Emerging Markets Report E-Book

Paulo Vicente Alves

• Herausgeber: AVEC Editora
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 151

Veröffentlichungsjahr: 2015

Beschreibung

This report is divided into three chapters: a discussion of what constitutes an emerging market and their classification, an analysis of the BRIMCs nations in geographical and economical terms, and a series of possible future economic scenarios. The first chapter, the discussion about what constitutes an emerging market divides the nations into three groups, each representing 70%, 80% and 90% of the world economy, and studies these groups using cluster analysis with the variables of GDP per capita, population size and fertility rate. Through this study it is possible to create three layers of emerging nations. The second chapter, an analysis of BRIMCs nations in geographical and economical terms brings studies of markets, internal geography and barriers to growth for the nations of Brazil, Russia, India, Mexico and China. The third chapter, a series of possible future economic scenarios presents a broad vision of the BRIMCs' nations in the 21st century using hegemonic cycles and Kondratieff cycles theories. The report examines each one of the BRIMCs on possible demographical, economical and political expansions. Each nation studied has specific approach in this scenario extrapolation and each scenario analysis contains a map of the most probable expansions with four possible variants and statistical probabilities assigned to each scenario.

Leseprobe

Emerging Markets Report

Paulo Vicente Alves

AVEC EDITORA

Porto Alegre, 2014

Copyright ©2014 Paulo Vicente Alves

All rights reserved to AVEC Editora

No part of this publication may be reproduced, distributed or transmitted in any form or by any means, or stored in a database or retrieval system, without the prior permition of the publisher

Publisher: AVEC Editora

Editor: Artur Vecchi

Cover and Epub Design: Tatiana Medeiros

1ª edition, 2014

Published in Brazil

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Dados Internacionais de catalogação na Publicação (CIP)

(Câmara Brasileira do Livro, SP, Brasil)

Catalogação na fonte: Ana Lúcia Merege - CRB-7 4667

A474

Alves, Paulo Vicente

Emerging markets report / Paulo Vicente Alves. – Porto Alegre : AVEC Editora, 2014.

ISBN 978-85-67901-05-3

10.200 kb; ePUB

1. Globalização 2. Economia mundial 3. Política econômica I. Título

CDD 330

Índice para catálogo sistemático:

1. Globalização 330

About the Author

Paulo Alves is a D.Sc. in Administration by Fundação Getúlio Vargas (FGV), M.Sc. in public administration by FGV, and Engineer by IME (Military Institute of Engineering).

He was a participant in the Global Colloquium in Participant Centered Learning (GLOCOLL) at Harvard Business School in 2012.

Currently works as full professor in Fundação Dom Cabral (FDC), one of the top ranked Business schools in the World.

In the past he was Under-secretary of Planning from Secretariat of Planning and Management of the Rio de Janeiro State (SEPLAG-RJ) from 2007 to 2009. His professional experience includes the sectors of government, defense, aerospace, education and energy. He was a Brazilian Army Officer serving from 1990 to 2002, reaching the rank of Captain as a Weaponry engineer.

As a lecturer he has worked by FDC in international programs for CKGSB, HULT, INSEAD, ISB, John Hopkins, Saint Gallen, Skolkovo and Vlerick. He worked by FDC at in-company trainings for several organizations including: Abril, APEX-Brasil, Cyrella, Duratex, EADS, Einstein Hospital, EMBRACO, Itaú Bank, Medabil, TIM, Randon, SENAI, Sírio-libanês Hospital, SOFTEX, Souza Cruz, and Suzano.

Previoulsy than FDC he has worked as a professor at ESPM, FGV, IBMEC, IME and PUC-RJ. As a consultant he worked for BAT, IADB/BID, Petrobrás, Shell, and the US DoC.

Currently he is a Fellow of the Strategic Planning society (SPS-UK), and Founder Member of the Strategic Management Forum.

He is author of the book “Jogos de Empresas” (Business games) published by Pearson/Makron Books in 2001.

He was winner of the Best marketing strategy prize given by Publicis, in the L’Oréal Marketing Award 2004 as professor. He was ranked 29th in the Best Business Professor Award promoted by The Economist Intelligence unit in 2012-13.

About the book

This project began as far back as my doctoral thesis in 2004 when I started using cluster analysis to classify nations. My thesis also used hegemonic cycles and Kondratieff cycles to create a framework for analyzing the past. At that time my major interest was Latin America.

When I joined FDC in 2010 one of my first tasks was to lecture in the BRICs on BRICs program where students moved all around the world studying the emerging nations from within.

In 2011 my lectures on emerging markets evolved to become a coherent body of knowledge on emerging markets, always trying to compare them and create forecast their future.

The main work on this project however came in 2012 when I was assigned to write this report and the many maps on territorial comparisons were created by me. In this year also my model on future scenarios finally became mature and the “century in four acts” logic finally appeared from forecasting with hegemonic cycles and Kondratieff cycles.

By mid 2013 the report was ready and it was released initially in early 2014.

Foreword

Emerging Economies have had a remarkable impact in the global economy in the last twenty-five years. They started from the supply side, mainly as sources of cheap labor, raw materials and other inputs that have dramatically lowered production costs in several global industries. Along the years, two other developments are transforming those countries and, as a consequence, their role in the world economy. First, companies operating in some emerging economies are moving up in the value chains of their industries, to the point that several multinational corporations are locating part of their R&D labs in emerging economies. Second, and simultaneously, continuous economic growth in emerging economies is allowing millions of workers and their families to join the middle classes, creating fast and growing demand in a volume and speed never seen in human history.

The forecast for the next decades is that emerging economies will continue to grow faster than developed countries. This is partly consequence of the fact that emerging economies have a much lower base to compare. Despite their growth potential, emerging economies do not have a smooth path to development. On the contrary, for the larger emerging economies the challenges are immense. In order to take their huge populations to middle or high-income levels, emerging economies will have to keep economic growth balanced with social and environmental policies. The richer they grow, the wider will be their global impact in several issues.

The Emerging Markets Report is a superb piece of work from Professor Paulo Vicente Alves, with the objectives of defining and classifying emerging economies, and proposing forecasts for the development of perhaps the five most relevant emerging economies: Brazil, Russia, India, Mexico and China. The Report is an essential reading for those who wish to better understand the growth perspectives of each of the five countries. Besides, it allows the reader to make insightful comparisons between the countries.

The Center for Strategy and International Business of Fundação Dom Cabral is proud to host and support the elaboration of the Emerging Markets Report. It is an important contribution for both academics and business people who are interested in understanding and/or developing business in Emerging Economies.

Aldemir Drummond

Director of the Center for Strategy and International Business

Fundação Dom Cabral – www.fdc.org.br

September, 2014

SUMMARY

EXECUTIVE SUMMARY

1 INTRODUCTION

2 CLASSIFICATION OF EMERGING MARKETS

2.1 – Methodology

2.2 – The 14 biggest nations (70% of world GDP)

2.3 – The 24 biggest nations (80% of world GDP)

3 SELECTED MARKETS COMPARISON

3.1 – Territorial comparison

3.2 – B2C markets comparison

3.3 – B2B markets comparison

3.4 – Barriers to growth comparison

3.5 – Political situation comparison

4 SCENARIOS AND FORECASTS

4.1 – XXIst century: a general forecast

4.2 – Scenarios

CONCLUSIONS

EXECUTIVE SUMMARY

This report is divided into three main parts: a discussion of what constitutes an emerging market and how to classify them, an analysis of the BRIMCs nations in terms of geography and economy, and a series of scenario forecasts.

In terms of classification we first divided the nations into three groups, each representing 70%, 80%, and 90% of the world economy, respectively. We then analyzed these groups using cluster analysis with the variables of GDP per capita, population size, and fertility rate. It was possible to create three layers of emerging nations. We have then focused on analyzing the markets, internal geography, and barriers to growth of the layer 1, that is, Brazil, Russia, India, Mexico and China, or BRIMCs.

All five nations are growing well, with large consumer markets, although there is little standard in consumption patterns. In general there are two kinds of consumer behavior. One kind includes India and China that behave similarly. The other kind includes Russia, Mexico and Brazil that also have a similar pattern.

While all five nations are growing well, barriers exist that may limit future development. We see the nations surpassing these barriers as happening in the following order: Russia, Brazil, Mexico, India, and China.

Finally, we have forecast for what may happen to these BRIMCs nations, by first making a general forecast of the XXIst century using hegemonic cycles and Kondratieff cycles forecast. Second, we examine each of the BRIMCs in relation to their possible expansions demographically, economically, and politically. Each nation has a main theme in this scenario view and each scenario analysis contains a map of the main possible expansions and four possible scenarios with probabilities assigned to each scenario.

Our examination shows that Russia and Brazil are expanding and Mexico is merging economically with USA. China and India are limited in their geographical expansions so they must move abroad and search for regions in Africa and the Middle East, which points to a new game of colonialism and corporate expansion in these regions.

1 INTRODUCTION

In the last decade the term ‘emerging market’ has become widespread. Because of this increasingly important theme in the current business environment, The Fundação Dom Cabral (FDC) has funded this report. It has been placed in the Strategy and Emerging Markets Research Nucleus because of the strategic theme. In addition, many of our clients, both corporate and public, are interested in better understanding what the emerging markets are, how they behave, and what will be their future and challenges. This is even more important because Brazil is an emerging market itself.

This report has three objectives:

1 – To analyze what an emerging market is and how to classify them. This will be discussed in Chapter 2 using several statistical techniques.

2 – To make a focused analysis of the most important emerging markets, which will be studied in terms of geography, economy, barriers to growth, and politics. This will be done in Chapter 3 using a number of quantitative and qualitative methods.

3 – To make a strong forecast of the future of the most important emerging markets, both in relation to the world and within their internal logic of expansion. This will be done in Chapter 4 using Hegemonic cycles, Kondratieff cycles, geopolitical analysis, and scenario forecasts.

In the end this report is an evaluation not only of how the main emerging markets function today, but also how they may develop in the future. This analysis will allow decision makers to weight their importance and plan ahead. Thus, this report is an important tool for strategic planning by companies operating in emerging markets.

2 CLASSIFICATION OF EMERGING MARKETS

The upsurge in the number of emerging markets has led to many definitions of what an emerging market is and confusion between large emerging markets like China and India, and relatively small ones like Peru or Chile.

The coinage of terms like BRICs, E-7, and G-20 was designed to refer to a specific group of nations considered either large economies or fast growing economies with potential for high consumption. Particularly, large middle classes gaining access to mass products and services are seen as important potential growth drivers during future years. Also, these economies are still in development, which means they are still imperfect in terms of competition, and not impacted as much by the hyper competition of mature markets. However, this also means they are not as deregulated and transactions costs are higher.

However, these earlier classifications were either ad-hoc or non-rigorous from the methodological point of view. It can be argued that the BRIC nations have little in common with these classifications. Recently, South Africa was considered by some to be part of the BRICs, but why include South Africa and not Mexico or Indonesia? Doing so seems to imply a politically ad-hoc choice rather than one as the result of strong methodological criteria.

Another example of the ad-hoc nature of the earlier classifications is that the E-7 included all BRICs, plus Mexico, Indonesia, and Turkey. Again these countries have little in common and so why not also include Iran, Poland, or Thailand to form an E-10? The reasons likely range from political to outright heuristic issues.

So our first question that must be addressed is: what is an emerging market and which subtypes of emerging markets exists?

Our approach is that an emerging market is a classification and the usual method for classification is the cluster analysis. This method is used in biology to differentiate species using their morphology to find whether there are different species or just a variation within the same species. We are not analysing biological entities but rather Leviathans, just as Hobbes described the modern Nation state in an analogy to a huge animal made of a collection of animals.

To prepare for that kind of analysis, we must choose a sample of markets we are willing to compare. Table 1 suggests three layers for any methodological approach. The layers consist of countries that represent 70%, 80%, and 90% of the world economy, respectively. The three layers include respectively 14, 24, and 45 countries.

Table 1 – Data for the 45 top economies of the world in 2012

The morphology used in this report is the one appropriate for our Leviathans, that is, secondary data found in national statistics. It’s important to remember that the birth of the term ‘statistics’ in the XVIIth century used the “stat” prefix because it was critical to understand the State in terms of demography. So since early times statistics has been associated with and even baptized because of its association with the study of the Leviathan.

The variables shown in the table above can be used to analyze which of these markets constitute an emerging market, but they can also help determine what kinds of emerging markets exist by means of a statistical classification methodology of the cluster analysis. Classification is important because when companies are deciding where and how to invest and search for clients, they almost always look at segmentation in one of its forms: geographical, demographical, economical, social, and psychographic.

Emerging markets represent a special challenge in this respect for multinational companies (MNCs) trying to enter a new market since MNCs have a number of options, a limited entry budget, and limited understanding of these new markets. This requires hard choices as to where and how to enter first and the follow-up list of countries to enter later. For the MNC’s already installed in emerging markets, they also face hard choices as to where invest, or divest, and the trends of each nation.

The logical decision is to go where consumption is higher and the costs of entry lower. In this sense, classifying those markets in terms of size of consumption and population size is a logical step in addition to looking at income per capita and costs of entry, which are associated with transaction costs. However, income per capita is difficult to estimate since the data available is not compatible for comparison, therefore GDP per capita in purchasing power parity (PPP) or a human development index (HDI) are more comparable across nations allowing for analogies in terms of nations or states with which the analyst is familiar.

This, however, is not the whole story. For example, while Portugal (HDI=0.909, Pop=10.6 million, GDP/cap=US$ 23 thousand), Shanghai (HDI=0.908, Pop=23 Million, GDP/cap=US$ 11 thousand), and Montana (HDI=0.908, Pop=1 Million, GDP/cap=US$ 42 thousand) are comparable in terms of HDI, their demographical patterns like population size, density, gender, age, and GDP per capita are not necessarily so. Also, we cannot affirm similarities in psychographic segmentation. Nonetheless, these comparisons are useful when trying to picture a foreign nation in familiar terms.

2.1 – Methodology

We paired the data of the countries in terms of variables trying to look at the overall attractiveness of each market but to also correctly separate them into logical clusters. For each of the pairings, a dendogram was first made to analyze the number of clusters that would better separate the countries, and then a K-cluster analysis was made. The statistical software used for that analysis was the SPSS 20.0.

The first analysis was the separation of GDP into its parts of GDP per capita and population, but since population was a very large number for some countries -- running in millions while GDP per capita was in the range of thousands -- this created a problem in Euclidian distances for the cluster methodologies. The solution was to use the logarithm of population reducing its size, and using GDP per capita in thousands. This analysis showed the best results in classification since the numbers were comparable. However, applying the logarithm to the population but not to the GDP per capita seemed to also create a bias, so it was tested with a logarithm applied to both variables. The results were also good but not as good as before.

The second analysis was used pairing HDI with the logarithm of the population, but since HDI is too short on variation, the population variable dominated, (even in the logarithm), creating distortions in classification making, for example, Japan fall in the same category of Brazil and Russia but apart from Germany and France. This makes sense when classifying countries by population size, but not emerging markets. We used HDI squared to enhance distances in HDI dimension, but the problems persisted.

The third analysis was made pairing energy consumption with population. This at first made sense since there is a high correlation between energy consumption and GDP per capita (R2=0.76, sig<0.01, linear regression, 24 countries dataset), and HDI (R2=0.83, sig<0.01, logarithmic regression, 45 countries dataset) so they could be interchangeable. However, when the data was analyzed some countries, like Canada and Russia, represented distortions. Canada, Sweden, and Norway, for example, fell into separate clusters depending on the sets analyzed, since they are cold-weather countries with high consumptions of energy. Russia and Saudi Arabia, being producers of energy, were classified among the developed nations instead of emerging markets. This pairing proved not very useful due to the distortions.

The fourth pairing was fertility rate and population which could indicate not only current demand but also the prospect of future demand. The result was very different and unexpected clusters appeared. USA with a fertility rate above 2, was included with other emerging markets like Brazil and Mexico. The U.K. and France separated themselves from the rest of Europe with fertility rates above 1.9, thus, they could be taxonomically termed self-sustaining markets, while many other nations could be termed mature markets. Also India could be segregated from China as they have very different fertility rates (India=2.62, China=1.54). In general, these clusters are complimentary to those found in the first pairing