global merchandise trade scenarios, methodology

Global Merchandise Trade Scenarios MethodologyDeveloped in October 2013

Commissioned by

1 The Economist Intelligence Unit Limited 2014

Global Merchandise Trade Scenarios Methodology

Contents

Global Merchandise Trade Scenarios Methodology 2

Development of qualitative scenarios for global merchandise trade out to 2030 3

Introduction 3 PESTLE analysis 3 Themes development 3 Narratives development 3

Development of scenario-based economic forecasts 5

Introduction 5 Definitions 5 Forecasts development overview 5 Box: Methodology for long-term forecasts 6

Consulted sources 10



Global Merchandise Trade Scenarios

Methodology

The Economist Intelligence Unit (EIU) was commissioned by the A.P. Moller-Maersk Group to develop a dataset on global merchandise trade in order to support the drafting of the book Creating Global Opportunities: Maersk Line in Containerisation 19732013. In order to support this debate, the EIU was subsequently tasked to independently develop three scenarios for global merchandise trade out to 2030. Given the disruptive changes that greater interconnectedness has generated over the past 40 years, this type of forward-looking analysis raises important strategic questions and sheds light on global trends that are likely to shape our future.

This methodology document outlines the conceptual framework the EIU adopted to develop the trade scenarios.

The research process followed two sequential work phases and was supported by an extensive peer-review process:

A. Development of qualitative scenarios for global merchandise trade out to 2030

B. Development of scenario-based economic forecasts



Development of qualitative scenarios for global merchandise trade out to 2030A

1. Introduction

In setting the scope of this research we drew from Michael Porters (1985) de nition of scenarios: an internally consistent view of what the future might turn out to benot a forecast, but one possible future outcome. In devising the scenarios we adopted Van der Heijdens (1997) ve criteria:

At least two scenarios need to re ect uncertainty;

Each of the scenarios must be plausible;

The scenarios must be internally consistent;

The scenarios must be relevant;

The scenarios must produce a new and original perspective.

We paid particular attention to situating our scenarios in a plausibility versus possibility matrix, building stories that would be suf ciently imaginative and yet believable. It should be noted that, particularly when moving away from event analysis and into systemic thinking, the number of scenarios one can develop becomes in nite. As with all scenario analyses, we have selected scenarios that would satisfy the conditions outlined above, but judgement calls were made in determining the impact of

speci c trends and their development under each scenario.

2. PESTLE analysis

The rst step in the scenario development took the form of a horizon scanning exercise. The EIU conducted an internal brainstorming process to develop a trends and factors analysis looking at the major forces that are set to in uence the future prospects of global trade. In a dynamic workshop we populated a PESTLE (Political, Economic, Social, Technological, Legal, Environmental) framework with some of the trends and game-changers with a 2030 time horizon, compiling a list of over 50. PESTLE analysis describes a framework of macro-environmental factors that allows for a systemic scanning of global trends.

3. Themes development

We ran a separate exercise and clustered our ideas around possible themes, narrowing them down to ve. Scenario analysis revolves around the combination of axes which represent opposite possible worlds; we conducted an exercise imagining how each one of these themes would develop if taken to an extreme. This allowed us to



test how each theme performs in the possibility versus plausibility matrix. We developed the following themes:

Trade legislation and global governance: integration vs protection;

Resources: plenty vs scarcity;

Technology: progressive vs disruptive;

Global economic geography: bipolarity vs multipolarity;

Migration, talent and labour markets: global village vs regional silos.

4. Narratives development

Scenario analysis is commonly undertaken through two alternative approaches (Maack 2001):

The scenario matrix approach is designed to isolate two sources (what in this analysis we

cluster in the themes) of great uncertainty and great importance for the success and sustainability of the work being planned. These are then developed into four alternative stories.

Alternative scenario plot types typically move away from the scenario matrix approach and include dichotomies such as winners and losers, or good news vs bad news. These scenarios tend to inform upside and downside risk assessments.

In this project we used an alternative plot type, outlining a baseline scenario and two alternative scenariosone upside and one downside. This allowed us to retain some of the complexity that emerged from the themes development and provided a more relevant framework for our subsequent modelling exercise.

The scenarios were developed into three narratives, providing a global macro picture identifying merchandise-trade relevant features of the global economy out to 2030.



Development of scenario-based economic forecastsB

1. Introduction

Upon developing the scenario narratives, we focused on generating numerical forecasts out to 2030. The gures were calculated through a manipulation of the EIUs proprietary long-term forecasting model (see box) and their development followed a combination of model-driven and qualitative analysis. In this sense, our research methodology combined a bottom-up and a top-down approach; we considered this combination necessary as countries would experience different patterns in each scenario and this would have not been re ected in a purely top-down approach.

2. De nitions

We adopted two country samples for this project. Our dataset for the country-level analysis covered 60 of the worlds largest economies which, taken together, account for more than 95% of global GDP. This sample was used to identify the growth rates for each series and for the regional-level analysis (see section 3). The second country sample is the EIU world aggregate, which refers to 120 countries that are aggregated by the EIU on a monthly basis to produce these world

estimates. This sample has been used for the global-level analysis and ultimately constitutes our de nition of world.

3. Forecasts development overview

Our approach in developing the forecasts was largely based on the EIUs own in-house methodologies. Although our thinking was informed by the analysis of Dean and Sebastia-Barriel (2004), we opted for the approach outlined below1. The process undertaken by the EIU ensured a rigorous and tractable method for producing global merchandise trade forecasts in a scenario framework.

The process for generating the aggregate forecasts followed these stages:

1. The country levelthe forecasts for a number of major economies were conducted at the country level for each of the scenarios. EIU country analysts adjusted country-speci c drivers under each scenario narrative and produced country-speci c forecasts.

2. The regional levelusing the regional expertise of EIU country analysts, in combination

1 The Economist Intelligence Unit recognises that this is only one way of generating global merchandise trade scenario forecasts. Our choice of starting our analysis with a bottom-up approach lies in the fact that different countries would be affected differently by each one of the scenarios, some more evidently than others (e.g, technological development would affect a country very differently, depending on the potential extent to which it could leapfrog). As a result, we felt that accounting for these differences would add depth to the analysis and provide a more accurate picture.



The regressions, which have high explanatory power for growth, allow us to forecast the long-term growth of real GDP per head for sub-periods up to 2030, on the basis of demographic projections and assumptions about the evolution of policy variables and other drivers of long-term growth.

De nitions of variables

The dependent variable is GDPG: Average annual growth in real GDP per head, in the 1970s, 1980s and 1990s, measured at national constant prices.

The independent variables include:

GDP: The natural logarithm of GDP (adjusted for purchasing power parityPPP) per worker (that is, per population aged 15 65) in constant 1980 US dollars at the start of each decade. Expressed as an index, US=1.

SCHOOL: The natural logarithm of the mean years of schooling of the population aged over 15 at the start of each decade. Missing values for some countries are lled in by estimating mean years of schooling on the basis of an equation relating mean years of schooling (where available) to gross primary school enrolment ten years previously, and to secondary and tertiary enrolment ratios ve years previously.

LIFE EXPECTANCY: The natural logarithm of life expectancy at birth at the start of each decade. This variable also enters the equation in squared form, re ecting diminishing returns to growth of increases in life expectancy at high levels.

OPENNESS: Updated Sachs-Warner index of opennessthe fraction of years during each decade in which a country is rated as an open economy according to the following four criteria: average tariff rates below 40%; average quota and licensing coverage of imports of less than 40%; a black-market exchange-rate premium that averaged less than 20%; and no extreme controls (taxes, quotas, state monopolies) on exports.

The Economist Intelligence Unit has developed a methodology for producing long-term economic forecasts. The key output of our long-term model is a forecast of real GDP growth per capita, which can be combined with population growth forecasts to give a real GDP growth forecast for each country.

We have considerable experience in tracking and forecasting a series of economic and institutional factors, which our analysis suggests are closely related to long-term growth prospects. These factors include the availability of an educated workforce, the openness of the economy to trade, the quality of institutions (including the legal framework and the quality of the bureaucracy), scal policy, the degree of government regulation, movements in the population of working age relative to the overall population, and the development of information and communication technology infrastructure. In addition, the income gap between each country and the global technological leader (the US) is important, as this illustrates the potential for economic catch-up by importing ideas and techniques. Forecasts of GDP growth per capita can then be combined with demographic projections (taken mainly from the US Census Bureau) to give forecasts for overall GDP growth. This is explained in more detail below.

Growth projections

The main building blocks for the long-term forecasts of key market and macroeconomic variables are long-run real GDP growth projections. We have estimated growth regressions (based on cross-section, panel data for 86 countries for the 1970-2000 period) that link real growth in GDP per head to a large set of growth determinants. The sample is split into three decades: 1971-80, 1981-90 and 1991-2000. This gives a maximum of 258 observations (86 countries for each decade); given missing values for some countries and variables, the actual number of observations is 246. The estimation of the pooled, cross-section, panel data is conducted on the basis of a statistical technique called Seemingly Unrelated Regressions (SUR), to allow for different error variances in each decade and for correlation of these errors over time.

Methodology for long-term forecasts



ICT: The natural logarithm of an index, on a scale of 1-10, of the development of information and communications infrastructure. ICT development is found to have in uenced growth signi cantly only from the 1990s, with little or no impact in previous decades. For 1990 the index is measured simply on the basis of xed telephone lines per 1,000 people. From 2000 a more sophisticated measure is constructed, re ecting the very rapid development of ICT. The composite ICT index is based on ten indicators. Six indicators are quantitative and rely on our forecasts of xed-line telephone penetration (lines per 100 people); mobile telephone penetration (subscribers per 100 people); the stock of personal computers (PCs per 100 people); Internet users (per 100 people); the number of Internet servers (per million people); and broadband penetration (per 1,000 people). In addition, there are four qualitative indicators from our e-readiness model. These include the quality of Internet connections, the development of e business, the development of online commerce and the exposure of the population to the Internet (Internet literacy). Each of the ten indicators is transformed into an index scaled 1-10. The composite ICT infrastructure/use index, on a 1-10 scale, is an average of the ten component indices.

Control variables include PRIMARY: Share of the exports of primary products in GDP at the start of a decade; TROPIC: Percentage of the land area within a country that has a tropical climate; COLONY: History of independent statehooda dummy variable taking the value of 1 if a country was a colony before 1945; and, in some speci cations, regional dummy variables.

INSTITUTION: Index of institutional quality (on a scale of 1-10) that is an average of ve sub-indices of measures of the rule of law: quality of bureaucracy, corruption, the risk of expropriation and the risk of government repudiation of contracts. Forecast values are based on corresponding indicators from our business environment rankings.

LABOUR POPULATION: The difference between the growth rate of the working-age population (aged 15-65) and the growth rate of the total population in each decade in the 1970-2000 period.

TERMS OF TRADE: The average annual rate of change of the terms of trade in a given decade.

GOVERNMENT SAVINGS: The average government savings ratio in each decade (current government revenue minus current government expenditure) expressed as a share of GDP.

TRADE SHARE: The average share of trade (exports and imports of goods and services) in GDP, lagged by one decade to deal with the endogeneity of growth and trade.

GOVERNMENT REGULATION: An index on a scale of 1-10 of regulation of product, credit and labour markets. For forecast periods, the composite index is based on seven indicators from three categories of our business environment rankings modelfrom Policy towards private enterprise (ease of setting up new businesses, freedom to compete, price controls); from Financing (openness of the banking system, nancial market distortions) and from Labour markets (restrictiveness of labour laws, wage regulation).

Methodology for long-term forecasts (continued)

with the country-level forecasts, we conducted calculations on how the remaining countries in those regions would behave.

3. The global perspectivewhile the rst two steps involved a bottom-up approach to generating a global aggregate, the EIU also conducted analysis to ensure the robustness of these forecasts at the global level.

3.1 The Country Level

We narrowed down the bottom-up analysis to a set number of countries and applied a number of criteria in order to develop a robust sample:

We selected the worlds largest regions so that at least 75% of world GDP would be represented (Western Europe, Asia and Australasia, and



North America were included, accounting for approximately 81% of world GDP).

For each one of these regions, we selected a number of countries that would comprise at least approximately 50% of the regions GDP.

For all remaining regions we selected a prominent economy.

As a result of this selection, we conducted individualised, country-level, scenario-based forecasting for the following economies:

North America: United States;

South America: Brazil;

Middle-East and Africa: South Africa;

Western Europe: France, Germany, United Kingdom;

Eastern Europe: Russia;

Asia and Australasia: China, Japan, Indonesia.

As far as indicators are concerned, we produced forecasts for the following series:

GDP (nominal and real2);

Total trade in goods (nominal and real);

Trade as a % of GDP.

EIU country analysts were asked to provide an upside and a downside forecast for their specialist country. Within the long-term model, the analyst manipulated the drivers that were

most relevant to the scenarios at hand. It was left to the judgement of the analyst how each element of the scenario would in uence the drivers of long-term growth. As described in the methodologies box, the main output from the model is GDP growth. Following a suitable pro le for GDP growth, the analyst also made changes to their baseline forecast for exports (of goods and services) out to 2030. The nal step at this country level involved the split between goods trade and services trade. Each analyst had to review the historical trend in these series and their share in overall exports. Given each scenario, the analyst had to decipher the long-run trend of this share.

3.2 Regional-level

The next stage of analysis involved the expertise of our regional teams to make quali ed assessments of what these scenarios would entail for each region of the world. Based on how representative key countries were of the rest of the region, country analysts assessed the relative similarities of these countries in terms of issues such as energy production, trade openness, institutional quality and technological infrastructure. If a country in the region was similar to the key country already analysed with respect to these issues, we made the assumption that the impact on growth in each scenario would be comparable. In other words, we used the growth differentialthe difference in growth from the baselineand applied this to the baseline forecast of the comparable country. Therefore, we did not assume that a comparable country grew at the same rate as the key country in a given scenario; rather we assumed that the impact of the scenario would be the same. Naturally, not all countries in each region could be considered "similar" to the key country of that region. There are obvious differences for countries within a region with respect to energy, technology and trade openness. For these countries, our experts had to decide how these

Regiona Share of world GDP (%)b

Asia and Australasia 31

Eastern Europe 5

Latin America 8

Middle East and Africa 6

North America 25

Western Europe 24

World 100a Aggregatesb Figures might not add to 100% due to rounding

2 A neutral view was taken regarding in ation. In both the upside and downside scenarios, there are demand-side effects that will push in ation in one direction, while at the same time there are supply-side effects that will be pushing in ation in the other direction. A precise examination of in ation dynamics in these scenarios was not conducted.



countries would be affected in a way that was different from the key country of that region.

3.3 Global-level analysis

In the nal stage of the analysis we adopted a top-down approach, where our Global Forecasting Team tested the coherence and robustness of the scenarios at a global level. Regional and global trends were veri ed and checked for consistency.



Consultedsources

Chermack, T.J., A review of scenarios literature, Futures Research Quarterly, Summer 2001.

Maack, N. Scenario Analysis: A Tool for Task Managers, in Social Development Papers, Paper number 36, The World Bank, June 2001.

Porter, M., Competitive Advantage. The Free Press, New York, 1985.

Van Der Heijden, K., Scenarios: The Art of Strategic Conversation. Jon Wiley, New York, 1997.

Dean, M & Sebastia-Barriel, M. (2004). Why has world trade grown faster than world output. Bank of England Quarterly Bulletin.

Prasad, E. S. & Gable, J. A. (1997) International evidence on the Determinants of Trade dynamics. International Monetary Fund Working Paper.

Ramirez, R. et al. (2011). Scenarios and early warnings as dynamic capabilities to frame managerial attention, Technological Forecasting and Social Change.

Schoemaker, P. (1995). Scenario Planning: A Tool for Strategic Thinking, MIT Sloan Management Review, January 15, 1995.

World Trade: Possible Futures. Foresight Horizon Scanning Centre, UK Government Of ce for Science.

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global merchandise trade scenarios, methodology

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global trends

development of scenario

scenario development

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themes development

narratives development