Supply chain and geographical concentration

Supply chain and geographical concentration

Autores y e-mail de la persona de contacto: Sofía Jiménez ([email protected]), Erik Dietzenbacher, Rosa Duarte, Julio Sánchez Chóliz. Departamento: Análisis Económico. Universidad: Universidad de Zaragoza. Área Temática: Localización de la actividad económica, especialización y clústeres. Resumen: If we make a review of economic literature we can see different positions respect to the evolution of concentration of supply chains. Some works stand up for the idea that in current years there has been an increasingly concentration of supply chains. But, in the other hand, studies related to global value chain claim that there has been an increasingly international fragmentation. Our main objective is to discover the story behind these two ideas, paying attention to supply chains since sellers and buyers perspective and by countries and sectors, inside input-output framework. In that sense, we use two measures, a variant of Herfindahl index and a variant of location quotient first use in Linden(1999). It is possible to observe some interesting results, such as a tendency to a common trade pattern between countries or an increasingly specialization of countries production in particular sectors. Palabras Clave: MRIO, Concentración geográfica, Índice Herfindahl, supply chains. Clasificación JEL: F, F1.

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1. Introduction

Since the 90s decade there has been a process of globalization that have created supply

chains between countries. In that way, enterprises can get intermediate products easily and

with fewer costs. It is almost clear that this process achieved the highest importance during

the first years of XXI century. However, it seems that literature find various tendencies that

are happening at the same time in the lasts years.

In that way, in one hand we have literature include in business and economic area where

is possible to find a variety of papers that find two main facts related to supply chain

evolutions at micro level; outsourcing of supply and reducing of number of suppliers of

enterprises, both creating dependencies between enterprises and so on a risk for their

production. In the other side, at macro level we can find several analysis about value chains.

All of them, such as Timmer, M. et al. (2013) or Los, B. et al (2013), conclude that in the two

last decades has been a process of fragmentation and internationalization of production,

coming value added from different countries and not only the domestic one.

To sum up, we have two complementary ideas. Enterprises are reducing the number of

suppliers and outsourcing their supply outside their borders. This enterprises are pieces of a

supply chain that, at the same time, is getting more fragmented and internationalized, which

could be a consequence of the external outsourcing of enterprises. Then, the main research

questions of our work are, how the dispersion and/or concentration of global supply chains

has evolved since globalization beginning until the second decade on XXI century? Which are

the main characters of global supply chains? In other words, our main objective is to study

global supply chains focusing our attention of their concentration as well as their main

composition.

In order to do that, we are going to work inside the input-output framework, particularly

with MRIO models, as it results really useful when the objective is to study interactions

between countries and/or sectors. Besides, we are going to focus not only on imports

(columns input-output tables) but also on exports perspective (rows input-output tables), in

order to have the complete image of the ‘story’. As measures of concentration we use the

well-known Herfindahl index and a variant of ‘location quotient’, which is a relative index

based on the one used by Linden(1999). Both indices can be applied to calculate geographical

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concentration, considered geographical when we it is analyzed the ‘number’ of countries

involved in supply chain, and sectoral concentration, considered sectoral the analysis made by

the chain form among sectors. However, our main contribution is the creation of links

between shares, being able to express these linkages through chains of successive

decompositions that are a generalization of different degrees of concentration.

In that way, first we will start explaining in detailed the literature review. In section 3 we

will introduce the methodology used in this work explaining how multiregional input-output

tables let us to calculate different levels of concentration depending on their disaggregation.

We will continue showing main results in section 4, distinguishing between imports and

exports and geographical and sectoral concentration. We will finish showing the main

conclusions obtained.

2. Literature review

Since a ‘micro’ perspective (firm o factory perspective), Wagner M.S. (2007) talks

about a tendency that is happening in current years. Enterprises ‘live’ in really globalize and

competitive world, so their main objective is to have an efficient production process. Because

of that it is observed a tendency to outsource, reduce inventories and to streamlining the

supply base.

Besides, it defines supplier concentration as the ‘scenario where the buying firm only

has a small number of suppliers’. This can have benefits, for example, it can improve product

or relationships between enterprises. However, as we can read in Wagner M.S. (2007), ‘when

a firm concentrates its sourcing activities on a small number, it loses the ability to switch to

contingency supplier in disruptive situations. Moreover, the buying firm is exposed to a

heightened risk of organizational hold-up’.

Following Choi. T. Y (2005), we can say that the more quantity of inputs buy outside

instead of make, the more dependent it is on the supply base. As we have seen before and this

paper shows, in general, companies reduced the number of suppliers in their supply base,

which is called ‘supply base optimization’. The objective of this is to reduce the

administrative and transaction costs. Some examples are General Motors (GM) and General

Electric (GE).

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As we can read in other papers, it is possible to observe in recent years a tendency to

outsource. ‘Outsourcing not only impact on the organization and its relationships, but also

changes supply network structure and processes.’ (Harland. C. (2002)). So, it is observed a

tendency to outsource together with a tendency to reduce the number of supplier, being this

the reason of Toyota’s success as Harland. C. (2002) comment.

However this can have a negative perspective through the increase of vulnerability.

This is what Norman. A. (2004) shows, some trends increase the vulnerability in supply

chains, such as increasingly use of outsourcing of manufacturing and R&D to suppliers,

globalization of supply chains or reduction of supplier base. It study the results of fire taken

place on 18 March 2000 at a sub-supplier’s plant of Ericsson in Albuquerque. The impact in

Ericsson was huge. In the spring of 2001, a major loss of about $400 million was indicated,

primarily due to gaps in the supply of radio-frequency chips from this supplier. This plant of

Albuquerque was Ericsson’s only source for this chip, so Ericsson was no able to sell one of

its key consumer products during this crisis. Business costs of this interruption of production

were calculated as approximately $200 million. Since that the company changes the way to

manage with risk. However, in order to increase their agility and responsiveness Ericsson has

increased outsourcing and reduced buffers (capacity to manage risk).

So, analyses done in business and administration area show a tendency to outsource,

associated to globalization process, but mainly a tendency of enterprises to work with few

suppliers. In that way, we can say that in the lasts years supply chains has become shorter.

We have also to take into account macro perspective (final goods perspective) literature.

There are some literature related to global value chains, where various enterprises or countries

are pieces of them. In that way we can mentioned Timmer, M. et al. (2013), Los, B. et al.

(2013) and Los, B. et al. (2015). The three papers focus their attention in global value chains

but since different perspectives. Despite that, the three conclude the same, it is observed a

high fragmentation of global value chain. For instance, Los, B. et al. (2013) says in its

concluding remarks ‘We find a strong tendency towards increased fragmentation for most

production processes, irrespective of the country of completion or the final product generated

by global value chain’.

Timmer, M. et al. (2013) studies fragmentation focusing their attention on the role play by

labour and capital, the two components of value added. In that way, the first conclusion they

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get is ‘…production has become increasingly internationally fragmented in the past two

decades, indicated by rising shares of foreign value added in production over the period 1995-

2008’.

More related to our field of study is Los, B. et al. (2015) that study if fragmentation of

value chains is mainly within or across regional blocs, finding two main facts. First, they find

that ‘…value chains have become increasingly internationally fragmented. …. This result is

observed for virtually all chains and does not depend on the nature or the product or the

country…’. The second fact that this paper highlights is that international fragmentation (of

value chains) has progressed much faster than the regional one.

To resume, from literature review we can extract two main conclusions that are

complementary of each other. The first one, enterprises are reducing the number of suppliers

especially at regional level at the same time that they are outsourcing their production. On the

other hand, linkages between all enterprises form what we call global value chains. Respect to

global value chains we can say that in lasts years it is observed a process of fragmentation and

internationalization. So, while enterprises reduce their supply base, global value chains

become more fragmented, especially in international terms, being possible a consequence of

the strong tendency to outsource of enterprises.

In that sense, our main objective is going to study dispersion and/ or concentration of

global supply chains; in other words, if global supply chains are becoming longer or shorter,

which countries are involved as well as the main sectors, in what extent supply chain of

different countries are getting similar o not…. In order to do that we will calculate two

indices, Herfindahl index and Location quotient index, since imports and exports perspective

for country s and r and for sectors i and j. As we will explain in the methodology, it is

possible to make the analysis for four levels of aggregation and, besides, it is possible to

create links among them, being this our main contribution.

3. Methodology

As we have commented in the previous section, our objective is to study global supply

chains, trying to explain the story behind the output dependence between countries and across

sectors, since the point of view of both imports and exports. In order to do that we apply two

indices, Herfindahl index and location quotient index. However, our main contribution is the

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creation of several types of shares, depending on their level of disaggregation, over which we

can apply our indices that could take more aggregated or disaggregated information.

In order to capture a macro perspective, we are going to make use of World Input-Output

Database (WIOD), particularly World Input-Output Tables (WIOT) at constant prices. WIOT

are multiregional input-output tables formed by 40 countries plus Rest of World and 35

sectors as we can see in more detailed in Timmer, M.P. (2012). In that way, the implicit

model we are going to make use is input-output model, where the basic expression is the

following;

x= Ax + f (1)

where x is outputs vector, A is the technical coefficients matrix and f is the vector of final

demand. We can transform expression (1) into expression (2) as follows;

x= (I-A)-1f (2)

being (I-A)-1 Leontief inverse, whose each column reflect the output increments of sector i if

there would be a unitary increment of sector j.

The analyses is going to be made in two steps. In a first step we are going to look at

direct relations. To capture this direct relations we are going to apply our indices in the

following expression:

(3)

where Z is a 1435x1435 matrix of intermediate deliveries. So, let the intermediate deliveries

of good or service i produced in country r to industry j in country s be given by , we define

if in order to focus on trade. Moreover, as we are working inside a

multiregional framework, it is possible to define different shares with different levels of

aggregation and focusing in different parts of the table. In that way, we have found the

possibility to join shares since the most disaggregated to the most aggregated.

In our model we have 4 levels of degree of freedom, ‘r’, ‘s’, ‘i’, ‘j’. Two of them have

a geographical character; ‘r’ representing the exporter country and ‘s’ being the importer

country. The other two are sectoral; being ‘i’ the exporter sector and ‘j’ the importer one. In

our analysis we are going to apply our concentration measures to one of these four indicators,

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although other alternatives are possible. In that way, we will have geographical concentration

and sectoral concentration indices. However, the difference among each case is the ‘area’ over

with we define concentration index and from where we can obtain different shares. This field

would be a subset define as If the subset has only one

degree of freedom we will say that it has 0 level of aggregation. If it has two degree of

freedom their shares should have 1 level of aggregation and successively. For instance, when

we analyze all the corresponding shares will have 3 levels of aggregation. In that case, our

concentration indices will measure concentration of imports and exports by countries or

sectors.

Let’s see previous explanation with an example. We are going to suppose that we have

a multiregional table of three countries and three sectors each one. Let’s see the following

table:

MRIO table three countries and three sectors.

s1 s2 s3

j1 j2 j3 j1 j2 j3 j1 j2 j3

i1 1 2 4 5 4 8 1 8 3

r1 i2 3 5 7 2 2 9 1 1 0

i3 1 1 3 5 72 7 9 0 1

i1 8 3 1 2 4 4 8 5 7

r2 i2 1 0 3 5 7 2 9 1 3

i3 0 1 1 1 3 72 7 3 1

i1 8 3 4 8 8 3 1 2 4

r3 i2 1 0 2 9 1 0 3 5 7

i3 0 1 2 7 0 1 1 1 3

In this table we have all and there are no aggregation. There are three countries

and, as we have said before, each one have three sectors. In that way, our table is showing

sells of each sector of each exporter country that goes to each sector of an importer country.

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As we have introduced previously, we are going only to obtain geographical and

sectoral concentration indices. This indices are going to depend of one of the four indices we

have in the table. We mean we that; r, s, i, j. If we would like, for example, to obtain exports

of the whole economy and their concentration on exporter countries, we need to calculate

and the corresponding shares . Particularly, we will aggregate

the previous table until have a three rows and one column table, the three exporter countries

and one column. The resultant table would be:

3 level aggregation data.

Exports

r1 165

r2 162

r3 85

Total 412

This table let us to say, for instance, that r1 is the country with the highest volume of

exports and, at the same time, that global exports have low level of concentration, as shows

the readjusted Herfindahl index1 that achieves a value of:

=357

5,62* = 363.42

Note that in order to obtain values of previous table and the shares used to calculate

Herfindahl we have had to aggregate the elements of initial table through the sum of three

indices. Because of that we have a Herfindahl level of 3 level of aggregation.

1 Traditional Herfindahl index has a minimum of 1/n and maximum value of 10000, as MG Lijesen (2004), JE Kwoka Jr (1985) or WA Kelly (1981) explains. If minimum values of two cases that we would like to compare are small, the fact that the minimum is not the same could be no important. We find the problem when the minimum is not relatively small when the number of elements is no so high. In particular, for two elements the minimum value of Herfindahl index is 5000 when the maximum value is 10000. For three elements the minimum is 333.33, for four elements is 2500, for 10 elements is 1000 and for 100 is 100. We can see, for example, that the size should take into account to define the index. Because of that, we use a readjusted index that has a minimum value of 0 and a maximum value of 10000. This let us to make comparisons and let us to apply the same criteria for low, medium and high in all the cases. The definition of the readjusted index is: HR = (H traditional – H minimum)*10000/(10000 – H minimum).

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We continue now with the following level of aggregation. To understand better the

meaning we suppose that we choose one of the previous exporter countries, in particular r1that

it is the main exporter. Now, we ask ourselves what sector are the destination of its exports

(165 units) as well as the higher or lower concentration of their exports. In fact, we are asking

for the information related to j knowing that r is fixed, it is r1. Then, we are no working now

with the whole table, we focus our attention only on that part associate with r1. Our reference

table is now;

MRIO table for an exporter country.

s1 s2 s3

j1 j2 j3 j1 j2 j3 j1 j2 j3

i1 1 2 4 5 4 8 1 8 3

r1 i2 3 5 7 2 2 9 1 1 0

i3 1 1 3 5 72 7 9 0 1

As we would like to know the sectorial distribution of r1exports since buyers

perspective, we should aggregate through the sum of indices i and s. That is and

their corresponding shares , being r1 r value. We can represent this in the

following table;

3 level of aggregation data.

j1 j2 j3 Total

r1 28 95 42 165

This information let us to say, for example, that from total exports of r1the industry

that receive less is j1. However, exports are distributed among all the sectors following a

medium concentrated pattern, as readjusted index is 1376.31 as we can see in next lines;

=4250.87* = 1376.31

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It is clear that concentration index obtained has 2 level of aggregation, as we have seen

before. Nevertheless, we can continue with another step, how r1 exports destined to industry

j1are geographically concentrated? To do that we should make fixed one part of initial table,

particularly;

MRIO table for one exporter country and one sector of destiny.

s1 s2 s3

j1 j1 j1

i1 1 5 1

r1 i2 3 2 1

i3 1 5 9

As we would like to know geographical distribution of r1exports destined to industry

j1, we should to aggregate through the sum of index i. That is and the corresponding

shares , being r1 r value and j1 j value. We represent these summations in a

table like the following;

1 level of aggregation data.

s1 s2 s3 Total

r1 5 12 11 28

From this table we can see that from the 28 units that r1 exported to industry j1, country

s3 imported 11 units, being a medium importer, in an importer structure with a low level of

concentration as readjusted Herfindahl index shows;

=3698.

98* = 548.47

Finally, we can analyze distribution and geographical concentration of r1 exports to

industry j1 of country s3, which knows are 11 units. To ask this question we will focus in a

part of initial table obtained from the selection of r1, j1 and s3. We show this in the following

matrix;

11

MRIO table for one exporter country; fixed sector and fixed country of destiny.

s3

j1

i1 1

r1 i2 1

i3 9

Total 11

It is no necessary to make here any aggregation as the information is directly given ,

being the shares now . In that way, we can affirm that

with a 0 level of aggregation almost all imports come from sector i3. In fact, the table shows a

high concentration in this sector, concentration that is measure with readjusted Herfindahl

index;

=6859.5

* = 5289.26

As it is clear from previous explanation the last index is an index of 0 level of

aggregation.

Seeing the previous example, we can say that the only thing we have made is calculate

indices selecting different data sets. This affirmation is totally true, but here is possible to find

their own ‘value’. MRIO tables have a lot of geographical and sectorial information. The

problem is how to manage this information easily and its interpretation. We have choose an

easy way to do it, although we don’t miss information from table set.

Notice that, besides, the chain used could be really different depending on the order of

election and chosen sectors. Following the order of the chain, it is immediately that we have

4!=4*3*2*1=24 types of chains, each one offering different kind of information. Moreover, if

we have into account that on each geographical election the chosen country could be any of

all we have and that the same happens in the case of sectorial election, from each type we

could obtain in our example 34 different chains. This issue could be generalize for n countries

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and m industries, so we can obtain 4! kind of chains and form each kind we can obtain n2m2

different chains.

This diversity of chains could be better understand through a basic equality that we

can derive from different shares of original MRIO table, in particular , that

also we can express as;

Notice that is a share with 3 levels of aggregation. An easy transformation let us to

express this expression depending on shares with 2 levels of aggregation as follows;

(1)

where are the weights that join level 3 shares with level 2 sones. Let’s see now

the same expression depending on shares of level 1.

(2)

being and the weights that join shares of 1 level of aggregation with the previous one.

Finally, let’s see how the same expression could be express in function of shares of

level 0. Making calculus in the same way as before we would have;

(3)

We shouldn’t forget that weights depends on the type of chain and we have 24 types as

we have seen before.

Similar expressions is possible to get for the final demands , where k indicates the

final demand category (e.g. household consumption, private investments, government

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expenditures) and defining if . We can use this similarity for extending our

analysis to the final demands concentrations.

Finally, in a second step, instead of considering the imports and exports themselves we

may consider the contributions to global supply chains, focusing on embodied links instead of

direct relations. Define , then its element indicates the production of

industry i in country r that is included (embodied) in the global supply chain of industry j in

country s. Just like we did with the intermediate deliveries , we have two options. Imports

perspective considered analysis by columns and exports perspective looked at rows. The

columns of the matrix Q reflect the global supply dependence of industry j in country s on

production in industries i in countries r. This is foreign production, so these are intermediate

inputs that are directly and/or indirectly imported. The rows of the matrix Q reflect the

contribution of industry i in country r to the global supply chain of industries j in countries s.

As this is a chain that leads to a foreign final product, the production of industry i in country r

is directly and/or indirectly exported.

Under this framework, we may also ask to what extent the direct and indirect imports

of global supply chains of country s are concentrated? Define if and define

shares as (similar to from (4)). We can also define the shares

, and

similar to , and And we can obtain specific Herfindahl index based on these

shares. From the other perspective, we may ask to what extent are the direct and indirect

contributions of country r to foreign global supply chains concentrated?

As we have said before here we are going to apply two indices; Herfindahl index and

location quotient index. As it is well known, Herfindahl index is define in the following way:

H=

However, here we are going to use a readjusted index in order to be able to make

comparisons. Readjusted Herfindahl index is define as follows, as we have said previously;

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where n is the number of elements we are taken into account. In our case we are going

to calculate the geographical and sectoral concentration of countries and sectors for imports or

sellers perspective and for exports or demand perspective. For example, for direct relations

two significant readjusted Herfindahl indices for geographical concentration of imports yields

into;

,

, being and

and being the index the measure of concentration (origin country) of imports made by

country s, and the index of imports made by industry j. Note that other geographical

index of imports can be obtained for from (4) and for from (5), but in this case the

destination fields are smaller. Moreover, we can also get geographical and sectoral indexes

whose references fields are larger, the total imports/exports of the world

In the other hand, in the case of sectoral concentration of imports (origin industry) we

use weights derived in expression (3) or any type of chain of 24 that are possible.

,

, being and

and being the index the measure of concentration (origin industry) of imports made by

country s, and the index of imports made by industry j.

It also is possible to get , , and , which are associated with the other

perspective. All of these eight indexes, four from import perspective and other four from

export perspective, are 2-aggregated level. The results associated with them are collected in

later section entitled 2-aggregated level results.

Remember us that for understanding the results, we also have to take into account that

the rule to consider a ‘market’ concentrated, following the rules of Justice Department of

USA, is if Herfindahl index is lower than 1000 is low concentrate, if it is between 1000 and

1800 is moderately concentrate and over 1800 we consider high concentration. In that way, if

our indices get values over 1000 this could be a signal of concentration of supply or demand.

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To complete our analysis we introduce a relative measure, as we have said before, in

particular a rewritten form of ‘location quotient’. Following Isard (1960) the ‘location

quotient’ is originally define as;

(17)

Where express the variable to analyze (production, exports, imports, domestic

demand,…) of commodity i in country r, is total value of variable analyzed (production,

exports, imports, domestic demand,…) of country r, represents the variable to analyze

(production, exports, imports,…) of commodity i in region e and is the total value of

variable analyzed (imports, exports…) of region e. Oosterhaven (1995) found that this

expression, which is a multiplicative measure overestimate small sectors deviations. Because

of that he propose an additive form. Later Linden (1999) explains it is possible to rewrite this

expression in order to have a specialization indicator. In that way, following Linden (1999)

index the country specific readjusted location quotient index from imports perspective,

complementary of , is given by

(18)

including expression (7) and that represents the share of total imports that comes

from country r respect total imports/exports of the ‘world’.

We can also obtain the location quotient index for specific industry from imports

perspective, complement of ;

(19)

Until here we have the geographical concentration. Similar expressions are possible to

obtain for the analysis of sectoral concentration, which are complementary of and ;

(20)

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(21)

Note that it also is possible to get other two and other two associated with

exports perspective. The analysis of these indices are going also to be include in section 2-

aggregated level results.

We can obtain similar indexes if we consider the contributions to global supply chains.

But in this case there also is a difference between both indices. For example, while the

geographical (sectoral) Herfindahl index is an absolute measure that gives us information

about concentration of imports of global supply chains of country s (industry j) or, in the

other side, distributions of exports of country r (industry i) between to foreign global supply

chains; the geographical (sectoral) location quotient index is a relative measure so the give us

information about to what extent imports of global supply chains of country s (industry j) or

exports from country r (industry i) to global supply chains are concentrated in comparison

with the average of the world. So, in that way with location quotient we can obtain not only

information about levels of concentration but also we may have knowledge about the levels of

‘convergence’ of trade patterns.

4. 3-aggregated level results.

We can start the explanation of these results giving a first image of the global situation of

world. In order to do that we calculate Herfindahl index for global imports and global exports,

both at geographical and sectoral level and for matrices Z and Q, that is to say, the 3-

aggregated levels results. In all the cases, values obtained are below 1000, so they are

indicating low levels of concentration. Note that we use matrix Q in order to capture the

whole supply chain and not only direct relations. In that way the most interesting fact appears

in the evolution of Herfindahl index values for matrix Q, which will be the main guide for our

comments.

Both at sectoral and geographical side, we get increases of the Herfindahl index,

which is especially important in the case of exports perspective. In the case of geographical

concentration of exporting countries ( ) Herfindahl index in 1995 is 30 and in 2011 is 672,

as we can see in table 1. So, this could be indicating that the number of characters involved in

global chain is decreasing and/or those that form part of it are increasing their participation.

For this particular case of exports or buyers perspective that means that along the time the

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number of significant buyers in the global chain is decreasing. In that sense, USA and RoW

increase their participation as main buyers. If we talk in terms of exporting sectors that means

that the number of sectors that finally sells in the global chain is decreasing, achieving more

importance sectors like Coke and Petroleum and Electrical and optical equipment.

Similar results we can obtain from imports perspective. Focusing on results associated

to matrix Q, we find also an increase of values of Herfindahl index, although the increment is

not as high as in the case of exports perspective, see table 1. When we talk about imports we

are making reference to the origin of sales. We can observe that the main sellers in the global

chain are USA, Germany and Japan at the beginning of the period and in 2011 other countries

such as China and RoW join them. If we focus on sectors again we observe an increase of

Herfindahl index. In this case, the main sellers are Basic metals and Fabricated Metal and

Renting of M&Eq and Other Business Activities (where we include activities related with

R&D) in 1995. However, we have to notice that in 2011 Chemicals and chemicals products

achieves the second position as main seller. This could be reflecting changes in productive

structures at global terms. With these all fact in mind we can start to detail our main results.

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Table 1: Herfindahl index for global economy.

1995 2011

Geographical concentration

Exports Z 633 588

Q 30 672

Imports Z 529 540

Q 475 558

Sectoral concentration

Exports Z 204 247

Q 72 385

Imports Z 388 457

Q 238 338

Source: Own elaboration.

5. 2-aggregated level results.

As we said previously we define geographical concentration as the measure of

concentration in terms of the number of countries that are involved in supply chains. Now we

are going to obtain two perspectives, imports perspective or exports perspective, but with a

lower aggregation level, showing the geographical or sectoral concentration in relation with a

given country or industry. We start talking about imports perspective.

5.1. Imports perspective (sellers).

We focus, first, on Herfindahl index of geographical concentration (see table 2) of matrix

Q that captures the influence of the whole supply chain. We can see that 20% countries with

the highest values of Herfindahl (orange colour) in 1995 are Austria, Bulgaria, Canada, India,

Ireland, Japan, Korea and Mexico, whereas in 2011 we find Australia, Canada, Indonesia,

India, Japan, Korea, Mexico and Taiwan. If we compare with Z matrix results we can observe

as main difference Japan and Korea’s behavior, which don’t achieve the highest values as in

matrix Q. This reflects a true possibility of concentrating its direct links removing

intermediate countries.

The main increments (blue color), both in matrix Z and Q, are found in Asiatic

countries, in particular Indonesia, India and Japan. The main explanation is that these

countries instead of diversify their demand they concentrate it in particular countries. For

instance, Indonesia main sellers are Europe and Japan, RoW in the case of India and USA and

RoW in the case of Japan.

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Paying attention of the 20% lowest (green color) Herfindahl values of matrix Q

appears European countries such as Germany, Finland, Hungary or Sweden. This could be

explained for the trade agreements and politics of opening of borders that were promoted

inside European Union. This may be also related with the decreases between 1995 and 2011

(yellow color) observed in Bulgaria and Estonia. It is also interesting to remark France case

that appear as one of the lowest concentrated when we take into account direct relations but

not when we focus on embodied perspective. A reason might be the geographical situation of

France inside European Union that let it to be a key country for the commerce of the rest

European countries and, most probably, because of that, its global supply chain is not as

shorter as others.

In the other hand, we said before that Canada and Mexico were two of the countries

with the highest values of concentration (respect both Q and Z). But it is important to notice

the evolution followed between 1995 and 2011. It is in these two countries where we find the

most significant decreases looking at both types of indexes. These two facts together shows

relevant information about the evolution of NAFTA agreements between USA, Mexico and

Canada, which increased their mutual trade reinforce the trade from USA to Mexico or

Canada, while Mexico and Canada maintain or increase their direct and global suppliers.

Finally, we can say that in average there is a tendency to reduce levels of

concentration. This is saying that for the majority of countries their supply chain (sellers), by

contrast with results related to global supply chain, are getting wider.

20

Table 2: Herfindahl index values, 1995 and 2011. (import countries)

AUS AUT BEL BGR BRA CAN CHN CYP CZE DEU DNK ESP EST FIN FRA GBR GRC HUN IDN IND IRL ITA JPN KOR LTU LUX LVA MEX MLT NLD POL PRT ROM RUS SVK SVN SWE TUR TWN USA RoW Average


1995

Z 859 1,511 930 1,681 1,251 3,876 1,016 483 1,081 399 750 778 1,331 551 717 646 790 680 832 1,569 1,445 679 1,227 1,274 1,326 1,293 743 4,613 1,269 945 887 860 891 931 1,148 883 700 879 1,228 887 1,090 1,145

Q 828 1,153 779 1,171 1,083 2,871 983 499 893 410 604 709 953 512 660 603 716 599 892 1,270 1,142 652 1,194 1,284 1,032 1,112 746 3,792 762 782 773 760 726 679 853 736 654 776 1,141 834 993 966

2011

Z 1,348 1,240 689 738 1,228 2,709 1,260 710 873 363 568 774 474 573 628 626 578 611 1,956 2,843 1,651 855 1,868 1,616 1,848 1,447 520 2,558 689 729 714 1,287 472 572 709 793 513 590 1,593 1,015 439 1,055

Q 1,295 834 526 543 957 1,982 1,079 639 678 415 516 648 464 495 577 583 541 505 1,487 1,962 1,181 739 1,637 1,392 944 763 476 1,787 508 655 593 946 418 592 558 642 487 646 1,253 976 525 840

DIFZ

489 -271 -241 -942 -23 -1,167244 227 -208 -36 -182 -5 -856 22 -89 -20 -213 -69 1,124 1,274 206 176 641 342 522 154 -223 -2,055-580 -216 -173 428 -419 -359 -439 -90 -188 -289 365 128 -651 -89

DIFQ

467 -318 -253 -628 -127 -889 96 140 -215 5 -88 -60 -489 -17 -83 -20 -175 -94 595 692 39 87 443 108 -89 -349 -270 -2,004-254 -128 -181 186 -308 -86 -295 -94 -167 -130 112 142 -468 -126


Table 3: ‘Location quotient’ values, 1995 and 2011 (import countries)



1995

Z 18.4 38.6 38 51 22.8 45.5 38.5 30.5 48 27.6 36.9 29.2 58.9 30.8 27.4 20.8 33.5 42.1 29 29.4 32.2 27.1 34.9 32.8 50.5 51 53.6 52.3 42.3 26.6 42.5 34.7 44.3 34.6 55.1 45 30.1 32.1 29.3 25.8 18.9 36.4

Q 15.3 49.7 53.5 50.8 44.3 51.9 46.1 56.4 48.7 41.9 47.8 44.1 53.6 45.9 45.5 44.5 46.9 49.1 47.6 44.4 54 43 44.4 49.4 54.4 66 53.3 53 60.8 52 43.4 49.3 46.5 41.8 50.4 50.8 48.6 44.4 76.5 44.7 49.4 48.9

2011

Z 26 40.7 35.6 45.6 18.7 42.9 29.4 31 37.6 28.7 32.4 26.1 36.1 35.3 27.8 20.7 24.5 38.7 40 40.2 42.6 28 37.3 34.7 56.9 51.8 43.9 44.3 39.4 22.6 32.6 39.6 41.8 26.5 39.6 37.5 26.2 24.5 36 26.9 15 34.3

Q 42.4 40.1 36.9 41.1 43 40 35.9 40.1 37.7 37.7 39.6 40.5 41 40.8 40.4 39.6 40.7 37.6 39.7 40.7 37.1 39.7 40.6 37 44.4 39.1 43.2 38.3 36 36.6 37.9 42.6 40.4 43.2 38.5 38.6 39.5 41.1 63.8 34.7 32.3 40

DIFZ 7.6 2.1 -2.4 -5.4 -4 -2.6 -9.1 0.5 -10.4 1 -4.5 -3.1 -22.7 4.5 0.4 -0.1 -9 -3.5 11 10.8 10.4 0.8 2.4 2 6.4 0.8 -9.7 -8.1 -3 -4 -9.9 4.8 -2.5 -8.1 -15.5 -7.5 -3.8 -7.6 6.7 1.2 -3.9 -2.1

DIFQ 27 -9.6 -16.6 -9.8 -1.3 -11.9 -10.2 -16.3 -10.9 -4.2 -8.1 -3.6 -12.6 -5.1 -5.1 -4.8 -6.1 -11.5 -8 -3.6 -

16.9-3.4 -3.8 -12.4 -10 -26.9 -10.1 -14.7 -24.8 -15.3 -5.5 -6.7 -6.2 1.4 -11.9 -12.3 -9 -3.3 -12.8 -10 -17.2 -8.9


21

Respect to ‘location quotient’ measure, first we have to emphasize that it is a relative

measure, what that’s means? This measure tell us if a country is concentrated in comparison

with the ‘shares’ of trade patterns of the whole world.. Moreover, if a country increase its

concentration moving to the world average, its Herfindahl index will increase but its location

quotient become smaller.

In that sense, it is possible to observe in table 3 that in 1995 the highest values of

geographical concentration, for analysis with matrix Q, are achieved by Taiwan and

Luxemburg, showing its specific trade patterns, technological for Taiwan and financial for

Luxemburg. This high value is maintained for Taiwan in 2011, but it disappear for

Luxemburg in 2011 because likely its financial advantages go down.

It is also interesting to remark the case of Russia. In 1995 appears as one with the

lowest levels of concentration and in 2011 with one of the highest values, but is concentration

indexes are similar in both years (41.8 and 43.2) while the world average changes a lot, from

48.9 to 40 reinforce some traditional trade links closing their borders especially to USA. The

other country where we also find increases in Australia, indicating a moving out from the

main world average.

With the exception of these two countries, between 1995 and 2011 it is possible to see

general decreases of location quotient values and the range of their variation in the case of

indirect relationships. The range is [15.3, 76.5] in 1995 and [63.8, 32.3] in 2011, showing the

effects of globalization and convergence in trade patterns.

Until now we have made the analyses for import countries, but it also results

interesting to see in which sectors the external supply concentration is higher and how they

have evolved. This can be seen in table 4, where we show both, geographical and sectoral

concentration of imports by each industry. We start with geographical concentration. When

we look at Herfindahl index we can see that concentration in direct relationships is higher

than in indirect relations and higher in 2011 than in 1995. For example, in 1995 the average

for matrix Z is 911 and for matrix Q is 769 and in 2011 923 and 906 respectively. But, this is

not happening with increases between 1995 and 2011. In average, in 1995 it is possible to

observe an increase of concentration of 12 for matrix Z whereas this values is around 137 for

matrix Q. These facts are showing that for each sector supply chain are getting shorter or/and

22

more concentrated being also stronger main sellers (China, USA and RoW, sometimes

Germany,…).

In general, direct Herfindahl index gets values in 1995 below 1000 in almost all

sectors with the exception of three sectors: Coke, refined petroleum and nuclear fuel,

Electricity, gas and water supply, and Private households with employed persons, which

achieve values over 3510, 1780, and 1190 respectively, being this a signal of the high foreign

dependency of many countries in these key sectors. In 2011, other sectors also have higher

levels of concentration (above 1,000) such as Textiles and textile products, Manufacturing,

nec. and recycling, and Financial intermediation. In fact, it is possible to observe the highest

increases between 1995 and 2011 in Textiles and textile products, Manufacturing, nec. and

recycling and Private households with employed persons, whereas in the majority of rest

sector the difference is negative or unappreciated. This is explained by the role of RoW in the

case of Textiles and India in the case of Manufacturing, representing around 50% of total

supply of this sectors.

By contrast, when we take into account indirect relations (Leontief inverse) it is

observed increases of Herfindahl index in almost all industries, which could be a symptom of

an extend trend of concentration of supply in few sellers; China, the US, RoW and Germany

in the case of industrial sectors. In table 4 we can see how other two sectors, Transport

equipment and Electrical and Optical equipment, achieve values of Herfindahl index above

the average in 1995, mainly for Q matrix. But, whereas in the case of Electrical and Optical

equipment there is a relatively important increase in 2011, where China represents around

61% of total sells of this country, in the case of Transport equipment it is observed decreases

that are related to the less specialization of European Union in this sector, representing only

25.9% of total sales.

As well as happened in geographical concentration, it is observed a tendency to shorten the

supply chain also in sectoral terms, with average increases of 12 and 51 for Z and Q matrix

respectively. In other words, the number of sectors or their weights involved in supply chains

is decreasing along the time. In particular, the sector where we find the highest increase is

Financial Intermediation. However, it is interesting to notice differences between services

sectors and industry, mainly high-tech industry. While in services we observed the lowest

values of Herfindahl index in high-tech industry such as electrical equipment we saw the

highest values. This is accordingly with geographical results and with literature review.

23

Table 4: Herfindahl index values, 1995 and 2011. (import sectors)

Geo

grap

hica

l con

cen

trat

ion

1995 2011

Z Q Z Q DifZ DifQ

Agriculture, Hunting, Forestry and Fishing 652 499 599 558 -53 59

Mining and Quarrying 705 506 805 567 100 61

Food, Beverages and Tobacco 662 497 628 580 -34 83

Textiles and Textile Products 397 441 823 953 426 512

Leather, Leather and Footwear 515 512 493 539 -22 27

Wood and Products of Wood and Cork 567 440 573 505 6 65

Pulp, Paper, Paper , Printing and Publishing 476 447 451 472 -25 25

Coke, Refined Petroleum and Nuclear Fuel 3,357 1,875 3,104 2,185 -253 310

Chemicals and Chemical Products 544 529 627 590 83 62

Rubber and Plastics 497 503 487 557 -10 54

Other Non-Metallic Mineral 467 453 480 508 13 55

Basic Metals and Fabricated Metal 442 474 554 567 112 93

Machinery, Nec 494 516 423 492 -71 -25

Electrical and Optical Equipment 761 557 690 715 -72 158

Transport Equipment 712 582 432 474 -280 -108

Manufacturing, Nec; Recycling 445 445 833 806 389 361

Electricity, Gas and Water Supply 1,580 949 1,670 1,316 89 367

Construction 426 438 441 551 15 113

Sale, Maintenance and Repair of Motor Vehicles

and Motorcycles; Retail Sale of Fuel 576 504 447 477 -130 -27

Wholesale Trade and Commission Trade, Except

of Motor Vehicles and Motorcycles 659 446 390 467 -269 21

Retail Trade, Except of Motor Vehicles and

Motorcycles; Repair of Household Goods 588 439 465 582 -123 142

Hotels and Restaurants 510 469 589 617 80 148

Inland Transport 632 524 492 657 -140 133

Water Transport 415 449 573 614 158 165

Air Transport 629 441 434 700 -195 259

Other Supporting and Auxiliary Transport

Activities; Activities of Travel Agencies 561 451 348 429 -213 -21

Post and Telecommunications 590 497 495 597 -96 99

Financial Intermediation 761 468 794 567 33 99

Real Estate Activities 565 449 547 512 -18 63

Renting of M&Eq and Other Business Activities 601 467 504 539 -98 72

24

Public Admin and Defence; Compulsory Social

Security 527 455 416 584 -110 129

Education 510 442 411 497 -99 56

Health and Social Work 563 511 515 581 -48 70

Other Community, Social and Personal Services 552 450 501 580 -51 130

Private Households with Employed Persons 978 700 2,315 1,806 1,337 1,106

Average 683 538 696 678 12 140


Results associated to ‘location quotient’ measure seems to be in the same way as

before (see table 4), both geographical and sectoral concentration. Again the values associated

with the direct relationships is higher in general than in indirect relations. In average terns

value for matrix Z is 14.9 in 1995 and 17.8 in 2011, whereas for matrix Q these values are 9

and 11.6 respectively. From the average values we can observed that values of location

quotient are low as they are close to 0, so this could be indicating the same geographical

commerce patterns. However, there is a tendency of changing this situation, with increments

between 1995 and 2011 of 2.9 percentage points for matrix Z and 2.6 for matrix Q. That

means that there is a light tendency of specialization of sales.

In general, we can observe that services are the sectors that achieved lower values of

geographical location quotient, with the clear exception of Financial Intermediation being UK

and Luxemburg their main contributors to this situation. Besides, if we focus on the evolution

between 1995 and 2011, main increments are found in Chemicals and Chemicals products,

Manufacturing, Nec and Recycling, Textiles and Textiles products and Electrical and Optical

equipment. This is especially observed when we take into account the whole supply chain. So,

it seems that only few countries are specializing their production in high-tech sector (as

literature review says). In particular, China is one of the major sellers to these sectors,

representing since 17% of total sales in the case of Chemicals and Chemicals products until

46% in the case of Manufacturing, Nec and Recycling.

Table 5: ‘Location quotient’ values, 1995 and 2011. (import sectors)

Geo

grap

hica

l con

cen

trat

ion

1995 2011

Z Q Z Q DIFZ DIFQ

Agriculture, Hunting, Forestry and

Fishing 8.5 17.5 10.6 16.6 2.2 -0.9

Mining and Quarrying 11.6 4.9 14.7 9.8 3.1 4.9

Food, Beverages and Tobacco 16 9.3 16.3 11.8 0.2 2.5

25

Textiles and Textile Products 23.3 14.2 28.7 20.4 5.4 6.2

Leather and Footwear 17.4 10.3 15.9 10.3 -1.5 0

Wood and Products of Wood and

Cork 16.7 9 12.6 12.8 -4 3.8

Pulp, Paper , Printing and

Publishing 17 9.6 17.2 12.3 0.2 2.7

Coke, Refined Petroleum and

Nuclear Fuel 52 31.3 43.7 35.6 -8.4 4.3

Chemicals and Chemical Products 10.3 5.5 9 10.9 -1.3 5.5

Rubber and Plastics 10.9 5.8 11.1 4.7 0.2 -1.1

Other Non-Metallic Mineral 8.1 4.8 7.3 9.2 -0.9 4.4

Basic Metals and Fabricated Metal 10 6.3 13.7 11.7 3.7 5.4

Machinery, Nec 12.7 7.6 17 8.4 4.2 0.9

Electrical and Optical Equipment 15.8 9.6 21.7 14.9 5.9 5.3

Transport Equipment 17.3 12.3 21.3 12.4 4 0.1

Manufacturing, Nec; Recycling 10.1 3.9 15.9 10.3 5.7 6.4

Electricity, Gas and Water Supply 30.7 16.3 28.5 22.2 -2.2 5.9

Construction 8.8 5 11.6 4.8 2.8 -0.2

Sale, Maintenance and Repair of

Motor Vehicles and Motorcycles;

Retail Sale of Fuel

16.8 8 19.5 13 2.7 5.1

Wholesale Trade and Commission

Trade, Except of Motor Vehicles

and Motorcycles

10.7 5.8 16.7 8.2 5.9 2.4

Retail Trade, Except of Motor

Vehicles and Motorcycles; Repair of

Household Goods

8.2 4.7 14.4 7.1 6.2 2.4

Hotels and Restaurants 13.9 8.4 13.6 10.5 -0.4 2.1

Inland Transport 9.6 4.4 11.2 6.3 1.6 1.9

Water Transport 22.6 10 26.3 7.3 3.7 -2.6

Air Transport 15.7 8.2 16.6 10.3 0.9 2.1

Other Supporting and Auxiliary

Transport Activities; Activities of

Travel Agencies

14.5 5.1 16.8 11.6 2.3 6.5

Post and Telecommunications 9.1 7.7 16.1 9.3 7 1.6

Financial Intermediation 19.7 13.8 32.2 18.8 12.5 5

Real Estate Activities 13.9 4.4 18.3 8.4 4.4 4

Renting of M&Eq and Other

Business Activities 9 7.3 14.1 6.6 5.2 -0.7

Public Admin and Defence;

Compulsory Social Security 7.1 5 12.7 6.3 5.5 1.4

Education 9.8 5.3 12.7 5.9 2.8 0.6

Health and Social Work 8.8 4.4 11.3 5.2 2.5 0.7

Other Community, Social and

Personal Services 8.3 5.7 11.5 4.8 3.2 -0.8

26

Private Households with Employed

Persons 27.2 24.7 42.5 27 15.3 2.3

Average 14.9 9 17.8 11.6 2.9 2.6


In summary, from the analysis of imports (columns of input-output tables) we can say

first that the evolution of tendencies is different for countries and for sectors. But we can get

some provisional and relevant conclusions. In the case of countries analysis we observed a

stronger globalization and a more intense trade although this not seems to be the case of

Asiatic countries that are shortening their supply chain, being their main relationships

between them with China as a center. This process of globalization goes together with a

tendency to homogenize trade patterns. Nevertheless, at the same time countries are

specializing their production along the years. In particular, countries are no so selective in the

case of services but they are in the case industry sectors. This is the case of China, which is

one of the main sellers to sectors as Chemicals and chemicals products or Electrical and

optical equipment. To complete the story we need to know what has happened since demand

side (rows in input-output tables). This is what we are going to show in next sub-section.

5.2. Exports perspective (buyers)

We should start commenting the results associated with Herfindahl index. This can be

seen in table 6. Again, we find that average values of Herfindahl index for direct relations are

higher than in the case of matrix Q when we consider indirect relations. In average terms, we

have a value of 1196 in 1995 and 1455 in 2011 for matrix Z and 657 in 1995 and 1267 in

2011 for matrix Q.

It is really interesting the cases of Brazil and Canada that achieve high values in the case

of matrix Z but happens the opposite with matrix Q. This could be indicating the strong direct

linkages with strong countries such as China, RoW or USA that at the same time are selling

the final goods to other countries. So, they have a small number of direct relations but the

demand chain, finally, is longer than others. By contrast, we have the situation of Estonia and

Finland, with high values in analysis of matrix Q but low values in analysis of matrix Z. In

these countries what could be happening is that they have commerce relations with many

other European countries. However, the number of significant countries involved in their

global supply chains is too small. In both cases, Z and Q matrices, many low and medium

values are found in European countries due to the politics that have taken place.

27

Taking into account the evolution between 1995 and 2011 we can find all type of

evolutions, but we can see a clear increasing trend, mainly when we analyze indirect relations.

In average terms, Herfindahl index in the case of matrix Z is 258 and in the case of matrix Q

is 610. The most important increases in global Herfindahl indices appear in Luxemburg,

Taiwan, Mexico and Canada, whose exports goes mainly to RoW and USA respectively. It is

remarkable the case of the USA with a decrease in ‘direct terms’ (its difference is -666) due to

the role of China but with an increase (+693) in its global index, following the tendency of

rest of countries and explaining the cases of Mexico and Canada (increases of 2647 and 3295

respectively) that, as we have said before, have strong relations with USA.

It is also observed increases in the case of sectoral concentration, which is higher for

matrix Z with an average increase of 439. The highest increases are found in Japan, Korea and

Taiwan where the main buyers in the three cases are Manufacturing, nec; Recycling, Sale,

maintenance and repair of motor vehicles and motorcycles, retail sale of fuel, and Transport

equipment. Respect to the lowest values we can mention the case of Denmark, as a signal of

the diversification of their exports becoming a key exporter inside European Union. It is also

observed decreases in the cases of Ireland and Luxemburg, against the general tendency, in

the embodied case. This is mainly explain for the role played by Financial Intermediation

sector and the taxes system of these two countries that attract enterprises around all the world.

28

Table 6: Herfindahl index values, 1995 and 2011. (export countries)

Grographical concentration


1995 Z 1659 1181 803 862 1362 3516 1024 2766 1278 537 1211 856 746 604 833 749 1305 839 1158 1063 894 769 2008 1188 643 756 1209 3619 1299 746 1338 731 1190 468 1252 1365 772 881 1311 1590 677 1196

Q 276 382 633 568 97 148 120 374 2351 109 383 496 2631 1803 139 291 2177 500 312 64 126 254 280 214 3325 278 2066 245 308 243 378 243 605 483 2180 458 362 465 267 251 39 657

2011 Z 1752 1202 609 847 1129 3201 897 2893 1090 724 2123 763 839 649 810 656 4881 858 1103 826 1892 794 1786 1403 1405 5144 1606 4345 787 730 882 980 1185 921 804 1060 1191 1065 2194 908 708 1455

Q 1506 991 585 773 1139 2795 973 2199 800 800 1654 784 795 688 789 857 3252 739 1126 901 1613 789 1599 1364 1263 4042 1477 3540 768 646 751 858 1039 764 609 916 1164 1067 1791 944 768 1266

DIFZ 93 21 -194 -15 -232 -315 -128 127 -188 187 912 -93 92 45 -23 -93 3576 19 -55 -237 998 25 -222 215 762 4388 397 726 -512 -15 -457 249 -5 453 -448 -304 419 185 884 -682 31 258

DIFQ 1230 609 -49 205 1042 2647 852 1825 -1551 691 1272 288 -1836 -1115 650 566 1075 239 814 837 1486 535 1319 1150 -2062 3763 -589 3295 460 402 373 615 433 281 -1571 458 801 602 1524 693 729 610


Table 7: ‘Location quotient’ values, 1995 and 2011. (export countries)



1996 Z 40.2 33.8 43.3 34.7 18.2 47.5 23.4 49.6 51.9 25.7 29.8 28.3 62.1 19.8 21.0 15.1 29.1 34.3 37.2 20.2 28.1 21.6 34.8 30.6 36.5 43.6 43.3 44.9 37.5 29.3 41.8 33.8 35.5 40.0 62.0 42.7 23.3 26.3 31.9 28.1 10.5 33.9

Q 45.9 44.3 41.1 45.0 47.3 43.5 46.0 47.7 43.6 42.3 44.9 46.4 46.5 43.7 44.0 44.0 48.7 44.7 46.0 47.5 41.9 44.8 39.9 44.7 44.9 36.9 44.1 45.9 42.0 42.2 46.0 46.6 46.3 44.8 46.0 43.8 43.6 48.2 43.5 35.8 40.3 44.3

2011 Z 45.3 35.2 35.7 44.7 18.0 44.4 18.5 52.8 44.0 26.4 41.3 29.2 47.5 16.4 26.9 24.0 55.0 38.5 32.8 16.3 34.8 28.6 34.4 30.4 45.8 56.2 47.8 55.6 25.7 36.0 38.5 34.6 41.2 28.9 56.6 45.3 29.6 35.5 32.9 22.2 9.8 35.7

Q 40.9 37.5 34.5 40.7 43.8 39.4 35.7 45.5 40.0 36.5 38.4 42.3 42.0 37.9 41.0 40.8 45.7 38.1 41.4 44.2 30.4 41.3 40.5 34.4 39.6 44.2 41.3 41.7 35.7 36.4 39.5 43.3 42.5 37.5 41.9 39.3 37.3 43.7 29.3 36.4 32.9 39.4

DIFZ 5.0 1.3 -7.6 10.0 -0.2 -3.1 -5.0 3.2 -8.0 0.7 11.5 0.9 -14.6 -3.4 5.9 8.8 26.0 4.3 -4.4 -3.8 6.7 7.0 -0.5 -0.2 9.3 12.6 4.5 10.7 -11.8 6.7 -3.3 0.8 5.7 -11.2 -5.4 2.6 6.3 9.2 0.9 -6.0 -0.7 1.7

DIFQ -5.0 -6.8 -6.6 -4.3 -3.5 -4.1 -10.3 -2.2 -3.7 -5.7 -6.5 -4.1 -4.5 -5.8 -3.0 -3.2 -3.0 -6.5 -4.6 -3.3 -11.5 -3.5 0.5 -10.3 -5.4 7.2 -2.8 -4.3 -6.2 -5.8 -6.5 -3.3 -3.8 -7.3 -4.0 -4.6 -6.3 -4.5 -14.2 0.6 -7.4 -4.9


29

Respect to ‘location quotient’ measure, that we can see in table 6, we have to

comment first geographical results. In average terms it is observed an increase when we

take into account only direct relations whereas we observe decreases when we focus on

the whole supply chain. In particular, the values are 1.7 and -4.9 respectively. This

indicating that countries tend to specialize their exports in particular countries but at the

same time there is a homogenization of demand chain.

We have to remark Luxemburg case that gets one of the highest increase, that

could be due to the participation of Japan together with some European countries in the

whole demand chain. On the other hand, in China, Ireland, Korea and Taiwan we

observe decreases of location quotient indexes, that could be explain for a tendency of

convergence in export trade patterns of these countries.

To finish with this section we have to explain the results related to sectors (see

table 8). We start talking about geographical concentration in this case. From the

analysis of the whole supply chain (matrix Q) it is possible to observed again an

increase in average terms with a value of 703. Moreover, no sector reduces this index

from 1995 to 2011. This could be shown a shorthen or higher concentration of demand

chain, thus the final buyers are at the end always the same.

As we saw previously, Electrical and optical equipment and Transport

equipment achieve higher increases in global terms. Then, only few countries focus

their demand in these sectors, mainly USA, Germany and Japan. Respect to decreases

or low increases we can say that those are specially seen in services sectors such as

Financial Intermediation, where as we said before, UK and Luxemburg have a key role.

Table 8: Herfindahl index values, 1995 and 2011. (export sectors)

Geo

grap

hica

l con

cen

trat

ion

1995 2011

Z Q Z Q DifZ DifQ

Agriculture, Hunting, Forestry and Fishing 527 42 681 599 154 557

Mining and Quarrying 746 50 902 802 156 752

Food, Beverages and Tobacco 462 50 870 690 408 640

Textiles and Textile Products 487 72 642 745 156 674

Leather, Leather and Footwear 1,905 143 1,198 992 -707 849

Wood and Products of Wood and Cork 780 145 522 532 -258 387

Pulp, Paper, Paper , Printing and Publishing 545 43 710 693 165 650

Coke, Refined Petroleum and Nuclear Fuel 400 302 537 630 137 328

Chemicals and Chemical Products 424 30 470 590 47 560

Rubber and Plastics 551 49 595 660 44 611

30

Other Non-Metallic Mineral 730 104 473 568 -257 464

Basic Metals and Fabricated Metal 500 42 519 660 19 618

Machinery, Nec 603 41 742 742 139 701

Electrical and Optical Equipment 821 59 917 903 96 844

Transport Equipment 1,059 63 739 831 -320 767

Manufacturing, Nec; Recycling 807 82 1,956 1,247 1,149 1,165

Electricity, Gas and Water Supply 522 86 541 553 19 468

Construction 1,428 76 540 566 -888 490

Sale, Maintenance and Repair of Motor Vehicles

and Motorcycles; Retail Sale of Fuel 863 50 3,824 881 2,961 831

Wholesale Trade and Commission Trade, Except

of Motor Vehicles and Motorcycles 4,447 52 3,592 1,307 -855 1,256


Motorcycles; Repair of Household Goods 1,154 51 2,466 733 1,311 682

Hotels and Restaurants 518 103 713 477 195 374

Inland Transport 1,800 110 1,253 810 -547 700

Water Transport 6,020 290 5,368 3,712 -652 3,422

Air Transport 849 108 778 810 -70 703


Activities; Activities of Travel Agencies 1,245 120 808 1,238 -438 1,118

Post and Telecommunications 480 46 310 509 -170 462

Financial Intermediation 1,135 67 1,691 1,236 556 1,169

Real Estate Activities 1,092 36 1,600 619 508 584

Renting of M&Eq and Other Business Activities 984 93 589 636 -395 543


Security 921 54 2,043 854 1,121 801

Education 1,485 122 957 548 -528 427

Health and Social Work 659 55 866 545 207 490

Other Community, Social and Personal Services 534 101 465 468 -69 366

Private Households with Employed Persons 1,761 110 3,079 725 1,318 614

Average 1,121 87 1,256 832 135 745


We finish talking about location quotient index and we pay attention, first, on

geographical concentration. As we can see in table 9, including indirect relationships

location quotient achieves relatively high value in the case of services sectors. This

could be another symptom of specialization of national economies in particular

products. But more interesting it seems to be the increments between 1995 and 2011.

We observe some high increases of relative index in both direct and indirect relations,

although in average terms they are not so signifcant. These increases of concentration

are especially important in the case of services block, as increases of concentration in

industry sectors are almost unappreciated with the exception of Manufacturing Nec;

Recycling. These could be explained for a few countries are specializing in these trade

services, whereas it is observed a general flattering of trade patterns in the case of

industry, being mainly China, Germany, Japan, USA and RoW the countries involved.

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We have to notice that the case of ‘Manufacturing’ is explained by the role played for

India, as we have explained before.

Opposite situation we can find when sectoral concentration analysis is done. In

that way, we see that the highest values are got in industrial sectors but an important

increment is observed again in Financial Intermediation sector. It is remarkable the case

of transport equipment. As it is possible to see, the concentration of Transport

Equipment in one of the highest during the whole analysis but its change is not

significant. We have to mark again the decreases observed in Textiles and Textiles

products, Leather and Footwear and Wood and Products of Wood and Cork, result also

visible in the case of Herfindahl index.

Table 9: ‘Location quotient’ values, 1995 and 2011. (export sectors) G

eogr

aphi

cal c

once

ntr

atio

n

1995 2011

Z Q Z Q DifZ DifQ

Agriculture, Hunting, Forestry and Fishing 19.0 11.1 17.5 9.5 -1.4 -1.6

Mining and Quarrying 22.8 15.1 34.0 24.2 11.2 9.1

Food, Beverages and Tobacco 14.8 8.9 18.8 10.5 4.0 1.6

Textiles and Textile Products 20.0 14.9 20.9 16.5 0.9 1.5

Leather, Leather and Footwear 47.2 25.1 32.5 20.8 -14.7 -4.3

Wood and Products of Wood and Cork 23.7 16.2 17.7 12.5 -6.0 -3.7

Pulp, Paper, Printing and Publishing 11.9 5.3 15.1 7.4 3.3 2.1

Coke, Refined Petroleum and Nuclear Fuel 16.2 24.7 16.3 7.7 0.1 -16.9

Chemicals and Chemical Products 13.3 5.6 12.1 6.9 -1.2 1.4

Rubber and Plastics 12.6 6.5 14.8 6.9 2.2 0.4

Other Non-Metallic Mineral 11.9 10.3 14.5 8.7 2.6 -1.6

Basic Metals and Fabricated Metal 11.4 4.4 12.4 4.8 1.0 0.4

Machinery, Nec 10.1 6.9 12.3 6.7 2.3 -0.3

Electrical and Optical Equipment 15.4 11.8 16.7 11.3 1.4 -0.5

Transport Equipment 19.2 13.0 20.0 14.2 0.8 1.2

Manufacturing, Nec; Recycling 22.0 11.7 37.5 26.5 15.6 14.7

Electricity, Gas and Water Supply 37.1 10.2 37.9 5.5 0.7 -4.7

Construction 42.2 7.5 36.9 10.2 -5.3 2.6

Sale, Maintenance and Repair of Motor Vehicles and

Motorcycles; Retail Sale of Fuel 25.5 3.3 43.0 9.6 17.5 6.3

Wholesale Trade and Commission Trade, Except of

Motor Vehicles and Motorcycles 44.2 6.1 42.4 14.7 -1.8 8.5


Motorcycles; Repair of Household Goods 25.1 4.2 34.4 5.3 9.3 1.1

Hotels and Restaurants 55.7 18.1 47.4 17.4 -8.3 -0.7

Inland Transport 29.1 16.0 22.9 8.5 -6.2 -7.5

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Water Transport 56.3 33.1 58.9 40.7 2.6 7.6

Air Transport 23.5 21.6 16.6 11.3 -6.9 -10.3


Activities; Activities of Travel Agencies 29.1 16.9 37.2 21.3 8.1 4.4

Post and Telecommunications 31.8 5.5 35.5 11.5 3.7 6.0

Financial Intermediation 26.1 8.8 35.2 17.1 9.1 8.3

Real Estate Activities 56.3 4.5 63.7 9.1 7.4 4.6

Renting of M&Eq and Other Business Activities 34.2 11.0 26.8 10.0 -7.4 -0.9


Security 18.3 7.1 39.9 14.0 21.7 6.9

Education 62.2 15.8 47.2 13.3 -15.0 -2.5

Health and Social Work 40.7 7.0 48.7 11.8 8.0 4.9

Other Community, Social and Personal Services 35.5 13.0 33.8 11.8 -1.6 -1.1

Private Households with Employed Persons 46.1 21.0 60.1 14.7 14.0 -6.3

Average 28.9 12.1 30.9 12.9 2.0 0.9


So, in this section, conclusions seems to be in the same direction than in the

previous one. It is observed a process of globalization and internationalization that

makes three countries to be always the final demanders. This is the case of China, USA

and RoW, the last one related to the process of outsourcing explained by previous

literature. Our results for exports perspective also give us two main tendencies, a

tendency to homogenize trade patterns, which could be associated to a process of

convergence among occidental countries and a tendency to specialized their productive

structure. It is also important to comment two particular cases as are the cases of

Financial Intermediation that detects that places that have some tax incentives, as is the

case of Luxemburg. But, it is also important to comment Electrical and Optical

Equipment and Transport Equipment sector where China have a relevant position both

as an importer and as exporter.

In next section we show the 1-aggregated level results that, as we explained in the

methodology, consist in the calculus of both indices for a particular country (for all

industries) and sector (for all countries). Here we show a first approximation in order to

reinforce our results.

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6. Conclusions.

At the beginning of the paper we have seen that there are different studies getting

different conclusions about structure and concentration of supply chains. While micro

studies claim that in current years there is a higher concentration of external supply,

mainly in high-tech industries; studies related to global value chains argue that there is a

high fragmentation and higher in an international way than in regional. So, our main

objective has been try to explain the overall story that it is behind this two hypothesis

focusing our attention directly in supply chains and making the analysis since buyers

and sellers perspective and for countries and sectors. However our main contribution is

methodological, being able to create links between different shares with different levels

of disaggregation.

We have found some relevant facts in this work. First, it seems to be a common

trade pattern between countries. Only a few countries are the mainly suppliers and

‘consumers’ of intermediate inputs, in particular at the end of the period USA, China

and RoW. The increasing role of RoW could be a consequence of the tendency to

international outsource in the current years.

In the other hand it is possible to observe a specialization of trade, and so on of

production, of countries. Because of that our measures tend to be higher when we

analyze sectors. It is relevant the case of Electrical and optical equipment sector, in the

case of supply side, due to the Chinese impressive growth. This is saying us that China

is not only specializing their economy in industry of low technology but also of high

technology. In the case of demand side it is observe some differences, as the increase of

concentration is mainly observed in block services.

So our results seem to confirm the complementary character of both ‘thesis’ we

introduced in literature review. It is true that it is observed an specialization of countries

in some particular sectors, mainly in the case of industry, accordingly with ‘micro’

studies. But, in the other hand, it is true that countries tend to have higher trade inside

the international context, with China and RoW at the main leaders, together with USA,

instead inside regional blocks, accordingly with value chains studies.

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