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Opening a Pandora’s Box: Modeling World Trade Patterns at the 2035 Horizon
Working paper by Lionel Fontagné, Jean Fouré, 2013
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Staff Working Paper ERSD-2013-09 Date: August 02, 2013
World Trade Organization
Economic Research and Statistics Division
Opening a Pandora’s Box:
Modeling World Trade Patterns at the 2035 Horizon
Lionel Fontagné & Jean Fouré
Manuscript date: August 2, 2013
Disclaimer: This is a working paper, and hence it represents research in progress. This paper
represents the opinions of the author(s), and is the product of professional research. It is not
meant to represent the position or opinions of the WTO or its Members, nor the official
position of any staff members. Any errors are the fault of the author(s). Copies of working
papers can be requested from the divisional secretariat by writing to: Economic Research and
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OPENING A PANDORA’S BOX:
MODELING WORLD TRADE PATTERNS AT THE 2035 HORIZON
& Jean Fouré
August 2, 2013
Economic projections for the world economy, particularly in relation to the construction of
Computable General Equilibrium (CGE) baselines, are generally rather conservative and take scant
account of the wide range of possible evolutions authorized by the underlying economic mechanisms
considered. Against this background, we adopt an ‘open mind’ to the projection of world trade
trajectories. Taking a 2035 horizon, we examine how world trade patterns will be shaped by the
changing comparative advantages, demand, and capabilities of different regions. We combine a
convergence model fitting three production factors (capital, labor and energy) and two factor-specific
productivities, alongside a dynamic CGE model of the world economy calibrated to reproduce
observed elasticity of trade to income. Each scenario involves three steps. First, we project growth at
country level based on factor accumulation, educational attainment and efficiency gains, and discuss
uncertainties related to our main drivers. Second, we impose this framework (demographics, gross
domestic product, saving rates, factors and current account trajectories) on the CGE baseline. Third,
we implement trade policy scenarios (tariffs as well as non-tariff measures in goods and services), in
order to get factor allocation across sectors from the model as well as demand and trade patterns. We
show that the impact of changing baselines is greater than the impact of a policy shock on the order of
magnitude of changes in world trade patterns, which points to the need for care when designing CGE
JEL Classification: E23, E27, F02, F17, F47
Key Words: Growth, Macroeconomic Projections, Dynamic Baselines
* PSE (Paris 1) and CEPII. Email: firstname.lastname@example.org
‡ CEPII. Email: email@example.com
What will be the main patterns of world trade in the 2030s? This is an important question since it
refers to whether the institutional environment of international trade will produce a level playing-field.
There is much uncertainty in such projections. There are two main sources of errors of judgment:
projection of the drivers of international trade, and the basic unpredictability of some key variables
which is an irreducible source of error.
Thus, it is essential to properly project the drivers of international trade: conditional on trade frictions
and prices, the volume of trade is basically a function of countries’ Gross Domestic Product (GDP),
according to the extensively documented principle of gravity. The key here is to have a sound
representation of economic growth, providing GDP projections for the largest number of world
countries. Is it best to adopt a very detailed model of growth that includes regulations for example, or a
detailed representation of the public sector, or to focus on the main mechanisms and treat national
idiosyncrasies as unobservable? There is a trade-off between restricting projections to the set of OECD
countries (plus a handful of emerging economies), and including a much larger set of countries in a
more stylized growth model.
The second prerequisite is to acknowledge the unpredictability of certain key variables. Energy prices
could vary widely over the next 20 years, not primarily as the result of uncertainties surrounding
economic growth, but due to geopolitical tensions (e.g. Middle-East) or technological breakthroughs
(e.g. shale gas or jumps in energy efficiency). Female participation in the labour force is expected to
increase as countries develop, but this driver of growth is also subject to uncertainties. Education
convergence may speed up or may be hampered, with sizeable impacts on productivity. The future of
international capital mobility is also very uncertain. Productivity convergence may accelerate or
decelerate as a result of methods of technology transfer and approaches to firm mobility. Lastly, we
may observe large migrations flows as a result of unexpected push factors, while fertility rates are
Against this background, this paper has a methodological aim. First, to demonstrate how to feed into a
dynamic sectoral model of the world economy projected economic growth for a larger set of countries.
These projections use a stylized conditional convergence model of economic growth fitting three
factors (capital, labor and energy), two types of technological progress (total factor productivity –
TFP – of the capital-labor combination, and energy efficiency) and international capital mobility. We
also project saving rates (based on a life cycle hypothesis) and current accounts. GDP and saving are
projected at the country level, while constraints are imposed in terms of global balance between saving
and investment. The second aim is to illustrate how induced GDP, saving, energy efficiency and
current account trajectories to 2035 can be imposed on a Computable General Equilibrium (CGE)
model of the world economy, relying on identical assumptions about population, labor force,
education and current accounts. The CGE, in turn, will provide factor allocation (across sectors),
demand patterns (preferences and budget shares) and, thus, trade patterns, conditional on trade costs.
In addition to illustrating step by step how best to combine the modeling tools, our exercise adopts
open-minded assumptions about the evolution of the key drivers of growth. For example, we give free
rein to economic mechanisms, hence opening a Pandora’s Box of growth projections. We examine the
impact on economic growth of large and combined shocks to its main drivers. Not all shocks will
affect developing and developed economies in the same way. We combine the effects of these shocks
with possible evolutions in trade costs. In addition to energy prices, we consider the situation of a
tariff war driving countries beyond the legal World Trade Organization (WTO) framework, to post-
Tokyo Round tariff levels. We examine the impact of generalized inspection of shipments, as a
response to pandemics or terrorism. Finally we address changes in the barriers to services trade. The
aim is not to suggest that any of these scenarios is plausible given current information on the world
economy. Instead we want to show how common modeling tools can be combined to characterize the
broad range of possible world trade patterns associated with the presence of high uncertainty. We
conclude that the impact of changing baselines is greater than the impact of a policy shock on the
order of magnitude of changes in world trade patterns, which points to the need for care when
designing CGE baselines.
This methodological paper contributes to a recent and growing body of literature on long term
prospects for the world economy. Qualitative scenarios combining the two modeling frameworks as a
background to a more multidisciplinary approach centered on Europe were developed at the 2050
horizon for the European Commission (EC, 2012). The International Monetary Fund (IMF, 2011) uses
a partial equilibrium approach to address the consequences of reductions in exchange rate
misalignment with trade patterns in the presence of global value chains and possibly imperfect pass
through. Fontagné et al. (2013) proposes possible scenarios to be used as background for
environmental studies, and considers the 2100 horizon in order to explore methodological issues
associated with the use of long term dynamic baselines in CGE models. World Bank (2007), which is
closer to our approach, relies on a multisectoral model of the world economy comparable to MIRAGE,
to sketch scenarios for the world economy at the 2030 horizon. None of these studies use an explicit
growth model and the scenarios are driven by assumptions about TFP imposed on the CGE. In
contrast, Petri and Zhai (2013) rely on Asian Development Bank growth projections using a growth
model similar to ours (ADB, 2011). They use this GDP series to derive scenarios at the 2050 horizon,
with a CGE model on which assumptions about TFP, higher food prices, higher energy prices, or
protectionism are imposed. Finally, Anderson and Strutt (2012) consider the 2030 horizon and build a
baseline for the GTAP CGE model by drawing on both ADB (2011) and Fouré et al. (2010).
The value added of the present paper is to highlight the technical issues and the wide range of
uncertainty raised by combining macroeconomic and multisectoral models which open a Pandora’s
box of long term projections. For example: (i) it builds scenarios for some 150 countries, taking
account of the interactions among capital mobility and trade in goods and services; (ii) it implements
scenarios in a consistent way in both the growth (Macroeconometrics of the Global Economy –
MaGE) and multisectoral dynamic CGE (MIRAGE) models; (iii) the scenarios tackle a wide range of
potentialities paying close attention to decomposition of effects. To our knowledge, this is, the only
exercise so far on such a scale, at the 2035 horizon, adopting a methodology of consistent scenario
construction for the growth and the CGE models.
The rest of the paper is organized as follows. Assumptions related to growth projections and our
scenarios are presented in Section 1. Section 2 describes the methodology and the scenarios are
implemented in the growth model in Section 3. Section 4 summarizes the results of the global and
sectoral models of the world economy. The last section concludes.
1 A companion paper discusses our main findings in depth: Fontagné L., Fouré J. and A. Keck (2013), Simulating
world trade in the decades ahead: Driving forces and policy implications. WTO working paper, Geneva.
1. MODELING GROWTH PROJECTIONS AND DESIGNING SCENARIOS FOR THE WORLD ECONOMY
This paper is positioned at the junction between three strands of the applied economic literature: (i)
economic growth projections; (ii) design of dynamic baselines in applied general equilibrium
modeling with a focus on the environment; and (iii) design of medium and long term scenarios for the
world economy. The first two are not independent: design of dynamic baselines relies on the first
literature strand (GDP driven baselines), or provides GDP projections directly based on assumptions
about changes in sector-specific TFP (TFP driven baselines). The third stream of literature combines
quantitative elements (potentially provided by projections and baselines) with qualitative and
sometimes multidisciplinary expertise on the main drivers of economic, social and environmental
change. Below, we briefly survey the literatures related to growth projections, dynamic baselines and
1.1. Growth projections
Increased interest in long-term economic-related issues, such as environment depletion and energy
scarcity, has motivated several growth projection exercises. The business community initiated
documentation of the huge shift towards the emerging economies (Wilson and Purushothaman, 2003;
Ward, 2011), which was added to by work from international institutions. With some exceptions
(Duval and de la Maisonneuve, 2010, Johansson et al., 2012), academic work in this area was sparse,
leading to a lack of well-documented and economically-grounded projection models. This can be
explained by the huge uncertainties surrounding projections that rarely prove accurate and are
conditional on changes in the geopolitical context. Nevertheless, long-term investigations are a
prerequisite for much downstream analysis, such as provided in this paper to address future patterns of
This lack of attention contrasts with the importance of economic growth factors in the economic
literature. Starting from the standard neo-classical Solow model, mechanisms for production factor
accumulation have been identified, for instance, in the demographic determinants of saving (see, for
instance, Masson et al., 1998) and capital formation (Feldstein and Horioka, 1980), or in human
capital catch-up and productivity improvements (Aghion and Howitt, 1992). Long-term growth
projection models build on this vast literature by combining existing analyses.
At least three drivers are common to all empirical studies: capital stocks, labour force and TFP. These
factors are taken into account by Wilson and Purushothaman (2003), which combines existing labour
force projections, constant investment rates, and a convergence scenario for TFP. Duval and de la
Maisonneuve (2010) identify human capital per worker as a driver, and calibrate conditional
convergence scenarios among countries for each of these four determinants. Using a similar
framework, Johansson et al. (2012) restrict their analysis to a smaller number of countries, but
emphasize the impact of structural and fiscal policies (retirement age, trade regulation, public debt,
credit availability). Finally, Fouré et al. (2013) introduce energy as a production factor along with
energy-specific productivity, and base their projection framework on econometric analysis of both
convergence mechanisms and structural relations. Comparing the projections in these papers we
observe sizeable differences in the results. In Fouré et al. (2013), the share of China in world GDP at
2050 is almost twice the share projected by Duval and de la Maisonneuve. These differences call for
transparency in assumptions and modeling frameworks and ’open-minded’ scenarios when introducing
projections into dynamic CGEs.
1.2. Dynamic baselines
Large scale policy simulations generally rely on multisectoral dynamic models of the word economy.
CGE is the most commonly used modeling framework. Policies are simulated as shocks and then the
deviation of the variables of interest from their reference trajectory is computed. It could be argued
that the modeler’s interest is in the deviation, not the initial equilibrium. However, this would be
flawed reasoning if the focus is medium or long run policies: an economic policy affecting China
would have a dramatically different impact on the world economy were China twice as large, which
will be the case in less than ten years at current growth trends.
Since the model is exploited to determine capital accumulation, energy and primary resources prices, it
is necessary to supplement it with world data including demographics. However, since CGE models
generally do not describe the intrinsic mechanisms of growth (conditional convergence) they provide
neither a satisfactory representation of efficiency gains from combining production factors, nor
plausible trajectories for countries at different levels of development. Accordingly, it is necessary to
constrain the CGE to reproduce a pre-defined GDP growth path or a pre-defined TFP path for each
world country (or region). This is the aim of dynamic baselines.
Many baselines focus on the period up to 2020 (e.g. the GTAP model), but some exercises extend to
2050 (e.g. the Linkage model). Relying on an ambitious approach, Fontagné et al. (2013) tentatively
consider the 2100 horizon in order to provide environmental studies with a theoretically consistent
baseline of the world economy. Whatever the horizon, the building blocks of a baseline are the same.
The first step is projection of a general trajectory of world growth, based on simple and robust
economic mechanisms. There are two competing approaches to CGE modelling. The first option is to
build a scenario for factor productivity growth in order to recover GDP from the CGE model. The
second is to build a GDP scenario such that the model recovers the relevant TFP gains.
Recovering GDP from TFP growth assumptions has the advantage that availability of detailed data on
demographics or education is not a limiting factor. Moreover, it allows different sector specific
trajectories to be encompassed without over-constraining the model. However, this approach is very
sensitive to assumptions related to TFP growth and its determinants. For instance, the EPPA model
(Paltsev et al., 2005) assumes identical logistic productivity growth for all countries and sectors, and
does not implement capital productivity.
The symmetric approach of imposing GDP growth trajectories onto a CGE, and recovering the
productivity gains, is more data demanding since it is necessary, first, to project growth for every
country. Its main advantage is that it enables proper modeling of growth by taking account of
conditional convergence and possibly different types of technical progress, in line with the vast
literature on macroeconomic growth. This is a very important advantage if the interest is in long run
modeling of different kinds (mature, emerging, developing) of economies. Also, this approach allows
greater reliance on the macro projections in the literature (see Fouré et al., 2013 for a short review).
For instance, the main projections used in the GTAP model and earlier versions of MIRAGE (Decreux
and Valin, 2007) were provided by the World Bank (Ianchovichina and McDougall, 2000).
The only crucial assumption in GDP-driven CGEs is the relative dynamism of productivity in broad
sectors. Several approaches to this difficult issue have been proposed. The LINKAGE model (Van der
Mensbrugghe, 2006-a) adds a sector-specific component – labor-only productivity – to endogenous
national TFP. This approach results in: (i) a constant exogenous agricultural TFP; and (ii) a constant 2-
percentage points difference between industry and services sector productivity (the former being more
productive). There is a separate literature on agriculture-specific productivity that draws on Nin et al.
(2001). Coelli and Rao (2005) and Ludena et al. (2007) depart from the usual analysis of yields, and
start from non-parametric productivity indices based on the use of agricultural inputs. They show that
productivity in agriculture is not constant, and that its growth rate is heterogeneous across countries.
In addition to these efforts to provide the modeling community with dynamic baselines for their policy
simulations that rely on CGEs, a related literature stream specifically tackles environmental issues.
Two key issues are raised: first the productivity of energy and its impact on CO2 emissions, and
second, natural resources scarcity and it’s the direct link to energy prices. In both cases, assumptions
focus on one variable such that the other adjusts.
Similar to environmental baselines, the first approach is to rely on CO2 emissions from other
institutions (or, equivalently, on energy demand), such as in the PACE model (Böhringer et al., 2009).
In this case, improvements to the carbon intensity of goods are deduced, although no comprehensive
framework for energy consumption is developed. In addition, particular attention has to be given to the
coherence between the emissions projections’ underlying growth assumptions and the growth model
projections because CO2 emissions depend heavily on economic activity.
The second approach consists of developing a scenario for Autonomous Energy Efficiency
Improvements (AEEI) as in the EPPA model. These AEEI encompass non-price induced, technology-
driven productivity changes. An exogenous time trend for energy productivity is imposed in order to
control for the evolution of demand reduction, which scales production sectors’ use of energy per unit
of output. These AEEI are specific to broad regions (10 regions in the EPPA) with two distinct
profiles. On the one hand, China and the Developed Countries face a regularly increasing AEEI. On
the other hand, other countries’ AEEI first decrease (up to around 2035) and then increase at different
rates. These discrepancies are driven by the empirical observation that energy productivity has
regularly increased in countries with well-developed industry and services sectors, and have stagnated
or even decreased in industrializing countries. The Linkage model implements a mixed framework, in
which energy demands are imposed to recover productivity changes with the exception of crude oil
consumption which is driven by an exogenous productivity scenario.
A problematic issue related to CO2 emissions and energy consumption is the limitation inherent to
CGE modeling. These two variables are measured in physical quantities, although variables in CGE
models traditionally are in dollars at constant prices. Laborde and Valin (2011) point out that using
Constant Elasticity of Substitution (CES) functional forms for monetary values leads to incoherence in
substitutions when commodities are relatively homogenous, as is the case for energy goods. There are
two ways to deal with this issue. One can build a world price matrix for physical quantities of energy
goods, such that they account for changes in both value and quantity. A more parsimonious approach
is to impose on the model that production, consumption and trade are coherent in both monetary units
and physical quantities.
Finally, the question of natural resource depletion can be approached in two ways. As underlined by
Paltsev et al. (2005), long run dynamics of energy prices are captured by natural resources depletion.
Therefore, it is possible to model this depletion and deduce the corresponding energy prices, or to do
the reverse. The first solution is chosen by the EPPA model, which incorporates resource-specific
natural resources use as well as additional recoveries. The second involves exogenously fixing energy
prices, as in the ENV-Linkage model (an option also available in EPPA), such that natural resources
adjust to match targeted prices. The assumption in ENV-Linkage is to rely on IEA’s world price
projections up to 2030 and then assume a 1% growth in oil prices.
1.3. Scenario design
In what follows, we briefly survey some medium term scenarios of the world economy relying on a
combination of growth projection and CGE modeling. These exercises were developed by the World
Bank, the OECD, Petri and Zhai (based on Asian Development Bank projections) and Anderson and
Strutt (based on Asian development Bank and our own projections).
World Bank (2007) relies on LINKAGE (a multisectoral model of the world economy comparable to
MIRAGE, described in van der Mensbrugghe, 2006) to draw scenarios for the world economy at the
2030 horizon. Instead of recovering TFP from the CGE on which GDP and factor accumulation would
be imposed, TFP assumptions are imposed on the CGE to obtain GDP. In addition, energy efficiency
is assumed to improve exogenously by 1% per year worldwide. Energy efficiency is derived
theoretically and projected on a country basis in MaGE, before being introduced in the CGE in our
exercise. Finally, international trade costs are assumed to decline by 1% per year, in line with our own
pre-experiment aimed at mimicking the historical income elasticity of international trade. This
exercise was calibrated on the GTAP-2001 database (we used GTAP-2004 for MIRAGE).
Petri and Zhai (2013) combine Asian Development Bank growth projections at the 2050 horizon
(ADB, 2011) with a CGE model in order to develop their scenarios. In addition to a focus on Asia
rather than a world-wide perspective, the big difference from our study is not the horizon considered,
but the methodology used to design the scenarios. Our approach involves three steps. We first design a
business–as-usual scenario of world growth, and run a pre-experiment in order that our CGE
reproduces income trade elasticity observed in the past. Second we construct two scenarios for the
growth model, which are then imposed on the CGE in a consistent way. Third, we shock the CGE,
completing our two scenarios with evolutions, such as possible changes in transaction costs, that can
be tackled only by the CGE. In contrast, Petri and Zhai use a business-as-usual macroeconomic
baseline and then proceed to our third step.
This is an important difference since many of the
assumptions of our scenarios (e.g. fertility, female participation in the labor market, education catch
up) will have cascading effects for growth and trade, channeled through the different mechanisms in
the two models.
Anderson and Strutt (2012) consider the 2030 horizon and build a baseline for the GTAP CGE model.
They combine growth rates for GDP, investment and population from ADB (2011), with our (previous
set of) projections for the world economy (Fouré et al., 2010) for those countries not included in the
ADB projections. Finally, skilled and unskilled labor growth rate projections are from Chappuis and
Walmsley (2011). Historical trends for agricultural land from the Food and Agriculture Organization
(FAO), and mineral and energy raw material reserves from British Petroleum (BP, 2010), are extended
over the next two decades. TFP growth rates are recovered from the CGE model. Scenarios are
implemented (as in Petri and Zhai) directly in the CGE: they show a drop in TFP and further trade
liberalization. Implications for world trade are derived. Two drawbacks to this approach are the
combining of different growth models (ADB and MaGE-V1.2), and the implementation of scenarios
directly in the CGE rather than using two modeling frameworks. Also, the scenarios can be questioned
since further trade liberalization is not necessarily an outcome for the future economy. Anderson and
Strutt acknowledge the absence of scenarios for trade costs, transport costs and current account
imbalances, all dimensions included in our analysis.
2 Their simpler approach allows deeper developments in terms of income distribution. While we rely on a
representative household and two labor categories (skilled, unskilled), Petri and Zhai supplement their CGE with an
income distribution module which allocates total consumption to four income bins.
Finally, the OECD (Chateau et al., 2012) uses the ENV-Growth model in order to design climate
change scenarios in line with the five Shared Socioeconomic Pathways (SSP) developed by the
Integrated Assessment Modeling Consortium. These scenarios are organized around the trade-off
between climate change mitigation and adaptation, both translated into demographic (population and
education), technological (catch-up speed and frontier growth) and natural resources (prices and
available resources) related scenarios, and both implemented in the growth model. They clearly
identify the drivers of growth as capital accumulation, TFP, labor force (and to a lesser extent human
capital and energy), but cannot directly investigate the saving-investment relationship due to the
original specifications of the SSPs, nor explicitly deal with uncertainty in labor force participation
with trade integration (except via positive externalities on in TFP). These scenarios are being
integrated with the OECD’s ENV-Linkages CGE model, following a method similar to ours; to our
knowledge, results are not yet available.
2. WHAT WE DO
This paper adopts the GDP-driven CGE approach described above. To proceed, we start with a growth
model derived theoretically, estimated, and used to make projections for more than 140 countries. The
building blocks of this model are conditional convergence (based inter alia on human capital
accumulation), energy use and efficiency, demographic transition, and saving behavior. This first step
is performed with the MaGE model (Fouré et al., 2013). Using this framework, we implement
scenarios for the world economy. The second step consists of imposing GDP trajectories (depicted in
the various scenarios) from our growth model onto the CGE, and using sector-specific constraints and
exogenous agricultural productivity to depict coherent sector disaggregation. To proceed we use a new
version of MIRAGE, known as MIRAGE-e – the ‘e’ referring to environment (Fontagné et al., 2013).
The CGE provides sector decomposition of growth, factor allocation, country specialization and world
trade patterns, these last being our ultimate objective. In the second stage, additional shocks are
imposed on the CGE. This two-step approach is ultimately mobilized to build alternative scenarios of
the world economy.
Below we describe the growth model (MaGE), the CGE model (MIRAGE-e), and the design of the
2.1. The growth model
Projections of world macroeconomic trends are elaborated with the MaGE model proposed in Fouré et
al. (2013). Based on a three-factor (capital, labor, energy) and two-productivity (capital-labor and
energy-specific) production function, MaGE is a supply-side oriented macroeconomic growth model,
defined at country level for 147 countries. It consists of three steps. First, production factor and
productivity data are collected for 1980 to 2009. Second, behavioral relations are estimated
econometrically for factor accumulation and productivity growth, based on these data. Third, these
relations are used to project the world economy.
Using World Bank, United Nations and International Labour Organization data, we built a dataset of
production factors and economic growth for the period 1980-2009. Our theoretical framework consists
of a CES production function of energy and a Cobb-Douglas bundle of capital and labor. This
theoretical framework allows recovery of energy-specific productivity from the profit-maximization
program of the representative firm, while capital and labor productivity are recovered as a Solow
Behavioural relations are econometrically estimated from this dataset for population, capital
accumulation and productivity. Population projections are given by United Nations population
projections, split across 5-year age bins. For each of these age groups, we estimate education and then
deduce labor force participation. Educational attainment follows a catch-up process to the leaders in
secondary and tertiary education, with region-specific convergence speeds. While male labor force
participation follows the logistic relation determined by the International Labor Organization, female
participation changes with education level.
Capital accumulates according to a permanent-inventory process with a constant deprecation rate. On
the one hand, investment depends on saving with a non-unitary error-correction relationship which
differentiates long-term correlation between saving and investment and annual adjustments around this
trend. Because of the significant differences we found between OECD and non-OECD members, both
levels of estimation are conducted separately for the two country groups. On the other hand, saving
depends on the age structure of the population consistent with both the life-cycle hypothesis and
Capital-labor and energy productivity follow catch-up behavior with the best-performing countries.
The former is conditional on and fuelled by education level (tertiary education for innovation and
secondary education for imitation); the latter is modified by the level of development to reflect the
sectoral organization of countries.
We are able to recover GDP and factor projections given the theoretical link between energy
productivity, energy price (exogenously imposed) and energy consumption.
2.2. The CGE model
We use a new version of the multisectoral, multi-regional CGE model MIRAGE (Bchir et al., 2002;
Decreux and Valin, 2007), which was developed and has been used extensively to assess trade
liberalization and agricultural policy scenarios (e.g., Bouët et al., 2005, 2007). For simplicity, we use
the version of the model fitting perfect competition. The MIRAGE-e version of the model proposes a
different modeling of energy use, and introduces modling of CO2 emissions (Fontagné et al., 2013).
MIRAGE-e was adapted to the exercise conducted here. MIRAGE has a sequential dynamic recursive
set-up which is consistent with the output of MaGE: capital accumulation and current account will be
driven by the results of the first step of our exercise. Macroeconomic closure consists of having the
share of each region in global current accounts imbalances varying yearly according to the projections
On the supply side, in this perfect competition version of MIRAGE, each sector is modeled as a
representative firm, which combines value-added and intermediate consumption in fixed shares.
Value-added is a bundle of imperfectly substitutable primary factors (capital, skilled and unskilled
labor, land and natural resources) and energy.
We assume full employment of primary factors, whose growth rates are set exogenously based on
MaGE projections. Installed capital is assumed to be immobile (sector-specific), while investment is
allocated across sectors according to their rates of return. The overall stock of capital evolves by
combining investment and a constant depreciation rate of capital. Skilled and unskilled labor are
perfectly mobile across sectors, while land is assumed to be imperfectly mobile between agricultural
sectors, and natural resources are sector-specific.
Firms’ energy consumption comprises five energy goods (electricity, coal, oil, gas and refined
petroleum), which are aggregated in a single bundle that mainly substitutes for capital. There is no
consensus in the literature about the extent to which capital and energy are substitutable. It can vary
according to the vintage of capital (e.g. from 0.12 to 1 in the GREEN model), or be fixed between 0.5
(GTAP-E model) and 0.8 (PACE model). Since energy consumption is very sensitive to this elasticity
of substitution, its calibration is vitally important. We choose to reproduce stylized energy
consumption trends as in International Energy Agency projections to 2025 (IEA, 2011), which leads
us to calibrate this elasticity as in GTAP-E. The architecture of the energy bundle defines three levels
of substitution. Energy used can be delivered by electricity or fossil fuels. Fossil fuels can be coal or
oil, gas or refined oil. Thus, oil, gas and refined oil are more inter-substitutable than with coal and,
finally, electricity. Values of the elasticities of substitutions were chosen in line with the literature:
electricity-fossil fuel substitution is based on Paltsev et al. (2005), the other two elasticities are from
Burniaux and Truong (2002).
Finally, the value of the energy aggregate is subject to the efficiency
improvements projected by the growth model. As stressed above, in CGE models CO2 emissions and
3 In order to avoid unrealistic results, we assume ‘constant energy technology’ in non-electricity energy production
sectors (coal, oil, gas, petroleum, coal products): it is impossible to produce crude oil from coal, or refined petroleum
from gas and electricity. In these sectors, substitutions between energy sources are not allowed (Leontief formulation).
energy consumption in physical quantities compared to variables measured in dollars at constant
prices, present a challenge. In practice, using CES functional forms with variables in monetary units
leads to inconsistencies when trying to retrieve physical quantities. In addition to the accounting
relations in constant dollars, MIRAGE-e integrates a parallel accounting in energy physical quantities
(in million tons of oil-equivalent) based on the use of two country- and energy-specific endogenous
adjustment coefficients, such that CO2 emissions can be computed in millions of tons of CO2. Carbon
dioxide emissions are recovered as proportional to energy consumption in quantity, using energy-,
sector- and country-specific parameters calibrated on the data.
Production factors in MIRAGE-e are evolving, in yearly steps, as follows. Population and
participation in the labor market evolve in each country (or region of the world economy) according to
the demographics used in MaGE. This determines the labor force as well as its skill composition
(skilled, unskilled). Primary resources and land are considered at their 2004 level: prices adjust
demand to this supply. Instead of modeling the fossil energy sectors, we rely on the more specialized
modeling of the International Energy Agency (IEA, 2011), which provides us with projections for
coal, oil and gas prices up to 2035. Given demand, resources adjust accordingly in MIRAGE. Capital
is accumulated according to the usual permanent inventory assumption. Capital usage is fixed (we use
a 6% depletion rate), while gross investment is determined by the combination of saving (the saving
rate from MaGE applied to the national income) and comparison of the current account and domestic
absorption. Finally, while total investment is saving-driven, its allocation is determined by the rate of
return on investment in the various activities. For simplicity, and because we lack reliable data on
Foreign Direct Investment (FDI) at country of origin, host and sectoral level, we allow capital flows
between regions only through the channel of current account imbalances. We are aware that FDI is
channeling technology transfer and productivity catch-up; this mechanism will be integrated separately
when building the scenarios.
Firms’ demand for production factors is organized as a CES aggregation of land, natural resources,
unskilled labor, and a bundle of the remaining factors. This bundle is a CES aggregate of skilled labor,
and another bundle of capital and energy. Finally, energy is an aggregation of energy sources as
On the demand side, a representative consumer from each region maximizes its intra-temporal utility
function under its budget constraint. This agent, which could be a household or government, saves a
part of its income. This behavior is determined by the saving rate projected by the growth model on
the basis of combining individual countries’ demographic profiles with a life-cycle hypothesis.
Expenditure is allocated to commodities and services according to a LES-CES (Linear Expenditure
System – Constant Elasticity of Substitution) function. This assumption means that, above a minimum
consumption at sectoral level, consumption choices between sectors are according to a CES. This
assumption is a tractable representation of the preferences in countries at different levels of
development. Thus, it is well suited to our purpose.
Then, within each sector, goods are differentiated by their origin. A nested CES function allows for a
particular status for domestic products according to the usual Armington hypothesis (Armington,
1969). We use elasticities provided by the GTAP database (Global Trade Analysis Project) and
estimated by Hertel et al. (2007). Total demand is built from final consumption, intermediate
consumption and investment in capital goods.
Efficiency in the use of primary factors and intermediate inputs is based on the combination of four
mechanisms. First, agricultural productivity is projected separately, as detailed in Fontagné et al.
(2013). Second, energy efficiency computed by MaGE is imposed on MIRAGE (it enters the capital-
energy bundle). Third, a 2 percentage point growth difference between TFP in manufactures and
services is assumed (as in van den Mensbrugghe, 2006). Fourth, given the agricultural productivity
and the relation between productivity in goods and services, MIRAGE-e is able to recover
endogenously country specific TFP from the exogenous GDP (from MaGE) and production factors.
While this TFP is recovered from the pre-experiment, it is set as exogenous in the simulations of the
scenarios, as explained later. Dynamics in MIRAGE-e is implemented in a sequentially recursive
approach. That is, the equilibrium can be solved successively for each period by adjusting to the
growth in the projected variables described above. For this long-run baseline, the time span is 31
years, the starting point being 2004.
Feeding the world population and providing the industry with its agro-related primary resources will
be continuing challenges in future decades. It is therefore essential to properly assess to what extent
technical progress in the agricultural sector will mitigate these problems. Whereas data on labour-force
in agriculture are available, there are no aggregated data on capital in agriculture, although there are
some disaggregated data (machinery, land, etc.). We need to implement a multi-input, non-parametric
methodology, such as the Malmquist productivity index, based on productivity distance to a global
(moving) frontier (for details, see Fontagné et al., 2013). We use FAO data for agricultural production
and inputs. We chose two agricultural outputs (crops and livestock) and, on the basis of their common
occurrence across the world, and data availability, five inputs (labor, land, machinery, fertilizers,
livestock). Inputs can be allocated either to crops or to livestock, or be shared between these sectors.
Table 1 – Sector and country aggregation in MIRAGE
European Free Trade Association
Australian and New Zealand
Association of Southeast Asian Nations
Rest of Africa
Rest of Europe
Rest of Latin America
Rest of the world
Petroleum and coal products
Cars and Trucks
Finance, Insurance and Business services
MIRAGE-e was calibrated on the GTAP dataset version 7, with 2004 as base year. Our data
aggregation isolates all energy sectors and combines other sectors into main representative sectors in
agriculture, manufacturing and services. For the regional aggregation, we retained the main developed
(e.g. EU, Japan and the US) and emerging (e.g. Brazil, Russia, China) economies, aggregated with the
rest of the world on a geographical basis (see Table 1). We include international transaction costs and
non-tariff measures (NTM) in services, modeled as an iceberg trade cost. Data to calibrate trade costs
associated with time were calibrated using a database provided by Minor and Tsigas (2008), which
adopts the methodology in Hummels and Schaur (2012); NTM in services are ad-valorem equivalents
taken from Fontagné et al. (2011).
2.3. The dynamic baseline calibration
A difficulty related to large scale CGE models is whether the main stylized facts of world trade can be
reproduced easily using this framework. Similar to the well-documented magnified reaction of world
trade to booms and busts in the world economy (see Figure 1), the exercise is hopeless. CGE represent
long term equilibrium, and cannot reproduce short term adjustments.
More importantly, we want our CGE to reproduce the medium term income elasticity of trade present
in historical data. Table 2 shows trade income elasticity for different sub-periods.
Figure 1 – World trade-to-income elasticity of trade (goods)
Source: Authors’ calculation. WTO data 1950-2011.
Table 2 – World trade to income elasticity (goods), for different sub-periods
1950-59 1960-69 1970-79 1980-89 1990-99 2000-09 1950-2009
1.62 1.54 1.31 1.19 2.82 1.42 1.64
Source: Authors calculation. WTO data 1050-2011.
The 1990s have been documented as conveying an increase in this elasticity (Freund, 2009, partly
because value chains have been fragmented globally and partly because the contributors to world
economic growth chose export-oriented growth (e.g. China). We can hardly assume that the
phenomenon will continue with the same intensity in the forthcoming two decades because there is a
physical limit to product fragmentation and because complexity costs are increasing while the
opportunities for exploiting new comparative advantages are most exhausted.
In a much longer perspective, trade in goods since 1950 has increased faster than industrial or
agricultural production, and even more than GDP. Long-term elasticity with respect to GDP was 1.46
over the period 1950-1989, before the rapid growth in world trade during the 1990s. This half-century
experience is of the order of magnitude that a model like MIRAGE should aim to reproduce. This
elasticity mirrors increases in world trade that have several determinants:
- energy prices (and especially the oil price) have been decreasing since the 1970s;
- technological progress has occurred in the transport sector;
- tariffs have decreased over time;
- some non-tariff measures have been phased out;
- global value chains have been fragmented, leading to increased discrepancy between trade
measured in gross terms, and GDP measured as value added terms.
Therefore, using MIRAGE-e, we develop two baselines in order to encompass elasticity of world trade
in goods (and manufacturing goods in particular). The first, ‘Past Trade’ tries to reproduce historical
evidence; ‘Pre-experiment’ adjusts the model in order to start with a plausible elasticity for the
In order to test whether MIRAGE-e can reproduce historical evidence, we first implement a sensitivity
reference case (‘Past Trade’) using different sets of assumptions. The basic case is the standard version
of the model with no changes in transaction costs. Tariffs are kept constant. There is no TFP growth in
the transport sector beyond what is endogenously determined by the model to match growth
projections from MaGE, as referred to above. There is no change in trade costs, which are kept at their
initial (2004) level. Energy prices are taken from the central scenario already discussed. We run the
model over 30 years and compute the trade to income elasticity.
Results reported in Table 3 show that the trade-to-income elasticity embodied in MIRAGE-e is low, as
usual for any model of this type: 1.22 (first row in Table 3). This elasticity matches what was observed
during the 1980s.
In order to reproduce the higher elasticity observed in the 1950s and 1960s, shown in the middle of
panel in Table 3, we integrate a combination of decreasing trade costs, progress in transport
technologies, low energy prices and trade liberalization, based on the following assumptions which
reproduce the above mentioned determinants of long term income trade elasticity:
- very low energy prices (decreasing by 3% yearly for oil, and no growth for coal and gas,
according to the 1980-2004 average from BP historical data);
- 2% additional TFP growth in the transport sector compared to other services (containerization,
standards, etc.), in line with estimations of sectoral TFP differentials by Wolff (1999) for the
- 50% cut in trade costs in the broad sense (time, red-tape, quality of the communications, etc.),
- 4% annual decrease in tariff rates (corresponding to the evolution of simple average tariffs
between 1973 and 2004 in Deardorff and Stern, 1983).
Table 3 – Long-term trade to income elasticity in MIRAGE-e under alternative assumptions
Baseline Assumptions on Elasticity
in transport Trade cost cut Tariffs cuts
Standard Ref 0% 0% 0% 1.22
Price Decreasing - - - 1.33
TFP transport - 4% - - 1.28
Trade cost - - 50% - 1.37
Tariffs - - - 4% annual 1.32
Past trade Decreasing 4% 50% 4% annual 1.65
TFP transport - 2% - - 1.27
Trade cost - - 25% - 1.29
Pre-experiment Ref 2% 25% 0% 1.34
Note: All the scenarios are implemented between 2004 and 2035, linearly (trade costs) or at constant
growth rate (TFP and decreasing energy price).
Source: MIRAGE, author’s calculations.
The elasticity observed in the 1970s can be reproduced only by introducing in the model the observed
tariff cuts. No additional assumption is required about transport technologies or trade costs. Indeed,
the assumption of low energy prices is irrelevant for that period.
The elasticity observed in the 2000s can be nearly matched with a (large) drop in trade costs.
alternatively introduce large TFP gains in the transportation sector or even a decreasing price for
Finally, what this kind of model cannot reproduce with plausible assumptions, is the trade to income
elasticity observed in the 1990s; as already stressed, this period might be unique and it should not be
reproduced in the baseline used for projections for future decades.
Regarding our reference scenario, we believe that many of the conditions of the 20th century that lead
to such high elasticity of trade to GDP will not be reproduced in upcoming decades, in particular those
regarding energy prices. For this reason, we implement a pre-experiment with the following
- 2% additional TFP growth in transport sector;
- 25% trade cost cuts;
- reference energy prices;
- no tariff cut.
As the decomposition shows, the boost in TFP for the transport sector and the drop in trade costs have
effects of similar magnitude. When combining these assumptions, MIRAGE-e reproduces a long term
elasticity of trade equal to 1.34, in line with what was observed in the 1970s and the 2000s. This
pattern of MIRAGE-e, shown in the last row of Table 3, is the new reference which we will apply the
scenarios described below.
4 Indeed, using a time span finishing in 2011 would give a higher elasticity.
2.4. Two scenarios for the world economy
We next illustrate construction of the two contrasting scenarios that will be applied in a consistent way
to the growth model – MaGE, and to the CGE of the world economy – MIRAGE-e. We first present
the scenarios in MaGE, followed by their implementation in MIRAGE-e.
In order to design contrasting scenarios of the world economy, we combine various shocks with the
aim of broadening the cone of possible trajectories, as follows. For simplicity, we refer to the resulting
scenarios that combine these differentiated shocks, as ‘low’ and ‘high’, to describe the expected
changes in world GDP.
For the low and high scenarios, we assume changes in education attainment, female participation in
the labor market, and energy prices that apply homogenously across countries. In contrast, high
income-countries, as opposed to low- and middle-income countries, are affected differently by
changes in fertility, migration, energy efficiency, TFP and capital mobility.
The shocks imposed on
MaGE in a first step are reported in Table 4.
The first variable to experience a shock is demography. We start from the UN’s low and high fertility
scenarios. The low case is defined as lower fertility in middle- and low-income countries. This will
have a negative impact on growth, although not necessarily on income per capita. We do not assume
any reduction in fertility in high-income countries. Symmetrically, the high case corresponds to higher
fertility in the middle- and low-income economies only. In certain developed countries it is possible
that an unexpected rebound in fertility will be observed, but this outcome cannot be considered a
The second variable of interest is migration. There are some migration flows embedded in the UN’s
demographic projections. These correspond to the ‘normal migration assumption’ where net migration
is generally kept constant, at least for our time horizon. The UN introduces changes on a country by
country basis, corresponding to anticipated immigration policy changes, and the imposed shock
enhances these flows. We consider migration from Sub-Saharan Africa (SSA), and the Middle-East
and North Africa (MENA) to Europe, and from Latin America to the US. First, annual additional
outflows to Europe amount to 1.2 million people from SSA and 800,000 from MENA. This
corresponds roughly to a doubling of net migration to Europe, compared to UN data for the period
2000-2010. Second, migration from Latin America to the US is 1.2 million persons per year, which is
double North America’s net inflow. We were not able to trace UN projected migrations precisely (by
sex, age group or education level). Therefore, the initial migrants in UN projections, who are present
in all scenarios, are assumed to resemble the local inhabitants. Additional migrants in the high case
belong to the working-age population (15 to 64), and are divided across age groups and gender
proportional to the shares of these categories in the population of the country of origin; they are
assumed to maintain their initial level of education. Only in relation to life expectancy do the
additional migrants mimic the host country natives.
We also address the impact of accelerated or decelerated convergence in education. In MaGE, catch up
to the education frontier plays an important role because it drives convergence in TFP. The
productivity frontier is not constant because the leading country (which can change over time) is
continuously improving its education level. For each region of the world, we estimated in MaGE the
5 In MaGE, countries are classified by income level, which drives conditional convergence. Our shocks are defined
using the World Bank income classification. For the presentation of the results and the simulations with MIRAGE, we
use the WTO country group classification of developed and developing. The correspondence between the two
classifications is provided in Appendix B.
structural speed of convergence to the education frontier. We consider the half-life time
process and increase it by 50% in the low case. We expect this to reduce technological catch-up and
hamper growth. Alternatively, we divide by two the estimated half-time in order to take account of
acceleration in the accumulation of human capital in middle- and low-income countries.
Table 4 – Shocks to MaGE
Variable \\ Scenario Low High
Reference fertility in high income
countries, low fertility in other
Reference fertility in high-income
countries, high fertility in other
Migrations Reference case Additional migrations from SSA and
MENA to EU and from SAM to
1.5 half-life time 0.5 half-life time
Female participation No improvements Reference case
Differentiated TFP -50% TFP growth rate for low and mid
income countries, -25% for high-income.
+50% TFP growth rate for low and mid
income countries, +25% for high-
Energy price High price scenario (EIA) Low price scenario (EIA)
+50 % high income in 2050, reference for
+50% for low and mid income in 2050,
reference for other
Capital mobility Convergence to I=S in 2050 Low correlation coefficient (non-
OECD) for everyone
The fourth variable to be affected by a shock in MaGE is female labor market participation. In the low
case we consider that the expected improvement in middle- and low-income economies, an additional
driver of growth, will not occur for societal reasons. In the high case we keep this improvement as in
the reference case.
The fifth variable of interest is TFP. As already explained, TFP is endogenous in MaGE. It is
determined by a catching up process in which distance to the technology frontier and education drive
convergence. We add an exogenous gain or loss of TFP respectively in the high and low cases, but
otherwise the process is kept unchanged. There will be a TFP gain as a result of additional technology
transfer via FDI, exports (e.g. via contracts related to utilities, armament, power generation) or
collaborative research. The TFP growth rate will increase with larger benefits for catching up countries
with higher initial TFP growth rates. In contrast, in the low case, a deteriorating economic
environment will produce the opposite evolution, which will be topped by capital destruction, and long
term unemployment. Again the impact will be greater for catching up countries, and will have a
detrimental effect on their growth rates. TFP shocks correspond to previously observed episodes.
Figure 2 shows that, during the past 30 years, there have been many periods and many countries when
TFP growth has slowed or become negative, or alternatively experienced buoyancy. The most notable
include the transition of Russia after the fall of the USSR, and Japan during the 1990s. Our scenarios
try to consider the impact of similar prolonged phases that are not captured by econometric estimation.
The mechanisms described above (FDI, technology transfer, collaborative research) are topped by an
overall technological boost in the high case, leading to a 50% increase in TFP for middle- and low-
6 Half-life time is defined as the time necessary to reduce by half the distance to the education frontier, assuming a
income countries, and a 25% increase for high income economies only. Everyone is better off, but the
technological leadership of high-income countries is eroded. The low case reflects ‘hard times’ when
limited TFP gains in the North (just three-quarter of the gains projected in the reference scenario) lead
to lower levels of technology transfer and tensions over intellectual property rights among this group
of countries. As a result, TFP gains are even more reduced (-50%) in the group of catching-up
countries. This is a less cooperative world in which everyone is worse off, but where the richest
countries preserve part of their initial advantage.
Figure 2 – Level of TFP and TFP leaders, 1980-2009
Notes: TFP level is corrected for oil rents bias. Leader countries each year are the 5
countries with highest TFP, excluding Luxemburg. These countries include the USA,
Denmark, Sweden, Ireland, Belgium, France, the Netherlands and Germany, depending on
the year considered.
Source: MaGE, authors’ calculation.
Another variable of interest is national energy efficiency. Technological breakthroughs could have a
major impact on energy efficiency, since sectoral transition to less energy-intensive activities is
endogenous, monitored by the conditionality of catching-up in energy productivity to GDP per capita,
as depicted in Figure 3 for the MaGE reference case.
Here, we assume that countries at different levels
of development will benefit unevenly from this progression. In the low case, technical progress occurs
in the high income countries, but is not passed on to the middle- and low-income ones. Efficiency
gains in energy accordingly are concentrated in the already most efficient countries (we assume a 50%
increase with respect to the reference scenario), with lower impact on the overall energy efficiency of
the world economy. In the high case efficiency gains are concentrated in the middle- and low-income
countries, based on the assumption of increased transfers of the existing technology.
7 At the beginning of projections, almost every country had passed the turning point between efficiency decrease and
Figure 3 – Energy intensity of the GDP in the reference scenario of MaGE, 1980-2035
(barrel of oil per 1,000 2005 USD of GDP)
Source: MaGE, authors’ calculation.
Figure 4 – Saving-Investment balance in the reference scenario, 1980-2035 (percentage of GDP)
Source: MaGE, authors’ calculation.
Finally, capital mobility is an important determinant of growth since it shapes the difference between
national saving and investment. Increased capital mobility should allow better allocation of capital
worldwide and, thus, enhance growth overall. In the high case, we align the correlation between
domestic saving and investment at world level with the lowest regional level, which is the one of the
non-OECD countries. Note that this choice leads to no gain in capital mobility for the latter group of
countries. However, in the low case, there is ‘financial de-globalization’, meaning that countries return
progressively to financial autarky by 2050 (beyond the horizon of this exercise: thus, not achieved at
the 2035 horizon).
Notice that assumptions about demographic profiles and capital mobility will modify the dynamics of
the saving-investment balance depicted in Figure 4, characterized by a natural rebalancing of the
The next step is to use the results of these two scenarios imposed on MaGE as inputs for MIRAGE.
This provides two baselines that will experience additional shocks to the variables not included in
MaGE (e.g. sectoral value added) and will become ‘High Sim’ and ‘Low sim’. For each scenario we
compute the baseline scenario in MIRAGE on which we impose GDP, population, total labour force,
skill level, saving, energy productivity, agricultural productivity, current account and energy price. We
reproduce global trends in transaction costs consistent with the observed income elasticity of
international trade (as already discussed) and recover TFP. As the energy price is exogenous, natural
resources adjust endogenously at the baseline. Our two baselines assume status quo for tariffs as well
as non-tariff barriers. We ultimately establish high and low for MIRAGE, which we call ‘High Ref’
and ‘Low Ref’ respectively (Table 5).
Table 5 – The two baselines of MIRAGE-e (2035 horizon)
Low Ref High Ref
MaGE scenario Low High
Energy prices High price Low price scenario
Transaction costs for goods* 25% cut 25% cut
Transports TFP* 2% annual growth 2% annual growth
Tariffs No change wrt 2007 No change wrt 2007
*As discussed in the text, these two trends were introduced in a pre-experiment in order to
reproduce long-term income elasticity of world trade.
The final step consists of implementing trade scenarios in each of the two baselines (tariffs, transaction
costs and NTM in services) with GDP and energy price set to be endogenous (TFP and natural
resources fixed at their baseline level). These scenarios are summarized in Table 6. In the ‘Low sim’
scenario an increase in transaction costs and tariff is applied to the low baseline. The ‘High sim’
scenario starts from a high baseline and describes a more cooperative world where the barriers to trade
in goods and services are reduced compared to their 2007 level.
In the ‘Low Sim’ scenario, we impose on the ‘Low Ref’ baseline a series of shocks reproducing an
increase in transaction costs and a tariff war. In the context of a low growth profile and, possibly,
geopolitical tensions, countries increase bureaucracy at the border, and systematize container
scanning. Developing countries are more affected by this since their exports are perceived as ‘unsafe’
by the advanced economies. This degradation of the world trading environment is progressive: we add
a 20% increase in the transaction costs for developed countries’ exports, linearly over the period
considered (2014-2035). The increase peaks at 50% for developing countries’ exports. The second
dimension to this degradation of the trading environment refers to protectionism. Countries either
respect de jure WTO commitments and rely almost exclusively on anti-dumping duties and
safeguards, or revert to using their bound tariffs, or contribute to the non-cooperative scenario in
which earlier commitments cease to be respected. To reproduce this outcome, we assume a
progressive return of the world economy to post-Tokyo Round levels of protection, over the two
decades considered. This is implemented as follows.
Table 6 – The two scenarios implemented in MIRAGE-e (2035 horizon)
Low Sim High Sim
Applied to baseline Low Ref High Ref
Transaction costs for
+50% from developing countries
+20% from developed countries
-50% from developing countries
-20% from developed
Tariffs on goods Trade war scenario (Tokyo round
-50% compared to 2004
NTM in services No change Liberalization in services (-50%)
For manufacturing, we try to reproduce post-Tokyo Round tariffs. We use available data from
Deardorff and Stern (1983) by sector. For aggregated regions, we adopt a simple average. For the rest,
we take the oldest data from the World Development Indicators (WDI) to which we add a 25%
increase. For the agro-food sector, we simply reverse the Uruguay round (Agreement on Agriculture)
and add 36% to developed countries’ tariffs and 24% to those of developing countries; for energy
goods, we keep tariffs constant. Tariffs within Free Trade Areas (FTAs) are assumed not to be subject
to a tariff war and are not increased in our scenario. This affects the EU27, EFTA, NAFTA, AU-NZ
and USA-AUNZ. These tariff increases provide a target for 2030, which we implement linearly
between 2013 and 2030. Tariffs are constant after 2030. We present this procedure in Table 7 for
The high scenario (‘High sim’) describes a more cooperative world. Firstly, tariffs on goods are
reduced (-50%) compared to their 2007 level. Secondly, benefiting from sustained growth and rapid
convergence of emerging countries (increasing income levels and reducing costs of competition),
countries address the issue of trade in services. There is a large decrease in the barriers to trade in
services, and transaction costs on goods continue to decrease and decrease faster for developing
countries where progress margins are bigger. We assume a 50% decrease in transaction costs for
developing countries and a 20% decrease for developed countries.
Regarding the reduction in the
barriers to trade in services, we start with the ad valorem equivalents computed by Fontagné et al.
(2011), modeled here as a trade cost. We then set the target to -50% in 2030, and this phasing out is
implemented linearly between 2013 and 2030. The outcome of this exercise is shown in Table 8.
Finally, the overall three-step method is summarized in Figure 5.
8 This methodology implies that targeted tariffs may be lower than or equal to the 2004 tariff in GTAP. For primary
products, tariffs are not available in Deardorff and Stern and we use 2004 levels. For 252 triplets out of 9,261 this
situation occurs as a result of averaging bias (sectors where the tariff decreased to less than the average value) or to
aggregation bias (our simple average does not match the GTAP weighted average).
9 Recall that the impact of energy prices on demand for transport is taken into account endogenously in MIRAGE-e.
Table 7 – Tariff scenario by importer for selected sectors, ‘Low Sim’ versus ‘Ref’
Sector Cars and Trucks
ASEAN 19.2 21.5 2.1 16.2 1.3 1.3 10.0 12.3 20.3 24.0
AUNZ 6.4 24.0 0.3 0.3 0.5 0.6 3.0 3.0
Brazil 14.0 45.2 3.1 45.2 7.0 8.7 10.8 45.2
Canada 3.0 3.4 0.1 0.1 0.7 0.9 10.3 10.3
China 17.2 43.3 1.2 43.3 3.5 3.5 6.4 8.0 10.5 43.3
EFTA 0.5 5.1 4.7 4.7 0.2 0.2 26.0 35.1 40.2 40.2
EU27 3.2 10.5 0.1 0.1 0.0 0.0 8.8 11.9 15.8 15.8
India 19.3 90.8 9.0 90.8 23.8 23.8 29.0 35.9 49.6 91.6
Japan 0.0 5.7 0.3 0.3 0.0 0.0 9.5 12.9 20.3 20.3
Korea 7.3 20.1 1.6 20.1 0.9 0.9 43.1 53.5 25.2 27.8
Mexico 14.6 17.6 8.6 15.4 5.1 5.1 8.1 10.0 16.4 18.1
Middle East 10.5 31.6 2.7 30.7 2.6 2.6 11.2 13.8 15.5 32.4
North Africa 19.5 32.2 6.7 31.0 7.5 7.5 19.1 23.4 18.7 31.5
Rest of Africa 13.1 16.3 5.7 16.1 3.3 3.3 11.1 13.7 16.9 18.5
Rest of Europe 5.0 10.9 1.6 10.7 0.4 0.4 9.4 11.6 21.8 21.9
Latin America 14.2 18.8 3.8 17.4 1.9 1.9 7.9 9.8 16.3 18.6
Rest of the
World 16.3 19.4 2.4 16.2 1.8 1.8 9.7 12.0 18.7 22.6
Russia 9.9 11.7 4.3 9.0 3.8 3.8 5.9 7.3 14.3 15.2
South Africa 15.3 17.4 0.3 16.0 6.1 7.5 14.0 19.6
Turkey 5.7 7.3 0.4 6.4 20.1 24.9 25.6 25.6
USA 1.8 3.5 0.1 0.1 6.1 8.3 5.4 5.4
Total 10.4 22.0 3.0 20.5 4.9 4.9 12.2 15.4 18.6 26.3
Note: Simple average. ‘Ref’ is the baseline tariff used for ‘Low Ref’ and ‘High Ref’. ‘Low Sim’ is the
Source: Authors’ calculations based on GTAP, Deardorff and Stern (1983) and WDI.
Table 8 – NTM tariff equivalent in services by importer
Sector Finance, Insurance,
Business serv. Other Services
ASEAN 44.2 22.1 48.0 24.0 34.4 17.2 26.6 13.3
AUNZ 62.4 31.2 78.0 39.0 44.5 22.3 29.4 14.7
Brazil 49.8 24.9 108.4 54.2 36.8 18.4 37.7 18.8
Canada 31.0 15.5 56.5 28.2 35.9 18.0 27.1 13.5
China 91.8 45.9 43.9 22.0 59.6 29.8 71.9 35.9
EFTA 47.4 23.7 65.2 32.6 28.9 14.4 31.8 15.9
EU27 30.0 15.0 39.0 19.5 29.9 15.0 19.4 9.7
India 105.5 52.7 103.6 51.8 68.4 34.2 51.3 25.6
Japan 47.2 23.6 38.8 19.4 48.4 24.2 27.7 13.8
Korea 40.8 20.4 71.7 35.9 36.2 18.1 13.4 6.7
Mexico 56.5 28.2 65.1 32.5 38.9 19.5 36.0 18.0
Middle East 68.0 34.0 72.2 36.1 46.8 23.4 48.0 24.0
North Africa 55.4 27.7 70.9 35.4 38.0 19.0 43.5 21.8
Rest of Africa 68.6 34.3 62.9 31.4 46.6 23.3 43.9 21.9
Rest of Europe 63.7 31.8 76.2 38.1 48.7 24.3 42.8 21.4
Latin America 65.5 32.8 77.2 38.6 39.3 19.7 32.6 16.3
Rest of the
44.7 22.4 53.6 26.8 27.6 13.8 22.2 11.1
Russia 41.1 20.5 44.3 22.1 42.1 21.1 22.8 11.4
South Africa 65.0 32.5 88.5 44.3 51.3 25.7 41.4 20.7
Turkey 70.4 35.2 81.6 40.8 50.0 25.0 54.1 27.1
USA 45.8 22.9 70.1 35.0 8.8 4.4 22.6 11.3
Total 44.2 22.1 48.0 24.0 34.4 17.2 26.6 13.3
Note: Simple average. Ref is the baseline level and ‘High Sim’ is the scenario values.
Source: Authors’ calculations based on Fontagné et al. (2011).
Figure 5 – Design of scenarios in MaGE and MIRAGE
3. IMPLEMENTING THE SCENARIOS IN MAGE
We start by considering the impact of alternative assumptions regarding the variables of interest,
shocked one at a time. We then consider the combination of differentiated shocks in the two scenarios.
3.1. Demography and migration
The demographic scenarios were defined as reference fertility for high income countries, and low
fertility for other countries in the low scenario, versus reference fertility in high-income countries and
high fertility in other countries in the high scenario.
The shock is quite symmetrical across the high and low scenarios, as shown in Table 9. Note that there
are five EU member countries not classified by the World Bank as high income economies – Bulgaria,
Lithuania, Latvia, Poland and Romania.
Table 9 – Differentiated population scenarios, 2035 (million people)
Ref low high
United States of America 373 +0.0% +0.0%
Japan 117 +0.0% +0.0%
European Union 513 -0.9% +0.9%
Brazil 223 -7.8% +8.1%
Russian Federation 134 -6.9% +7.0%
India 1580 -7.5% +7.7%
China 1382 -6.8% +6.9%
Latin America 452 -7.6% +7.8%
Middle east and North Africa 544 -6.6% +6.6%
Sub-Saharan Africa 1320 -6.4% +6.4%
Rest of Asia 1238 -7.2% +7.3%
Rest of the World 193 -3.8% +3.9%
Total world 8068 -6.1% +6.2%
Source: MaGE, authors’ calculation.
The next step for demography is to introduce migration (beyond the conservative migration flows
included in the UN’s demographic projections). In the low scenario, migration shows no change
compared to our baseline projection. Thus, here we discuss only the high scenario. We assume annual
migration outflow of 1,200,000 people from SSA to the EU, migration of 800,000 from the MENA
countries to the EU, and 1,200,000 people to the US from Latin America every year. Age, sex and
education levels are the same for both destination and origin countries, but mortality and activity rates
assume the levels of the destination country (although female labor force participation will be affected
by the integration of migrants in the average education level computation). The results for population
are presented in Table 10. Notice that total world population is affected because mortality is lower in
the destination countries of migrants than in their countries of origin.
Table 10 – Total population in the presence of additional migrations, 2035 (million people)
United States of America 373 +6.6%
European Union 513 +8.3%
Latin America 452 -5.0%
Middle east and North Africa 544 -2.4%
Sub-Saharan Africa 1320 -1.8%
Total world 8068 +0.1%
Note: Other regions are not impacted by the migration scenario.
Source: MaGE, authors’ calculation.
As already noted, when migrants leave their origin country, their initial level of education remains
unchanged. Given the numbers considered, migrants will have a significant impact on the share of the
population at each education level. Table 11 shows the outcome of our assumptions related to
education attainment at secondary and tertiary levels. Education increases in MENA and Latin
America because of the age group aggregation. Due to different mortality rates among age groups and
among countries within a country group, the drop in population numbers distorts the age structure
across time (although migrants at time t are equally distributed across age groups). Not surprisingly,
origin countries have (on average) less human capital than destination countries and, therefore,
immigrants work to reduce education levels, which explains the results for the EU and the US.
Table 11 – Secondary and tertiary education, 2035 (percentage of working-age population)
Reference high Reference high
United States of America 99 -0.5 64 -1.0
European Union 93 -1.5 38 -1.4
Latin America 74 +0.2 25 +0.1
Middle east and North Africa 72 +0.1 25 +0.1
Total world 71 +0.3 20 +0.3
Note: Other regions are unaffected by the migration scenario.
Source: MaGE, authors’ calculation.
The last direct consequence of the migration considered is for saving rates due to the distortion of the
age structure (in both the origin and destination countries), which is the main determinant of saving in
our life-cycle framework. Saving also determines investment capacity for a given level of international
financial flows. Table 12 shows how the migrations scenarios affect investment and saving.
Table 12 – Investment and saving rates, 2035 (percentage GDP)
Reference High Ref High
United States of America 14 +0.21 13 +0.34
Japan 21 -0.11 21 -0.18
European Union 17 +0.33 16 +0.56
Brazil 17 -0.00 14 -0.03
Russian Federation 21 -0.01 27 -0.06
India 20 +0.00 22 -0.01
China 31 -0.00 30 -0.04
Latin America 18 -0.24 18 -0.65
Middle east and North Africa 20 -0.11 23 -0.28
Sub-Saharan Africa 16 -0.06 16 -0.30
Rest of Asia 22 -0.04 23 -0.08
Rest of the World 19 -0.10 21 -0.16
Total world 20 -0.08 20 -0.08
Source: MaGE, authors’ calculation.
The arrival of young age groups in ageing countries such as EU countries and the USA, tends to
increase these countries saving rates (+0.6% and +0.3% of GDP, respectively). However, this is
accompanied by a fall in saving rates in origin countries due to the departure of working-age
population (but not younger and older people). At the global level, the saving loss is greater than the
saving increase (-0.1% of world GDP). Thus, the global investment envelope decreases while the
share of EU and USA in this envelope increases and investment reduces in all other countries.
3.2. Human capital accumulation and female participation
In addition to demography, we need to investigate shocks to investment in human capital. Education
catch-up over the past 50 years has been very diverse across regions and has been one of the main
drivers of differences in per-capita income performance across countries. For estimation purposes, we
estimate catch-up speed according to geographical regions. However, average speed does not
encompass the diversity of situations depicted in Figure 6. Shocks to MaGE try to frame educational
convergence and represent lower and upper bounds of catch-up using a standard catch-up measure:
half-life time. Half-life time is the time it takes for a country to reduce its difference with the education
leader by half, assuming a constant education frontier.
The increase in education levels observed for the emerging countries is shown in Figure 8, as the
evolution in educational attainment in some Asian countries over the period 1980-2010. The pattern
differs across countries. The achievements observed for Korea and Singapore fuelled productivity
gains and helped to elevate income levels in those countries. By contrast, China remains at low levels,
and its comparative advantage is mainly in low value added segments and low value added products.
In the high scenario, shown in Table 13, catch up is accelerated (50% reduction in the half-life of
convergence). Even assuming this investment in education is observed worldwide, its impact will be
concentrated in countries far from the (moving) education frontier. In the USA and Japan, the increase
in the percentage of the population completing tertiary education is respectively 3 and 2 percentage
points. The impact is larger for the EU (+6 p.p.), due to the differentiated levels currently observed for
member countries. The impact is also sizeable for Latin America (+7 p.p.). In China gains in relative
terms are less important, though the absolute number of educated people is very large, which is
important for world growth overall. China gains 4 percentage points for tertiary education and more
than 1 percentage point for secondary education. However, most of the gains are concentrated on
secondary education for the poorest countries (+8 p.p. in SSA). In India both levels benefit by 5
percentage points each. The low scenario shows symmetric results, although attenuated as a result of
the assumption made (half-life increased by 50%).
Figure 6 – Share of tertiary educated population in selected Asian countries, 1980-2010
Key: KOR = Korea, SGP = Singapore, THA = Thailand,
LAO = Laos, CHN = China, KHM = Cambodia
Source: Barro and Lee (2013).
Table 13 – Secondary and Tertiary education, 2035 (% of population)
Reference low high Reference low high
United States of America 99 -0.1 +0.1 64 -1.8 +3.1
Japan 97 -0.1 +0.2 64 -1.3 +2.2
European Union 93 -0.7 +1.3 38 -2.7 +5.7
Brazil 71 -1.8 +3.9 17 -2.4 +7.2
Russian Federation 98 -0.1 +0.2 63 -1.6 +2.8
India 66 -2.3 +5.2 12 -1.5 +5.2
China 86 -0.7 +1.4 14 -1.3 +4.0
Latin America 74 -1.9 +4.4 25 -2.6 +7.2
Middle east and North Africa 72 -2.1 +4.4 25 -2.6 +6.9
Sub-Saharan Africa 40 -2.7 +7.9 5 -0.7 +2.5
Rest of Asia 62 -2.5 +5.8 17 -1.4 +4.3
Rest of the World 94 -0.7 +1.2 46 -2.8 +5.4
Total world 71 -1.7 +4.1 20 -1.6 +4.5
Source: MaGE, authors’ calculation.
Another important labor market trend is female participation which ranges from 37% (India) to 54%
(USA) to 57% (China).10 Any convergence in participation rates to the highest level will have an
impact on the labor force and its skill composition. Two opposing effects compete. First, better
educated women tend to participate more to the labor force. However, this is true only for women aged
more than 25 years. For the younger age groups (15-19 and 20-24), longer studies work to decrease
participation in the labor force. If the former (resp. latter) effect is greater, then the overall impact on
labor force will be positive (resp. negative). In our baseline scenario, based on historical evidence we
consider that there will be some form of convergence. We do not discuss a ‘high’ case which would
mean women’s participation increasing even more. Here, the high case is the same as the baseline. In
the ‘low’ case, however, we constrain women’s participation to be constant after 2010. This has an
unexpected outcome for ageing economies where women’s participation is expected to decrease
naturally, for demographic reasons. By constraining this mechanism from applying in the future, the
low scenario increases women’s participation with respect to the reference scenario, for example, in
Europe and Russia. The impacts of our scenario are shown in Table 14.
Table 14 – Female participation in the labour force, 2035 (percentage of female population)
United States of America 56 -1.5
Japan 47 -2.3
European Union 43 +2.1
Brazil 54 +5.5
Russian Federation 51 +3.0
India 37 -5.9
China 57 -1.7
Latin America 46 +3.9
Middle east and North Africa 30 -4.3
Sub-Saharan Africa 63 +1.7
Rest of Asia 50 -0.8
Rest of the World 53 +2.4
Total world 49 -1.1
Source: MaGE, authors’ calculation.
3.3. Energy price and technical progress in energy use
The next shock we consider is to energy prices, using EIA scenarios (Figure 7). It induces substitution
and technical progress (although the link between the energy price and productivity is not integrated in
our model), such that the energy intensity of GDP will be reduced in the case of high prices. Table 15
shows how this phenomenon cushions the impact of energy prices for different countries. The
reference scenario is shown in column 1, where values are energy intensity expressed as barrels of oil
equivalent (boe) per US$1,000 of GDP in 2035. Energy productivity can be observed independently,
but does not include possible substitution for labor and capital. Thus, we present the results as energy
intensity of GDP (E/Y). In general, lower productivity leads to higher energy intensity.
Columns 2 and 3 in Table 15 show changes in energy intensity measured in the same units. We
observe first that the world economy is much more energy efficient under the high price scenario. For
oil producing countries, this effect is reinforced by the oil rent boosting growth and facilitating
10 Female participation is highest in SSA (63%).
convergence in energy efficiency. The biggest gains are expected for SSA, and Russia. Japan, Europe
and the US are expected to achieve lower efficiency gains in this respect, although in Japan and
Europe, from very low initial energy intensity in Japan and Europe.
Figure 7 – Observed energy price and EIA scenarios 1980-2035 (constant 2005 USD)
Energy use is determined by the energy price and induced adjustment of economies towards less
intensive technologies in the case of high energy prices. Beyond this ‘economic’ adjustment, we need
to consider an autonomous strand of technical progress in the energy domain. Major technological
breakthrough or societal changes may occur in the next two decades, leading to a dramatic increase in
the energy efficiency of our economies. Such changes are not embedded in existing data and therefore
are not included in our econometric estimations. They can be introduced in the projections as
Results for energy intensity are shown in Table 15 along with the impacts of energy price scenarios.
As in the previous case, columns 4 and 5 of Table 15 show the results of our assumptions about the
energy price on energy use efficiency, measured in boe per US$1,000 of GDP. Although in this case,
the impact on countries differs, the world average order of magnitude is the same as for the scenarios
for energy price.
Regarding technological progress, for the low scenario we assume a 50% increase (compared to our
reference scenario) in energy efficiency for the high income countries at the 2050 horizon, but no
change for the other countries. In the high scenario, we assume a 50% increase in energy efficiency for
low and middle income countries at the 2050 horizon, but no change for the other countries.
Table 15 – Energy intensity, 2035 (barrel per $1,000 of GDP)
Impact of energy price Impact of techno. progr.
Ref low high low high
United States of America 0.92 -0.06 +0.13 -0.18 +0.00
Japan 0.65 -0.04 +0.09 -0.12 +0.00
European Union 0.72 -0.05 +0.10 -0.12 -0.01
Brazil 1.30 -0.09 +0.20 +0.00 -0.25
Russian Federation 2.83 -0.32 +0.59 +0.00 -0.54
India 2.28 -0.14 +0.32 +0.00 -0.44
China 2.21 -0.13 +0.31 +0.00 -0.42
Latin America 1.22 -0.11 +0.21 -0.00 -0.23
Middle east and North Africa 1.43 -0.19 +0.35 -0.08 -0.19
Sub-Saharan Africa 2.51 -0.23 +0.44 +0.00 -0.48
Rest of Asia 1.76 -0.12 +0.26 -0.10 -0.23
Rest of the World 1.03 -0.08 +0.17 -0.15 -0.04
Total world 1.36 -0.11 +0.22 -0.09 -0.17
Source: MaGE, authors’ calculation.
3.4. Total factor productivity
Technological progress in the various regions of the world is an important mechanism, which varies
according to the scenario. These changes can be driven by national autonomous technological
developments or international transfers of technology. In the low scenario we consider a 50% drop in
TFP growth rates for low- and middle-income countries compared to our baseline scenario, and a 25%
drop for high-income countries. In the high scenario, we assume a 50% increase in TFP growth rates
for low- and middle-income countries, and a 25% increase in TFP growth rates for high-income
Table 16 – Average annual TFP growth rate, 2012-2035
Ref low high
United States of America 0.98 -0.25 +0.25
Japan 1.71 -0.34 +0.30
European Union 1.33 -0.29 +0.30
Brazil 1.65 -0.77 +0.68
Russian Federation 3.91 -1.80 +1.54
India 3.26 -1.50 +1.28
China 4.19 -1.89 +1.56
Latin America 1.76 -0.83 +0.75
Middle east and North Africa 1.54 -0.60 +0.67
Sub-Saharan Africa 2.28 -0.95 +0.85
Rest of Asia 2.32 -0.37 +0.37
Rest of the World 1.66 -0.22 +0.26
Total world 1.58 -0.13 +0.25
Source: MaGE, authors’ calculation.
3.5. International capital mobility
The last factor of production is capital. Capital is depreciating at a constant rate in our growth model,
consistent with the specification used in MIRAGE. In considering gross investment, we need to take
account of two drivers. First, saving is driven by demography (as already discussed) and per capita
income. Second, the difference between domestic saving and domestic investment is driven by the
international mobility of capital to which we apply a shock. We have two opposite cases: in the low
case, countries are returning to capital autarky, in the high case, they converge to the highest level of
capital mobility observed (with no direct gains for low- and middle-income countries). Low mobility
has a positive impact for current account surplus countries (India, Russia or China).
Under the high mobility assumption, we reduce the Feldstein-Horioka correlation coefficient of the
OECD countries: the correlation between domestic saving and domestic investment is lower for the
OECD country group and takes the value estimated for non-OECD countries which remains
unchanged. The total amount of saving (which equals world investment) remains almost unaffected by
this change, whereas the allocation of investment among countries is modified. Since the correlation is
smaller for the OECD countries, investment in these countries diminishes for a given amount of
saving. In turn, investment is increasing in the non-OECD countries where it is associated with lower
TFP. At the world level, this induces a negligible decrease in overall GDP, while developed countries
suffer a -0.2% loss in GDP compensated for by a gain of a similar magnitude in the developing
countries (Table 17).
Table 17 – Average investment rate, 2012-2035 (percentage of GDP)
Ref low high
United States of America 15.3 -0.73 -2.36
Japan 20.7 -0.15 -3.02
European Union 17.4 -0.07 -2.54
Brazil 17.6 -1.71 +2.33
Russian Federation 21.2 +3.70 +6.32
India 20.7 +1.08 +4.39
China 32.2 +0.35 +6.78
Latin America 18.8 -0.36 +0.39
Middle east and North Africa 21.0 +2.05 +3.71
Sub-Saharan Africa 16.6 -0.36 +2.73
Rest of Asia 23.4 +0.56 +1.55
Rest of the World 19.9 +0.82 -2.01
Total world 18.8 +0.20 -0.66
Source: MaGE, authors’ calculation.
3.6. Combining the different assumptions of the two scenarios in MaGE
We now implement jointly all the assumptions in the two scenarios simulated with MaGE. Table 18
shows the results for GDP growth which take account of all the relations and feed-backs among the
variables in the MaGE model.
In the first panel in Table 18, column (1) presents annual growth rates for the regions included in
MIRAGE and columns (2) and (3) show the difference between each scenario and the reference case.
China shows a 6% average growth rate to 2035. This is the highest figure among regions considered.
The high scenario corresponds to 2.7 percentage points additional growth and symmetrically -2.7
percentage points corresponds to the low scenario. This symmetry is not observed systematically, but
depends on regional composition. We observe contrasts for the two scenarios for developing
economies, with a range of 3.7 percentage points growth between the two scenarios, but much less so
for the developed economies (resp. 0.6 percentage points). In the second panel, column (4) shows
GDP levels at 2005 US$ exchange rates and prices. Columns (5) and (6) indicate the percentage
deviations from the 2035 level. We observe that the scenarios combining different assumptions show
stark contrasts. World GDP is 33% higher (15% lower) in the high (low) scenario, compared to the
Table 18 – GDP projections under the high and low scenarios
GDP growth GDP in 2035 Share of world GDP
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Ref low high Ref low high Ref low high
United States of America 1.74 -0.12 +0.44 20562 -2.75 +10.49 20.3 +2.99 -3.40
Japan 1.53 -0.12 +0.20 6749 -2.63 +4.53 6.7 +0.99 -1.42
European Union 1.43 -0.02 +0.80 20458 -0.37 +19.81 20.2 +3.55 -1.97
Brazil 2.97 -1.01 +1.31 2299 -20.31 +33.78 2.3 -0.14 +0.02
Russian Federation 4.13 -1.51 +2.34 2481 -28.55 +66.66 2.5 -0.38 +0.63
India 5.96 -2.33 +2.48 5450 -40.10 +70.23 5.4 -1.58 +1.52
China 6.07 -2.70 +2.76 17217 -44.79 +80.48 17.0 -5.93 +6.12
Latin America 3.34 -0.79 +0.76 4674 -16.22 +18.38 4.6 -0.05 -0.50
Middle east and North Africa 3.47 -0.57 +0.79 5440 -11.86 +19.05 5.4 +0.21 -0.55
Sub-Saharan Africa 5.09 -1.43 +1.68 2727 -27.04 +43.99 2.7 -0.37 +0.23
Rest of Asia 3.98 -0.91 +1.37 7154 -18.24 +35.05 7.1 -0.25 +0.12
Rest of the World 2.69 -0.07 +0.63 6039 -1.61 +14.99 6.0 +0.96 -0.80
Total world 2.84 -0.74 +1.27 101251 -15.24 +32.73 100.0
Total Developed 1.64 -0.04 +0.52 52842 -0.95 +12.57 52.2 +8.80 -7.93
Total Developing 4.72 -1.67 +2.01 48409 -30.84 +54.73 47.8 -8.80 +7.93
Note: Column (4) is in 2005 US$ billions. Columns (1), (5) , (6) and (7) are percentages and columns (2), (3),
(8), (9) are percentage points.
Source: MaGE, authors calculations.
GDP trajectories are plotted in Figure 8 and also show large contrasts. This is the result of opening a
Pandora’s box of growth projections. The continuous line corresponds to the ‘High Ref’ scenario for
each country. Table 18 shows that China will achieve US$ 17,217 GDP in 2035 in the reference
scenario, and 80.5% more in the high scenario, that is, US$31,073 GDP in 2035, shown on the upper
dotted curve in Figure 8. The interpretation is symmetric (‘Low Ref’ scenario) for each country and
each low dotted curve. Figure 8 confirms that the range of outcomes for GDP is very large for
developing countries, compared to the developed ones. The highest in the range is China which
overtakes the US in 2030 in the high scenario. Similarly, India overtakes Japan at the same horizon in
the high scenario. Our objective in this section is to discuss the implications for world trade patterns of
such extreme assumptions.
Figure 8 – GDP in the ‘High Ref’ and ‘Low Ref’ scenarios, 2000-2035 (2005 US$ billion)
Note: continuous line is ‘High Ref’; dotted line is ‘Low Ref’.
Source: MaGE, authors’ calculation.
Due to the intrinsic link between all the factors that receive shocks in our scenarios, it is not possible
to determine precisely the relative impact of each shock. However, we can observe the impact of
individual variables on GDP in 2035. Results are shown in Figure 9.
The individual shocks (colored bars in Figure 9) allow us to infer that TFP is the main contributor to
GDP changes in our differentiated scenarios, along with capital mobility, energy price, female labor
participation and migration. For example, in the USA and the European Union, migrant inflows
account for around the same amount of extra GDP as TFP gains in the high scenario. Although they
are considered one at a time, demography, education and energy productivity seem to have marginal
impacts; their sum is not negligible and act more as catalysts: education fuels TFP; energy
productivity inflates the benefits of a low energy price; and demography drives saving.
If we compare this outcome with the outcome if all the variables suffer shocks simultaneously (black
crosses in Figure 9), orders of magnitude are maintained compared to the sum of individual variable
shocks, for all developing countries. This pattern is not observed for the three main developed country
blocks (USA, EU and Japan), due mainly to the following two effects: world equilibrium of saving
and investment, and the conjunction of TFP and education scenarios. For instance, in the ‘High Ref’
scenario, developing countries increase their share of world investment, leading to capital shortage in
Japan, whose growth depends heavily on capital accumulation.
Figure 9 – Contributions from scenarios on individual variables to GDP level, 2035.
a. Low ref b. High ref
Source: MaGE, authors’ calculation.
4. CONTRASTING WORLD TRADE PATTERNS AT THE 2035 HORIZON
We next present the results of the two scenarios (‘Low sim’ and ‘High sim’) simulated in MIRAGE.
4.1. World trade patterns
World trade patterns are fundamentally shaped by gravity-like determinants. We expect the volume of
world exports to be determined by changes in GDP, comparative advantage, and trade costs. We now
add the shocks on transaction costs and tariffs for goods and NTM in services, as described in Table 6,
to obtain the ‘Low sim’ and ‘High sim’ scenarios. The overall result is depicted in Figure 10. Under
the ‘High Sim’ scenario, developing economies will have buoyant exports of goods, growing at 8.4%
annually (7.4% in the ‘High Ref’ scenario with no further trade liberalization). This figure is even
larger for services. This contrasts with the ‘Low Sim’ scenario where GDP grows much slower,
especially for the developing countries, and where trade costs (tariffs and transaction costs) are
increasing. The low scenarios are particularly penalizing for goods, especially the ‘Low Sim’: goods
have to be transported and are subject to tariff wars, while services are only subject to regulations,
which are assumed to be constant in the low scenario. Finally, developing countries’ exports are
strongly affected by the combination of transaction costs, tariff and growth assumptions in the low
Figure 10 – GDP (volume) and exports (volume, incl. intra) growth rates in Low-vs-High Ref
and Sim scenarios, 2012-2035
Note: average annual growth rates (percentages). Developing countries’
trade in services grows annually by 7% in the ‘High Ref’ scenario, compared
to 9% in the ‘High Sim’ scenario.
Source: MIRAGE, authors’ calculation.
The annual growth rates for GDP and exports of goods and services shown in Figure 10, suggest that
trade to income elasticity will differ across the two scenarios and the two regions of the world
economy. Table 19 presents the computed trade elasticity. In the ‘High Sim’ scenario elasticity is 1.5
at the world level. This is low compared to historical levels, and particularly compared to the 1990s,
the decade when many global value chains were established: elasticity in the 1990s was 2.8. In our
projection, we return to the values observed in the 1960s. Under all circumstances, elasticity is higher
for the developed economies which will increase trade with a faster growing developing world. Under
the ‘Low Sim’ scenario, we have (slow) growth and no trade in the developing world (elasticity 0.4),
while trade and income follow the path of the developed economies.
Although our trade scenarios have only a marginal effect on GDP compared to the determinants of
growth in MaGE, they tend to reshape trade patterns profoundly over the next 20 years. For instance,
for goods, a trade war would imply an almost three-fold reduction in growth in trade of goods at world
level, compared to an 11% increase in growth in the case of further liberalization.
Table 19 – Trade to income elasticity (goods), 2012-2035
High Sim Low Sim
Developed 1.78 1.00
Developing 1.19 0.38
World 1.49 0.69
Note: elasticity computed on the exports.
Source: MIRAGE, author’s calculation
4.2. Intra-regional trade
Trade patterns are also affected by the share of intra-regional trade in world trade. As a result of the
gravity forces shaping world trade, we expect South-South trade to grow faster. However, growth in
South-South trade may not necessarily be driven by the presence of FTAs because the current levels of
market integration currently agreed for the South are too low to have a significant impact. This
contrasts with integrated regions such as the EU and, to a lesser extent, NAFTA. Thus, in the ‘Low
Sim’ scenario, we observe that the EU and NAFTA manage to maintain their initial share of world
trade at the 2035 horizon, notwithstanding a 35% increase in the volume of world exports. The fact
that tariffs within FTAs are supposed not to be subject to tariff wars in the ‘Low Sim’ scenario also
contributes to this result.
In contrast, in the high scenario, most trade growth occurs in the developing economies, shifting the
center of gravity of international trade to the South. World trade will quadruple in volume, EU exports
will increase by 55%, and the EU’s share dramatically decreases. A similar pattern is observed for
NAFTA (with a 52% increase in exports). FTAs in the South do not deliver since the shares of
ASEAN and Mercosur are kept constant in all scenarios. This reduction in the share of regional trade
in world trade in the high scenario, could be reversed were ambitious and new FTAs (e.g. those under
negotiation for the Pacific and to a lesser extent the Transatlantic levels) to be signed. We do not
model such outcomes.
4.3. Regional specialization
Alternative growth paths for the world economy will be conducive to different country export
specialization. In the ‘High Sim’ and ‘Low Sim’ scenarios considered here, accumulation of human as
well as physical capital will be different. This will shape countries’ comparative advantages. A good
illustration of this mechanism is provided by China’s specialization, shown in Figure 11. The ‘High
Sim’ scenario will further increase China’s specialization in the electronic sector as opposed to
textiles. In the ‘Low Sim’ scenario, with less physical and human capital per worker, China will
maintain its specialization in textiles and increase specialization in other manufacturing, while
progressively losing ground in electronics. These results suggest that countries’ specialization is not
More details on the future of regional trade are provided in our companion paper (Fontagné L., Fouré J. and A.
completely pre-determined if one considers seriously the wide range of possible outcomes for medium
Figure 11 – Share of sectors in China total export (volume)
Notes: Solid lines correspond to ‘High Ref’ (left) and ‘Low Ref’ (right) scenarios; dotted lines correspond
respectively to ‘High Sim’ and ‘Low Sim’.
Source: MIRAGE, authors’ calculation.
Figure 11 shows the patterns for the different baselines. The difference between the ‘High Ref’ and
‘High Sim’ scenarios for China remains small, showing only a slight shift towards services exports
away from electronics. In the low scenarios, almost all the shift in Chinese exports to textiles away
from electronics is due to shocks to trade costs. These patterns occurs because these sectors represent
China’s most important exports, and because reverting to post-Tokyo Round tariffs would lead to a
multiplication of tariffs on electronics of between 1.1 and 123 (for Canada), and a factor of 10 for the
USA and 20 for Australia and New Zealand. In contrast, for the textile sectors it is lower than 5.3.
This supports the claim that, except for the huge sectoral differentiation provided by trade policy
shocks, the main determinants of national specialization in a CGE are baseline assumptions.
These widely contrasting scenarios for country specialization have a profound impact on the shape of
world production in several sectors. Figure 12 depicts the effects for the automotive sector. We
observe that Chinese production of cars rapidly overtakes European production in the high scenario,
but not in the low scenario. This pattern applies also to comparison between China and the US in this
sector. In all the scenarios, the Japanese car industry is overtaken by China. Again, with the exception
of electronics (see Figure 13) and textiles, in the trade war scenario, countries’ market shares are
determined mainly by the baseline, rather than the trade policies we implemented.
Figure 12 – Regional shares in world car and truck production (volume)
Notes: Solid lines correspond to ‘High Ref’ (left) and ‘Low Ref’ (right) scenarios; dotted lines correspond
respectively to ‘High Sim’ and ‘Low Sim’.
Source: MIRAGE, authors’ calculation.
Figure 13 – Regional shares in world Electronic Devices production (volume)
Notes: Continuous lines correspond to ‘High Ref’ (left) and ‘Low Ref’ (right) scenarios; dotted lines
correspond respectively to ‘High Sim and ‘Low Sim’.
Source: MIRAGE, authors’ calculation.
This paper has developed a comprehensive methodological framework for projecting ‘open-minded’
world trade patterns at a medium and long-run horizon, based on scenarios for the world economy. We
investigated the determinants of past economic growth and international trade developments in order
to build sound baseline assumptions. We implemented contrasting scenarios encompassing a wide
range of potentialities. The combination of MaGE, a growth model, with MIRAGE a dynamic
multisectoral CGE model of the world economy proved fruitful. Our results point to the need for
careful building of baseline scenarios when evaluating policies in dynamic general equilibrium, given
that the magnitude of the changes in world trade patterns when baselines differ is at least as large as
when trade policy shocks are imposed. Due to the huge uncertainty surrounding future paths for the
global economy, our work highlights the important role of key assumptions in their modeling. These
results call for transparency, replicability and inter-operability of modeling frameworks. In order to
make general equilibrium inference as reliable and transparent as possible we plead for the Pandora’s
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APPENDIX A: TRADE WAR DATA
Table A1 – Post-Tokyo round tariffs by sector
Source: Deardorff and Stern (1983).
Table A2 – Weighted average applied tariff, First available year
Zone Corresp. region Tariff Year
Brazil Brazil 37.96 1989
China China 36.41 1992
ASEAN East Asia (developing) 13.53 1996
RoEurope Europe and Central Asia (developing) 8.91 1996
India India 76.28 1990
Korea Korea 16.91 1988
RoLAC Latin America (developing) 14.57 1996
Mexico Mexico 13.03 1991
MiddleEast Middle-East and North Africa (developing) 25.74 1996
NorthAfr Middle-East and North Africa (developing) 25.74 1996
RoW Middle Income 13.23 1996
Russia Russian Federation 7.52 1993
SouthAfr South Africa 13.42 1988
RoAfr Sub-Saharan Africa (developing) 13.33 1996
Turkey Turkey 5.37 1993
APPENDIX B: COUNTRY CLASSIFICATION
Table B1 – Country list, codes, income classes and region
code name Inc.
AGO Angola M SSA 0
ALB Albania M ROW 0
ARE United Arab
H MENA 0
ARG Argentina M SAM 0
ARM Armenia M ROW 0
AUS Australia H ROW 0
AUT Austria H EU27 1
BDI Burundi L SSA 0
BEL Belgium H EU27 1
BEN Benin L SSA 0
BFA Burkina Faso L SSA 0
BGD Bangladesh L ROAS 0
BGR Bulgaria M EU27 1
BHR Bahrain H MENA 0
BHS Bahamas, The H SAM 0
BLR Belarus M ROW 0
BLZ Belize M SAM 0
BOL Bolivia M SAM 0
BRA Brazil M BRA 0
BRB Barbados H SAM 0
BRN Brunei Darussalam H ROAS 0
BTN Bhutan M ROAS 0
BWA Botswana M SSA 0
CAF Central African
L SSA 0
CAN Canada H ROW 1
CHE Switzerland H ROW 1
CHL Chile M SAM 0
CHN China M CHN 0
CIV Cote d'Ivoire M SSA 0
CMR Cameroon M SSA 0
COG Congo, Rep. L SSA 0
COL Colombia M SAM 0
CPV Cape Verde M SSA 0
CRI Costa Rica M SAM 0
CZE Czech Republic H EU27 1
DEU Germany H EU27 1
DJI Djibouti M SSA 0
DNK Denmark H EU27 1
DOM Dominican Republic M SAM 0
DZA Algeria M MENA 0
code name Inc.
EGY Egypt, Arab Rep. M MENA 0
ESP Spain H EU27 1
EST Estonia H EU27 1
ETH Ethiopia L SSA 0
FIN Finland H EU27 1
FJI Fiji M ROW 0
FRA France H EU27 1
GAB Gabon M SSA 0
GBR United Kingdom H EU27 1
GEO Georgia M ROW 0
GHA Ghana L SSA 0
GIN Guinea L SSA 0
GMB Gambia, The L SSA 0
GNB Guinea-Bissau L SSA 0
GRC Greece H EU27 1
GTM Guatemala M SAM 0
GUY Guyana M SAM 0
HKG Hong Kong, China H ROAS 0
HND Honduras M SAM 0
HTI Haiti L SAM 0
HUN Hungary H EU27 1
IDN Indonesia M ROAS 0
IND India M IND 0
IRL Ireland H EU27 1
IRN Iran, Islamic Rep. M MENA 0
ISL Iceland H ROW 1
ISR Israel H MENA 0
ITA Italy H EU27 1
JOR Jordan M MENA 0
JPN Japan H JPN 1
KAZ Kazakhstan M ROW 0
KEN Kenya L SSA 0
KGZ Kyrgyz Republic L ROAS 0
KHM Cambodia L ROAS 0
KOR Korea, Rep. H ROAS 0
KWT Kuwait H MENA 0
LAO Lao PDR L ROAS 0
LBN Lebanon M MENA 0
LCA St. Lucia M SAM 0
LKA Sri Lanka M ROAS 0
code name Inc.
LSO Lesotho M SSA 0
LTU Lithuania M EU27 1
LUX Luxembourg H EU27 1
LVA Latvia M EU27 1
MAR Morocco M MENA 0
MDA Moldova M ROW 0
MDG Madagascar L SSA 0
MDV Maldives M ROW 0
MEX Mexico M SAM 0
MLI Mali L SSA 0
MLT Malta H EU27 1
MNG Mongolia M ROAS 0
MOZ Mozambique L SSA 0
MRT Mauritania L SSA 0
MUS Mauritius M SSA 0
MWI Malawi L SSA 0
MYS Malaysia M ROAS 0
NER Niger L SSA 0
NGA Nigeria M SSA 0
NIC Nicaragua M SAM 0
NLD Netherlands H EU27 1
NOR Norway H ROW 1
NPL Nepal L ROAS 0
NZL New Zealand H ROW 1
OMN Oman H MENA 0
PAK Pakistan M ROAS 0
PAN Panama M SAM 0
PER Peru M SAM 0
PHL Philippines M ROAS 0
PNG Papua New Guinea M ROW 0
POL Poland M EU27 1
PRT Portugal H EU27 1
PRY Paraguay M SAM 0
QAT Qatar H MENA 0
ROM Romania M EU27 1
RUS Russian Federation M RUS 0
RWA Rwanda L SSA 0
SAU Saudi Arabia H MENA 0
SDN Sudan M MENA 0
SEN Senegal L SSA 0
SGP Singapore H ROAS 0
SLB Solomon Islands M ROW 0
SLE Sierra Leone L SSA 0
SUR Suriname M SAM 0
code name Inc.
SVK Slovak Republic H EU27 1
SWE Sweden H EU27 1
SWZ Swaziland M SSA 0
SYR Syrian Arab
M MENA 0
TCD Chad L SSA 0
TGO Togo L SSA 0
THA Thailand M ROAS 0
TJK Tajikistan L ROW 0
TTO Trinidad and Tobago H SAM 0
TUN Tunisia M MENA 0
TUR Turkey M MENA 0
TZA Tanzania L SSA 0
UGA Uganda L SSA 0
UKR Ukraine M ROW 0
URY Uruguay M SAM 0
USA United States H USA 1
VCT St. Vincent and the
M SAM 0
VEN Venezuela, RB M SAM 0
VNM Vietnam L ROAS 0
VUT Vanuatu M ROW 0
YEM Yemen, Rep. L MENA 0
ZAF South Africa M SSA 0
ZMB Zambia L SSA 0
Table B2 – Labels for zones and income groups:
Label Zone Developed / Developing
EU27 European Union All developed
MENA Middle-East and North Africa All developing
SSA Sub-Saharan Africa All developing
USA United States of America Developed
JPN Japan Developed
CHN China Developing
IND India Developing
BRA Brazil Developing
RUS Russian Federation Developing
ROAS Rest of Asia All developing
SAM Latin America All developing
ROW Rest of the World All developing excepted Switzerland, New Zealand, Iceland, Norway, Australia and Canada
H High income (WB)
M Medium income (WB)
L Low income (WB)