: Export and import trade shares, a
dummy variable for Final goods, a dummy variable for Food products and a dummy variable for
Industrial goods. The results of this regression can be compared to the results found by Blonigen and
Wilson (1999) and Lopez and Pagoulatos (2002).
28
4. Data
4.1 Databases
In order to estimate the Armington elasticities price and quantity data has to be obtained for the
following variables of equation (4) of chapter 3.
-
PD: Domestic prices
PM: Foreign prices
D: Consumption of domestic goods
M: Consumption of imported goods
To obtain reliable estimates of the Armington elasticities, data on these variables has to cover a
substantial time period. Furthermore, in order to determine the country characteristic determinants
of Armington elasticities, data is needed for a number of countries and for a number of goods.
Finally, to be able to compare the trade and production data, data should be gathered at the same
goods aggregation level and all data should be obtained using a similar methods.
Europroms is a database which combines all these prerequisites. In Europroms the Prodcom and
Eurostat foreign trade databases are combined into one aggregation level. Prodcom is an extensive
database on EU production data for a variety of industrial goods at a very disaggregate level. The
Eurostat foreign trade database provides data on EU trade. Combined, this provides an accessible
source for readily comparable trade and production data over time and for a large number of EU
countries.
Eurostat databases
Prodcom (Production Communitaire) is the title of the production statistics for manufacturing,
mining and quarrying, electricity, gas and water supply. EU Countries have to report data on an
annual basis but are free to collect it quarterly or monthly. Countries gather the data using a
standardized survey system which is the same for all countries. Prodcom data is reported on a 8-digit
level and is compatible with the international CN and HS nomenclatures. The sectors available in
Prodcom are: mining industries, agrifoods, intermediate products, capital goods and consumer
goods. In total a number of close to 5,000 products are listed in a nomenclature common to all EU
countries (Prodcom 2006).
The data collected for Prodcom are the physical volume of production sold during the survey period
as well as the value of production sold during the survey period. Both are reported as quantity/value
sold and quantity/value produced. The production of any enterprise of 20 or more employees is
included in Prodcom. In each product category if the national production is less than 1% of the EU
total production data is reported as zero. This can have significant consequences, especially for the
production data of smaller countries (Prodcom 2006).
The Eurostat foreign trade database covers both trade of member states with countries outside the
EU (extra-EU trade) as well as trade between member states (intra-EU trade). The database forms a
harmonised source of information about imports and exports of the EU. The data is gathered
according to the Combined Nomenclature (CN) which comprises around 10.000 product codes.
29
Europroms
Europroms combines the production data for the 5.000 Prodcom products with trade data from the
EU foreign trade database. Like Prodcom, Europroms is published in the Comext database tables,
only here trade and production data are reported together. Yearly data is available from 1995 until
2011 for the EU-15. Europroms follows the foreign trade data (CN) nomenclature (Europroms 2008).
Combining trade data with production data enables the calculation of the ‘apparent consumption’:
production + imports – exports (Europroms 2008). The apparent consumption is very similar to the
domestic consumption (production – exports) featured in the Armington regression equation. The
calculation of the apparent consumption was ‘one of the principle aims’ of linking production
statistics to foreign trade statistics (Prodcom 2006). According to the website of Eurostat (2012) ‘The
Europroms database is very powerful for users searching for data on the "apparent consumption" of
specific products in EU countries’. However, the manual (Europroms 2008) cautions that there are
limitations to the reliability of the data. It describes the values for the apparent consumption as
unrealistic and sometimes even negative and therefore advises that calculating the apparent
consumption cannot always be recommended. For additional details on the properties of the data
see Prodcom (2006) and Europroms (2008).
Issues with trade data
There is a difference in the methodology used to gather data for extra-EU and intra-EU trade which
has some consequences for the trade statistics. Eurostat statistics on extra-EU trade are based on the
so-called ‘special trade system’. The special trade system requires goods to go into free circulation
within the destination country for them to be counted towards total trade. By requiring free
circulation after going through customs, the special trade system avoids the problem that goods in
transit, or those placed under a customs procedure for temporary entry, are counted towards extraEU trade (Eurostat 2008).
Where extra-EU trade is based on the special trade system, intra-EU trade statistics are defined in
terms of the ‘Intrastat’ system. The main difference with the special trade system is that the Intrastat
system does not have a direct link to customs procedures. Instead, intra-EU traded goods are goods
in free circulation within the EU which enter the ‘statistical’ territory of a given member state. Transit
trade (the transportation of a good through a member state to another member state) is not
counted towards intra-EU trade (Eurostat 2008).
However, there is a problem. Intra-EU trade goods can also be goods which have first cleared
customs from outside the EU into one EU member state and are then re-exported to a different
member state (Eurostat 2006). Goods which are imported by a EU-member state from a non-EU
country, but which travel first through another EU-member state, are thus counted towards imports
twice; once for the initial importer and once for the final importer. The reason for this is that goods
which have come from outside the EU have to clear customs and are therefore counted towards the
extra-EU imports of the entry country under the special trade system. If the goods subsequently
travel to another country within the EU they count as being re-exported to that country by means of
intra-EU trade.
30
This methodological issue gives rise to the so called ‘Rotterdam phenomenon’. Countries with a high
level of goods transit trade coming from outside the EU traveling to other member states experience
an overestimation of their imports and exports. This is particularly true for The Netherlands,
(Rotterdam harbour) and Belgium (with Antwerp harbour) (Gambini 2010). Also, the eventual
recipient (Germany), has a lower amount of extra-EU imports than is realistically the case. Germany’s
total imports do remain the same as the extra-EU import has been replaced by an intra-EU import of
the same size.
Belgium & Luxembourg
Normally the match between foreign trade and Prodcom is relatively simple. However up to 1998
Belgium and Luxembourg declared their trade in a joint manner as Belgian trade. At the same time
their Prodcom data was reported separately from the beginning (Europroms 2008). This can be a
problem because it causes an overestimation of imports and exports for those years. There are three
ways to deal with this problem: 1. Assume the effect is negligible. 2. Delete the observations in
question. 3. Attempt to correct for the overestimation. Since Luxembourgian trade is a factor 20
smaller than Belgian trade, including Luxembourgian trade should have little impact on the
Armington elasticities. Therefore, we use option 1 as default and assume the effect is negligible.
The results of options 2 and 3 are reported in table 5.8. Option 2, deleting the observations, has the
obvious drawback of reducing our already short time series with 4 more years. Correcting for the
overestimation is done by extrapolating Luxembourgian trade data after 1998 to that between 1995
and 1998. The averages of the three trade values following 1998 (1999-2001) were used. Next, the
obtained values were subtracted from the ‘Belgian trade’ between 1995 and 1998 to obtain the
corrected values for Belgian trade.
4.2 Data selection
The Europroms database provides readily comparable data for the values and quantities of
production, imports and exports from 1995 until 2011 for 15 countries. This set of data allows for the
estimation of all the necessary variables for the Armington regression equation (eq. 4). The import
quantity is directly available and its price can be gathered from the quantity and value of imports.
The domestic consumption quantity can be obtained through subtracting exports from production
and the domestic price is equal to the domestic production value (production value - export value)
divided by the domestic production quantity.
Products
For the estimation of the Armington elasticities a number of goods should be chosen for which to
estimate the Armington. Time restrictions limit us to around 30 goods. The next question is which
goods should be chosen out of 3900 possible candidates. Preferably the goods used should be clearly
defined and relatively homogeneous. Goods should be clearly defined to avoid unreliable reporting
from declarant countries. Homogeneity of the goods is advised because heterogeneous goods will
likely have lower Armington elasticities (Zhang and Verikios 2006), which makes it harder to obtain
31
significant results. Furthermore, an as wide as possible variety of products should be chosen to avoid
possible biases for certain product categories. Finally, for each of the goods there should be as many
observations as possible for as many countries as possible.
Table 4.1 Product descriptions
Product Code Product Description
Category*
22221930
Plastic stoppers, lids, caps and other closures (excluding for bottles)
Con
31091100
Metal furniture (excluding office, medical, surgical, dental or veterinary furniture; televisions)
Con
10921030
Dog or cat food
Con
15201351
Men's town footwear with leather uppers (including boots and shoes)
Con
14131430
Women's or girls' jackets and blazers, of knitted or crocheted textiles
Con
10111140
Fresh or chilled Carcasses, half carcasses and quarters with bone, in beef and veal
Fd
10121010
Fresh or chilled whole chickens
Fd
10201100
Fresh or chilled fish fillets and other fish meat without bones
Fd
11071930
Waters, with added sugar, other sweetening matter or flavoured, i.e. soft drinks (including aerated)
Fd
11051000
Beer made from malt (excluding non-alcoholic beer, beer containing <= 0.5% by volume of alcohol,
alcohol duty)
Fd
10822239
Chocolate blocks, slabs or bars (excluding filled, with added cereal; fruit or nuts, chocolate biscuits)
Fd
10612100
Wheat or meslin flour
Fd
10513030
Butter of a fat content by weight <= 85%
Fd
10392230
Citrus fruit jams, marmalades, jellies, purees or pastes, being cooked preparations (excluded
preparations)
Fd
10201400
Frozen fish fillets
Fd
10841230
Tomato ketchup and other tomato sauces
Fd
10721255
Sweet biscuits (including sandwich biscuits; excluding with chocolate)
Fd
22221100
Sacks and bags of polymers
Int
16241133
Flat pallets and pallet collars of wood
Int
17211300
Cartons, boxes and cases, of corrugated paper or paperboard
Int
22221200
Plastic sacks and bags (including cones) (excluding of polymers of ethylene)
Int
25992987
Base metal sign-plates, name-plates, address-plates and similar plates, numbers, letters excluding
illuminated)
Int
23701100
Worked monumental/building stone and articles thereof, in marble, travertine and alabaster
(excluding tiles, cubes)
Int
22212157
Rigid tubes, pipes and hoses of polymers of vinyl chloride
Int
22221300
Plastic boxes, cases, crates and similar articles for the conveyance or packing of goods
Int
08121190
Construction sands such as clayey sands; kaolinic sands; Feld spathic sands (excluding silicon bearing
sands)
Int
20301150
Paints and varnishes, based on acrylic or vinyl polymers dispersed or dissolved in an aqueous
medium
Int
22214150
Cellular plates, sheets, film, foil and strip of polyurethanes (foam)
Int
23611130
Building blocks and bricks of cement, concrete or artificial stone
Int
* Con is consumption good, Fd is food and Int is intermediate good
32
In the end trade-offs had to be made between data-availability and the variety of the products
chosen. All in all 29 products were found to be suitable. Goods were separated into different
categories: consumer goods, food products and intermediate goods. Of these categories food had
the best data availability. Since food generally also fits the precondition of homogeneity this product
type is the most prevalent in the dataset. Unfortunately, data on consumer goods was very limited
and only 5 products of this category could be included. Table 4.1 shows the 29 products, their
product (CN) codes, and their assigned categories.
Countries
As with the products, the availability of data is not equally distributed over the different countries. At
least 5 years of observations were required for a product to be included in the regression analysis.
Table 4.2 shows that the Netherlands has the least goods for which sufficient observations are
available with only 18 goods. This has to do with the fact that data for the Netherlands is often
classified as confidential. The average number of goods for which data is available per country is
almost 25. There are also differences in the total number of observations from which Armington
elasticities are derived and the number of observations (or years) per product. Interestingly it is not
The Netherlands but Sweden which has the fewest number of observations per product.
Table 4.2 Data availability by country
Country
Observations
Products
Obs./Product
France
429
27
15.9
Netherlands
220
18
12.2
Germany
377
26
14.5
Italy
437
28
15.6
U.K .
373
26
14.3
Ireland
298
22
13.5
Denmark
273
21
13.0
Greece
341
26
13.1
Portugal
374
27
13.9
Spain
449
28
16.0
Belgium
269
21
12.8
Sweden
231
23
10.0
Finland
371
27
13.7
Austria
347
24
14.5
Average
342.1
24.6
13.9
Total
4789
344
x
4.3 Outliers
The time series of each Armington regression is maximally 17 years, which is fairly limited. Outliers in
the data can therefore have significant impact on the individual Armington elasticities. Because of
this, obvious outliers in the data are removed. Outliers were in first instance identified by looking at
33
import and domestic prices. Import prices and domestic prices are both derived from the original
data on quantity and values. Variation in prices thus captures both variation in quantities and values.
Identifying outliers was mostly a matter of using own judgement and common sense. Removals of
observations were restricted to only the most clearly erroneous cases. Besides the prices, values and
quantities were also scrutinised for outliers. Taking into account patterns over time a reasonable
expectation of an observation’s value for production, consumption and exports could be made. As a
rule of thumb everything over a factor 10 different from this reasonable expectation was considered
an outlier. Finally, a number of systematic errors were identified and the observations removed.
Figure 3.1 shows the variation of import and domestic prices for ‘men’s footwear’ after outliers were
removed. It also the illustrates the correlation between import prices and domestic prices. As would
be expected, domestic and import prices are reasonably equal across countries.
8
Price
6
4
2
0
FR NL GE
IT
GB IR
DE GR PO SP BE LU SW FI
Country
Domestic Price
AU
Import Price
Fig. 4.1 Domestic and Import prices of fresh and chilled veal and beef (10111140) for 15 EUcountries
Besides removing obvious outliers from the initial data and obtained prices, the estimated Armington
elasticities were separately checked for outliers. Only if an Armington was is an outlier for both its
country and its product it was considered an actual outlier and is subsequently removed. A total of 3
Armingtons where removed for the OLS estimations and 8 Armingtons were removed from the
Cochrane-Orcutt estimates. In the Cochrane Orcutt procedure 2 extra Armington elasticities were
manually removed because they had only 3 observations remaining.
Negative values
Some of the obtained values for domestic consumption are negative. Theoretically this is clearly
impossible because the lowest possible value for domestic consumption is zero. (i.e. no domestically
produced goods are consumed domestically). The occurrence of negative values could have to do
with the problem of re-exports and the Rotterdam phenomenon. This is supported by the fact that
the Netherlands, Belgium and Denmark are the countries where the most negative values occur.
However, since the log of a negative value cannot be defined, all years with negative domestic
consumption are automatically excluded from the dataset. This (at least cosmetically) eliminates the
theoretical problem of the negative values.
34
4.4 Data on country characteristics
For the second tier regressions we require data on number of country characteristic determinants.
The first of these is wealth, represented as GDP per capita. Eurostat provides data on GDP per capita
for all EU countries in its statistics database (Eurostat 2013). To obtain relevant estimates we take
the average values over the period 1995-2011. The Eurostat statistics database also provides data on
total extra and intra EU imports and FDI. FDI is defined as the fraction of FDI stocks in the economy
(FDI/GDP). We take FDI in the form of stocks rather than flows because stocks best represent the
involvement of foreign actors in the domestic economy. Eurostat defines FDI stocks as a direct
investment enterprise in which a direct investor owns 10 % or more of the ordinary shares or voting
rights (Eurostat 2012). After we have calculated the values of FDI/GDP for each year, we again take
the average values over the time period 1995-2011. A similar procedure is followed for obtaining
intra- and extra- EU imports/GDP. Exports and imports per good could be easily obtained from the
Prodcom database used to obtain the Armington elasticities (Prodcom 2006). Again these variables
where defined as a fraction of GDP and averaged over the relevant time period. It is noteworthy that
these determinants are the only determinants which vary for each separate Armington and not only
per country, as is the case with all the other determinants.
We continue with the social determinants national pride and multiculturalism and urbanization.
Values for the national pride of the individual nations where obtained from Smith and Kim (2006),
who compare nationalism in 1998 and 2004. Unfortunately, not every country was included in both
years. This means that while for most countries the values of 2004 where used, for some (Italy and
Denmark) we used the values of 1998. For Belgium and Greece national pride was not estimated in
either of the years and therefore no data is available. Belgian nationalism is set equal to Dutch
national pride while Greek national pride is set equal to the average. For the variable
multiculturalism we use the immigrant stock of people born outside the EU as a proxy. This data
could be obtained from the Eurostat statistics database (Eurostat 2013). Urbanization levels could
also be obtained from the Eurostat statistics database. They are divided in areas classified as ‘dense’
‘intermediate’ and ‘low’ with the percentage of area of each classification reported. For the analysis
we use the combination of ‘dense’ plus ‘intermediate’ to define urbanized areas.
The final determinant included in the regression is not a country characteristic but a good
characteristic, namely a dummy variable for final goods. An overview of which goods are categorized
as final goods is given in Appendix 2, which also shows the assigned categories for the regressions on
goods characteristics.
35
36
5. Results of Armington Regressions
5.1 Results Armington elasticity estimations
The results of the estimation of Armington elasticities are the first step in the two-tier estimation
procedure used to arrive at the country-characteristic determinants of Armingtons, as defined in
chapter 2. In this chapter Armington elasticities are estimated for 14 countries and 29 goods using
OLS. This means that a total of 406 possible Armington elasticities could be estimated. However,
taking into account missing values and outliers in the original data, only 347 Armington elasticities
remain. Removing Armington elasticities that are outliers amongst the other Armingtons ( as
described in chapter 4) leaves us 344 Armington elasticity estimates (table 5.1). These 344 Armington
elasticity estimates will be used in the second step to estimate the country characteristic
determinants of Armingtons.
Table 5.1 Summary statistics of all reliable Armingtons estimated by OLS and Cochrane-Orcutt
Method
Armingtons
Avg. Armington
std. dev.
Min
Max
OLS
C-O
344
325
1.187
1.143
1.819
1.408
-6.15
-3.987
8.739
9.197
As can be seen in table 5.1 the average Armington elasticity for all the estimated products and
countries is 1.187. This is well within the range of results of between 1 and 2 commonly found in
literature (Mc Daniel and Balistreri 2003). However, the minimum and maximum are quite far out,
with -6.15 and 8.74 being the minimum and maximum respectively. The occurrence of Armingtons
below zero is theoretically impossible and is considered a result of estimation errors. Considering
estimation errors are biased upward as often as downward, the negative Armingtons are maintained.
Separate from the previous OLS estimation another regression was performed, using a CochraneOrcutt procedure to correct for autocorrelation. Out of 333 Armington elasticities that could be
estimated 8 where considered outliers, leaving 325 estimated Armington elasticities. Table 5.1 gives
a summary of the properties of these Armington elasticities. The difference between the CochraneOrcutt average (1.14) and the average of the OLS estimation (1.19) is quite small. However, the
standard deviation is smaller for the Cochrane-Orcutt estimation than that of the OLS-estimation.
Again the minimum and maximum are quite far out and similar to those found in the OLS estimation
(-3.99 and 9.20 respectively). The R2’s of both estimation methods averaged around 0.2 with big
differences between estimations. It is interesting to see that the values of the Armington elasticities
average around one, as this is also commonly found in literature (Mc Daniel and Balistreri 2003).
A detailed list of all OLS and Cochrane-Orcutt estimated Armingtons is included in Appendix 1.
37
5.2 Results of Armington elasticities per country
Next, let us take a closer look at the Armington elasticities per country. Armingtons per country are
of interest because they are related to the purpose of this thesis, namely the country-characteristic
determinants of Armingtons. Although the country averages are not directly used in the second step
regression they give an indication of which countries have higher or lower Armington elasticities.
Also, it provides an overview of the data-availability per country. Table 5.3 shows the average
Armington elasticities per country obtained with the OLS regression. Table 5.4 contains the same
country averages for the Cochrane-Orcutt estimation.
Table 5.2 OLS regression on Armingtons; per country summary statistics
Country
Avg. Armington
Avg. Std.
error
Avg.
p-value
Avg. R2
Min
Max
Obs.
Products
France
1.155
0.744
0.151
0.427
-1.967
8.664
429
27
Netherlands
1.186
0.857
0.236
0.389
-2.372
2.820
220
18
Germany
0.730
1.076
0.265
0.347
-3.699
7.105
377
26
Italy
1.338
0.645
0.205
0.361
-0.35
4.137
437
28
U.K .
1.290
0.887
0.225
0.378
-1.139
8.739
373
26
Ireland
1.161
0.707
0.201
0.440
-1.305
4.641
298
22
Denmark
0.461
1.279
0.287
0.323
-6.152
5.757
273
21
Greece
1.269
0.558
0.161
0.410
-0.739
3.319
341
26
Portugal
1.207
0.967
0.324
0.267
-1.172
5.821
374
27
Spain
1.419
0.538
0.094
0.473
-1.578
3.801
449
28
Belgium
2.198
1.377
0.115
0.471
-2.096
7.302
269
21
Sweden
0.699
1.001
0.320
0.280
-2.55
3.975
231
23
Finland
1.587
0.743
0.196
0.423
-2.182
4.592
371
27
Austria
0.808
1.364
0.209
0.339
-5.188
4.317
347
24
Average
1.18
0.91
0.21
0.38
-2.32
5.36
342.1
24.6
38
Table 5.3 Cochrane-Orcutt regression on Armingtons per country; per country summary statistics
Variable
Avg. Armington Avg. Std. Avg.
error
p-value
Avg. R2
Min
Max
Obs.
Products
France
0.935
0.525
0.202
0.383
-1.859 9.197
324
28
Netherlands
0.721
0.630
0.183
0.428
-2.315 2.670
127
17
Germany
0.881
0.588
0.185
0.348
-1.179 1.909
279
23
Italy
1.030
0.535
0.212
0.378
-1.135 3.694
357
28
U.K .
0.939
0.452
0.143
0.466
-3.413 8.179
283
26
Ireland
1.060
0.552
0.129
0.534
-3.987 6.380
187
21
Denmark
0.643
0.942
0.235
0.280
-1.883 2.963
198
19
Greece
1.553
0.437
0.102
0.580
-0.020 5.090
225
25
Portugal
0.409
0.551
0.300
0.278
-9.915 4.049
245
24
Spain
1.266
0.391
0.113
0.490
-0.074 3.685
359
28
Belgium
1.739
1.381
0.251
0.436
-0.221 5.181
202
19
Sweden
1.325
0.616
0.107
0.543
-1.611 4.243
161
21
Finland
1.360
0.689
0.182
0.399
-1.097 5.827
277
26
Austria
1.421
0.590
0.151
0.442
-2.688 6.430
266
23
Averages
1.09
0.63
0.18
0.43
-2.24
249.3
23.4
4.96
Looking at the per country results of the OLS estimation (table 5.3) Denmark has the smallest
Armington with 0.46 and Belgium has the largest Armington with 2.2, which makes Belgium
somewhat of an outlier. Denmark, Belgium and Austria have the largest average standard error,
indicating some level of uncertainty in the results of these countries. The best model fit is observed
for Belgium and Spain (R2 for both is 0.47), with Sweden (0.27) and Portugal (0.28) having the poorest
model fit. Spain has the most observations (449) and the Netherlands the fewest (220).
Considering the Cochrane-Orcutt estimation (table 5.3), shifts in countries’ Armington elasticities can
be observed. Portugal now has the smallest Armington elasticity with 0.41, which was 1.21 using the
OLS estimation. This indicates that while the average Armingtons do not differ much, the average
elasticities of individual countries do differ between estimation methods. In fact the absolute
average difference between the per country average OLS and Cochrane-Orcutt Armingtons is 0.353.
The biggest average Armington is again Belgium with 1.74. The highest R2 is Greece’s 0.580 and the
lowest is again for Portugal with 0.278. Spain again has the most observations, with 359 observations
for 28 products and the Netherlands the fewest, 127 observations for 17 products. In general, the
number of observations are substantially lower for the Cochrane-Orcutt estimations because of a
loss of observations for each performed iteration. This means that insufficient observations remain
to estimate Armington elasticities for some products.
5.3 Results of per country panel data Armington regressions
In the previous section the Armington of a country as a whole was defined as the average of the
Armingtons of all the products. An alternative approach to take the average of a country’s
Armingtons elasticities is to run a regression for that country alone, i.e. regressing irrespective of the
39
product, either by means of a simple OLS regression or with a Newey-West panel data approach. In
table 5.4 below, the results of these regressions are compared with the results of the averages of
good specific Armington estimations of the previous section.
Table 5.4 Per country Armington estimation results
Country
OLS
Newey-west
OLS
Cochrane-Orcutt
One Regression
Panel Data
Average Armingtons
Average Armingtons
France
2.011
1.100
1.369
0.936
Netherlands
1.544
1.671
1.186
0.721
Germany
3.271
0.554
0.730
0.881
Italy
2.224
1.322
1.338
1.033
United Kingdom
2.397
1.046
0.944
0.940
Ireland
1.388
1.126
1.161
1.063
Denmark
1.169
0.742
0.461
0.643
Greece
1.033
0.813
1.269
1.553
Portugal
1.724
1.137
1.207
0.409
Spain
1.842
1.005
1.419
1.266
Belgium
1.621
1.912
2.198
1.740
Sweden
1.246
0.937
0.699
1.326
Finland
1.626
2.043
1.587
1.361
Austria
1.861
1.752
1.255
1.421
Average
1.783
1.226
1.202
1.092
Table 5.4 shows a clear difference between the OLS (1.78) and the Newey-West approach (1.23)
averages. The Newey-West panel data approach results in an average similar to that of the average
Armingtons of OLS per product. However, there are some differences between countries; Austria for
example has an Armington of 1.75 in the panel data case, while this is only 1.26 using the average of
OLS. In the Newey-West approach Finland has the largest Armington (2.043) and Germany the
smallest (0.554).
5.4 Adjusting for Belgium
As mentioned in chapter 4, the first 4 years of the Belgian trade data are inaccurate because they
include trade data from Luxembourg. In chapter 4, two approaches were defined to deal with this
problem. The first, deleting the biased observations and the second; correcting the biased
observations. Both these approaches were applied using OLS and C-O regression methods. Table 5.5
reports the results of these regressions.
40
Table 5.5 Adjusted data approaches for Belgium
Regression
OLS
OLS
OLS
C-O
C-O
C-O
Country
Armington
Std. Error
t-statistic
p-value
R2
Obs.
Products
Belgium
-(95/98)
adjusted
Belgium
17-(95/98)
17-adjusted
2.20
2.09
2.26
1.92
1.73
2.08
1.38
1.30
1.44
1.37
1.33
1.39
3.35
3.06
3.57
3.74
3.45
3.69
0.11
0.24
0.17
0.24
0.26
0.44
0.47
0.44
0.45
0.46
0.46
0.43
269
186
271
202
161
238
21
18
21
20
16
20
The results of table 5.5 show that removing the values from the period 1995 until 1998 results in
smaller Armington elasticities, especially for the Cochrane-Orcutt regression. The adjusted values
however, yield Armingtons similar to the regular approach. In terms of standard errors or R2 nothing
much seems to be happening. The t-statistics and p-values do show some differences. The p-value of
the regular regression is the smallest for both OLS and Cochrane-Orcutt. While the adjusted
regression has the largest p-value for Cochrane-Orcutt and the ‘17-(95/98)’ has the largest for OLS.
5.5 Armington elasticities per product
In this paragraph we show the results of the average Armingtons per product type. Although not
necessary for the second stage estimation of the country-characteristic determinants of Armingtons,
these results are of interest in their own right. A brief inspection of some product characteristics may
provide results similar to those obtained by Blonigen and Wilson (1999) and Lopez and Pagoulatos
(2002). The averages of Armingtons per product using OLS and Cochrane-Orcutt estimations, as well
as the results of a panel data Newey-West regression per product are reported in table 5.6.
41
Table 5.6 Results of per product Armington estimation
Product Name
OLS
Cochrane-Orcutt
Newey-West
Fresh or chilled beef carcasses
1.511
1.984
2.761
Sacks and bags of polymers
1.646
1.082
1.984
Flat pallets of wood
0.497
0.849
0.236
Fresh or chilled whole chickens
0.607
0.704
0.768
boxes and cases of paperboard
1.000
0.956
1.567
tubes & pipes and hoses (polymers)
0.883
0.851
0.880
Plastic lids, caps and other closures
1.006
1.321
1.129
Plastic boxes, cases and crates
1.406
1.618
1.231
Metal furniture
Waters, with added sugar
Plastic sacks and bags
Base metal sign-plates
Construction sands
Beer made from malt
Dog or cat food
Chocolate blocks, slabs or bars
Wheat or meslin flour
Butter (fat content by weight < 85%)
Citrus fruit jams, marmalades
Frozen fish fillets
Fresh or chilled fish fillets
Tomato ketchup and tomato sauces
Men's leather footwear
Worked monumental/building stone
Paints based on acrylic polymers
polyurethanes (foam)
Women's jackets of knitted textiles
Sweet biscuits
Building blocks of cement, concrete
2.075
0.991
1.483
1.023
1.158
0.992
1.121
-0.496
1.185
0.133
0.391
2.099
2.734
1.153
3.830
1.600
0.888
1.627
2.134
1.062
1.667
1.865
0.618
0.974
1.110
1.163
1.006
0.946
0.522
1.488
0.014
0.778
0.138
1.889
0.738
3.473
1.636
0.897
1.608
2.515
0.868
1.432
1.642
0.730
1.424
1.183
1.293
1.050
1.384
0.181
1.788
0.230
0.711
1.131
1.216
1.345
3.387
1.452
1.182
2.436
2.116
1.538
0.975
1.29
1.21
1.34
Average of all products
The results show that the estimation methods do not result in widely varying average Armingtons.
The regular OLS Armington average (1.29) is only slightly larger than that of the Cochrane-Orcutt
(1.21). As with country averages, the differences between individual goods are larger than the
differences between averages.
42
6. Results of Country Characteristics Regressions
6.1 Results of country characteristic regressions
The second step in our analysis is to regress the country characteristic determinants defined in
equation (3.6) on the OLS and C-O Armington elasticities obtained in the first tier regression. In order
to achieve this, we must correct for heteroscedasticity and non-normality of the errors as described
in chapter 3. The choice of regressors was made in part with the intention to avoid multicollinearity.
The origins and definitions of each of the regressors were explained in chapter 4.
The first regression performed is a standard OLS regression using robust standard errors to correct
for heteroscedasticity. The estimated coefficient and their p-values for both the OLS and CochraneOrcutt Armington estimates are reported in table 6.1. The table also depicts the expected signs of the
coefficients. From table 6.1 it is evident that most of the p-values are high, thus indicating that the
results for the coefficients are not significant. The fraction of explained variance of the regression is
low for the OLS (R2= 0.04) and somewhat higher for the C-O estimations (R2= 0.08).
Table 6.1 Results Country characteristics regression on Armington elasticities OLS an CO with
robust standard errors
Determinant
Expected
sign
Coefficient
OLS
p-value
OLS
Coefficient
C-O
p-value
C-O
GDPcap
?
-0.424
(0.092)*
-0.136
(0.333)
ImpEUextra
+
5.495
(0.052)*
2.350
(0.283)
ImpEUintra
+
-2.830
(0.274)
0.401
(0.815)
Impgood
+
3.469
(0.616)
-5.631
(0.208)
Expgood
+
-0.721
(0.834)
4.728
(0.188)
InwFDI
+
-0.146
(0.769)
-0.391
(0.271)
MigrEUextra
+
-0.616
(0.203)
0.297
(0.472)
Urban
?
-0.988
(0.187)
-1.559
(0.007)*
Natpride
-
0.826
(0.488)
0.573
(0.470)
Finalgood
-
-0.152
(0.483)
-0.308
(0.032)*
2.121
(0.352)
1.337
(0.354)
Constant
*significant at a 90 percent level
For the OLS Armingtons, at a 90 percent level, only ImpEUextra (p = 0.052) and GDPcap (p = 0.092)
show significant p-values. Urban (p = 0.187) also has a relatively low p-value but is still insignificant. It
is noteworthy that significant p-values occur only for coefficients that have the expected sign.
Overall, there are a number of coefficients with the sign opposite of what is expected, but all of these
show insignificant p-values. The first significant coefficient is for GDP per capita, which has a negative
impact on the Armington elasticities; i.e. richer countries are less willing to substitute between
foreign and domestic. This could make sense if rich countries are less price sensitive than poorer
countries, as has been suggested (Frank 1991). Furthermore rich countries may consume more luxury
goods which are less price sensitive or place more value on the origin of a goods as a kind of luxury.
43
ImpEUextra is the other significant coefficient and is positive, which was expected (see chapter 3.2).
This means that the level of imports from outside of the EU has a positive impact on the Armington
elasticity.
Next, if we look at the results reported for the Cochrane-Orcutt Armingtons, we find that these
results are substantially different from those found using OLS-Armingtons. Coefficients with p-values
which are significant at the 90 percent level are Urban (p = 0.007) and Finalgood (p = 0.032). Like
with OLS, all significant coefficients have the expected sign. Urban, representing the level of
urbanisation in a country , has a negative effect on the Armington elasticity. Therefore countries with
higher levels of urbanisation experience lower Armington elasticities. If urban areas are wealthier
than rural areas there may be a link to the effect of wealth we found earlier. Alternatively, urban
communities might be more inclined to buy luxury products and therefore place more value on the
origins of a good. Finalgood is a dummy variable for Final goods. As expected from literature (Mc
Daniel and Balistreri 2003) these goods are found to have lower Armington elasticities. Final goods
are generally consumer goods where the good’s origin is more easily distinguishable. Finally Expgood,
also has a relatively low p-value (0.188) and is of the expected (positive) sign. If this result holds true
it means that the quantity of exports of a particular good increases the Armington elasticity of that
good.
6.2 Alternative estimation methods for country characteristic regressions
To confirm the robustness of the previous results and to test the effectiveness of the robust standard
errors two alternative regression methods were performed as described in chapter 3. First, a General
Least Squares (GLS) approach and second an OLS regression with robust standard errors clustered
over product types. The results are shown in table 6.2. Since these approaches affect only the
standard errors, the coefficients are the same as in table 6.1. Looking at the p-values reveals similar,
although slightly different, patterns as those found with OLS with robust standard errors. The GLS
approach hardly changes the results compared to the regular OLS. Mainly, the difference is that
GDPcap is now significant at the 95 percent level. The clustered standard error approach, on the
other hand, does yield slightly different p-values for some variables. Particularly the determinant
Finalgood is no longer significant at the 90 percent level. Also, the p-value of Urban and Expgood are
somewhat higher.
44
Table 6.2 Results of alternative regression methods for CO and OLS Armington elasticities
Determinant
Expected
sign
Coefficient
OLS
Coefficient
CO
OLS
GLS
C-O
GLS
OLS
Clustered
C-O
Clustered
GDPcap
?
-0.42
-0.14
(0.04)*
(0.34)
(0.07)*
(0.30)
ImpEUextra
+
5.50
2.35
(0.07)*
(0.27)
(0.03)*
(0.17)
ImpEUintra
+
-2.83
0.40
(0.27)
(0.82)
(0.32)
(0.80)
Impgood
+
3.47
-5.63
(0.64)
(0.30)
(0.71)
(0.21)
Expgood
+
-0.72
4.73
(0.80)
(0.07)*
(0.84)
(0.10)*
InwFDI
+
-0.15
-0.39
(0.80)
(0.34)
(0.79)
(0.30)
MigrEUextra
+
-0.62
0.30
(0.26)
(0.43)
(0.22)
(0.44)
Urban
?
-0.99
-1.56
(0.18)
(0.00)*
(0.20)
(0.01)*
Natpride
-
0.83
0.57
(0.50)
(0.49)
(0.40)
(0.46)
Finalgood
-
-0.15
-0.31
(0.45)
(0.03)*
(0.56)
(0.12)
2.12
1.34
(0.30)
(0.34)
(0.26)
(0.35)
Constant
*significant at a 90 percent level
Table 6.3 shows the results of a number of diagnostics test performed on the OLS country
characteristic regressions. It is evident from the tests that heteroscedasticity is an issue for both OLS
and C-O regressions, although C-O has diverging insignificant Breusch-Pagan test statistics.
Furthermore, skewness and kurtosis are more of a problem for C-O than for OLS. Multicollinearity is
low as the respective mean VIF’s are 1.57 and 1.54 for OLS and C-O. The maximum VIFs are 2.24 and
2.25 respectively. Finally, the Shapiro-Wilkinson test for normality rejects normality for both
estimation’s residuals, but more so for the C-O Armington regression. Next, figure 6.1 and 6.2
illustrate normality of the residuals for OLS and C-O. It is evident that the C-O regression is somewhat
lacking in normality compared to the OLS.
Table 6.3 Regression diagnostics OLS regression on country characteristic determinants
Test
OLS
p-values
C-O
p-values
White Test
0.0008
0.0010
Skewness
Kurtosis
VIF value
Shapiro-Wilkinson
0.6158
0.0300
1.5700
0.0011
0.1048
0.0148
1.5400
0.0000
-10
-4
-2
0
Residuals
-5
0
Residuals
2
5
4
6
10
45
-4
-2
0
Inverse Normal
2
4
Figure 6.1 Normal Q-Q plot OLS errors
-4
-2
0
Inverse Normal
2
4
Figure 6.2 Normal Q-Q plot CO errors
In order to check the robustness of results for outliers and influential observations, a robust
estimation of the Armington elasticities was performed using Iteratively Reweighted least Squares
(IRLS). This approach weights observations in accordance to their goodness of fit to the model. Note
that this approach does not only alter the standard errors (and p-values) but also the regression
coefficients themselves. Table 6.3 gives an overview of the results for both the OLS and the C-O
Armingtons.
Table 6.3 Results of a robust estimation of country characteristic regression on Armington
elasticities
Determinant
Expected sign Coefficient
OLS robust
p-value
Coefficient
C-O robust
p-value
GDPcap
?
-0.28
(0.13)
-0.02
(0.89)
ImpEUextra
ImpEUintra
Impgood
Expgood
InwFDI
MigrEUextra
Urban
Natpride
Finalgood
Constant
+
+
+
+
+
+
?
-
5.81
-2.12
-1.05
-2.11
-0.21
-0.46
-0.95
1.42
-0.17
0.65
(0.04)*
(0.37)
(0.88)
(0.43)
(0.69)
(0.37)
(0.17)
(0.21)
(0.37)
(0.73)
2.32
-0.79
-3.07
4.06
-0.01
0.53
-1.25
0.27
-0.21
1.13
(0.19)
(0.59)
(0.48)
(0.02)*
(0.99)
(0.09)*
(0.00)*
(0.70)
(0.07)*
(0.34)
*significant at a 90 percent level
The OLS coefficients and standard errors observed in table 6.3 show only minor changes compared to
the OLS estimations. This indicates that the consequences of heteroscedasticity and non-normality
are relatively minor (Chen, Ender et al. 2003). For the Cochrane Orcutt Armington estimates
however, there are some large changes in coefficients and p-values. Particularly, both MigrEUextra
and Expgood have both become significant. Interestingly both of these have shifted signs to become
positive as expected. If the IRLS estimation is accurate for C-O this indicates that migration from
outside the EU positively impacts the Armington elasticity and that exports in a good also increases
46
Armington substitution. The difference in results between IRLS and OLS for the C-O Armingtons may
bode ill for the reliability of the estimations as it may indicate that the results of the C-O Armington
regressions are not robust to outliers and influential observations (Chen, Ender et al. 2003).
Finally, table 6.4 shows the robust OLS regression results without Armingtons for Belgium and the
Netherlands for C-O and OLS Armingtons. This regression is relevant because Belgium and the
Netherlands experience large amounts of transit trade which could influence their Armingtons as
well as their trade data. For the OLS estimation, results without these two countries are different,
particularly for Imports from outside the EU which no longer has a significant p-value. This provides
some indication that the ‘Rotterdam phenomenon’ has been a biasing factor in the previous results.
The results for C-O Armingtons show little difference with the previous results and all results can be
duplicated using alternative regression methods. Also with respect to Belgium, it was found that
including or excluding Luxembourg trade data for 1995-1998 did not have any impact on the
regression results.
Table 6.4 OLS Country characteristic regression without Netherlands and Belgium
Determinant
Coefficient
OLS
p-value
Coefficient
C-O
p-value
GDPcap
-0.43
(0.04)*
-0.14
(0.34)
ImpEUextra
7.44
(0.49)
9.11
(0.23)
ImpEUintra
-2.54
(0.38)
2.15
(0.30)
Impgood
0.11
(0.99)
-4.91
(0.38)
Expgood
0.82
(0.79)
-2.87
(0.61)
InwFDI
-0.19
(0.77)
-0.44
(0.42)
MigrEUextra
-0.59
(0.30)
0.27
(0.54)
Urban
-0.96
(0.20)
-1.24
(0.03)*
Natpride
0.78
(0.53)
0.49
(0.55)
Finalgood
-0.06
(0.79)
-0.14
(0.19)
*significant at a 90 percent level
6.3 Panel data approaches on country and good characteristics
Finally, we take a brief look at the regression results using the Newey-West Armingtons from table
5.5 and table 5.7. Remember that these are estimates of the Armington elasticity for each country
using a panel data approach. This provides a single Armington elasticity for each country. Regressing
these Armington elasticities on the relevant country characteristics resulted in the coefficients
depicted in table 6.5. None of the coefficients are significant, but ImpEUextra, InwFDI and Urban
show relatively low p-values. The direction of the coefficients of these variables are the same as
those found in previous estimations.
47
Table 6.5 Country characteristic regression using Newey-West Armington estimates
Determinant
Coefficient
Newey-West
p-value
GDPcap
-0.01
0.67
ImpEUextra
6.36
0.13
ImpEUintra
1.46
0.65
InwFDI
-1.00
0.19
MigrEUextra
-0.83
0.37
Urban
-1.18
0.23
0.79
0.61
Natpride
Similarly, a Newey-West regression was performed on a per good basis. The results of the average
Armingtons per good for OLS and C-O and the Armingtons found in the Newey-West regression per
good of table 5.6 where regressed on a number of good characteristics. These characteristics
included the good specific imports and exports as a fraction of GDP averaged per product. The other
characteristics are represented by dummy variables. They are: final goods vs. intermediate goods,
food vs. non-food and industrial vs. consumer goods. The results of regressions on OLS, C-O and
Newey-West Armington estimates are depicted in the next table.
It is interesting to see that the variable Imp/GDP shows significant and positive coefficients for both
the Newey-West and OLS estimations. This result shows that goods which are imported in greater
quantities experience larger Armington elasticities. A similar effect could not be confirmed for
exports. The Newey-West values illustrate that final and food products have lower Armington
elasticities. This is very comprehendible if you realise that for food products and final goods the place
of origin is often easy to distinguish. It is perhaps surprising however that industrial products show a
lower Armington than consumer goods (either final or intermediate). A priori, one might think
consumers to be more sensitive to origins than companies, but this is not what these results show.
The estimates where obtained from a regression using 29 goods and one Armington for each good.
The R2 was 0.34 for the Newey-West Armington estimates regression.
Table 6.6 Results of good characteristic regressions
Determinant
Coefficient
Newey-West
p-value
Coefficient
OLS
p-value
Coefficient
C-O
p-value
Imp/GDP
3.43
(0.06)*
3.97
(0.06)*
0.283
(0.87)
Exp/GDP
-1.25
(0.32)
-1.40
(0.35)
-0.09
(0.94)
Final good
-0.67
(0.07)*
-0.07
(0.86)
-0.29
(0.44)
Food product
-0.82
(0.03)*
-0.75
(0.10)*
-1.03
(0.01)*
Industrial good
-0.90
(0.09)*
0.46
(0.45)
0.87
(0.11)
*significant at a 90 percent level
48
6.4 Overview of results on country characteristic determinants
Here we provide an overview of the estimation results for each of the country characteristic
determinants of the Armington elasticities. We will discuss each of the determinants in turn.
GDP per capita
The regression results for GDP per capita unanimously have a negative coefficient for all estimation
methods. For the OLS estimation these results are consistently significant, but for the C-O they are
not. In the robust regression the results are significant even for the C-O estimation. These results
provide strong evidence that GDP per capita negatively impacts the Armington elasticity.
Theoretically this makes sense if wealthy consumers are less price sensitive (Frank 1991). Also
wealthy consumers may consume more luxury goods and it is feasible that luxury goods are more
origin specific. Finally, origin (domestic or foreign) itself may provide some kind of luxury status (for
example Belgian chocolate) which would decrease its Armington substitution elasticity. It is plausible
that wealthy countries would consume relatively more of such products than poor countries and
therefore would have a higher Armington elasticities
Imports
Imports are expected to have a positive impact on the Armington elasticity because they indicate a
greater connection to world markets. The first variable is the quantity of imports from outside the EU.
OLS Armington regressions reveal positive and significant coefficients. C-O estimates however, whilst
also positive, lack significant p-values. Nevertheless, using clustered robust standard errors and IRLS,
the coefficients are significant at an 80 percent level. Again, like with GDP per capita, the overall
evidence from these results does seem to point to a significant result. However, removing Belgium
and the Netherlands from the regression resulted in highly insignificant results. This may indicate
that the results are biased by the ‘Rotterdam phenomenon’. Therefore, whilst there is some
evidence for a positive effect imports from outside the EU, this results should be treated with
caution. Imports from inside the EU display mixed and insignificant coefficients only. Therefore it
cannot be confirmed that intra-EU imports positively affect the Armington elasticity.
Urbanisation
Cochrane-Orcutt estimate regressions reveal a negative relation between Armington elasticities and
the determinant urbanisation. OLS estimates paint a similar picture, but fail to find estimates more
significant than an 80 percent level. Given the robustness of the sign across estimation methods and
the high levels of significance of the C-O regressions the negative relation between urbanisation and
the Armington elasticity can be confirmed. On beforehand this relationship is not directly evident.
Because wealth and urbanisation are positively correlated it is possible that we actually measure the
effect of wealth, which was found to be also negatively correlated to the Armington. However, the
low multicollinearity value does not point to this. Another explanation could be that urbanised
countries consume more origin specific (luxury/final) goods, which are expected to have lower
Armingtons. Alternatively it could be so that consumers from an urban area consume similar
products to those from rural areas but are just more origin sensitive in their consumption. Either
way, the result that Armington elasticities are lower in urbanised countries seems robust.
49
Final goods
Another robust finding is the negative coefficient of the dummy variable final goods. The finding that
final goods have lower Armingtons than intermediate goods is in line with results found in literature.
Final goods are more difficult to substitute because they are more easily differentiable than
intermediate goods. Significant values are found for the C-O Armington estimations, which are
significant across regression methods. OLS Armington estimations provide results of the same sign
but are not significant. The significant and positive coefficients found for final good provide evidence
that our estimated Armingtons are accurate.
Multiculturalism
The stock of immigrants from outside the EU was taken as a proxy for multiculturalism. On
beforehand it could be expected that a greater multiculturalism would increase the substitutability
between foreign and domestic goods, especially if those foreigners come from different cultures (i.e.
outside the EU). The only significant value was the C-O robust regression which had a positive
coefficient. On the other hand, some OLS estimations even had negative coefficients which
approached significant levels. Cochrane-Orcutt estimates for the imports from inside the EU report
positive coefficients for most estimation methods, but these results are all highly insignificant. All
considering , we cannot reliably confirm that multiculturalism is positively correlated with the
Armington elasticity.
Goods imports, FDI and national pride
Finally, we briefly discuss the remaining variables, goods specific imports and exports and FDI. First of
all, both variables on goods specific imports could not be confirmed to have an impact on the
Armington elasticities. Good specific imports and exports reported mixed signs on the coefficients
and mostly insignificant p-values. Therefore there is insufficient evidence that the quantity of trade in
a certain good positively impacts the Armington elasticity related to that good. Inward FDI could also
not be shown to have a positive impact on the Armington elasticity. On the contrary, the coefficients
are all negative, although nowhere significant. Finally, the variable National pride was expected to
have a negative influence on the Armington elasticity but again this could not be confirmed.
50
7. Conclusion and Discussion
In this chapter we will try to answer the main research questions posed at the start of this thesis by
providing the conclusions of the results obtained in chapter 5 and 6. In the second part we will
discuss some of the issues associated with the methods and data, as well as providing a short
discussion on the relevance of this study and options for further research.
7.1 Conclusion
In the first part of this thesis we tried to answer the research question: what are the Armington
elasticities for goods in different countries? Therefore, the first part of the thesis consisted of an
estimation of Armington elasticities for 14 countries and 29 products. The Armington estimates
described in chapter 5 have averages close to one, which is in line with the results reported in similar
literature (Mc Daniel and Balistreri 2003). An Armington elasticity of approximately one indicates
that, given a price change in one good variety, the demand of the substitute good variety has a
change of similar size in the same direction. As expected, we found substantial heterogeneity of the
Armington elasticities across countries. In the Cochrane-Orcutt approach for example, Armington
elasticities ranged from 0.41 for Portugal to 1.74 for Belgium. In this case Portugal is the least willing
to substitute foreign goods with domestic goods. Belgium on the other hand is found to have a large
Armington elasticity and therefore substitutes relatively easily between foreign and domestic goods
given a change in relative prices (Gibson 2003).
In the second tier of the thesis we focussed on the main research question: What are the country
characteristics determining heterogeneity of Armington elasticities across countries? To answer this
question a number of determinants where regressed on the Armington elasticities obtained in the
first tier regressions. A number of these determinants were found to be correlated to the size of
Armington elasticities. The first determinant is GDP per capita, which was found to be negatively
correlated to the size of countries’ Armington elasticities. This result, while not evident a priori, can
be explained if wealthy consumers are less price sensitive than poorer consumers. Furthermore,
wealthy consumers may consume more luxury goods where the origin of the good is part of that
luxury status.
Furthermore, we found that the level of imports into a country is related to that country's
Armington elasticities. However, this was only true for imports from outside the EU. This indicates
that trade from neighbouring or nearby countries is not related to openness towards foreign
products, while trade with nations that are further away is. An explanation for this can be found in
the fact that goods imported from further away tend to be more different from the domestic variety
than those imported from neighbouring countries. As such, countries which import a lot from far
away nations are more familiar with goods that are different from their own, and therefore
substitute more easily between foreign and domestic goods. Alternatively, countries with a high
propensity to substitute foreign with domestic goods are more likely to import goods even if those
goods are recognisably different from the domestic varieties. Therefore, countries with high
Armington elasticities are likely to import more from countries which are further away. Either way,
we can conclude that the level of imports from non-neighbouring countries is positively related to
the Armington elasticity, which can be a useful result for future CGE trade modelling practices.
51
The final country characteristic determinant we found was urbanisation. Urbanisation was found to
be negatively related to the Armington elasticity. This negative relationship can be explained if urban
populations are more conscious of their consumption choices, either because urban consumers are
wealthier, or because they are simply more sensitive (snobbish) when it comes to the origin of their
products.
Summarising, we can conclude that the country characteristics determining the heterogeneity of the
Armington elasticity across countries are GDP per capita, the level of imports from ‘distant’ nations
and the level of urbanisation of a country. Our results also confirmed the result found in literature
that final goods have lower Armington elasticities than intermediate goods. However, the effect of
other possible determinants such as FDI, exports, multiculturalism and national pride, could not be
confirmed. It must be added that for all estimations the fraction of explained variation of the
regressions was low. This may indicate that many more factors play a role in the determination of the
Armington elasticity than those captured in this study.
A number of separate regressions where performed on the characteristics of goods on the size of the
Armington elasticity. It was found that the average imports of a good are positively correlated with
the Armington elasticity. This supports the previous result that imports are positively correlated with
the Armington elasticity. Furthermore, final goods and food products where both found to have
lower Armingtons than other goods. This is an expected result because these types of goods are
generally more heterogeneous than other goods. A less obvious result was found for industrial
goods, which appear to have lower Armingtons than consumption goods. This result indicates that
apparently firms are more sensitive to the origin of products than consumers.
7.2 Discussion
The Armington estimates
In this thesis two regression methods for estimating the Armington elasticities were performed; an
OLS and a Cochrane-Orcutt approach. A priori, the results of C-O Armingtons should be preferred
because of their correction for autocorrelation. However, this approach also has some
disadvantages. For example, there are substantially less observations when using C-O Armingtons,
which could have a negative impact on the reliability of the estimations. Also, the C-O Armingtons
show larger heteroscedasticity and weaker normality than the OLS estimations. Furthermore, the
robust IRLS regression revealed that the C-O characteristic determinants regressions might be
sensitive to influential observations. Together, these issues cast some doubts on the C-O
characteristic estimates’ increased reliability compared to the OLS estimates. Therefore the OLS
estimations were used in supplement of the C-O elasticities for the estimation of country
characteristic determinants.
Blonigen and Wilson (1999) discuss alternative estimations of the Armington elasticities but find that
these make little difference for the results. In our case such alternative estimations where not
investigated because they would require a lot of work in regressing all the separate Armington
elasticities. As mentioned, the average Armington elasticities are of similar size as those found in
comparable studies. Still, more sophisticated estimation methods have sometimes found higher
Armington elasticity estimates than those found in this study (Feenstra, Obstfeld et al. 2010).
52
Country characteristic determinants
The country characteristic determinants regressions revealed different results for OLS Armingtons
and C-O Armingtons. Sizes and even signs differ between the two estimation methods. Still, the
reliability of the results is not bad if we consider that all significant results are of the expected sign.
Another indication of the reliability of the results is the significant positive result found for final
goods. This is in line with previous studies of the same kind. These results provide an indication that
estimations of the country characteristic determinants can be confirmed with some degree of
confidence.
Another issue with the reliability of the determinant estimates is with respect to outliers. At different
stages of the process outliers where identified and removed. It is difficult to assess if sufficient, or too
many observations were thus removed. For the Cochrane-Orcutt approach in particular, results
where sensitive to certain observations.
Limitations of the sample data
There are some limitations to this study concerning the size of the dataset. While efforts were made
to attain an as large as possible sample size, we could find only 14 countries for which the right trade
data was available for a sufficiently long time period. 14 countries is not that large a sample size to
determine the country characteristics determinants. Furthermore, only EU countries where
considered, which could be a drawback particularly if these countries are much alike. A further
drawback of using only EU countries is that they share the same trade barriers. As such, it was not
possible to investigate the effects of trade barriers on the Armington elasticity. The length of the
time series is another point of concern. While the 17 years covered should be sufficient in principle,
there are many examples for which the time series is more limited, thus reducing the reliability of the
Armington estimates. Finally there are some issues with negative values for domestic consumption,
probably caused by the ‘Rotterdam phenomenon’. These negative observations were removed from
the dataset, which may cause some bias, especially for the countries where this problem was
particularly substantial: The Netherlands, Belgium and Denmark.
Relevance and future research
This study is relevant in both an academic and practical sense. First of all this study adds to the
knowledge of the determinants of Armington elasticity, a topic where research is still sparse. Second,
the determinants of Armington elasticities can be useful for trade modelers wanting to endogenize
the Armington elasticity in their model. Country characteristic determinants can be relevant in (CGE)
trade models. Examples are particularly those characteristics connected to trade and other economic
variables. The results from this study suggest that the variables wealth, final goods and imports from
outside the EU could be used as variables for the Armington elasticity. In this case imports from
outside the EU could also be considered as imports from outside some established other region.
In a CGE modeling situations these relations would be represented by elasticities. This study
however, does not give any indication of these. To have a chance of reliably estimating the
elasticities associated with the determinants of Armington elasticities would require a substantially
larger sample size than achieved in this study. (Also a solution would have to be found for the
obtained negative Armington elasticities in case of a log linear function). Constructing endogenous
Armington elasticities would be a next step in dynamic general equilibrium trade models.
53
54
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56
Appendix 1
Table A.1 Armington elasticity estimates per country
Good
Country
1011114
0
2222110
1624113
0
3
1012101
0
1721130
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
France
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
0
2221215
7
2222193
0
2222130
0
3109110
0
1107193
0
2222120
0
2599298
7
0812119
0
1105100
0
1092103
0
1082223
9
1061210
0
1051303
0
1039223
0
1020140
0
1020110
0
1084123
0
1520135
1
2370110
0
2030115
0
2221415
0
1413143
0
1072125
5
2361113
0
Average
1011114
0
2222110
1624113
0
3
1012101
0
1721130
0
2221215
7
2222193
0
2222130
0
3109110
0
1107193
0
2222120
0
2599298
7
OLS
Armingto
n
0.60
1.67
1.03
1.30
1.13
-1.16
1.11
0.61
1.64
-1.97
-0.85
1.14
2.25
2.80
2.13
Std.
Error
pvalue
0.53
1.04
-1.85
1.05
2.33
-1.51
8.66
1.31
1.16
1.74
1.12
0.19
0.24
0.97
0.25
0.65
0.14
1.13
0.09
0.28
0.53
0.40
1.43
0.91
0.50
4.33
0.47
0.67
0.79
1.17
0.42
0.34
1.77
0.59
0.33
0.38
0.60
0.00
0.00
0.20
0.00
0.10
0.00
0.60
0.00
0.00
0.13
0.01
0.14
0.01
0.00
0.14
0.28
1.55
0.03
0.39
0.00
0.00
0.00
0.05
0.00
0.00
1.44
1.83
1.16
1.25
2.82
0.20
1.21
1.45
1.18
0.42
0.32
0.74
1.47
0.84
0.52
0.94
0.45
0.28
0.00
0.00
0.15
0.41
0.01
0.71
0.22
0.01
0.00
2.26
0.68
0.01
2.70
0.47
0.00
R2
0.0
0.8
2
4
0.6
3
0.1
1
0.6
7
0.1
7
0.8
1
0.0
2
0.9
8
0.7
9
0.1
5
0.4
1
0.1
7
0.3
9
0.5
4
0.2
8
0.0
8
0.1
4
0.2
7
0.0
5
0.6
7
0.5
8
0.6
8
0.2
9
0.4
5
0.5
9
0.4
4
0.7
3
0.4
3
0.0
5
0.6
1
0.0
1
0.1
0
0.4
1
0.6
5
0.5
8
0.7
1
C-O
Armingto
n
Std.
Erro
r
pvalue
2.39
1.07
0.68
Armingto
1.30
1.26 n
0.61
0.99
0.82
1.67
-1.86
-0.09
1.20
1.08
-0.04
1.16
0.48
0.47
0.16
-1.60
0.27
2.05
-1.22
9.20
1.17
0.05
1.40
1.08
0.15
0.24
0.62
0.24
0.33
0.17
0.35
0.11
0.35
0.33
0.33
0.50
0.39
0.16
1.01
0.40
0.65
0.92
0.94
0.72
0.44
2.82
0.20
0.24
0.32
0.04
0.00
0.02
0.06
0.00
0.09
0.00
0.04
0.00
0.00
0.79
0.01
0.06
0.91
0.00
0.66
0.26
0.80
0.11
0.78
0.01
0.02
0.01
0.00
0.84
0.00
0.39
1.14
0.94
-2.31
2.67
0.66
0.98
1.13
0.87
0.25
0.40
0.52
1.60
0.32
0.63
0.65
0.29
0.16
0.15
0.02
0.20
0.18
0.00
0.33
0.15
0.00
0.00
1.29
0.48
0.04
0.36
0.51
0.50
R2
0.2
0.7
6
9
0.4
8
0.2
4
0.7
8
0.1
9
0.7
1
0.2
8
0.9
8
0.7
1
0.0
1
0.6
0
0.3
4
0.0
0
0.7
9
0.0
4
0.0
9
0.0
0
0.1
8
0.0
1
0.3
7
0.3
7
0.5
2
0.8
0
0.0
0
0.5
9
0.1
4
0.4
7
0.3
8
0.1
6
0.9
6
0.1
2
0.1
4
0.5
2
0.7
8
0.5
5
0.0
4
57
0812119
0
1105100
0
1092103
0
1082223
9
1061210
0
1051303
0
1039223
0
1020140
0
1020110
0
1084123
0
1520135
1
2370110
0
2030115
0
2221415
0
1413143
0
1072125
5
2361113
0
Average
1011114
0
2222110
1624113
0
3
1012101
0
1721130
0
2221215
7
2222193
0
2222130
0
3109110
0
1107193
0
2222120
0
2599298
7
0812119
0
1105100
0
1092103
0
1082223
9
1061210
0
1051303
0
1039223
0
1020140
0
1020110
0
1084123
0
1520135
1
2370110
0
2030115
0
2221415
0
1413143
0
1072125
5
2361113
0
Average
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherlands
Netherland
s
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
0.4
7
0.6
2
0.0
3
0.7
6
0.2
0
2.10
2.30
0.12
-1.69
0.72
0.66
1.27
0.67
0.03
0.04
0.93
0.07
0.6
3
0.8
0
0.0
0
0.6
1
0.97
0.0
0
-1.10
0.58
0.16
0.5
4
0.28
1.22
0.00
0.80
1.89
0.38
0.33
0.76
0.00
0.63
2.46
2.71
1.19
3.76
1.32
1.30
-1.78
-1.44
3.45
-0.85
0.44
0.35
2.27
0.39
1.08
0.86
0.94
1.51
0.33
1.18
1.17
0.66
0.68
2.60
0.05
0.78
0.00
0.03
0.24
0.00
0.40
0.00
0.15
0.24
0.00
0.23
0.87
0.08
0.01
1.26
1.38
0.72
1.52
0.98
1.54
-0.35
0.58
1.66
1.45
1.56
0.46
0.61
0.63
0.63
0.50
0.42
0.54
0.34
0.53
0.28
0.25
0.02
0.04
0.18
0.03
0.07
0.00
0.53
0.11
0.01
0.00
0.00
1.29
0.83
0.14
1.59
0.63
-1.79
-0.02
0.61
2.39
-1.12
-3.70
0.17
0.13
1.24
0.57
2.08
0.78
1.19
3.14
0.00
0.00
0.17
0.98
0.77
0.01
0.36
0.26
1.52
0.75
0.05
1.31
0.80
1.74
-0.90
1.30
0.00
0.20
0.52
0.86
0.92
0.48
1.31
1.60
0.00
0.00
0.93
0.15
0.40
0.00
0.50
0.43
0.7
3
0.0
3
0.3
6
0.3
0
0.4
3
0.2
9
0.2
1
0.5
3
0.0
3
0.1
7
0.4
1
0.6
5
0.7
3
0.1
5
1.0
0
0.5
0
0.0
0
0.1
4
0.0
5
0.4
9
0.0
3
0.0
5
7.10
-0.94
5.24
0.48
0.22
0.07
-1.18
0.59
0.06
1.85
0.20
-0.97
1.45
-0.04
2.92
0.73
0.08
0.73
0.23
1.11
0.54
0.39
1.08
0.00
0.79
0.00
0.26
0.94
0.08
0.26
0.7
6
0.0
1
0.7
4
0.3
1
0.3
9
0.5
2
0.0
5
0.5
5
0.1
3
0.0
9
0.6
5
0.1
0
0.0
0
0.9
8
0.3
6
0.9
4
0.6
0
0.1
2
0.0
0
0.0
1
0.3
8
0.0
6
0.2
6
0.2
1
0.2
0
0.9
7
0.0
1
0.5
3
0.3
0
0.0
0
0.9
8
0.3
5
1.73
1.91
-0.73
1.02
0.72
0.09
0.45
0.21
1.11
0.86
0.00
0.00
0.00
0.45
0.43
0.88
0.59
0.19
2.18
-1.24
-0.44
-2.37
0.94
0.40
0.87
0.59
0.06
0.02
0.63
0.01
2.77
2.81
0.38
0.04
1.20
1.86
0.32
0.2
2
0.9
6
0.5
6
0.4
5
0.3
0
0.0
7
0.3
5
58
1011114
0
2222110
1624113
0
3
1012101
0
1721130
0
2221215
7
2222193
0
2222130
0
3109110
0
1107193
0
2222120
0
2599298
7
0812119
0
1105100
0
1092103
0
1082223
9
1061210
0
1051303
0
1039223
0
1020140
0
1020110
0
1084123
0
1520135
1
2370110
0
2030115
0
2221415
0
1413143
0
1072125
5
2361113
0
Average
1011114
0
2222110
1624113
0
3
1012101
0
1721130
0
2221215
7
2222193
0
2222130
0
3109110
0
1107193
0
2222120
0
2599298
7
0812119
0
1105100
0
1092103
0
1082223
9
1061210
0
1051303
0
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
Ireland
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
-0.12
1.16
0.97
-0.27
1.28
0.56
-0.28
1.53
0.29
0.24
0.58
1.56
0.41
0.33
0.49
0.14
0.69
0.00
0.12
0.86
0.01
0.11
0.58
0.00
0.49
3.09
0.83
1.55
0.83
4.14
-0.19
1.60
-0.35
0.12
0.83
3.88
2.19
3.76
0.59
1.53
1.38
2.39
1.76
2.20
1.34
-1.14
1.01
-0.33
0.88
1.79
0.73
0.40
1.95
0.31
1.87
0.20
0.45
0.17
1.56
0.33
0.50
0.99
0.50
2.02
0.77
1.29
0.40
0.31
0.16
0.25
0.24
0.64
1.06
0.64
0.71
1.03
0.45
0.68
0.38
0.54
0.66
0.79
0.13
0.12
0.00
0.00
0.00
0.02
0.57
0.01
0.73
0.82
0.69
0.00
0.13
0.00
0.08
0.00
0.00
0.00
0.02
0.06
0.20
0.13
0.34
0.48
0.21
0.00
0.20
0.56
0.04
1.06
2.08
0.56
1.30
0.85
1.03
0.79
-0.01
-0.14
0.22
0.22
1.03
0.15
0.18
0.48
0.78
0.53
0.57
0.00
0.00
0.68
0.00
0.00
0.05
0.33
0.99
0.81
0.0
1
0.6
4
0.1
8
0.0
0
0.4
3
0.1
6
0.0
2
0.8
9
0.1
4
0.1
9
0.5
4
0.4
6
0.6
4
0.3
5
0.0
2
0.4
1
0.0
1
0.0
1
0.0
1
0.6
3
0.2
4
0.8
6
0.2
1
0.8
8
0.7
0
0.9
0
0.3
3
0.2
3
0.3
6
0.1
5
0.0
6
0.0
4
0.1
0
0.6
0
0.1
3
0.0
4
0.4
3
0.6
1
0.8
6
0.2
3
0.9
0
0.5
9
0.2
3
0.0
7
0.0
0
0.0
0
-0.16
0.84
1.17
0.24
1.30
0.46
0.03
1.46
0.35
0.21
0.59
1.66
0.38
0.33
0.44
0.16
0.66
0.00
0.07
0.89
0.01
0.18
0.94
0.00
-1.13
-0.34
0.93
1.24
0.76
2.58
-0.24
1.11
0.99
0.40
0.39
3.44
1.65
3.69
1.23
1.76
0.92
2.46
0.87
0.86
1.03
0.29
0.44
0.77
0.91
0.99
0.90
1.23
1.61
0.51
1.25
0.18
0.34
0.22
0.62
0.33
0.44
1.13
0.49
1.54
0.57
0.61
0.66
0.31
0.10
0.31
0.38
0.51
0.37
0.54
0.67
0.46
0.43
0.53
0.07
0.24
0.60
0.73
0.04
0.79
0.00
0.00
0.01
0.00
0.49
0.03
0.39
0.43
0.80
0.00
0.03
0.00
0.00
0.00
0.01
0.00
0.11
0.04
0.21
0.67
0.35
0.10
0.11
0.00
0.00
0.07
0.07
0.62
1.25
0.15
0.34
0.00
0.00
1.07
0.75
0.73
0.46
0.21
-1.03
0.19
0.14
0.11
0.62
0.45
0.66
0.00
0.00
0.00
0.47
0.64
0.14
0.0
2
0.5
9
0.2
4
0.0
0
0.5
2
0.1
2
0.0
0
0.8
6
0.2
6
0.0
1
0.6
6
0.5
1
0.5
2
0.6
1
0.0
4
0.3
1
0.0
5
0.0
6
0.0
1
0.7
2
0.5
2
0.7
0
0.5
4
0.9
7
0.4
5
0.8
2
0.1
7
0.2
9
0.3
8
0.0
1
0.0
6
0.2
1
0.1
7
0.9
4
0.6
2
0.3
5
0.4
5
0.5
5
0.5
2
0.8
4
0.6
7
0.7
8
0.0
4
0.0
2
0.1
6
59
1039223
0
1020140
0
1020110
0
1084123
0
1520135
1
2370110
0
2030115
0
2221415
0
1413143
0
1072125
5
2361113
0
Average
1011114
0
2222110
1624113
0
3
1012101
0
1721130
0
2221215
7
2222193
0
2222130
0
3109110
0
1107193
0
2222120
0
2599298
7
0812119
0
1105100
0
1092103
0
1082223
9
1061210
0
1051303
0
1039223
0
1020140
0
1020110
0
1084123
0
1520135
1
2370110
0
2030115
0
2221415
0
1413143
0
1072125
5
2361113
0
Average
1011114
0
2222110
1624113
0
3
1012101
0
1721130
0
2221215
7
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
U.K.
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
0.10
2.97
2.80
8.74
0.90
1.33
0.99
0.32
9.28
0.69
0.69
2.05
0.26
0.13
0.41
0.76
0.43
0.00
0.00
0.01
0.00
0.00
0.03
2.01
0.89
1.29
2.61
1.75
1.31
-0.15
1.27
-0.15
1.00
1.10
1.48
-0.26
0.93
1.17
0.92
0.95
2.71
0.53
0.16
0.89
1.26
0.39
0.43
0.61
0.17
0.70
0.27
0.17
0.15
0.64
0.12
0.15
0.66
0.81
1.37
0.00
0.00
0.22
0.06
0.00
0.01
0.81
0.00
0.83
0.01
0.00
0.00
0.70
0.00
0.00
0.18
0.36
0.10
-1.31
1.53
0.41
2.31
-0.32
4.64
1.41
0.88
2.46
0.12
0.72
0.10
1.07
1.36
1.07
0.19
0.00
0.00
1.15
1.16
-1.09
-3.52
0.11
0.71
0.83
0.67
0.00
0.20
0.21
0.00
2.95
-0.42
0.15
0.72
1.92
0.46
0.00
0.83
0.76
0.0
1
0.1
3
0.7
0
0.6
7
0.8
2
0.4
6
0.9
1
0.3
1
0.4
9
0.7
0
0.3
8
0.1
0
0.5
7
0.5
0
0.0
0
0.7
9
0.0
0
0.6
0
0.7
3
0.9
0
0.0
1
0.9
5
0.8
2
0.1
2
0.1
1
0.3
9
0.67
-3.41
2.80
0.11
8.18
0.90
0.92
1.05
0.37
0.73
0.50
0.90
1.54
0.25
0.10
0.46
0.10
0.02
0.00
0.91
0.01
0.00
0.00
0.04
0.2
5
0.8
8
0.8
4
0.0
0
0.9
0
0.5
0
0.9
2
0.3
2
0.4
4
0.6
6
0.4
7
0.3
7
0.8
9
0.2
0
0.3
5
0.8
0
0.0
4
0.4
9
0.7
9
0.8
9
0.1
9
0.9
3
0.2
7
0.0
1
1.29
0.72
0.94
2.75
1.63
0.68
1.31
1.14
-0.48
0.86
1.25
1.65
0.80
0.39
0.15
0.45
0.99
0.15
0.51
0.52
0.15
0.78
0.31
0.17
0.23
0.52
0.01
0.00
0.14
0.02
0.00
0.22
0.03
0.00
0.56
0.02
0.00
0.00
0.16
1.23
0.89
0.10
0.39
0.00
0.04
-0.10
0.77
0.91
0.0
6
0.1
7
0.0
1
0.3
4
0.8
1
0.7
9
-3.99
1.63
0.04
1.10
1.06
6.38
1.90
0.28
1.31
0.58
0.00
0.01
1.01
1.10
0.09
0.10
0.00
0.00
0.8
8
0.4
4
0.1
0
0.6
6
0.5
3
0.0
0
0.0
1
1.10
1.07
1.25
-0.65
0.13
0.55
0.57
0.48
0.00
0.13
0.05
0.20
0.8
3
0.5
3
0.2
5
0.1
3
0.84
0.69
0.25
0.61
0.01
0.29
0.4
9
0.1
4
0.4
0
0.0
3
0.5
2
0.8
6
0.8
9
0.9
1
60
2222193
0
2222130
0
3109110
0
1107193
0
2222120
0
2599298
7
0812119
0
1105100
0
1092103
0
1082223
9
1061210
0
1051303
0
1039223
0
1020140
0
1020110
0
1084123
0
1520135
1
2370110
0
2030115
0
2221415
0
1413143
0
1072125
5
2361113
0
Average
1011114
0
2222110
1624113
0
3
1012101
0
1721130
0
2221215
7
2222193
0
2222130
0
3109110
0
1107193
0
2222120
0
2599298
7
0812119
0
1105100
0
1092103
0
1082223
9
1061210
0
1051303
0
1039223
0
1020140
0
1020110
0
1084123
0
1520135
1
2370110
0
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
Greece
2.14
0.39
0.00
0.8
1
2.28
0.52
0.01
0.8
0
1.29
0.29
0.00
0.55
0.31
0.10
-0.28
2.00
4.64
-2.18
-6.15
1.95
-1.48
-0.33
0.64
0.32
1.36
1.01
1.66
1.64
1.77
0.80
0.69
0.00
0.01
0.07
0.00
0.26
0.42
0.69
0.6
1
0.0
6
0.7
4
0.4
9
0.4
0
0.5
3
0.1
0
0.0
6
0.0
1
-0.99
0.65
2.96
-1.88
-0.99
2.88
1.05
0.51
0.06
0.41
0.66
1.77
1.02
1.44
2.57
0.84
0.00
0.14
0.00
0.34
0.36
0.07
0.69
0.55
0.2
3
0.9
9
0.1
7
0.6
7
0.1
8
0.0
8
0.2
9
0.0
2
0.0
3
3.26
5.76
0.61
3.98
0.00
0.22
0.34
1.32
0.40
4.55
0.41
0.79
-0.45
0.79
1.54
5.10
0.78
0.89
0.40
0.31
0.23
1.30
-0.63
0.46
0.93
1.86
0.17
0.26
1.50
1.55
1.99
1.29
1.45
1.73
1.08
1.04
1.01
0.65
1.87
0.60
1.74
0.91
3.20
0.88
0.24
1.28
0.88
0.21
0.39
0.42
0.50
0.49
0.69
1.19
0.30
0.36
0.55
0.37
0.15
0.43
1.49
0.44
0.38
0.19
0.46
0.16
0.04
0.29
0.34
0.00
0.68
0.54
0.01
0.01
0.01
0.30
0.02
0.00
0.07
0.04
0.00
0.16
0.24
0.19
0.00
0.00
0.00
0.6
5
0.3
4
0.0
1
0.0
1
0.1
3
0.5
3
0.3
2
0.1
5
0.8
6
0.0
2
0.0
3
0.4
1
0.4
4
0.4
1
0.0
9
0.8
8
0.6
4
0.2
5
0.6
1
0.9
0
0.1
5
0.1
4
0.1
3
0.6
2
0.6
4
0.7
9
1.53
-0.50
0.64
3.08
0.51
-0.02
0.80
1.21
1.30
1.84
2.75
1.45
1.04
0.92
1.50
1.17
0.54
0.86
0.26
0.94
0.85
0.30
0.40
0.16
0.27
0.56
0.83
1.04
0.08
0.15
0.14
0.16
0.08
0.48
0.10
0.13
0.23
0.02
0.13
0.96
0.00
0.00
0.04
0.05
0.03
0.00
0.00
0.00
0.00
0.00
0.28
1.23
2.00
1.91
3.17
0.20
0.24
0.19
0.51
0.00
0.00
0.00
0.00
0.64
-0.74
2.49
1.43
1.04
0.44
0.66
0.49
0.00
0.0
2
0.0
4
0.7
2
2.67
0.79
1.60
1.24
0.25
0.60
0.06
0.01
0.02
0.0
5
0.0
3
0.1
2
0.1
8
0.4
8
0.2
8
0.7
6
0.2
2
0.0
0
0.7
0
0.6
5
0.3
3
0.3
5
0.4
4
0.9
9
0.8
2
0.8
2
0.9
7
0.9
9
0.1
0
0.7
7
0.8
6
0.9
0
0.7
8
0.3
2
0.4
7
0.4
2
61
2030115
0
2221415
0
1413143
0
1072125
5
2361113
0
Average
1011114
0
2222110
1624113
0
3
1012101
0
1721130
0
2221215
7
2222193
0
2222130
0
3109110
0
1107193
0
2222120
0
2599298
7
0812119
0
1105100
0
1092103
0
1082223
9
1061210
0
1051303
0
1039223
0
1020140
0
1020110
0
1084123
0
1520135
1
2370110
0
2030115
0
2221415
0
1413143
0
1072125
5
2361113
0
Average
1011114
0
2222110
1624113
0
3
1012101
0
1721130
0
2221215
7
2222193
0
2222130
0
3109110
0
1107193
0
2222120
0
2599298
7
Greece
Greece
Greece
Greece
Greece
Greece
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Portugal
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
0.54
3.32
0.38
0.71
0.17
0.00
1.43
0.47
1.27
0.80
1.77
0.14
3.07
-0.23
1.21
1.50
1.35
3.92
-0.10
3.43
1.60
0.49
0.07
1.33
0.43
1.53
0.35
-0.58
5.82
0.20
0.41
0.56
0.77
0.14
0.28
1.07
0.86
0.45
1.54
0.59
0.71
0.43
0.76
0.25
0.72
0.52
0.47
0.48
1.28
0.82
0.64
6.54
0.00
0.27
0.16
0.32
0.00
0.63
0.01
0.79
0.02
0.35
0.04
0.00
0.81
0.00
0.00
0.57
0.89
0.02
0.39
0.25
0.68
0.43
0.44
2.56
0.18
2.97
-1.17
-0.57
2.11
1.04
0.60
0.55
1.25
0.27
0.87
0.00
0.05
0.66
-0.95
1.69
1.21
1.80
3.79
0.86
-1.50
1.37
0.44
1.78
0.49
2.78
-1.58
3.21
0.71
0.82
0.43
0.97
0.31
0.55
0.16
0.86
0.25
0.33
0.24
0.26
3.40
0.88
1.09
0.25
0.26
0.00
0.32
0.00
0.00
0.00
0.10
0.00
0.21
0.00
0.08
0.44
0.09
0.01
0.01
0.1
4
0.6
9
0.8
1
0.0
9
0.4
1
0.0
7
0.9
1
0.0
2
0.3
6
0.0
0
0.3
3
0.0
9
0.3
2
0.7
5
0.0
0
0.7
0
0.7
3
0.1
9
0.0
0
0.5
0
0.0
7
0.0
9
0.0
1
0.2
2
0.2
1
0.2
0
0.0
0
0.6
2
0.2
3
0.0
2
0.0
8
0.5
1
0.2
7
0.6
9
0.7
6
0.7
0
0.1
7
0.6
6
0.1
0
0.7
8
0.1
9
0.1
0
0.1
8
0.4
0
0.3
6
0.05
5.09
0.46
1.01
0.91
0.00
1.01
1.24
1.55
0.71
1.83
0.41
0.16
0.68
0.97
1.33
1.43
0.70
-0.05
4.05
0.99
0.23
0.49
0.44
0.51
0.15
0.41
1.63
0.34
0.23
1.15
0.70
0.84
0.21
0.69
0.18
0.00
0.03
0.10
0.18
0.00
0.33
0.92
0.07
0.00
0.28
0.08
0.43
0.82
0.00
0.00
0.06
0.25
-0.40
0.70
0.62
0.22
0.44
0.10
0.56
0.40
0.78
0.61
0.00
0.23
0.14
0.44
3.43
0.64
-0.44
1.07
0.76
0.36
0.55
0.69
0.00
0.09
0.45
-0.20
1.44
0.86
1.77
1.12
1.17
0.03
0.81
0.45
1.65
0.90
1.81
2.15
2.11
0.99
0.80
0.41
0.55
0.36
0.29
0.12
0.54
0.18
0.34
0.12
0.40
0.48
0.55
0.41
0.20
0.80
0.00
0.30
0.00
0.00
0.00
0.96
0.00
0.21
0.00
0.04
0.02
0.00
0.00
0.0
0
0.8
1
0.6
9
0.3
7
0.5
8
0.1
2
0.9
2
0.0
8
0.0
0
0.2
2
0.5
6
0.1
4
0.3
4
0.0
8
0.0
0
0.8
7
0.6
9
0.0
1
0.1
0
0.6
4
0.1
0
0.1
5
0.0
1
0.5
9
0.1
9
0.1
0
0.0
0
0.4
7
0.2
8
0.6
4
0.5
2
0.9
0
0.0
0
0.5
9
0.1
1
0.9
3
0.2
7
0.7
8
0.5
2
0.0
0
0.6
3
62
0812119
0
1105100
0
1092103
0
1082223
9
1061210
0
1051303
0
1039223
0
1020140
0
1020110
0
1084123
0
1520135
1
2370110
0
2030115
0
2221415
0
1413143
0
1072125
5
2361113
0
Average
1011114
0
2222110
1624113
0
3
1012101
0
1721130
0
2221215
7
2222193
0
2222130
0
3109110
0
1107193
0
2222120
0
2599298
7
0812119
0
1105100
0
1092103
0
1082223
9
1061210
0
1051303
0
1039223
0
1020140
0
1020110
0
1084123
0
1520135
1
2370110
0
2030115
0
2221415
0
1413143
0
1072125
5
2361113
0
Average
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Spain
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
Belgium
1.01
1.24
1.31
-0.10
3.32
0.09
0.26
0.19
0.37
0.59
0.00
0.00
0.00
0.79
0.00
0.12
2.83
1.85
2.03
3.80
1.15
1.01
3.00
1.59
0.72
0.73
1.42
0.32
1.32
0.93
0.35
0.35
0.19
0.33
0.41
0.29
0.22
0.27
0.54
0.71
0.08
0.07
0.00
0.00
0.00
0.01
0.00
0.00
0.01
0.02
0.09
4.86
1.36
1.44
0.31
0.66
0.79
0.12
0.17
0.17
0.49
0.00
0.00
0.00
0.09
0.20
5.02
5.29
1.15
7.91
0.64
0.36
0.55
0.00
0.01
1.60
-0.96
2.41
1.23
-2.10
2.72
0.23
0.38
1.05
0.32
1.20
1.28
0.00
0.24
0.04
0.01
0.10
0.05
5.72
1.89
0.01
7.30
7.32
0.36
2.62
2.61
3.56
0.38
1.02
0.85
0.00
0.02
0.00
-1.24
0.57
2.20
1.95
0.41
1.38
0.54
0.18
0.11
0.8
9
0.6
1
0.7
7
0.0
0
0.6
8
0.0
1
0.4
3
0.2
1
0.6
9
0.8
9
0.7
1
0.0
1
0.7
8
0.7
4
0.4
1
0.3
3
0.4
7
0.8
4
0.9
1
0.8
3
0.1
8
0.1
1
0.0
7
0.9
1
0.5
3
0.7
6
0.8
7
0.2
7
0.7
1
0.1
8
0.2
3
0.4
1
0.94
1.45
1.08
1.51
2.74
0.09
0.35
0.13
0.22
0.53
0.00
0.00
0.00
0.00
0.00
-0.07
2.17
0.56
1.26
3.68
0.99
0.69
1.25
0.75
0.73
0.76
1.27
0.20
2.14
0.68
0.38
0.32
0.22
0.15
0.41
0.64
0.22
0.30
0.39
0.72
0.39
0.42
0.01
0.00
0.00
0.00
0.01
0.26
0.00
0.02
0.11
1.43
1.63
0.97
1.56
0.12
0.21
0.27
0.35
0.00
0.00
0.00
0.00
4.14
5.18
0.76
5.93
0.56
0.38
0.54
0.00
0.09
1.62
0.26
0.00
0.25
2.61
-0.17
2.23
0.44
0.44
1.14
0.77
0.58
0.00
0.88
0.01
2.58
0.57
0.00
0.1
7
0.8
9
0.3
3
0.5
4
0.0
4
0.1
2
0.4
7
2.87
0.79
2.75
0.21
1.57
10.1
7
0.46
0.90
0.96
-0.22
1.10
1.74
1.87
0.44
1.38
0.91
0.03
0.25
0.00
0.82
0.13
0.8
9
0.5
5
0.8
2
0.7
8
0.6
5
0.0
1
0.2
6
0.0
5
0.4
4
0.9
1
0.6
0
0.6
0
0.3
9
0.1
2
0.4
5
0.3
2
0.4
9
0.9
2
0.8
2
0.4
9
0.5
8
0.1
4
0.9
4
0.3
6
0.7
3
0.0
3
0.9
0
0.0
0
0.3
7
0.6
5
0.0
2
0.8
8
0.0
0
0.1
6
0.0
0
0.3
1
0.4
4
63
1011114
0
2222110
1624113
0
3
1012101
0
1721130
0
2221215
7
2222193
0
2222130
0
3109110
0
1107193
0
2222120
0
2599298
7
0812119
0
1105100
0
1092103
0
1082223
9
1061210
0
1051303
0
1039223
0
1020140
0
1020110
0
1084123
0
1520135
1
2370110
0
2030115
0
2221415
0
1413143
0
1072125
5
2361113
0
Average
1011114
0
2222110
1624113
0
3
1012101
0
1721130
0
2221215
7
2222193
0
2222130
0
3109110
0
1107193
0
2222120
0
2599298
7
0812119
0
1105100
0
1092103
0
1082223
9
1061210
0
1051303
0
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Sweden
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
1.60
-0.04
-0.32
-0.60
2.36
2.22
0.85
3.87
1.24
1.14
0.62
0.59
1.12
0.62
0.69
0.97
0.80
0.36
0.00
0.09
0.21
0.0
2
0.0
0
0.0
4
0.0
8
0.5
9
0.3
6
0.1
9
3.35
1.66
1.58
1.36
0.07
0.29
-0.71
0.58
2.14
1.40
0.30
0.29
1.23
0.19
0.04
0.08
0.13
0.00
0.00
-0.13
1.08
1.08
0.94
0.49
-2.55
-0.55
2.17
1.51
0.44
0.15
0.27
0.32
0.79
0.55
2.03
0.60
0.75
1.69
1.00
0.55
0.01
0.01
0.26
0.44
0.26
0.39
0.10
0.39
0.0
0
0.4
2
0.7
0
0.5
6
0.1
1
0.2
1
0.2
4
0.0
8
0.8
1
0.0
8
1.32
0.30
0.00
2.22
0.93
1.23
0.49
1.63
1.32
2.07
-0.25
0.45
0.23
0.37
0.25
1.26
0.43
1.25
0.42
0.00
0.01
0.01
0.19
0.29
0.02
0.35
0.57
-2.44
1.14
0.06
-1.61
0.44
0.01
0.72
0.50
1.81
0.88
0.25
0.41
0.43
0.08
0.00
1.12
0.82
2.57
0.77
0.24
0.40
0.19
0.01
0.00
1.42
3.98
0.70
4.59
1.77
0.88
1.15
3.47
1.07
1.04
-1.18
0.43
3.10
1.00
0.85
0.88
1.16
1.25
0.42
0.61
1.14
0.55
0.01
0.24
0.32
0.00
0.09
0.47
0.37
0.00
0.10
0.38
0.06
1.31
4.24
1.33
4.73
0.66
2.54
1.52
2.01
0.21
0.73
-1.10
0.33
0.87
0.62
0.89
0.69
0.91
0.71
0.49
0.45
0.98
0.71
0.00
0.00
0.11
0.00
0.40
0.02
0.05
0.00
0.65
0.47
0.16
3.64
0.50
1.21
1.67
3.37
2.84
0.77
2.81
-2.18
0.26
1.03
0.24
0.20
0.31
0.27
0.24
0.88
1.69
0.00
0.64
0.02
0.00
0.00
0.00
0.01
0.01
0.22
0.3
4
0.0
6
0.2
6
0.6
4
0.5
0
0.1
9
0.2
8
0.6
8
0.3
6
0.0
6
0.0
6
0.8
2
0.1
7
0.0
6
0.0
6
0.9
4
0.0
3
0.8
9
0.8
7
0.8
9
0.8
8
0.4
3
0.4
2
0.1
1
2.20
-1.08
0.47
0.80
0.00
0.22
1.79
1.39
2.11
0.91
1.77
-1.03
0.17
0.34
0.31
0.23
0.84
0.86
0.00
0.00
0.00
0.00
0.05
0.26
0.3
9
0.2
7
0.3
8
0.3
1
0.3
3
0.9
3
0.6
4
0.8
3
0.7
0
0.5
0
0.6
5
0.3
6
0.5
7
0.7
3
0.0
4
0.6
6
0.2
3
0.5
7
0.8
2
0.6
3
0.8
3
0.5
4
0.7
0
0.1
8
0.4
9
0.2
8
0.5
5
0.0
1
0.0
4
0.2
3
0.6
4
0.2
3
0.9
5
0.5
5
0.7
7
0.5
6
0.2
6
0.1
1
64
1039223
0
1020140
0
1020110
0
1084123
0
1520135
1
2370110
0
2030115
0
2221415
0
1413143
0
1072125
5
2361113
0
Average
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
Finland
1011114
0
2222110
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
Austria
1624113
0
3
1012101
0
1721130
0
2221215
7
2222193
0
2222130
0
3109110
0
1107193
0
2222120
0
2599298
7
0812119
0
1105100
0
1092103
0
1082223
9
1061210
0
1051303
0
1039223
0
1020140
0
1020110
0
1084123
0
1520135
1
2370110
0
2030115
0
2221415
0
1413143
0
1072125
5
2361113
0
Average
3.46
-0.24
1.32
0.75
0.48
0.95
0.93
0.29
1.64
0.48
0.00
0.80
0.00
0.65
0.89
0.85
2.88
3.10
-0.81
3.63
1.59
4.04
2.84
-1.11
0.54
0.18
0.46
1.40
2.02
-0.33
3.45
0.30
0.41
1.37
1.16
0.51
0.74
3.19
2.03
1.03
1.28
0.84
0.31
0.26
0.59
1.18
4.51
0.01
0.00
0.07
0.50
0.00
0.20
0.25
0.20
0.34
0.68
0.83
0.16
0.00
0.01
0.79
0.47
1.04
1.11
-1.84
-0.75
4.30
-3.80
0.95
-3.08
0.28
0.53
0.46
0.79
0.69
1.71
0.98
3.75
0.00
0.06
0.00
0.36
0.00
0.04
0.35
0.47
-5.19
1.60
0.01
1.94
2.81
2.89
0.39
0.51
0.33
4.99
1.27
0.61
1.36
0.00
0.00
0.00
0.14
0.00
0.07
0.21
4.32
1.20
0.81
0.5
2
0.0
1
0.6
1
0.0
1
0.0
0
0.3
5
0.8
6
0.4
6
0.0
3
0.8
5
0.4
2
0.1
9
0.1
8
0.2
3
0.0
1
0.0
0
0.1
5
0.6
6
0.5
9
0.0
1
0.0
8
0.4
9
0.2
2
0.5
1
0.0
6
0.7
2
0.2
5
0.0
7
0.1
8
1.08
0.44
1.44
-0.72
0.48
0.47
0.96
0.37
0.60
1.11
0.05
0.68
0.00
0.25
0.67
0.3
7
0.0
6
0.5
8
0.0
9
0.0
1
0.3
9
0.9
2
0.4
9
0.0
1
0.8
6
0.4
0
0.9
0
0.1
4
1.0
0
0.1
8
0.0
0
0.2
4
0.8
7
0.8
7
0.1
4
0.82
2.93
5.83
0.21
3.52
1.36
6.43
1.31
-0.83
1.35
-0.11
0.58
2.05
3.31
0.60
0.28
0.33
2.94
0.47
0.54
0.69
2.15
1.23
0.02
0.77
0.60
0.30
0.21
0.56
0.48
0.01
0.00
0.12
0.66
0.00
0.18
0.21
0.32
0.00
0.10
0.86
0.08
0.00
0.00
0.24
1.00
1.36
1.43
1.85
3.25
0.68
0.30
0.20
0.20
0.39
0.82
0.77
0.76
0.24
0.00
0.00
0.00
0.04
0.00
0.38
0.25
0.6
5
0.7
6
0.4
9
0.2
7
0.5
6
0.0
5
0.1
2
0.5
1
0.6
2
0.6
7
0.8
4
0.7
4
0.4
3
0.0
7
0.3
4
0.15
0.46
0.77
2.07
1.29
3.30
0.42
0.61
0.36
0.00
0.05
0.00
2.39
1.61
1.61
0.86
0.57
0.59
0.02
0.01
0.15
0.0
2
0.6
3
0.2
4
0.8
8
0.3
5
0.3
6
0.4
4
Where the goods are defined according to the Comtrade nomenclature coding. Consult table 4.1 and
A.2 for their full descriptions.
65
66
Appendix 2
In chapter 6 the Armington elasticities are regressed on goods characteristics. Table A.2 shows the
dummy variable goods characteristics used in this regression. A ‘1’ indicates that the good is defined
according to the category in question, whereas a ‘0’ indicates a product with the characteristics
opposite to that category.
Table A.2 Definitions of good properties
Product
code
Product name
10111140
Fresh or chilled beef carcasses
22221100 Sacks and bags of polymers
16241133 Flat pallets of wood
Final
goods
0
Food
Industrial
products goods
1
0
0
0
1
1
0
1
10121010
Fresh or chilled whole chickens
0
1
0
17211300
Boxes and cases of paperboard
0
0
1
22212157
Tubes & pipes and hoses (polymers)
0
0
1
22221930
Plastic lids, caps and other closures
0
0
1
22221300 Plastic boxes, cases and crates
31091100 Metal furniture
0
0
1
1
0
0
11071930 Waters, with added sugar
1
1
0
22221200 Plastic sacks and bags
0
0
1
25992987 Base metal sign-plates
1
0
0
08121190 Construction sands
0
0
1
11051000 Beer made from malt
1
0
0
10921030 Dog or cat food
1
0
0
10822239 Chocolate blocks, slabs or bars
1
1
0
10612100 Wheat or meslin flour
0
1
0
10513030 Butter (fat content by weight < 85%)
0
1
0
10392230 Citrus fruit jams, marmalades
1
1
0
10201400 Frozen fish fillets
1
1
0
10201100 Fresh or chilled fish fillets
1
1
0
10841230 Tomato ketchup and tomato sauces
1
1
0
15201351 Men's leather footwear
1
0
0
23701100 Worked monumental/building stone
0
0
1
20301150 Paints based on acrylic polymers
0
0
1
22214150 Polyurethanes (foam)
0
0
1
14131430 Women's jackets of knitted textiles
1
0
0
10721255 Sweet biscuits
1
1
0
23611130 Building blocks of cement, concrete
0
0
1