Mapping-out export opportunities for Brazilian products to the BRICS

FUNDAÇÃO GETULIO VARGAS

ESCOLA DE ECONOMIA DE SÃO PAULO

DIOGO RIBAS BASSETTI

MAPPING-OUT EXPORT OPPORTUNITIES FOR BRAZILIAN

PRODUCTS TO THE BRICS

SÃO PAULO

2017

DIOGO RIBAS BASSETTI

MAPPING-OUT EXPORT OPPORTUNITIES FOR BRAZILIAN

PRODUCTS TO THE BRICS

Dissertação apresentada a Escola de

Economia de São Paulo da Fundação

Getulio Vargas como requisito para

obtenção do título de Mestre em Economia

Campo de conhecimento:

Comércio Internacional

Orientador: Prof. Dr. Lucas Ferraz

SÃO PAULO

2017

Bassetti, Diogo Ribas.

Mapping-out export opportunities for Brazilian products to the BRICS /

Diogo Ribas Bassetti. - 2017.

279 f.

Orientador: Lucas Pedreira do Couto Ferraz

Dissertação (mestrado) - Escola de Economia de São Paulo.

1. Comércio internacional. 2. Relações econômicas internacionais. 3.

Brasil – Comércio exterior. 4. Países do BRICS. I. Ferraz, Lucas Pedreira do

Couto. II. Dissertação (mestrado) - Escola de Economia de São Paulo. III.

Título.

DIOGO RIBAS BASSETTI

MAPPING-OUT EXPORT OPPORTUNITIES FOR BRAZILIAN PRODUCTS TO

THE BRICS

Dissertação apresentada a Escola de

Economia de São Paulo da Fundação

Getulio Vargas como requisito para

obtenção do título de Mestre em Economia

Campo

de

Conhecimento:

Comércio

Internacional

Data de aprovação:

__/__/____

Banca Examinadora:

______________________

Prof. Dr. Lucas Ferraz (Orientador)

FGV-EESP

______________________

Prof. Dr. Vera Thorstensen

FGV-EESP

______________________

Prof. Dr. Gervasio Santos

UFBA

Acknowledgements

It was a great pleasure to be given the opportunity to write this dissertation. I

wouldn’t have done it without those around me, providing me with understanding, support,

guidance, love and care. For all of this, I am very grateful.

I would like to thank in particular Prof. Lucas Ferraz, for all the knowledge,

assistance, inspiration and encouragement given to me.

A very special gratitude to Prof. Ermie Steenkmap, who offered me all the assistance

needed, providing profound knowlodge, support, patience and guidance throughout this

process.

Lastly, I thank those who raised me – my mother Eliana and my father Eduardo,

giving me conditions to achieve this goal; my wife Nathalia, for the love and support during

this process; my sister Giovana, my family and my colleagues – specially Bruno Tebaldi for

the great help with some calculations, and friends.

Resumo

Este estudo tem como objetivo identificar oportunidades de exportação de produtos brasileiros para outros

países do BRICS. Utilizou o Decision Support Model (DSM), criado por Cuyvers et al (1995), com a intenção

de auxiliar políticos e instituições, identificando e filtrando mercados externos e, como resultado, detectando

oportunidades de exportação. Esta é a primeira aplicação do DSM para o Brasil, portanto, a literatura não tem

história sobre as oportunidades de exportação brasileiras em referência a este modelo. A análise realizada teve

como entrada o nível de desagregação de produtos de 6 dígitos do HS, podendo manter um alto nível de

profundidade em relação à seleção correta de produtos no mercado de importação do BRICS, além do Brasil.

A análise geral foi feita entre 2011 e 2015. Os resultados do modelo mostraram 1.113 produtos que podem ser

exportados com sucesso para a Rússia, China, Índia e África do Sul, avaliados como oportunidades de

exportação realistas e, após uma análise da Marketshare, entre 80% e 95 % desses produtos, com pequenas

variações de ano para ano, não são explorados ou explorados muito pouco pelos exportadores brasileiros. O

modelo também expôs 292 produtos em que o Brasil já possui ou tem experiência em exportar. Além disso,

foi calculado o valor potencial dessas exportações: o ano de 2014 totalizou US$ 136,9 bilhões; para o ano de

2015, US$ 101,7 bilhões; e considerando produtos selecionados pela capacidade de exportação do Brasil, para

2014, US$ 62,3 bilhões; para 2015, US$ 43 bilhões.

Palavras-chave: Decision Support Model; oportunidades de exportação; promoção de exportações; exportações

brasileiras para o BRICS; Brasil.

Abstract

This study aims to identify export opportunities for Brazilian products to other countries of the BRICS. It

utilized the Decision Support Model (DSM), created by Cuyvers et al (1995), with the intent of assisting policy

makers and instituitions by identifying and filtering foreign markets and as a result detecting export

opportunities. This is the first application of the DSM to Brazil, therefore the literature has no history about

Brazilian export opportunities in reference of this model. The analysis made had as input the HS 6-digit level

of disaggregation of products, being able to retain a high level of depth regarding the right selection of products

in the BRICS’ import market, aside from Brazil. The overall analysis was made between 2011 and 2015. The

results of the model showed 1,113 products which can be successfully exported to Russia, China, India and

South Africa, evaluated as realistic export opportunities, and following a Marketshare analysis, between 80%

and 95% of those products, with small variations from year to year, are not explored or explored very little by

Brazil’s exporters. The model also exposed 292 products in which Brazil already has or had expertise in

exporting. Also, it was calculated the potential value of those exports: the year of 2014 a total US$ 136.9

billions; for the year of 2015, US$ 101.7 billions; and considering products selected by Brazil’s export capacity,

for 2014, US$ 62.3 billions; for 2015, US$ 43 billions.

Key words: Decision Support Model; export opportunities; export promotion; Brazil exports to the BRICS;

Brazil.

List of Graphics

Graphic 1: Brazil export to the BRICS – 2011 to 2015 ... 12

Graphic 2 :Brazil’s export scenario to the BRICS (%) – 2011 to 2015 ... 12

Graphic 3: BRICS imports from Brazil (%) – 2011 to 2015 ... 13

List of Tables

Table 1: Papadopoulos et al (2002) trade-off model ... 19

Table 2: Short and long terms cut-off values ... 31

Table 3: Market-size cut-off values ... 32

Table 4: Categorization of product-country combinations in filter 2 ... 33

Table 5: Risk assessment of Russia ... 39

Table 6: Product Categorization from 2011 to 2015 – Results of Filter 2 on the

application to Russia ... 40

Table 7: Final categorization of realistic export opportunities – indication of cells

according to its Market and Marketshare ... 43

Table 8: Final categorization of realistic export opportunities to Russia – 2011 .... 43

Table 9: Final categorization of realistic export opportunities to Russia – 2012 .... 44

Table 10: Final categorization of realistic export opportunities to Russia – 2013 .. 45

Table 11: Final categorization of realistic export opportunities to Russia – 2014 .. 46

Table 12: Final categorization of realistic export opportunities to Russia – 2015 .. 46

Table 13: Final categorization of realistic export opportunities to Russia – 2014 with

RCA ... 48

Table 14: Final categorization of realistic export opportunities to Russia – 2015 with

RCA ... 48

Table 15: Risk assessment of China ... 51

Table 16: Product Categorization from 2011 to 2015 – Results of Filter 2 on the

application to China ... 52

Table 17: Final categorization of realistic export opportunities to China – 2011 ... 54

Table 18: Final categorization of realistic export opportunities to China – 2012 ... 55

Table 19: Final categorization of realistic export opportunities to China– 2013 .... 56

Table 20: Final categorization of realistic export opportunities to China– 2014 .... 56

Table 21: Final categorization of realistic export opportunities to China – 2015 ... 57

Table 22: Final categorization of realistic export opportunities to China– 2014 with

RCA ... 58

Table 24: List of Products in Cell 5 with RCA>0.5 - 2014 ... 62

Table 25: List of Products in Cell 5 with RCA>0.5 - 2015 ... 71

Table 26: Risk assessment of India ... 80

Table 27: Product Categorization from 2011 to 2015 – Results of Filter 2 on the

application to India ... 81

Table 28: Final categorization of realistic export opportunities to India – 2011 ... 83

Table 29 - Final categorization of realistic export opportunities to India– 2012 .... 84

Table 30 - Final categorization of realistic export opportunities to India– 2013 .... 85

Table 31 - Final categorization of realistic export opportunities to India– 2014 .... 85

Table 32: Final categorization of realistic export opportunities to India – 2015 ... 86

Table 33: Final categorization of realistic export opportunities to India – 2014 with

RCA ... 87

Table 34: Final categorization of realistic export opportunities to India – 2015 with

RCA ... 88

Table 35: List of Products of India’s market in Cell 5 with RCA>0.5 - 2014 ... 90

Table 36: List of Products of India’s market in Cell 5 with RCA>0.5 - 2015 ... 93

Table 37 - Risk assessment of South Africa ... 98

Table 38 - Product Categorization from 2011 to 2015 – Results of Filter 2 on the

application to South Africa ... 99

Table 39: Final categorization of realistic export opportunities to South Africa – 2011

... 101

Table 40 - Final categorization of realistic export opportunities – 2012 ... 102

Table 41 - Final categorization of realistic export opportunities to South Africa– 2013

... 102

Table 42 - Final categorization of realistic export opportunities to South Africa –

2014 ... 103

Table 43 - Final categorization of realistic export opportunities to South Africa– 2015

... 104

Table 44 - Final categorization of realistic export opportunities to South Africa –

2014 with RCA ... 105

Table 45: Final categorization of realistic export opportunities to South Africa – 2015

with RCA ... 106

Table 46 - Brazil exports to Russia 2015 – Top 10 ... 110

Table 47 - Potential exports to Russia considering products in Brazil's export

specialization – top 30 ... 111

Table 48 - Brazil exports to China 2015 - top 10 ... 115

Table 49 - Potential Exports to China considering products in Brazil's export

specialization - top 30 ... 115

Table 50 - Brazil exports to India 2015 - top 10 ... 118

Table 51 - Potential Exports to India considering products in Brazil's export

specialization - top 30 ... 119

Table 52 - List of Realistic Export Opportunities to Russia ... 128

Table 53 - List of Realistic Opportunities to China ... 141

Table 54 - List of Realistic Export Opportunities to India ... 218

Table 55 - List of Export Opportunities to South Africa ... 243

Table 56 - Realistc Export Opportunities to Russia considering country’s export

capacity ... 244

Table 57 - Realistc Export Opportunities to China considering country’s export

capacity ... 249

Table 58 - Realistc Export Opportunities to India considering country’s export

capacity ... 266

Table 59 - Realistc Export Opportunities to South Africa considering country’s

export capacity ... 272

List of Figures

Figure 1: Brazil’s export partners – 2015 ... 11

Figure 2: What does Brazil export to China? – 2015 ... 14

Figure 3: Two-dimensional matrix - Papadopoulos et al (2002) trade-off model ... 20

Figure 4: Decision Support Model ... 23

Figure 5: Walwoord’s model for market selection ... 26

Figure 6: DSM filter sequence ... 27

Figure 7: Final Categorization of Realistic Export Opportunities of the Decision

Support Model ... 36

Table of Contents

1.

INTRODUCTION ... 10

1.1.

Context background ... 10

1.2.

Study objectives ... 14

1.3.

Summary ... 15

2.

LITERATURE REVIEW ... 16

2.1.

Country Level Quantitative methods ... 17

2.1.1.

Green and Allaway’s shift-share Model ... 17

2.1.2.

Russow and Okoroafo’s global screening model ... 18

2.1.3.

Papadopoulos et al’s trade-off model ... 18

2.1.4.

The International Trade Centre’s (ITC) multiple criteria method ... 20

2.1.5.

Assessment of export opportunities in emerging markets ... 21

2.1.6.

The gravity model ... 21

2.1.7.

Export Development Canada‟s Trade Opportunity Matrix ... 22

2.1.8.

Decision Support Model ... 22

2.1.9.

Applications of the Decision Support Model (DSM) ... 23

3.

METHODOLOGY ... 26

3.1. Filter 1 – Identifying preliminary market opportunities: ... 28

3.1.1. Filter 1.1: Political and commercial risk assessment ... 28

3.1.2. Filter 1.2: Macro-economic size and growth ... 29

3.2. Filter 2: Identifying possible opportunities ... 29

3.3. Filter 3 – Identifying probable and realistic export opportunities ... 33

3.3.1. Filter 3.1 – Degree of import market concentration ... 33

3.3.2. Filter 3.2 – Trade Barriers ... 34

3.5. Product selection by country’s export capacity ... 37

3.6. Export Potential Values ... 37

3.7. Inclusion of Estimated applied tariffs and non-tariff barriers ... 38

4.

APPLICATION OF THE DSM ... 39

4.1.

DSM application on Russia ... 39

4.1.1.

Filter 1 – Identifying preliminary market opportunities ... 39

4.1.2.

Filter 2 – Possible export opportunities ... 40

4.1.3.

Filter 3 – Realistic export opportunities ... 41

4.1.4.

Filter 4 – Categorization of the Realistic Export Opportunities ... 42

4.1.5.

Product Selection by country’s export capacity ... 47

4.1.6.

Export potential values ... 49

4.2.

DSM application on China ... 51

4.2.1.

Filter 1 – Identifying preliminary market opportunities ... 51

4.2.2.

Filter 2 – Possible export opportunities ... 51

4.2.3.

Filter 3 – Realistic export opportunities ... 52

4.2.4.

Filter 4 – Categorization of the Realistic Export Opportunities ... 54

4.2.5.

Product Selection by country’s export capacity ... 58

4.2.6.

Export potential values ... 60

4.2.7.

Addressing Potential Products in Cell 5 ... 61

4.3.

DSM application on India ... 80

4.3.1.

Filter 1 – Identifying preliminary market opportunities ... 80

4.3.2.

Filter 2 – Possible export opportunities ... 80

4.3.3.

Filter 3 – Realistic export opportunities ... 81

4.3.4.

Filter 4 – Categorization of the Realistic Export Opportunities ... 83

4.3.5.

Product Selection by country’s export capacity ... 87

4.3.7.

Addressing Potential Products in Cell 5 ... 90

4.4.

DSM application on South Africa ... 98

4.4.1

Filter 1 – Identifying preliminary market opportunities ... 98

4.4.2. Filter 2 – Possible export opportunities ... 98

4.4.3.

Filter 3 – Realistic export opportunities ... 99

4.4.4.

Filter 4 – Categorization of the Realistic Export Opportunities ... 101

4.4.5.

Product Selection by country’s export capacity ... 105

4.4.6.

Export potential values ... 106

5.

CONCLUSION ... 108

5.1.

Summary ... 108

5.2.

Results and Conclusion ... 110

5.3.

Recommendations ... 124

REFERENCES ... 125

APPENDIX A – LIST OF REALISTIC EXPORT OPPORTUNITIES TO

RUSSIA

128

APPENDIX B – LIST OF REALISTIC EXPORT OPPORTUNITIES TO

CHINA

141

APPENDIX C – LIST OF REALISTIC EXPORT OPPORTUNITIES TO

INDIA

218

APPENDIX D – LIST OF REALISTIC EXPORT OPPORTUNITIES TO

SOUTH AFRICA ... 243

APPENDIX E – REALISTIC EXPORT OPPORTUNITIES TO RUSSIA

WITHIN COUNTRY’S EXPORT CAPACITY ... 244

APPENDIX F – REALISTIC EXPORT OPPORTUNITIES TO CHINA

WITHIN COUNTRY’S EXPORT CAPACITY ... 249

APPENDIX G – REALISTIC EXPORT OPPORTUNITIES TO INDIA

WITHIN COUNTRY’S EXPORT CAPACITY ... 266

APPENDIX H – REALISTIC EXPORT OPPORTUNITIES TO SOUTH

AFRICA WITHIN COUNTRY’S EXPORT CAPACITY ... 272

10

1. INTRODUCTION

1.1.Context background

Jim O’Neill (2001) firstly introduced the BRIC’s as a possible economic group,

referring to, at first, Brazil, Russia, India and China, as they, although not sharing demografic

and geographic similiarities, were immersed in the same macroeconomic state, being an

emerging economy, having large populations and being recently open to the globalization

trend, and also sharing a high prospect for GDP growth when related to Purschasing Power

Parity (see “Building Better Global Economic BRICs” – Jim O’Neill, 2001). Later, O’Neill

(2003) stated that by 2039 the BRIC’s would emerge as top 10 large economies, which would

overtake large part of the traditional high developed economies. In 2011, South Africa was

added to the group, henceforth BRICS.

Since 2009, the BRICS group holds an annual reunion, objectifying mutual

cooperation, encompassing subjects as financial and environmental sustaintability, but only

in 2015 the group discussed strategies for intensifying, diversifying and expanding trade

relations

(<http://www.itamaraty.gov.br/pt-BR/politica-externa/mecanismos-inter-regionais/3672-brics>).

Even with the high empowerment attained in the 2000 decade by the GDP growth of

the group, there are those skeptical to the idea of an economic group with such inequalities,

as points out the paper from Centre for the Study of Governance Innovation - On the BRICS

of Collapse? (2013), which implied that it takes more than GDP similarities for BRICS to

grow, as the foundation of the group was immersed basically in commodity extraction and

cheap labor, and little was worked in social and political development, leading inequivocaly

to a downfall to the group.

Althought this came, partially, to happen, as the cases of Brazil and Russia, in 2015,

both China and India were still having GDP growth above 7% (source:

<http://www.businessinsider.com/world-bank-fast-growing-global-economies-2015-6>).

11

Degaut (2015) recognized that the BRICS countries are forces that can’t be ignored,

but argues that without mutual cooperation, the BRICS, as a group, is most likely to fail.

This could impy that overlooked opportunities of trade between the members of the BRICS

could have the positive effect that, not only, bolster the exporting country’s economy, but

also the group as a whole.

Countries that develop an export policy do so in order to prosper the domestic market,

and consequently lead to an economic development and growth, especially when in countries

in process of industrialization and economic expansion, as they have a series of markets not

yet exploited, increasing the standard of living of its inhabitants. However, given the large

number of variables and possibilities, the great difficulty is to identify which of these

possible windows of opportunity are the most adequate, both for the internal level of

production and for the efficiency of the export process (Steenkamp, 2011).

The Department for International Development of United Kingdom Government

(DFID, 2011) on its publication noted that is common to firms not only to underestimate the

benefits of exporting, also overestimate the difficulties in foreign markets, caused by

asymmetric information. It is also mentioned that exporting firms have more propensity to

be innovative and are usually more productive, achieving growth otherwise not possible.

Observing the main 2015 commercial partners of Brazil, as detailed in Figure 1, it

can be seen that China represents 18.63% of Brazil’s total exports, being the top Brazilian

commercial partner. India holds 1.89%, Russia 1.29% and South Africa 0.71%.

12

An indepth look over the trade relations from Brazil with another members of the

BRICS over time can further assert that aside from China, Brazil’s export has little impact

over markets of India, Russia and South Africa (Graphics 1. 2, and 3):

Graphic 1: own graphic – Brazil export to the BRICS – 2011 to 2015 (source: wits.worldbank.org)

Graphic 2 :own graphic – Brazil’s export scenario to the BRICS (%) – 2011 to 2015 (source:

wits.worldbank.org)

0,00 5.000.000,00 10.000.000,00 15.000.000,00 20.000.000,00 25.000.000,00 30.000.000,00 35.000.000,00 40.000.000,00 45.000.000,00 50.000.000,00 2011 2012 2013 2014 2015

BRAZIL EXPORT TO THE BRICS - 2011 to 2015 (US$ x 1000)

Sout Africa China Russia India

0,00% 2,00% 4,00% 6,00% 8,00% 10,00% 12,00% 14,00% 16,00% 18,00% 20,00% 1 2 3 4 5

BRAZIL'S EXPORT SCENARIO TO THE BRICS (%)

13

As noticed, Brazil has an intensive focus over exports to China, possibly overlooking

other partners inside the BRICS. Further on, however, will be denoted that although this

holds true, those imports are very concentrated.

Graphic 3: own graphic – BRICS imports from Brazil (%) – 2011 to 2015 (source:

wits.worldbank.org)

Graphic 3 gives the perception that Brazil explores very little from South Africa,

Russia and India’s import market.

Even though Brazil holds a stronger position in China’s import market than in India,

Russia and South Africa, it is visible, by figure 2, that there is little diversity over the

exported products, in which 75% of the $ 35.9 billions of dollars of exports from Brazil to

China are concentrated on the top 3 products, that are non-industrial/raw materials. These

products are also responsible for 24.6% of Brazil’s worldwide export value, being 11%

Soybeans, 7.6% iron ores and 6% crude petroleum oils.

This indicates that over three quarters or more of the exports to China are composed

by products that don’t have or have little aggregated effects that could better render Brazil’s

employment level and economic development.

0,00% 0,50% 1,00% 1,50% 2,00% 2,50% 3,00% 3,50% 2011 2012 2013 2014 2015

BRICS IMPORTS FROM BRAZIL - 2011 TO 2015

14

Figure 2: What does Brazil export to China? – 2015 (source: OEC – http://atlas.media.mit.edu)

It is unsure however, that these economies have new and/or overlooked markets for

other Brazilian products, and given that usually export promotion agencies have limited

resources (Steenkamp, 2011), the selection of the right markets for export opportunities are

crucial.

In this sense, along with the intent of further exploration of export opportunities to

BRICS’ members, to support policy makers and export promotion agencies, this study

proposes to find, given the proper market identification method, new and overlooked

opportunities to the BRICS, the top growing economies of the group, that could lead in an

enhancement of Brazil’s export market, and thus rise its contribution to the country’s GDP

and strengthening the position of the BRICS as a group.

1.2.Study objectives

The main objective of this study is to uncover realistic export opportunities that can

further enhance Brazilians exports to the BRICS (Russia, India, China and South Africa),

15

using the Decision Support Model, in its first application regarding Brazilian export

products.

Also, the analysis of the Marketshare shifts between 2011 and 2015, with emphasis

on more interesting groups; an evaluation of realistic export opportunities that Brazil is

already specialized in exporting and its Marketshare analysis.

Furthermore, include the potential values of the selected products of the new or

overlooked import markets as a form to prioritize the selected products, for both general

products and products in which Brazil has expertise in exporting.

1.3.Summary

Introduction: contains the problem contextualization and exposes why the study is

needed; which its objetvives are and the summary of the study’s structure.

Literature Review: positions the aim of the study over international literature;

presents market selection methods on country level basis, and determine which can provide

best results according to the objective.

Methodoly: explains how the DSM is applied according to previous applications.

Application of the DSM: provides the results made from the application of the model

to Brazil’s exports to each of the BRICS countries – Russia, India, China and South Africa,

explaining adaptations made to suit the purposes of the study, and provides analysis of the

findins.

Conclusion: provides conclusions of the present study and recommendations for

further studies.

16

2. LITERATURE REVIEW

Papadopoulos and Denis (1988) identified two different methods for exports market

selection, i.e. qualitative and quantitative approaches. Qualitative approaches were then

considered inaccurate, due to the fact that it is biased and based on perceptions of

government agencies, distributors, banks and chambers of commerce (Papadopoulos and

Denis, 1988 – as quoted by Steemkamp, 2011). Quantitative approach was defined as the

analysis and comparison of trade data, and was divided in market grouping – where firms

and countries are grouped regarding their resemblance in the sense that a market similar to

one already penetrated by a firm should be subject of attractiveness, being therefore applied

to countries with similar social, political and economic indicators, but not accounting its

demand level and being more likely to overlook lucrative possibilities (

Steemkamp, 2011)

;

and market estimation methods – that evaluates market potential and attractiveness through

measuring economic indicators.

Market estimation methods were further divided by Cuyvers and Viviers (2012) into

firm-level and country level, where (Cuyvers and Viviers, 2012):

1. Firm level: the estimation methods focus on a limited range of products,

considering firm’s objectives, profitability, experience and knowledge, customer

standards and attitudes and product adaptation requirements, and usually follow

a three-stage process (Cuyvers and Viviers, 2012, 30):

i)

a preliminary screening to select more attractive countries to investigate in

detail, based on countries‟ demographic, political, economic and social

environment;

ii)

ii) an in-depth screening in which these products‟ potential (market size and

growth), competitors, market access and other market factors for the countries

selected in stage one are analysed; and

iii)

iii) a final selection that involves the analysis of company sales potential,

profitability and possible product adaptation.

17

2. Country Level: market estimation methods that evaluates a wide range of

product-country combinations for a specific exporting country, selecting export

opportunities, analyzing market size and growth, indicators of economic

development, domestic consumption, factors of production, tariff and non-tariff

barriers, exchange rates, distances between countries and current international

trade data.

2.1.Country Level Quantitative methods

Steenkmap (2011) listed country level market selection methods that are capable of

screening through a wide range product-country combinations to provide opportunities to an

exporting country.

2.1.1. Green and Allaway’s shift-share Model

In this model, international market selection is based on estimating the average

growth of the products-countries combinations under investigation, then identifying growth

differentials by market share changes, and compared with the average growth, calculating

the net shift of each combination. Therefore, the net shit is the difference of the actual

performance and the estimation on the average growth

(Green and Allaway, 1985 – as quoted

by Steenkamp, 2011)

.

Papadopoulos et al (2002) found the Shit-Share Model’s results to be dependent on

the years of choosing, i.e. biased, and whilst its main strength is to be industry-specific, it

has very low predictive power and is volatile.

18

While Russow and Okoroafo (1996) argued that there should be statistical analysis

over Green and Allaway’s shift-share Model variables, Williamson et al (2006) tested the

significance of variables designated to indicate import market size and growth – much like

Green and Allaway’s net shift; import market competitiveness and barriers. It was concluded

that all three explanatory variables should be used together only, and therefore discrediting

Green and Allaway’s shift-share model (Steenkamp, 2011).

2.1.2. Russow and Okoroafo’s global screening model

According to Steenkamp (2011), Russow and Okoroafo (1996) defined their method

as a start point to evaluate potential foreign markets, as it uses three criteria to identify those

opportunities, that being product market size and growth; production factor of exporting

country and economic development of importing country, and classifying markets according

to their potential accordingly to each product.

2.1.3. Papadopoulos et al’s trade-off model

Viviers and Cuyvers (2012) argue that Papadopoulos et al (2002) trade-off model is

based on the balance of demand potential and trade barriers. Their model is detailed in the

table 1, as the model states four variables for each

main group

:

19

Table 1: Papadopoulos et al (2002) trade-off model

Source: Summary of Papadopoulos et al (2002:170-171))

Papadopoulos et al (2002) trade-off model measures from 0 to 10 for each variable

according to data available, and calculates the average of each main group, and furthermore

plotting them into a two-dimensional matrix, where:

20

Figure 3: Two-dimensional matrix - Papadopoulos et al (2002) trade-off model (source:

Papadopoulos et al (2002: 174))

Although clear that the upper right cell would be the preferential markets for an

exporting country/firm, Papadopoulos et al (2002) states that a firm with offensive market

strategy could target the upper left market opportunities cell even meaning that it would be

a larger effort to penetrate them, as it is seeking for higher demand potential rather than

diminishing costs of trade barriers.

Even though this model contains its limitations, it was argued by Papadopoulos et al

(2002) that it was an improvement over models that would only capture import demand.

2.1.4. The International Trade Centre’s (ITC) multiple criteria method

ITC’s multiple criteria method, according to Freudenberg (2006), offers both

qualitative and quantitative analysis. As for qualitative analysis, it includes information

based on interviews and surveys. As an online data-base, the ITC’s quantitative analysis

contains up to date official information from countries to the UN Comtrade database, such

as trade flows and market access barriers, using this data to provide indicators to evaluate

market export potential.

The method calculates indicators based on export performance, size and dynamism

and trade balance of the exporting country, for different sectors and markets; and

international market demand (size and growth) and market accessibility (trade barriers)

(Cuyvers and Viviers, 2012).

21

Freudenberg and Paulmier (2005) indicate that there are some limitations to ITC’s

multiple criteria method, as is relies on data availability and provides a “state of the moment”

characteristic of export opportunities.

2.1.5. Assessment of export opportunities in emerging markets

As discussed by Cuyvers and Viviers (2012), there were contributions for export

promotion identification methods regarding emerging markets. These were the cases of

Cavusgil (1997), Arnold and Quelsh (1998) and Sakarya et al (2007).

According to Cuyvers and Viviers (2012, p. 42):

Cavusgil (1997:87-91) attempted to rank the total market potential

of 25 emerging countries. Cavusgil only used country-level

indicators and no product specificy was introduced.

Arnold and Quelsh (1998) utilized country-level – macroeconomic indicators -

measures with the purpose of identification of long-term market potential, using firm-level

information afterwards to identify prospects and export profits (Cuyvers and Viviers, 2012).

From Arnold and Quelsh (1998)’ method, Sakarya et al (2007) assessed

product-country combinations for emerging markets regarding information that are not immediately

available, i.e. social values, product quality, customer service, wages, etc; and included

customer receptiveness, competitive strength of the industry and cultural distance

(Steenkamp, 2011).

2.1.6. The gravity model

This model is utilized to explain and estimate trade flows, and it was introduced by

Tinbergen (1962) and Linneman (1966), utilizing the concept of Newton’s gravity theory.

The idea was to relate economies by their attractiveness amongst each other, being able to

22

determine the trade flows between them, also taking into account distance, tariffs and

barriers (Cuyvers and Viviers, 2012), and being exemplified by:

Fij = O

i

,

.

D

j .

R

ij –

where:

Fij

– flow of goods between economies i and j;

O

i

– origin;

D

j

– destination

R

ij

– measure of restrictiveness.

2.1.7. Export Development Canada‟s Trade Opportunity Matrix

This method looked to overcome that previous methods weren’t industry specific,

i.e. the analysis was based on generic economic information. It uses historical and forecasted

data as input for its estimation models and then ranking the best industries per country and

best countries per industry, following Canadian exports (Verno, 2008 – as quoted by

Steenkamp, 2011).

Steenkamp (2011, as inferred by Verno, 2008) argues that although this method is

dynamic, as the latest information can be verified into an updated result, its reliability on

data being available and the fact that industry data is widely aggregated could be an issue

for this method, being then limited.

2.1.8. Decision Support Model

This model assumes the hypothesis that all worldwide markets have potential to

become an export market opportunity, and according to Cuyvers et al. (1995), the DSM is a

filtering process of successive eliminations, with the use of cut-off values constructed from

economic indexes and data, where all global product-country combinations are inserted. The

screening process consists of four filters.

23

The first evaluates, through 2 filters, the political and commercial risk and size of

each country's economy and its growth. The second assesses the market potential for a given

product to be exported, according to the market opening of a given economy and the growth

of imports of this product in this economy. The third filter evaluates, among the remaining

countries, those that have commercial barriers, through 2 filters – market concentration and

import restriction. From the third filter on there is already a relation of products-countries

combinations as potential export possibilities. The fourth filter categorizes and generates a

ranking according to the size of each market of each country-product combination and its

market growth already validated in previous filters, evaluated along with the Marketshare

(Cuyvers, 1995). The figure 4 illustrates the DSM process:

Figure 4: Decision Support Model (source: Viviers et al, 2014a)

As stated by Steenkamp (2011), the DSM model incorporates previous assessed

models, and includes all possible product-country combinations, being ideal to address the

identification of export market opportunities, thus being chosen to further evaluate this

study.

24

In order to improve the process of choosing international commercial opportunities,

and avoiding that resources are wasted due to failures in market identification, Cuyvers et al

(1995) created the Decision Support Model – DSM process for the evaluation of export

opportunities for Belgium, a process of successive eliminations where the conditions and

quantitative assessments of each economy and its internal markets, in cross-checking of data

with products available for export are listed, and through the process undesirable and/or

non-viable opportunities are discarded, and resulting a ranking of combinations of

product-countries.

In an application of the model to the Netherlands, Viviers et al (2014a), it is

emphasized that exports are fundamental to the well-being and competitiveness of the

country in question, generating a large contribution to its employment level and GDP,

reaching approximately two-thirds at the time which it was studied. The Netherlands has a

strategic location, excellent infrastructure and logistics, which makes it one of the countries

with the highest export and import rates on the globe. However, exports were geographically

concentrated and highly dependent on the European region, and the exported products were

of low and medium added value in most cases, which could be an easy opportunity for

competitors of emerging and developing suppliers to take its place. The DSM, in this way,

could evaluate potential markets, considering Netherlands productivity advantages and the

dynamics of change in world’s demand. With the application, it was perceived that the

Netherlands currently exploits less than thirty percent of the global potential, except for

Europe. Among the potential markets evaluated, whether by underutilization of products to

be exported or even new markets, were identified United States, China, Japan, Brazil and

Australia.

It is observed, according to the paper, that the products with the highest export

potential in the Netherlands are those with high added value, such as chemicals, machinery

and equipment, indicating a possible transition from the import market to low and medium

value products that can be produced by developing countries. It does not mean, however,

that the Netherlands should abandon the export of these products, but rather evaluate the

strategy for maintaining its overall export position.

The DSM was also applied by Viviers et al (2014b) for South Africa. In this paper,

which was motivated by low rate of development and economic growth, as well as poverty,

25

unemployment and social inequality, it was assessed that the growth and diversification of

South Africa’s imports could increase its economic position. It was implied that among the

greatest difficulties is the identification of markets according to the skills, capacities and

commercial fundamentals of the country in question. Using the DSM was convenient due to

the extensive combination of products and services put together with countries demand data,

and through the filtering process being able to find the best combination between them,

identifying previously unseen opportunities and thus being able to diversify export markets,

thereby making DSM a strategic tool for identifying these potential markets.

One premise of the paper was to evaluate only unexplored or nontraditional markets.

It was then verified through the sequential filters that more than half of the products with

export capacity and in current production can be inserted in export markets that have not yet

been explored.

26

3. METHODOLOGY

The Decision Support Model (DSM) was first developed by Cuyvers et al. (1995),

taking as its fundamental framework the model developed by Walvoord (1980) – as

demonstrated in figure 6 – in order to identify the product-country combinations with the

highest export potential for a specific country - Belgium. It was specifically designed to

promote exports with a more technical focus on how to determine the products and countries

of destination, to which the usually scarce export promotion resources should be

concentrated.

27

The initial DSM step is taken by including all countries and products, and consists of

a screening process in which realistic export opportunities (REO) are identified. As already

mentioned, the model basically consists of four consecutive filters that sequentially eliminate

less realistic and interesting product-country combinations, and then be able to categorize

them – as indicated in fig. 11:

Figure 6: DSM filter sequence (source: Cuyvers and Viviers, 2012, constructed from Jeannet and

Hennessey, 1988)

In order to establish the criteria of analysis, the indices that may be considered

undesirable, as well as the way to evaluate which opportunities may be more compatible and

which are of greater interest, there is a need to detail how the filters are formed, according

to Vivers et al (2014, N).

28

3.1. Filter 1 – Identifying preliminary market opportunities:

This filter is intended to eliminate countries that demonstrate high political or

commercial risk for the exporting country, in the case of the filter 1.1, and also those that do

not have adequate growth and economic size (Cuyvers et al, 1995).

3.1.1. Filter 1.1: Political and commercial risk assessment

In this filter, Vivers et al (2014, N) used the risk classification of the Belgian

organization Office National du Ducroire, whose methodology complies with the OECD

(Organization for Economic Co-operation and Development) for export support guidelines,

so it can be used for any country that wishes to establish the level of risk involved in the

export process.

Commercial risk assesses the likelihood of non-payment for products imported into

the country, depending on the financial deterioration of the importing country. They are

measured, amongst others, by economic and financial indicators, such as Gross Domestic

Product, inflation and interest rate; Also through payment history and institutional context

indicators such as corruption and economic transition processes (ONDD, 2016).

Political risk considers probabilities of force majeure events, such as wars, natural

disasters, and even currency shortages and government actions, and is based on economic

and financial assessment indices, political situation and payment history analysis (ONDD,

2016).

The political risk classification indexes by ONDD (2016) range from 1 to 7, with 1

being the lowest political risk and 7 the largest. The commercial risk indices are A, B or C,

with A being low risk and C being the greatest risk.

In order to evaluate such risks, Cuyvers and Viviers (2012), proposed a

transformation of determined values, whereas the political risk in short, medium and long

terms defined from 1 to 7, is transformed to 1 to 10, and the commercial risk A, B and C are

transformed to 3.33, 6.66 and 10, respectively, and defined a critical value of 9.286 as the

29

intended value for a country not to advance in further analysis. This value represents a

country with average political risk of “6” and a commercial risk “C”.

3.1.2. Filter 1.2: Macro-economic size and growth

This filter evaluates macroeconomic data regarding Gross Domestic Product (GDP),

such as GDP itself, GDP per capita, GDP growth and per capita GDP growth, in order to

eliminate countries that do not meet the desired potential.

The values determined for the sub-sequential filter must be greater than the world’s

average for three consecutive years in the case of GDP and GDP per capita, and higher than

the two-year global average growth of the last three years for growth assessment of the GDP

and per capita GDP growth (Steenkamp, 2011), in which:

CV = X

m

- ασ

x

where:

CV = Critical Value;

X

m

= Average of GDP and GDP per capta;

α = indicator of marginal variations, in order to not eliminate too many countries;

σ

x

= standart deviation of X.

Filter 1 will be calculated, but the results might be disregarded in the current study,

since its object is the diversification of Brazilian exports only to BRICS, consequently even

if the results are for a specific country are to be eliminated, the calculations of the subsequent

filters will be performed.

3.2. Filter 2: Identifying possible opportunities

With the exclusion of undesirable countries made in filter 1, filter 2 evaluates the

import demand for a given product by a given country (Cuyvers et al, 1995).

30

For this, 3 criteria are used to determine this compatibility, being short term import

growth, long term import growth and size of the import market. Vivers et al. (2014, N) used

CEPII BACI database (www.cepii.fr), constructed from the database of Nations Statistics

Division's UN Comtrade.

The growth of short-term imports is simply the most recent annual growth of a

particular country, while long-term growth considers the period of five years. The size of the

import market is the total import of a country from a particular product or category of

products.

Thereby it is necessary to calculate the cut-off value for each criterion. Cuyvers et al.

(1995) suggests that if a country already has a high degree of specialization in the export of

a product, the country's cutoff value must necessarily be less rigorous. Thus, the use of a

specialization coefficient denoted as Revealed Comparative Advantage – RCA, firstly

introduced by Balassa (1965), being defined by:

RCA = (X

i,j

/X

w,j

)/(X

i,tot

/X

w,tot

), where:

X

i,j

= exports of country i (which is the exporting country for which realistic export

opportunities are identified) of product j ;

X

i,tot

=

total exports of country i;

X

w,j

=

worldwide exports of product j;

X

w,tot

= worldwide exports of all product categories;

Vivier et at (2014, N) stated that in the case of a coefficient close to “1”, it is

considered that the country has great expertise in the export of the product concerned. For a

coefficient close to 0, the country has little or no product specialization.

A scaling factor is defined for short- and long-term growth, designated as Sj, to take

into account the degree of specialization in the export of country i of the product j in the

determination of the cut-off values (Willeme And Van Steerteghem, 1993, as indicated by

Cuyvers, 1997), that being:

31

S

j

= 0,8 + 1/ (RCA

j

+ 0,85) exp(RCA

j

– 0,01);

By this, the cut-off values are determined (Willeme and Van Steerteghem, 1993, as

indicated by Cuyvers, 1997):

g

i,j

≥ G

j

, with g

i,j

being the rate of growth of imports of the product j by country i, and

G

j

= g

w,j

S

j

if g

w,j

> 0, or G

j

= g

w.j

/S

j

if g

w,j

< 0, with g

w,j

being The total imports of the

product j. Table 2 demonstrate the reasoning of the cut-off values for short and long term

growth:

Table 2: Short and long terms cut-off values

Source: Steenkamp, 2011.

If this criterion is met by a country for a particular product evaluated, the value "1"

is indicated, otherwise "0", and these steps are performed for both the short term and the

long term (Steenkamp, 2011).

32

Regarding the relative size of the import market, this is considered, according to

Cuyvers et al (1997), to be sufficiently wide if:

M

i,j

≥ S

j

, in which M

i,j

is the size of the product import market j by country i; and

S

j

= 0,02 M

w,j

if RCA ≥ 1 or S

j

= [(3 – RCA

j

)/100] M

w,j

if RCA < 1.

Table 3 explain the reasoning of the cut-off values for the market size:

Table 3: Market-size cut-off values

Source: Steenkamp, 2011.

Again, "1" is applied for occasions when the criteria are met, and "0" for occasions

when they are not met.

It is important to highlight that the cut-off values are dependent of the country RCA

and world’s demand data, meaning that for each product there will be a different cut-off

value, that being for short term growth, long term growth or market size.

With the determination of which combinations of product-countries have their

criteria met, Cuyvers et al (1997) categorizes these combinations according to the table 4:

33

Table 4: Categorization of product-country combinations in filter 2

. Source: Cuyvers (1997).

The product-country combinations that are categorized in filters 3, 4, 5, 6, and 7 are

those that will be moving forward and being analyzed in filter 3 (Cuyvers, 1997). It can be

observed that the markets must have a growing demand for the analyzed product, whether

in the short and/or long term, and / or the total volume of demand.

3.3. Filter 3 – Identifying probable and realistic export opportunities

Even if there is growth and large size of an economy, that does not mean that the

market is necessarily easily penetrated (Cuyvers et al, 1995). Filter 3 then assess the

restrictions for market penetration, being:

3.3.1. Filter 3.1 – Degree of import market concentration

A concentrated market is usually difficult to penetrate, since if there are few suppliers

controlling the Marketshare, implying that there is already a certain knowledge of this

market, and thus may have developed better commercial relations with their consumers

(Cuyvers et al, 1995).

Cuyvers et al. (1995) used Hirshmann's Herfindahl-Hirshmann Index (HHI) (1964),

which calculates the market concentration:

34

HHI

i,j

=

Σ

(Z

k,i,j

/Z

tot,i,j

)

2

In which Z

k,i,j

is the import of country i from country k of product or category of

products j, and Z

tot,i,j

is total imports of product j by country i.

An HHI value close to 1 (one) indicates high market concentration and a value close

to 0 (zero) indicates low concentration (Cuyvers et al, 1995).

Cuyvers et al (1995) indicates that cut-off values for the Hirshmann's

Herfindahl-Hirshmann Index should take into account the level of specialization of the given export

country, since it would be less of an issue to compete with already established competitors

if the country already has specialization on exporting the product/market, being therefore

less strict in this case. And so, the cut-off value for filter 3.1 would be dependent of the

categories assigned in filter 2, as below (Viviers et al, 2014a):

hk = 0.4 for category 3 – This implies that in a relative non-growing large market,

a concentration of 40% is acceptable;

hk = 0.5 for category 4, 5 and 6 – This implies that in a relative large and/or

growing market, a concentration of 50% is acceptable;

hk = 0.6 for category 76 – This implies that in a relative large and growing in short

and long terms market, a concentration of 60% is acceptable.

3.3.2. Filter 3.2 – Trade Barriers

Cuyvers et al. (1995) assess the hypothesis that if the neighboring countries of the

exporting country have a relatively strong position in a particular market, it would not be

difficult for the exporting country to overcome the trade barriers, and thus denotating the

absence of barriers, and it is calculated as:

Mi,j = X

V1,i,j

/X

V1,i

+ X

V2,i,j

/X

V2,i

+ X

V3,i,j

/X

V3,i

+...

35

Mi,j is the marketshare of the neighboring countries of the exporting country to the

country i of product or product category j, X

Vy,i,j

is the exports from each neighboring

country (v = 1, 2, 3...) of the exporting country of the product or category of products j to

the country i, X

Vy,i

is the total exports of neighboring countries of the exporting country to

the country i, X

w,i,j

is the total exports in the world of the product or category of products j

to country i, nd X

w,i

the world exports to country i.

It is verified that the greater the Marketshare defined by Mi,j the easier it is to enter

the market in question, that is, the less the difficulty of overcoming trade barriers.

It was suggested by Cuyvers et al (1995) that if a neighboring country of the

exporting country had a Revealed Comparative Advantage in exporting to the analyzed

market, it would reveal the absence of trade barriers for the exporting country the model is

applied, defining that, with a 5% margin of error, a value of Mi,j ≥ 0.95% would suffice for

the product-country combination to be successful.

Both conditions of filter 3 should be attended for a product-country combination to

enter filter 4 (Vivier et al, 2014, N).

3.4. Filter 4 – Categorization of the Realistic Export Opportunities

With the definition of the Realistic Export Opportunities, filter 4 has the purpose of

categorizing the opportunities, without making any eliminations at this stage (Viviers et al,

2014a). The categorization is given by information obtained in filter 2, the size and growth

of the import market, in cross with relative Marketshare data, calculated in this step.

The Marketshare (

µ

m, i, j

) of the exporting country of the product or category of

36

µ

m,i,j

= Xn,i,j/X

six,i,j

Being defined X

n,i,j

as the exports of the exporting country of the product or category

of products j to the country i, and X

six,i,j

the exports of the six largest exporters of the product

or category of products j to the country i.

Cuyvers et al (1997) classifies the relative market share as:

•

µ

m,i,j

≤ 0,05 – Relatively small Marketshare

• 0,05 <

µ

m,i,j

< 0,25 – Intermediately small Marketshare

• 0,25 <

µ

m,i,j

< 0,5 – Intermediately large Marketshare

•

µ

m,i,j

≥ 0,5 – Relatively large Marketshare

With the data provided by the previous filters, it is possible to generate a matrix for

the categorizations of the realistic export opportunities, according to Viviers et al (2014a):

Figure 7: Final Categorization of Realistic Export Opportunities of the Decision Support Model

(source: Vivers et al, 2014a).

It identifies in this way twenty different types of markets with product-country

combinations, and are allocated in the proper cells according to their classifications defined

37

in filters 2 and 4. The model delivers these opportunities already categorized according to

size and growth of the import market with the relative Marketshare of the exporting country,

i.e. whether there is proper use of export capacity.

Vivers et al (2014a) argue that the product-country combinations in the cell between

1 and 10 are very unexploited opportunities, since they have low Marketshare allied to a

high size and growth of the import market by the product featured.

3.5. Product selection by country’s export capacity

So far, the DSM selected product-country combinations taking into consideration the

market size, growth, penetration and concentration, i.e. the demand side of the process, but

leaving unattended the supply side, which is the capacity of the exporting country to meet

its demand. Therefore, Viviers et al (2014a) suggest that a measure for such is the country’s

RCA, and that a RCA ≥ 1 means such conditions are met (Balassa, 1964, as quoted by

Viviers et al, 2014a), meaning that these products, beforehand selected through the filters,

are satisfyingly produced by the exporting country.

3.6. Export Potential Values

According to Viviers et al (2014a), given that the results of product-country

combinations might be extensive, there could be difficulties in identifying priority markets,

since none of the steps before-mentioned addresses the export value of given opportunities.

This could, however, be measured by (Viviers et al, 2014a):

Pot_exp

i,j

= average (Z

six1,i,j

, Z

six2,i,j

, Z

six3,i,j

,…), where:

Z

six1,i,j

: is country i’s imports of product j from each of the top six competitors

(excluding the exporting country for which the model is applied).

Viviers et al (2014a, argues that calculating the average value of exports of a given

product by the top six suppliers can be considered as the potential export value of the

38

product-country combination selected, thus being able to prioritize between opportunities by

its potential.

3.7. Inclusion of Estimated applied tariffs and non-tariff barriers

This section concerns new features added to the model, specific in filter 3, which

regards market accessibility. It aims to address a possible gap between products that may be

exported by neighbor countries that have its market more restricted to products from Brazil.

Estimated applied tariffs (EAT) are simply the tariffs submitted to World Trade

Organisation (WTO), in a Harmonized System product code from each country. The data is

provided by WITS – World Integrated Trade Solution (

http://wits.worldbank.org

) on a HS

6-digit dissagregation level, and its bilateral data provides tariffs applied to every product

traded between countries.

This, however, cannot be used solely to determine the level of protection, according

to Kee et al (2008), as there are many forms for a country to impose barriers to another

country’s export products. Kee et al (2008) proposed to find an estimated index that could

represent trade restrictiveness. The main result from the paper was an “Overall Trade

Restrictiveness Index” (OTRI), which was composed by, amongst others, an ad-valorem

equivalent (AVE) for non-tariff measures (NTM). The concept utilized was to capture the

movement in import prices based on a general equilibrium model considering variables that

could explain it, for instance relative factor endowments, GDP, distance, agricultural

domestic support, tariffs, demand elasticity and parameters that measure ountry

characteristics and the presence of non-tariff barriers (see Kee et al “Estimating Trade

Restrictiveness Indices”).

Following the core of the methodology, there was the need for applying a cut-off

value in order to screen possible undesirable markets. The International Trade Centre

(www.intracen.org) considers 30% as a high level of protection, being then stablished as the

cut-off value of the sum of the estimated applied tariffs and the ad-valorem equivalent of

non-tariff measures provided by Kee et al (2008).

39

4. APPLICATION OF THE DSM

This section regards the application of the DSM over Russia, India, China and South

Africa’s import market. The data for the calculations was retrieved from BACI, which is the

World trade database developed by CEPII – French Research Centre in International

Economics (http://www.cepii.fr). This database was chosen due to being constructed from

official data provided by the United Nations Statistical Division (COMTRADE) and then its

information reconciled, in assessment of differences between CIF and FOB costs declared

by exporters and importers. The data was collected in a HS 6-digit product disaggregation

over the period from 2006 and 2015, with the analysis occurring from 2011 and 2015. Once

the database was retrieved, it was used Microsoft Access, due to the lack of lines on

Microsoft Excel, to work the database and then used Microsoft Excel to proceed with the

calculations.

4.1.DSM application on Russia

4.1.1. Filter 1 – Identifying preliminary market opportunities

The database used for filter 1 was collected from ONDD - Office National Du

Ducroire <www.delcredereducroire.be/en /country-risks/rating>.

Table 5: Risk assessment of Russia

RISK ASSESSMENT

Political Risk

Commercial

Risk

Short

Medium

Long

Russia

ONDD

4

3

4

C

Transform.

5.71

4.29

5.71

6.67

Average

5.24

6.67

Risk Assessment

7.62