A global approach to energy and environment: the G-Cubed model

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CHAPTER1
15
5
A Global Approach to Energy
and the Environment: The G-Cubed
Model
Warwick J. McKibbin*, Peter J. Wilcoxen**
*
The Australian National University and The Brookings Institution
**
Syracuse University and The Brookings Institution
Abstract
G-Cubed is a multi-country, multi-sector, intertemporal general equilibrium model that has been used to
study a variety of policies in the areas of environmental regulation, tax reform, monetary and fiscal policy,
and international trade. It is designed to bridge the gaps between three areas of research eeconometric
general equilibrium modeling, international trade theory, and modern macroeconomics eby incor-
porating the best features of each. This chapter describes the theoretical and empirical structure of the
model, summarizes its applications and contributions to the literature, and discusses two example
applications in detail.
Keywords
Intertemporal modeling, international trade, international finance, energy policy, environmental policy,
monetary and fiscal policy, trade liberalization, trade agreements, tax policy, climate policy, financial crises,
pandemics, demographic change
JEL classification codes
C54, C68, D58, E27, E44, E6, F1, F21, F3, F4, H23, H6, J11, O24, O4, O5, Q3, Q4, Q5
15.1 INTRODUCTION
G-Cubed is a multicountry, multisector, intertemporal general equilibrium model that has
been used to study a variety of policies in the areas of environmental regulation, tax reform,
monetary and fiscal policy, and international trade.
1
It is designedto bridge the gaps between
three areas of research eeconometric general equilibrium modeling, international trade
theory and modern macroeconomics eby incorporating the best features of each.
2
1Many applications are summarized in Section 15.3 below.
2The type of inter temporal general equilibrium model represented by G-Cubed with macroeconomic dynamics and
various nominal rigidities is closely related to the dynamic stochastic general equilibrium models appearing in the
macroeconomic and central banking literatures
Handbook of CGE Modeling - Vol. 1 SET 2013 Elsevier B.V.
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From the trade literature, G-Cubed takes the approach of modeling the world
economy as a set of autonomous regions e12 in the version used in this paper e
interacting through bilateral trade flows.
3
Following the Armington approach
(Armington, 1969), goods produced in different regions are treated as imperfect
substitutes.
4
Unlike most trade models, however, G-Cubed distinguishes between
financial and physical capital. Financial capital is perfectly mobile between sectors
and from one region to another, and is driven by forward-looking investors who
respond to arbitrage opportunities. Physical capital, in contrast, is perfectly immobile
once it has been installed: it cannot be moved from one sector to another or from
one region to another. In addition, intertemporal budget constraints are imposed on
each region: all trade deficits must eventually be repaid by future trade surpluses.
Drawing on the general equilibrium literature, G-Cubed represents each region by
its own multisector econometric general equilibrium model.
5
Production is broken
down into Nindustries and each is represented by an econometrically estimated cost
function. Unlike many general equilibrium models, however, G-Cubed draws on
macroeconomic theory by representing saving and investment as the result of forward-
looking intertemporal optimization. Households maximize an intertemporal utility
function subject to a lifetime budget constraint, which determines the level of saving,
and firms choose investment to maximize the stock market value of their equity.
6
Finally, G-Cubed also draws on the macroeconomic literature by representing inter-
national capital flows asthe result of intertemporal optimization, and by including liquidity-
constrained agents, a transactions-based money demand equation and slow nominal wage
adjustment. Unlike typical macro models, however, G-Cubed has substantial sector detail
and many of its parameters are determined by estimation rather than calibration.
This combination of features was chosen to make G-Cubed versatile. Industry detail
allows the model to be used to examine environmental and tax policies which tend to
3Some well-known examples of other models with international trade flows include Deardorff and Stern (1985),
Burniaux et al. (1992), and Hertel (1997).
4Given the model’s level of aggregation, this is more a simple acknowledgement of reality than an assumption. Even if
individual products from different countries were perfect substitutes, the aggregate products appearing in the model
would not be because the composition of the aggregates differs between domestic production and imports. In motor
vehicles, for example, even if there were individual domestic cars for which there were identical imported products,
the mix of economy cars, luxury cars, trucks and vans in the overall motor vehicle aggregate differs between domestic
production and imports.
5The computable general equilibrium (CGE) literature is quite large. Some well-known examples of single-country
models are Johansen (1960), Dixon et al. (1982), Ballard et al. (1985), Jorgenson and Wilcoxen (1990), and Goulder
and Summers (1989). See Shoven and Whalley (1984) for a survey.
6G-Cubed builds on elements from throughout the literature on macroeconomics. Our representation of saving and
investment, in particular, descends from Abel and Blanchard (1983). Other intertemporal general equilibrium models
that include some of the features in G-Cubed include Auerbach and Kotlikoff (1987), Goulder and Summers (1989),
Jorgenson and Wilcoxen (1990), McKibbin and Sachs (1991),andGoulder (1992). The latter is also described in
Bovenberg and Goulder (1996).
996 Warwick J. McKibbin and Peter J. Wilcoxen

have their largest direct effects on small segments of the economy. Intertemporal
modeling of investment and saving allows G-Cubed to trace out the transition of the
economy between the short run and the long run. Slow wage adjustment and liquidity-
constrained agents improves the empirical accuracy with which the model captures the
transition. Overall, the model is designed to provide a bridge between computable
general equilibrium models, international trade models and macroeconomic models by
combining key features of each approach. The cost of this versatility is that G-Cubed is
a fairly large model. It has over 10000 equations holding in each year, is typically solved
annually for 100 years in each simulation, and has over 100 intertemporal costate
variables. Nonetheless, it can be solved using software developed for a personal
computer.
15.2 STRUCTURE OF THE MODEL
The key features of G-Cubed are summarized in Tab l e 1 5. 1 . There are several different
versions of G-Cubed that have beendeveloped, depending on the question being analyzed.
Versions have been built with two sectors (macroeconomic issues), six sectors (trade and
growth issues), 12 sectors (energy and environmental issues), 21 sectors (India) and 57
sectors (Australia). There are also a large number of different country disaggregations.
However, all versions of G-Cubed are global: each represents the economic activity of all
countries in the world, either modeled individually or aggregated into regions.
Table 15.1 Key features of G-Cubed
1 Developed and developing countries are modeled in detail, including all trade and
financial links between countries.
2 A full menu of financial assets is included and the valuations of those assets are driven by
the real economy.
3 International flows of financial capital are modeled. An important distinction is made
between the stickiness of physical capital within sectors and within countries and the
flexibility of financial capital, which quickly flows to where expected returns are
highest. This important distinction leads to a critical difference between the quantity of
physical capital that is available at any time to produce goods and services, and the
valuation of that capital as a result of decisions about the allocation of financial capital.
4 Households and firms are represented as mixtures of two types of agents: one group
which bases its decisions on forward-looking expectations and a second group which
follows simpler rules of thumb which are optimal in the long run, but not necessarily in
the short run.
5 The model allows for short-run wage rigidity (varying in degree across countries) and
therefore allows for significant periods of unemployment depending on the labor
market institutions in each country. This assumption, when taken together with the
explicit modeling of money and other financial assets, gives the model more realistic
macroeconomic properties than conventional general equilibrium models.
A Global Approach to Energy and the Environment: The G-Cubed Model 997

The most frequently used model and the version most relevant for environmental and
energy questions is the 12 sector model. In this paper we will focus on the structure and
specification of this version of G-Cubed. Within each region, production is dis-
aggregated into 12 sectors: five energy sectors (electric utilities, natural gas utilities,
petroleum refining, coal mining, and crude oil and gas extraction) and seven non-energy
sectors (mining, agriculture, forestry and wood products, durable goods, non-durable
goods, transportation and services). This disaggregation, summarized in Table 15.2,
enables us to capture the sector level differences in the impact of alternative environ-
mental policies.
Each economy or region in the model consists of several economic agents: house-
holds, the government, the financial sector and the 12 production sectors listed above.
We now present an overview of the theoretical structure of the model by describing the
decisions facing these agents. To keep our notation as simple as possible we have not
subscripted variables by country except where needed for clarity. Throughout the
discussion all quantity variables will be normalized by the economy’s endowment of
effective labor units. Thus, the model’s long-run steady state will represent an economy
in a balanced growth equilibrium.
Table 15.2 Regions and sectors in G-Cubed
Regions (codes shown in parentheses)
1 US (U or USA)
2 Japan (J or JPN)
3 Australia (A or AUS)
4 Western Europe (E or EUW)
5 Rest of the OECD (O or OEC)
6 China (C or CHI)
7 Other developing countries (L or LDC)
8 Eastern Europe and the Former Soviet Union (B or EEB)
9 Oil exporting countries and the Middle East (P or OPC)
Sectors
1 Electric utilities
2 Gas utilities
3 Petroleum refining
4 Coal mining
5 Crude oil and gas extraction
6 Other mining
7 Agriculture
8 Forestry and wood products
9 Durable goods
10 Non-durables
11 Transportation
12 Services
998 Warwick J. McKibbin and Peter J. Wilcoxen

15.2.1 Firms
We assume that each of the 12 sectors can be represented by a price-taking firm, which
chooses variable inputs and its level of investment in order to maximize its stock market
value. Each firm’s production technology is represented by a tier-structured constant
elasticity of substitution (CES) function. At the top tier, output is a function of capital,
labor, energy and materials:
Qi¼AO
i0
@X
j¼K;L;E;M
ðdO
ij Þ
1
sO
iX
sO
i1
sO
i
ij 1
A
sO
i
sO
i1
;(15.1)
where Q
i
is the output of industry i,X
ij
is industry i’s use of input j, and A
i
O
,dO
ij and s
i
O
are parameters. A
i
O
reflects the level of technology, s
i
O
is the elasticity of substitution and
the dO
ij parameters reflect the weights of different inputs in production; the superscript
‘O’ indicates that the parameters apply to the top, or ‘output’, tier. Without loss of
generality, we constrain the ds to sum to one.
At the second tier, inputs of energy and materials, X
iE
and X
iM
, are themselves CES
aggregates of goods and services. Energy is an aggregate of goods 1e5 (electricity
through crude oil) and materials is an aggregate of goods 7e12 (mining through
services). The functional form used for these tiers is identical to (1) except that the
parameters of the energy tier are A
i
E
,dE
ij and s
i
E
, and those of the materials tier are A
i
M
,
dM
ij and s
i
M
.
The goods and services purchased by firms are, in turn, aggregates of imported and
domestic commodities, which are taken to be imperfect substitutes. We assume that all
agents in the economy have identical preferences over foreign and domestic varieties of
each commodity. We represent these preferences by defining 12 composite commodities
that are produced from imported and domestic goods. Each of these commodities, Y
i
,is
a CES function of inputs domestic output, Q
i
, and imported goods, M
i
.
7
For example,
the petroleum products purchased by agents in the model are a composite of imported
and domestic petroleum. By constraining all agents in the model to have the same
preferences over the origin of goods we require that, for example, the agricultural and
service sectors have the identical preferences over domestic oil and oil imported from the
Middle East.
8
This accords with the input-output data we use and allows a very
convenient nesting of production, investment and consumption decisions.
Finally, the production function includes one additional feature to allow the model to
be used to examine the effects of emissions quotas or tradable permit systems: each input
7The elasticity of substitution in this function is the Armington elasticity.
8This does not require that both sectors purchase the same amount of oil, or even that they purchase oil at all; only that
they both feel the same way about the origins of oil they buy.
A Global Approach to Energy and the Environment: The G-Cubed Model 999

is used in fixed proportions to the use of an input-specific permit. The permits are
owned by households and included in household wealth. Permit prices are determined
endogenously by a competitive market for each type of permit. To run simulations
without a permit system, the supply of permits can be set large enough so that the price
of a permit goes to zero.
In each sector the capital stock changes according to the rate of fixed capital
formation ( J
i
) and the rate of geometric depreciation (d
i
):
_
Ki¼JidiKi:(15.2)
Following the cost of adjustment models of Lucas (1967), Treadway (1969) and
Uzawa (1969), we assume that the investment process is subject to rising marginal costs of
installation. To formalize this we adopt Uzawa’s approach by assuming that in order to
install Junits of capital a firm must buy a larger quantity, I, that depends on its rate of
investment ( J/K):
Ii¼1þfi
2
Ji
KiJi;(15.3)
where fis a non-negative parameter. The difference between Jand Imay be interpreted
various ways; we will view it as installation services provided by the capital-goods vendor.
The goal of each firm is to choose its investment and inputs of labor, materials and energy
to maximize intertemporal risk-adjusted net of tax profits. For analytical tractability, we
assume thatthis problem is deterministic (equivalently, the firm could be assumed to believe
its estimates of future variables with subjective certainty). Thus, the firm will maximize:
9
Z
N
t
ð1s2ÞpieðRðsÞþmeinÞðstÞds;(15.4)
where meiis a sector- and region-specific equity risk premium, s
2
is the effective tax rate
on capital income, and variables are implicitly subscripted by time. The firm’s profits, p,
are given by:
pi¼P
iQiWiLiPE
iXiEPM
iXiMð1s4ÞPIIi;(15.5)
where s
4
is an investment tax credit and P
)
is the producer price of the firm’s output. R(s)
is the long-term interest rate between periods tand s:
RðsÞ¼ 1
stZs
t
rðvÞdv:(15.6)
9The rate of growth of the economy’s endowment of effective labor units, n, appears in the discount factor because the
quantity and value variables in the model have been scaled by the number of effective labor units. These variables must
be multiplied by exp(nt) to convert them back to their original form.
1000 Warwick J. McKibbin and Peter J. Wilcoxen

As all real variables are normalized by the economy’s endowment of effective labor
units, profits are discounted adjusting for the rate of growth of population plus
productivity growth, n. Solving the top-tier optimization problem gives the following
equations characterizing the firm’s behavior:
Xij ¼dO
ij AO
isO
i1QiP
i
PjsO
i
j˛fL;E;Mg(15.7)
li¼1þfi
Ji
Ki1s4PI(15.8)
dli
ds¼ðrþmeiþdiÞlið1s2ÞP
i
dQi
dKi
ð1s4ÞPIfi
2Ji
Ki2
;(15.9)
where l
i
is the shadow value of an additional unit of investment in industry i.
Equation (15.7) gives the firm’s factor demands for labor, energy and materials, and
equations (15.8) and (15.9) describe the optimal evolution of the capital stock. By inte-
grating (15.9) along the optimum path of capital accumulation, it is straightforward to show
that l
i
is the increment to the value of the firm from a unit increase in its investment at time t.
It is related to q, the after-tax marginal version of Tobin’s Q (Abel, 1979), as follows:
qi¼li
ð1s4ÞPI:(15.10)
Thus, we can rewrite (15.8) as:
Ji
Ki
¼1
fi
ðqi1Þ:(15.11)
Inserting this into (15.3) gives total purchases of new capital goods:
Ii¼1
2fi
ðq2
i1ÞKi:(15.12)
In order to capture the inertia often observed in empirical investment studies we assume
that only fraction a
2
of firms making investment decision use the fully forward-looking
To b i n ’s qdescribed above. The remaining (1 ea
2
) use a slowly-adjusting version, Q,driven
by a partial adjustment model. In each period, the gap between Qand qcloses by fraction a
3
:
Qitþ1¼Qit þa3ðqit Qit Þ:(15.13)
As a result, we modify (15.12) by writing I
i
as a function not only of q, but also the slowly
adjusting Q:
Ii¼a2
1
2fi
ðq2
i1ÞKiþð1a2Þ1
2fi
ðQ2
i1ÞKi:(15.14)
A Global Approach to Energy and the Environment: The G-Cubed Model 1001

This creates inertia in private investment, which improves the model’s ability to mimic
historical data and is consistent with the existence of firms that are unable to borrow. The
weight on unconstrained behavior, a
2
, is taken to be 0.3 based on a range of empirical
estimates reported by McKibbin and Sachs (1991).
So far we have described the demand for investment goods by each sector. Investment
goods are supplied, in turn, by a 13th industry that combines capital, labor and the
outputs of other industries to produce raw capital goods. We assume that this firm faces
an optimization problem identical to those of the other 12 industries: it has a nested CES
production function, uses inputs of capital, labor, energy and materials in the top tier,
incurs adjustment costs when changing its capital stock, and earns zero profits. The key
difference between it and the other sectors is that we use the investment column of the
input-output table to estimate its production parameters.
15.2.2 Households
Households have three distinct activities in the model: they supply labor, they save, and
they consume goods and services. Within each region we assume household behavior can
be modeled by a representative agent with an intertemporal utility function of the form:
Ut¼Z
N
t
ðln CðsÞþln GðsÞÞeqðstÞds;(15.15)
where C(s) is the household’s aggregate consumption of goods and services at time s,G(s)
is government consumption at s, which we take to be a measure of public goods provided,
and qis the rate of time preference.
10
The household maximizes (15.15) subject to the
constraint that the present value of consumption (potentially adjusted by risk premium
mh) be equal to the sum of human wealth, H, and initial financial assets, F:
11
Z
N
t
PcðsÞCðsÞeðRðsÞþmhnÞðstÞ¼HtþFt:(15.16)
Human wealth is defined as the expected present value of the future stream of after-
tax labor income plus transfers:
Ht¼Z
N
t
ð1es1ÞðWðLGþLCþLIþX
12
i¼1
LiÞþTRÞeðRðsÞþmhnÞðstÞds;(15.17)
10 This specification imposes the restriction that household decisions on the allocations of expenditure among different
goods at different points in time be separable.
11 As before, nappears in (15.16) because the model’s scaled variables must be converted back to their original basis.
1002 Warwick J. McKibbin and Peter J. Wilcoxen

where s
1
is the tax rate on labor income, TR is the level of government transfers, L
C
is
the quantity of labor used directly in final consumption, L
I
is labor used in producing the
investment good, L
G
is government employment, and L
i
is employment in sector i.
Financial wealth is the sum of real money balances, MON/P, real government bonds in
the hand of the public, B, net holding of claims against foreign residents, A, the value of
capital in each sector and holdings of emissions permits, Q
i
P
:
F¼MON
PþBþAþqIKIþqCKCþX
12
i¼1
qiKiþX
12
i¼1
PP
iQP
i:(15.18)
Solving this maximization problem gives the familiar result that aggregate
consumption spending is equal to a constant proportion of private wealth, where private
wealth is defined as financial wealth plus human wealth:
PCC¼ðqþmhÞðFþHÞ:(15.19)
However, based on the evidence cited by Campbell and Mankiw (1990) and Hayashi
(1982) we assume some consumers are liquidity-constrained and consume a fixed
fraction gof their after-tax income (INC).
12
Denoting the share of consumers who are
not constrained eand choose consumption in accordance with (15.19) eby a
8
, total
consumption expenditure is given by:
PCC¼a8ðqþmhÞðFtþHtÞþð1a8ÞgINC:(15.20)
The share of households consuming a fixed fraction of their income could also be
interpreted as permanent income behavior in which household expectations about
income are myopic.
Once the level of overall consumption has been determined, spending is allocated
among goods and services according to a two-tier CES utility function.
13
At the top tier,
the demand equations for capital, labor, energy and materials can be shown to be:
PiXCi¼dCiPCCPC
PisO
C1
;i˛fK;L;E;Mg;(15.21)
12 There has been cons iderable debate about the empir ical validity of the permanent income hypothesis. In addition the
work of Campbell, Mankiw and Hayashi, other key papers include Hall (1978) and Flavin (1981). One side-effect of
this specification is that it prevents us from computing equivalent variation. Since the behavior of some of the
households is inconsistent with (15.19), either because the households are at corner solutions or for some other
reason, aggregate behavior is inconsistent with the expenditure function derived from our utility function.
13 The use of the CES function has the undesirable effect of imposing unitary income elasticities, a restriction usually
rejected by data. An alternative would be to replace this specification with one derived from the linear expenditure
system.
A Global Approach to Energy and the Environment: The G-Cubed Model 1003

where X
Ci
is household demand for good i,sO
Cis the top-tier elasticity of substitution
and the d
Ci
are the input-specific parameters of the utility function. The price index for
consumption, P
C
, is given by:
PC¼ X
j¼K;L;E;M
dCjPsO
C1
j!1
sO
C1
:(15.22)
The demand equations and price indices for the energy and materials tiers are similar.
Household capital services consist of the service flows of consumer durables plus
residential housing. The supply of household capital services is determined by consumers
themselves who invest in household capital, K
C
, in order to generate a desired flow of
capital services, C
K
, according to the following production function:
CK¼aKC;(15.23)
where ais a constant. Accumulation of household capital is subject to the condition:
_
KC¼JCdCKC:(15.24)
We assume that changing the household capital stock is subject to adjustment costs so
household spending on investment, I
C
, is related to J
C
by:
IC¼1þfC
2
JC
KCJC:(15.25)
Thus, the household’s investment decision is to choose I
C
to maximize:
Z
N
t
ðPCKaKCPIICÞeðRðsÞþmznÞðstÞds;(15.26)
where P
CK
is the imputed rental price of household capital and mzis a risk premium on
household capital (possibly zero). This problem is nearly identical to the investment
problem faced by firms, including the partial adjustment mechanism outlined in equa-
tions 15.13 and 15.14, and the results are very similar. The only important difference is
that no variable factors are used in producing household capital services.
15.2.3 Labor market
We assume that labor is perfectly mobile among sectors within each region but is
immobile between regions. Thus, wages will be equal across sectors within each region,
but will generally not be equal between regions. In the long run, labor supply is
completely inelastic and is determined by the exogenous rate of population growth.
Long-run wages adjust to move each region to full employment. In the short run,
1004 Warwick J. McKibbin and Peter J. Wilcoxen

however, nominal wages are assumed to adjust slowly according to an overlapping
contracts model where wages are set based on current and expected inflation and on
labor demand relative to labor supply. This can lead to short-run unemployment if
unexpected shocks cause the real wage to be too high to clear the labor market. At the
same time, employment can temporarily exceed its long-run level if unexpected events
cause the real wage to be below its long run equilibrium.
15.2.4 Government
We take each region’s real government spending on goods and services to be exoge-
nous and assume that it is allocated among inputs in fixed proportions, which we set to
2006 values. Total government outlays include purchases of goods and services plus
interest payments on government debt, investment tax credits and transfers to
households. Government revenue comes from sales taxes, capital and labor taxes, and
from sales of new government bonds. In addition, there can be taxes on externalities
such as carbon dioxide emissions. The government budget constraint may be written
in terms of the accumulation of public debt as follows:
_
Bt¼Dt¼rtBtþGtþTRtTt;(15.27)
where Bis the stock of debt, Dis the budget deficit, Gis total government spending on
goods and services, TR is transfer payments to households and Tis total tax revenue net
of any investment tax credit.
We assume that agents will not hold government bonds unless they expect the bonds
to be paid off eventually and accordingly impose the following transversality condition:
lim
s/N
BðsÞeðRðsÞnÞs¼0:(15.28)
This prevents per capita government debt from growing faster than the interest rate
forever. If the government is fully leveraged at all times, (15.28) allows (15.27) to be
integrated to give:
Bt¼Z
N
t
ðTGTRÞeðRðsÞnÞðstÞds:(15.29)
Thus, the current level of debt will always be exactly equal to the present value of
future budget surpluses.
14
14 Strictly speaking, public debt must be less than or equal to the present value of future budget surpluses. For trac-
tability we assume that the government is initially fully leveraged so that this constraint holds with equality.
A Global Approach to Energy and the Environment: The G-Cubed Model 1005

The implication of (29) is that a government running a budget deficit today must run
an appropriate budget surplus as some point in the future. Otherwise, the government
would be unable to pay interest on the debt and agents would not be willing to hold it. To
ensure that (15.29) holds at all points in time we assume that the government levies a lump
sum tax in each period equal to the value of interest payments on the outstanding debt.
15
In effect, therefore, any increase in government debt is financed by consols and future
taxes are raised enough to accommodate the increased interest costs. Other fiscal closure
rules are possible, such as requiring the ratio of government debt to GDP to be unchanged
in the long run or that the fiscal deficit be exogenous with a lump sum tax ensuring this
holds. These closures have interesting implications but are beyond the scope of this paper.
15.2.5 Financial markets and the balance of payments
The nine regions in the model are linked by flows of goods and assets. Flows of goods are
determined by the import demands described above. These demands can be summarized
in a set of bilateral trade matrices which give the flows of each good between exporting
and importing countries. There is one nine by nine trade matrix for each of the 12 goods.
Trade imbalances are financed by flows of assets between countries. Each region with
a current account deficit will have a matching capital account surplus, and vice versa.
16
We
assume asset markets are perfectly integrated across regions.
17
With free mobility of
capital, expected returns on loans denominated in the currencies of the various regions
must be equalized period to period according to a set of interest arbitrage relations of the
following form:
ikþmk¼ijþmjþ
_
Ej
k
Ej
k
;(15.30)
where i
k
and i
j
are the interest rates in countries kand j,m
k
and m
j
are exogenous risk
premiums demanded by investors (possibly zero), and Ej
kis the exchange rate between
the currencies of the two countries.
18
However, in cases where there are institutional
rigidities to capital flows, the arbitrage condition does not hold and we replace it with an
explicit model of the relevant restrictions (such as capital controls).
15 In the model the tax is actually levied on the difference between interest payments on the debt and what interest
payments would have been if the debt had remained at its base case level. The remainder einterest payments on the
base case debt eis financed by ordinary taxes.
16 Global net flows of private capital are constrained to be zero at all times ethe total of all funds borrowed exactly
equals the total funds lent. As a theoretical matter this may seem obvious, but it is often violated in international
financial data.
17 The mobility of international capital is a subject of considerable debate; see Gordon and Bovenberg (1994) or
Feldstein and Horioka (1980).
18 The one exception to this is the oil-exporting region, which we treat as choosing its foreign lending in order to
maintain a desired ratio of income to wealth.
1006 Warwick J. McKibbin and Peter J. Wilcoxen

Capital flows may take the form of portfolio investment or direct investment but we
assume these are perfectly substitutable ex ante, adjusting to the expected rates of return
across economies and across sectors. Within each economy, the expected returns to each
type of asset are equated by arbitrage, taking into account the costs of adjusting physical
capital stock and allowing for exogenous risk premiums. However, because physical
capital is costly to adjust, any inflow of financial capital that is invested in physical capital
will also be costly to shift once it is in place. This means that unexpected events can cause
windfall gains and losses to owners of physical capital, and ex post returns can vary
substantially across countries and sectors. For example, if a shock lowers profits in
a particular industry, the physical capital stock in the sector will initially be unchanged
but its financial value will drop immediately.
15.2.6 Money and monetary rules
We assume that money enters the model via a constraint on transactions.
19
We use
a money demand function in which the demand for real money balances is a function of
the value of aggregate output and short-term nominal interest rates:
MON ¼PYi3;(15.31)
where Yis aggregate output, Pis a price index for Y,iis the interest rate, and 3is the interest
elasticity of money demand. Following McKibbin and Sachs (1991) we take 3to be e0.6.
On the supply side, the model includes an endogenous monetary response function
for each region. Each region’s central bank is assumed to adjust short-term nominal
interest rates following a HendersoneMcKibbineTaylor rule as shown in the equation
below. The interest rate evolves as a function of actual inflation (p) relative to target
inflation (p
T
), output growth (Dy) relative to growth of potential output (Dy
T
) and the
change in the exchange rate (De) relative to the bank’s target change (De
T
):
it¼it1þb1ðptpT
tÞþb2ðDytDyT
tÞþb3ðDetDeT
tÞ:(15.32)
The parameters in (32) vary across countries. For example, countries that peg their
exchange rate to the US dollar have a very large value of b
3
.
15.2.7 Parameterization
To estimate G-Cubed’s parameters we began by constructing a consistent time series of
input-output tables for the US. The procedure is described in detail in McKibbin and
Wilcoxen (1999a) and can be summarized as follows. We started with the detailed
19 Unlike other components of the model we simply assume this rather than deriving it from optimizing behavior. Money
demand can be derived from optimization under various assumptions: money g ives direct utility; it is a factor of
production; or it must be used to conduct transactions. The distinctions are unimportant for our pur poses.
A Global Approach to Energy and the Environment: The G-Cubed Model 1007

benchmark US input-output transactions tables produced by the Bureau of Economic
Analysis (BEA) and converted them to a standard set of industrial classifications and then
aggregate them to 12 sectors.
20
Then, we corrected the treatment of consumer durables,
which are included in consumption rather than investment in the US National Income and
Product Accounts (NIPAs) and the benchmark input-output tables. Third, we supple-
mented the value added rows of the tables using a detailed dataset on capital and labor input
by industry constructed by Dale Jorgenson and his colleagues.
21
Finally, we obtained prices
for each good in each benchmark year from the output and employment data set con-
structed by the Office of Employment Projections at the Bureau of Labor Statistics (BLS).
This dataset allowed us to estimate the model’s parameters for the US. To estimate the
production side of the model, we began with the energy and materials tiers because they
have constant returns to scale and all inputs are variable. In this case it is convenient to
replace the production function with its dual unit cost function. For industry i, the unit
cost function for energy is:
cE
i¼1
AE
i X
5
k¼1
dE
ik p1sE
i
ik !1
1sE
i
:(15.33)
The cost function for materials has a similar form. Assuming that the energy and
materials nodes earn zero profits, cwill be equal to the price of the node’s output. Using
Shephard’s lemma to derive demand equations for individual commodities and then
converting these demands to cost shares gives expressions of the form:
sE
ij ¼dE
ij Pj
AE
iPi1sE
i
;j¼1; ::: ; 5;(15.34)
where sE
ij is the share of industry is spending on energy that is devoted to purchasing input
j.
22
A
i
E
,s
i
E
and d
ij
E
were found by estimating (15.33) and (15.34) as a system of equations.
23
Estimates of the parameters in the materials tier were found by an analogous approach.
20 Converting the data to a standard basis was necessary because the sector definitions and accounting conventions used
by the BEA have changed over time.
21 Primary factors often account for half or more of industry costs so it is particularly important that this part of the data
set be constructed as carefully as possible. From the standpoint of estimating cost and production functions, however,
value added is the least satisfactory part of the benchmark input-output tables. In the early tables, labor and capital are
not disaggregated. In all years, the techniques used by the BEA to construct implicit price deflators for labor and
capital are subject to various methodological problems. One example is that the income of proprietors is not split
between capital and imputed labor income correctly. The Jorgenson dataset corrects these problems and is the work
of several people over many years. In addition to Dale Jorgenson, some of the contr ibutors were L. Christensen,
Barbara Fraumeni, Mun Sing Ho and Dae Keun Park. The original source of the data is the Fourteen Components
of Income tape produced by the Bureau of Economic Analysis. See Ho (1989) for more information.
22 When s
E
is unity, this collapses to the familiar CobbeDouglas result that s¼dand is independent of prices.
23 For factors for which the value of s was consistently very small, we set the corresponding input to zero and estimated
the production function over the remaining inputs.
1008 Warwick J. McKibbin and Peter J. Wilcoxen

The output node must be treated differently because it includes capital, which is not
variable in the short run. We assume that the firm chooses output, Q
i
, and its top-tier
variable inputs (L, E and M) to maximize its restricted profit function, p:
pi¼piQiX
j¼L;E;M
pjXij;(15.35)
where the summation is taken over all inputs other than capital. Inserting the production
function into (15.35) and rewriting gives:
pi¼PiAO
i0
@d
1
sO
i
ik K
sO
i1
sO
i
iþX
j¼L;E;M
d
1
sO
i
ij X
sO
i1
sO
i
ij 1
A
sO
i
sO
i1
X
j¼L;E;M
PjXij;(15.36)
where K
i
is the quantity of capital owned by the firm, d
ik
is the distributional parameter
associated with capital, and jranges over inputs other than capital. Maximizing (15.36)
with respect to variable inputs produces the following factor demand equations for
industry i:
Xij ¼dijPsO
i
jd
1
sO
i1
ik Ki PiAO
iÞ1sO
iX
k
dikP1sO
i
k!
sO
i
1sO
i
;cj˛fL;E;Mg:(15.37)
This system of equations can be used to estimate the top-tier production parameters. The
results are listed in McKibbin and Wilcoxen (1999a).
Much of the empirical literature on cost and production functions fails to account for
the fact that capital is fixed in the short run. Rather than using (15.37), a common
approach is to use factor demands of the form:
Xij ¼dijPsO
i
i
Qi
AO
i X
k¼K;L;E;M
dikP1sO
i
k!
sO
i
1sO
i
:(15.38)
This expression is correct only if all inputs are variable in the short run. In McKibbin and
Wilcoxen (1999a) we show that using equation (15.38) biases the estimated elasticity of
substitution toward unity for many sectors in the model In petroleum refining, for
example, the fixed-capital estimate for the top tier elasticity, s
3
O
, is 0.54 while in the
variable elasticity case it is 1.04. The treatment of capital thus has a very significant effect
on the estimated elasticities of substitution.
Estimating parameters for regions other than the US is more difficult because time-
series input-output data is often unavailable. In part, this is because some countries do
not collect the data regularly and in part it is because many of G-Cubed’s geographic
entities are regions rather than individual countries. As a result, we impose the
A Global Approach to Energy and the Environment: The G-Cubed Model 1009

restriction that substitution elasticities within individual industries are equal across
regions.
24
By doing so, we are able to use the US elasticity estimates everywhere. The
share parameters (the ds in the equations above), however, are derived from regional
input-output data taken from the GTAP version 7 database and differ from one region
to another. In effect, we are assuming that all regions share a similar but not identical
production technology. This is intermediate between one extreme of assuming that
the regions share common technologies and the other extreme of allowing the
technologies to differ in arbitrary ways. The regions also differ in their endowments of
primary factors, their government policies, and patterns of final demands.
Final demand parameters, such as those in the utility function or in the production
function of new investment goods were estimated by a similar procedure: elasticities were
estimated from US data and share parameters were obtained from regional input-output
tables. Trade shares were obtained from 2009 UN Standard Industry Trade Classification
(SITC) data aggregated up from the four-digit level.
25
The trade elasticities are based on
a survey of the literature and vary between 1 and 3.
26
15.2.8 Numerical implementation
G-Cubed is implemented via three software components. The first consists of a sequence
of programs written in the Ox language that construct G-Cubed’s dataset from raw
data.
27
The second component consists of a set of files specifying the model’s economic
structure in a portable, general-purpose language we developed called ‘Sym’. Sym is
a set-driven matrix language that descends from GAMS and GEMPACK. It imposes
rigorous conformability rules on all expressions to eliminate a broad range of potential
errors in the design and coding of the model. A useful consequence of these rules is that
subscripts are generally unnecessary and the model can be expressed very concisely and
clearly. The third component is a suite of Ox programs that are used for setting up
simulations and solving the model according to the two-point boundary value algorithm
described in McKibbin (1986).
28
. It allows models with large numbers of forward-
looking costate variables (G-Cubed has more than 100) to be solved quickly on
computers with limited resources.
24 For example, the top-tier elasticity of substitution is identical in the durable goods industries of Japan and the US.
This approach is consistent with the econometric evidence of Kim and Lau (1994). This specification does not mean,
however, that the elasticities are the same across industries within a country.
25 A full mapping of SITC codes into G-Cubed industries is contained in McKibbin and Wilcoxen (1994).
26 For a sensitivity analysis examining the role of the trade elasticities and several other key parameters, see McKibbin
et al. (1999a, 1999b).
27 Ox is available from www.doornik.com and described in Doornik (2007).
28 For a more detailed description of the algorithm, see McKibbin and Sachs (1991, appendix C).
1010 Warwick J. McKibbin and Peter J. Wilcoxen

15.2.9 Generating a baseline
Because G-Cubed is an intertemporal model, it is necessary to calculate a baseline, or
‘business-as-usual’, solution before the model can be used for policy simulations. In
order to do so we begin by making assumptions about the future course of key exog-
enous variables. We take the underlying long-run rate of world population growth plus
productivity growth to be 2.5% per annum and take the long-run real interest rate to be
5%. We also assume that tax rates and the shares of government spending devoted to each
commodity remain unchanged. Our remaining assumptions are listed by region in
Table 15.3.
As these assumptions do not necessarily match the expectations held by agents in the
real world, the model’s solution in any given year, say 2006, will generally not
reproduce that year’s historical data exactly. In particular, it is unlikely that the costate
variables based on current and expected future paths of the exogenous variables in the
model will equal the actual values of those variables in 2006. This problem arises in all
intertemporal models and is not unique to G-Cubed, but it is inconvenient when
interpreting the model’s results.
To address the problem we add a set of constants, one for each costate variable, to
the model’s costate equations. For example, the constants for Tobin’s qfor each sector
in each country are added to the arbitrage equation for each sector’s q.Similarly,
constants for each real exchange rate are added to the interest arbitrage equation for
each country, and a constant for human wealth is added to the equation for human
wealth.
29
To calculate the constants we use Newton’s method to find a set of values
that will make the model’s costate variables in 2006 exactly equal their 2006 historical
values. After the constants have been determined, the model will reproduce the base
year exactly given the state variables inherited from 2005 and the assumed future paths
of all exogenous variables.
30
One additional problem is to solve for both real and nominal interest rates consis-
tently since the real interest rate is the nominal interest rate from the money market
equilibrium less the ex ante expected inflation rate. To produce the expected inflation
rate implicit in historical data for 2006 we add a constant to the equation for nominal
wages in each country.
31
Finally, we are then able to construct the baseline trajectory by solving the model for
each period after 2006 given any shocks to variables, shocks to information sets
(announcements about future policies) or changes in initial conditions.
29 One interpretation of these constants is that they are risk premiums; another is that they are simply the residuals left
between the actual data and the econometrically fitted values calculated by the model.
30 In general, these constants affect the model’s steady state, but have little or no effect on the transitional dynamics.
31 One way to interpret this is as a shift in the full employment level of unemployment. In that case this approach is
equivalent to using the full model to solve for the natural rate of unemployment in each country.
A Global Approach to Energy and the Environment: The G-Cubed Model 1011

15.3 SUMMARY OF KEY APPLICATIONS AND INSIGHTS
Originally developed to evaluate climate change policies, G-Cubed has been used to
analyze trade policy, monetary and fiscal policy, financial crises, projections of global
economic growth, the impacts of pandemics, and global demographic change.
32
It has
been used by agencies within the governments of the US, Japan, Canada, Australia and
New Zealand, as well as in reports by the Intergovernmental Panel on Climate Change,
the UN, the Organization for Economic Cooperation and Development (OECD), the
World Bank, the International Monetary Fund, the Asian Development Bank, and
a number of corporations. Academic users can be found in the US, the UK, Germany,
Austria, Australia, Indonesia and Japan. The remainder of this section outlines key
applications of G-Cubed in the six areas: climate and energy policy, trade policy, analysis
of financial crises, macroeconomic policy, the analysis of pandemics, and global
demographic change.
15.3.1 Climate and energy policy
G-Cubed was designed to contribute to the debate on environmental policy and
international trade, with particular emphasis on climate change. It has been used for
that purpose since 1992 and work using the model has roughly fallen into two areas
of focus. One has been on generating projections of the future evolution of the world
Table 15.3 Regional assumptions used in generating the baseline
USA Japan Australia
Other
OECD China
Other
developing
countries
Eastern Europe
and Former
Soviet Union
Population
growth (%)
0.5 0.0 0.8 0.7 1.5 1.0 0.5
Non-energy
productivity
growth (%)
2.0 2.5 2.2 2.3 4 2.5 2.0
Energy sector
productivity
growth (%)
1.5 2.0 1.7 1.8 4 2.5 1.5
Energy efficiency
growth (%)
11 1 1 1 1 1
Monetary policy
(fixed money
growth rate, %)
2.9 1.25 1.64 3.98 12.84 6.48 23.81
32 See details in the summary that follows.
1012 Warwick J. McKibbin and Peter J. Wilcoxen

economy and exploring the sensitivity of these projections to a variety of assump-
tions. The second focus has been on evaluating the impacts of a variety of policy
changes on these projections. These two strands of research will be dealt with
separately below.
15.3.1.1 Baseline issues
In a study for the United Nations University, Bagnoli et al.(1996) found that over a 30-
year horizon, assumptions about productivity growth and structural change are crucial
for understanding an economy’s energy intensity. Using the model, the authors made
two projections of the world economy from 1990 to 2020. The first scenario assumed
that all sectors in a given region experienced a uniform rate of technical change
characteristic of that region. However, the rate varied across regions based on their
historical performance, with higher rates in particular developing economies such as
China. The second scenario allowed technical change to be heterogeneous at the sector
level. Within each region, sectoral technical change followed historical patterns, but
scaled so that each economy had the same average economy-wide GDP growth rate as
in the first scenario.
The two scenarios produced dramatically different projections of world energy
intensity by 2020. Countries had approximately the same GDP growth rates in both
scenarios (by construction), but energy use was far lower in the second scenario. Sector-
level differences in technical change caused structural changes that reduced economy-
wide energy per unit of GDP by around 1% per year independent of any autonomous
energy efficiency improvement (AEEI). This difference was purely due to the changing
structure of economies over time in response to relative price changes induced by
different sectoral rates of technical change. The difference was shown clearly in the
carbon taxes required for stabilizing emissions: in the second scenario the taxes were
typically half those for the first scenario.
This study and subsequent papers by McKibbin et al.(2007, 2009a) emphasized that
a simple projection of GDP growth was insufficient for projecting carbon emissions.
Although overall GDP growth matters, sectoral-level differences in productivity are
critical for future emissions.
33
The other issue that was emphasized in this study and related studies, is that the effect
of small changes in low-level growth rates over 20 or more years can have enormous
effects on composition of the economy. The large range of possible outcomes from small
changes in growth rates is always a sobering reminder of the degree of uncertainty
underlying climate policy. In particular, there is empirical evidence to suggest that many
economic variables have a unit root or a stochastic trend. If this is correct, or even
33 See also McKibbin (2000) for a discussion of the use of G-Cubed in a forecasting context.
A Global Approach to Energy and the Environment: The G-Cubed Model 1013

approximately correct, then standard errors for projected levels of variables would
quickly become large.
15.3.1.2 Policy issues
G-Cubed has been used for a range of studies of alternative greenhouse policies. Carbon
taxes are examined in McKibbin and Wilcoxen 1993, 1994, 1997. These studies all
highlight that a surprise carbon tax leads to a reduction in real output with the greatest
losses occurring in the short run. McKibbin et al.(1999a, 1999b) show that the
adjustment of capital flows are important for the impacts of climate policy. An increase in
the price of energy inputs makes goods produced using energy relatively more expensive
in world markets. The conventional view is that the current account of a country would
deteriorate as a result of a carbon tax. In McKibbin and Wilcoxen (1994) we showed on
the contrary that the current account could improve if the revenue from the tax was used
to reduce the fiscal deficit (i.e. holding government spending and transfers constant in
spite of the rise in tax revenue). The rise in saving and fall in investment could easily lead
to an improvement in the overall current account balance reflecting a capital outflow.
The composition of the trade account would reflect the simple partial equilibrium
reasoning but the economy-wide general equilibrium effect could go the other way.
This paper also illustrated that the way in which the revenue from a carbon tax is used
can have important consequences for the costs of the carbon abatement policy. If the
revenue is used to reduce another tax in the economy, then the costs of abatement can be
reduced. For example, in the US if the revenue is used to reduce the fiscal deficit, there
can be a fall in interest rates which stimulates economic growth and reduces the costs of
the carbon abatement. However, this effect does not occur in a country like Australia
because it is not a major participant in global capital markets and has very little impact on
world interest rates. Nonetheless, using the revenue to reduce taxes on capital can help to
offset the negative effects of a carbon abatement policy in Australia.
The trade implications of environmental policyare the focus of McKibbin and Wilcoxen
(1993, 1999a, 1999b). These papers show that changes in environmental policy are unlikely
to lead to major changes in trade flows through relocation of industry because the costs of
environmental policy are generally small relative to the cost of relocating production
facilities. This does not mean that environmental policies lead to small losses in economic
output, but that policies are unlikely to be fully offset by substitution toward goods that are
not subject to the same environmental regulation. In the context of US climate policy, the
papers above have shown that for every 100 tons of reduction in US emissions, global
emissions fall by 80e90 tons; only 10e20 tons are offset due to higher emissions elsewhere.
A key insight from this research is that a significant part of energy use is for domestic
transportation which is largely non-traded and therefore is unlikely to move overseas.
In McKibbin and Wilcoxen (1997) we found that many aggressive permit trading
scenarios were infeasible in G-Cubed because of the instability they caused in the global
1014 Warwick J. McKibbin and Peter J. Wilcoxen

trade system. The main problem was the extent of stabilization proposed in the scenarios,
which implied very high prices for emission permits. The result was wild fluctuations in
real exchange rates and consequently in patterns of international trade. This pointed to
a fundamental flaw in the global emission permit trading schemes frequently proposed,
such as the Kyoto Protocol. These regimes could generate large transfers of wealth
between countries. Supporters of a global permit system regard this as an advantage,
because it would allow developed countries to compensate developing countries for
reducing their emissions. However, G-Cubed suggests that such an approach would put
enormous stress on the world trade system depending on the tightness of the emission
targets, the extent to which the allocation of permits was different from the permits
required to meet the targets, and the marginal cost of abatement in different countries,
amongst other things. A developed country importing permits would see its balance of
trade deteriorate substantially. Equally serious problems would be created for developing
countries. Massive exports of permits would lead to exchange rate appreciation and
a decline or collapse in traditional exports. In the international economics literature this
is known as the ‘Dutch Disease’ or in Australia as the ‘Gregory Thesis’. It occurs because
the granting of permits has an impact on the wealth of the receiving countries, which
changes their consumption patterns and comparative advantage.
In McKibbin et al.(1999a, 1999b) international capital flows are shown to play an
important role in the adjustment process to emissions policies. A rise in the price of carbon
leads to a fall in the return on capital in carbon-intensive economies and to capital outflow
from carbon-intensive economies into large economies and less carbon-intensive econ-
omies. Although developing countries are generally less carbon intensive, they cannot
absorb a large capital inflow because of the adjustment costs in physical capital formation.
There is, therefore, much less carbon leakage in G-Cubed than in other trade models
because of the impact of capital flows and adjustment costs in developing countries.
The appeal of an international permit program is strongest if participating countries
have different marginal costs of abating carbon emissions. The analysis in McKibbin et al.
(1999). suggests that abatement costs are quite heterogeneous and international trading
offers large potential benefits to parties with relatively high mitigation costs. The analysis
also highlights that in an increasingly interconnected world in which international
financial flows play a crucial role, the impact of greenhouse abatement policy cannot be
determined without attention to the impact of these policies on the return to capital in
different economies. To understand the full adjustment process to international green-
house abatement policy it is essential to explicitly model international capital flows.
An important but often neglected issue in climate policy design is the effect that the
climate policy regime has on the transmission of economic shocks within a country and
between countries. McKibbin et al.(2009d) explore potential interactions between
climate policy, unanticipated macroeconomic events, and carbon emissions. They
examine two kinds of unanticipated macroeconomic shocks under two global climate
A Global Approach to Energy and the Environment: The G-Cubed Model 1015

policy architectures and pay special attention to outcomes that could undermine indi-
vidual countries’ incentives to remain party to the global agreement. They find that
a regime of fixed emissions targets strongly propagates growth shocks between regions
while price-based systems do not. Under a quantity-based policy, a positive growth
shock in developing countries can raise the global price of permits enough that GDP in
some economies actually contracts, creating an incentive for such countries to withdraw
from the arrangement. They also find that in a global downturn, a price-based system
exacerbates the economic decline. Overall, quantity-based policies perform badly during
unexpected economic booms and price-based policies perform badly during downturns.
They argue that a hybrid policy would be superior eperforming like a price-based
policy during a boom and like a quantity-based policy in a downturn.
G-Cubed also has been used to explore the characteristics of particular international
agreements such as the Kyoto Protocol in McKibbin et al.(1999a, 1999b) and McKibbin
and Wilcoxen (2004, 2007), and the Copenhagen Accord in McKibbin et al.(2010).In
the latter paper, the authors used G-Cubed to convert a heterogeneous set of
commitments by countries at Copenhagen into comparable policy effort by calculating
the ‘carbon price equivalence’ of policies. Among other results, they showed that China’s
intensity targets, which some observers at the time regarded as insignificant, are actually
a commitment to very significant reductions relative to the expected trajectory of
Chinese emissions in the absence of the policy. India’s intensity targets, on the other
hand, are essentially non-binding.
G-Cubed also has been used for evaluating national carbon policy proposals such as
the Carbon Pollution Reduction Scheme in Australia by the Australian Government
(2008) and various national schemes in the US by McKibbin et al.(2009b, 2009c).In
addition, we examined border tax adjustments for embodied carbon in McKibbin and
Wilcoxen (2009a). Border taxes are calculated based on the carbon emissions associated
with production of each imported product, and would be intended to match the cost
increase that would have occurred had the exporting country adopted a climate policy
similar to that of the importing country. We estimated how large such tariffs would be in
practice, and then examined their economic and environmental effects. We found that
the tariffs would be small on most traded goods, would reduce leakage of emissions
reduction very modestly and would do little to protect import-competing industries.
The benefits produced by border adjustments would be too small to justify their
administrative complexity or their deleterious effects on international trade.
A consistent theme in analyses of climate policies using G-Cubed is that climate
policy design should be robust to uncertainties about future economic conditions. The
sensitivity of longer run projections to small changes in assumptions as shown in
McKibbin et al.(2007) suggests that policies that rely heavily on precise forecasts about
the future are likely to be vulnerable to collapse. This experience led to the development
of a ‘Hybrid’ policy of taxes and permit trading set out in McKibbin and Wilcoxen
1016 Warwick J. McKibbin and Peter J. Wilcoxen

(2002a, 2002b, 2004, 2007, 2009b). A policy that is able to manage uncertainty is key in
the climate policy debate.
34
15.3.2 Trade policy
15.3.2.1 North American Free Trade Agreement
In a study for a report by the US Congressional Budget Office (CBO), G-Cubed was used
to assess the North American Free Trade Agreement (NAFTA) (Congressional Budget
Office, 1993; McKibbin, 1994; Manchester and McKibbin, 1994). At the time NAFTA
was being evaluated, many studies suggested that it would lead to a flood of cheap goods
into the US economy and a loss of jobs in the US. G-Cubed, however, showed the
opposite. In these studies, the key aspect of NAFTA was not only the removal of US tariffs
on Mexican goods, but the impact of the agreement on expected future productivity in
Mexico and the reduction in the risk premium attached to Mexican assets by international
investors. In the studies we followed the empirical link between closer economic inte-
gration and productivity growth surveyed in the case of Europe by Catinat and Italianer
(1988). The risk premium shock was based on estimates by the Congressional Budget
Office (1993a) that on average investment in Mexico required roughly a 10% higher
return than investments in the US. We assumed that the risk premium which drove this
differential was eliminated in three years from the announcement of NAFTA. G-Cubed
predicted that NAFTA would lead to a large flow of financial capital from the rest of the
world into the Mexican economy in response to a rise in the expected return to capital and
a reduction in the Mexican risk premium. The Mexican real exchange rate was predicted
to appreciate, crowding out net exports and leading to a rise in the Mexican current
account deficit. The short-term impacts of NAFTA were consistent with G-Cubed
predictions. The medium to long-run predictions from G-Cubed were more consistent
with the majority of studies at the time. The additional insight from G-Cubed was the
short-run adjustment process was largely driven by capital flows driving trade adjustment.
The model predicted a large impact from expected long-term productivity improvements,
and showed how, through the operation of intertemporal forces, this stimulated short-
term capital inflows to Mexico. In the short term, this completely dwarfed the static effect
(i.e. changing the composition of trade) of the tariff changes that was the focus of other
studies. The scale of economies, as well as the sectoral adjustment within economies, can
change significantly in dynamic models. Financial markets contain important information
about absolute and relative returns to current and future activities.
G-Cubed has also been applied to the Free Trade Agreement of the Americas
(FTAA) ea proposed extension of NAFTA. That analysis is discussed in detail in
Section 15.4.
34 This is the focus of McKibbin and Wilcoxen (2009b).
A Global Approach to Energy and the Environment: The G-Cubed Model 1017

15.3.2.2 Trade liberalization
The six-sector version of G-Cubed has been used to explore the impact of trade
liberalization under alternative regional and multilateral arrangements
35
as well as
unilateral trade liberalization in China.
36
In many of these studies, which are based
on actual agreements, the trade liberalization is generally announced to be phased in
over time. In this case, the key dynamic adjustment to the various trade policy
changes is the instantaneous change in rates of return to capital and asset prices in the
liberalizing economies. Changes in the return to capital change financial capital flows
which cause exchange rate adjustments. These exchange rate adjustments then drive
trade adjustment in the short run, even before substantial tariff reductions are
implemented.
McKibbin (1998a, 1998b) examined different regional groupings for trade liber-
alization. Countries were assumed to reduce tariff rates from 1996 levels to zero by 2010
for developed countries, and by 2020 for developing countries Figure 15.1 shows the
impact on Australian real GDP of liberalization in alternative groupings. Liberalization
within the regional groupings that include Australia (World, APEC and Australia) results
in short-term losses as the tariff reductions are phased in. Over time however there are
significant medium to long-term gains relative to the base scenario. There are significant
−.2 0 .2 .4 .6 .8
Percent Change
1995 2000 2005 2010 2015 2020
Year
APEC ASEAN
World Australia
Figure 15.1 Effects on Australian real GDP of trade liberalization. On graphs showing percentage
changes, series are omitted when the corresponding reference case variable is very small (e.g. in
a group of variables whose values are generally tens of billions of dollars, a variable changing from
0.01 to 0.02 billion would be omitted rather than shown as a 100% increase). Source: McKibbin (1998a).
35 See McKibbin (1994, 1998a,1998b), McKibbin and Salvatore (1995), and Stoeckel et al. (2000) on multilateral
trade agreements; McKibbin et al. (2004) on Korea and Japan; Berkelmens et al. (2001) on AustraliaeUS Free
Trade Agreement.
36 McKibbin and Tang (2000) and McKibbin and Woo (2004) on China.
1018 Warwick J. McKibbin and Peter J. Wilcoxen

additional benefits to joint liberalization in the short run but the majority of medium to
long-term gains occur through own liberalization. Liberalization by other countries
(ASEAN) results in only small GDP gains for Australia.
The adjustment path to phased liberalization can therefore exhibit short-run costs as
resources begin to be reallocated before the trade reforms are implemented. Once the
liberalization is announced, the return to capital in some sectors rises and capital flows in,
appreciating the real exchange rate. This further dampens demand for exported goods as
they temporarily become more expensive. Liberalization by other countries at the same
time can help to reduce these short-run adjustment costs and real exchange rate changes.
In the long run, own reforms give larger gains than foreign reforms and there is little
benefit from a policy of free riding.
The key insight provided by G-Cubed is the short-run adjustment process. The
impact of a policy change can be perverse in the short run in the sense that capital
flowing into a liberalizing economy can cause such a large real exchange rate appreci-
ation that there is a significant deterioration in the trade account as real resources flow
into the economy. If the adjustment process is poorly understood, policy makers can
become disaffected or can implement inappropriate policy responses such as tightening
macroeconomic policy in order to improve the external balance thus slowing down
economic activity. However, the capital inflows are needed to build future capacity in
expanding sectors. The appreciation of the real exchange rate and worsening of the trade
balance is not a loss of underlying competitiveness because of a bad policy change. The
reallocation of resources is driven by the signals in financial markets of where expected
returns are highest after the reforms are implemented.
15.3.2.3 Trade imbalances
G-Cubed has been used in a number of studies to explore the role of macroeconomic
policies and shocks in generating trade imbalances between regions of the world. In G-
Cubed the trade deficit of a country not only represents a excess of imports of goods and
services over exports of goods and services. A trade deficit also reflects a excess of
investment over savings in a country. Lee et al.(2006) used the model to explore the
sensitivity of the trade flows between the US and Asia. They found the fundamental cause
of trade imbalance since 1997 is changes in savingeinvestment gaps, attributed to the surge
of the US fiscal deficits and the decline of East Asia’s private investment after the 1997
financial crisis. In exploring the impact of nominal exchange rate realignment the results
from G-Cubed show that a revaluation of East Asia’s exchange rates by 10% (effectively
a shift in monetary policy) cannot resolve the imbalances. They also found that a concerted
effort by East Asian economies to stimulate aggregate demand can have significant impacts
on trade balances globally, but the impact on the US trade balance is not large. US fiscal
contraction was estimated to have large impacts on the US trade position overall and on the
bilateral trade balances with East Asian economies. These results suggest that in order to
A Global Approach to Energy and the Environment: The G-Cubed Model 1019

improve the transpacific imbalance, macroeconomic adjustment will need to be made on
both sides of the Pacific.
15.3.3 Macroeconomic policy
An antecedent of G-Cubed called the McKibbineSachs Global (MSG) model was
originally designed to explore macroeconomic policy issues. G-Cubed has similar
macroeconomic properties and has been used to explore a wide range of issues in
macroeconomics. Monetary and fiscal regime design in Europe has been explored using
G-Cubed by Allsop et al.(1996, 1999), Gagnon et al.(1996), Haber et al.(2001),
McKibbin and Bok (2001),andNeck et al.(2000, 2005). In these papers the key insight
was that the fixed exchange rate regime of the euro zone would be under serious stress if
fiscal policies in Europe were not coordinated in the face of various economic shocks.
Macroeconomic policy issues in Japan have been examined using G-Cubed by
McKibbin (2002) and Callen and McKibbin (2003) where the experience of Japan
during the 1990s was captured by the model as a serious of policy errors particularly in
announcing fiscal expansion and generating crowding out through asset markets, but
then not delivering the fiscal spending causing a persistent downward drop in GDP; in
India by McKibbin and Singh (2003) where nominal income targeting was shown to be
a far better monetary regime than inflation targeting given the prevalence of supply side
rather than demand-side shocks in the Indian economy; in China by McKibbin and
Tang (2000) and McKibbin and Huang (2000) where financial reforms where found to
have profound effects on economic growth and the balance of payments adjustment but
that a loss in confidence in China could devastate economic growth; and in Asia in
McKibbin and Le (2004) and McKibbin and Chanthapun (1999) where flexible
exchange rate regimes were found to be far better at insulating East Asian economies
against global economic shocks that pegging to either the US dollar or a common Asia
currency.
Theoretical issues in monetary policy design are investigated using G-Cubed in
Henderson and McKibbin (1993); and McKibbin (1997). Trade imbalances caused by
macroeconomic policies and shocks are explored in Lee, McKibbin and Park (2006).
The impacts of the end of the cold war and large shift in military spending on the global
economy are explored by McKibbin and Thurman (1995),McKibbin (1996) and
Congressional Budget Office (1996b); the spillover of macroeconomic policies between
countries are explored in McKibbin and Bok (1995) and McKibbin and Tan (2009); and
theoretical issues in the design of models for policy analysis are explored in McKibbin
and Vines (2000) and Pagan et al.(1998).
Global fiscal consolidation is explored in McKibbin and Stoeckel (2012) which
examines the direct impact of a large-scale reduction in government outlays on
economies as well as the implications that a global fiscal adjustment might have on
country risk premia. One key result in this paper is that substantial fiscal consolidation by
1020 Warwick J. McKibbin and Peter J. Wilcoxen

high-income economies (in proportion to the size of their debt problem) has the
temporary effect of lowering economic activity in those economies, but has a positive
effect on developing countries and a few high-income economies not undertaking fiscal
consolidation. The reason is that the negative flow-on effects through trade linkages by
high-income economies reducing imports and stimulating exports with the developing
world are offset by favorable financial flow-on effects, which provides capital for
developing countries to increase GDP. Secondly a credible phasing in of fiscal cuts can
reduce expected future tax liabilities of households and firms which dampens the
negative direct effects of cuts in government spending. The paper also explores the
outcome if all countries coordinate their fiscal adjustment except the US. A coordinated
fiscal consolidation in the industrial world that is not accompanied by US actions is likely
to lead to a substantial worsening of trade imbalances globally as the release of capital in
fiscally contracting economies flows into the US economy, appreciates the US dollar and
worsens the current account position of the US. The scale of this change is likely to be
sufficient to substantially increase the probability of a trade war between the US and
other economies. In order to avoid this outcome, a coordinated fiscal adjustment is
clearly in the interest of the global economy.
15.3.4 Financial crises
15.3.4.1 Asian crisis
In McKibbin and Martin (1998), the six-sector version of G-Cubed was used to simulate
the Asian currency and economic crisis. Data from the key crisis economies of Thailand,
Korea and Indonesia were used as inputs for simulations to see if the model could
generate the scales of adjustment in asset markets as well as the sharp declines in
economic activity that occurred.
The study considered three key factors in explaining the qualitative and quantitative
events that unfolded in the crisis economies: (i) revisions to growth prospects, (ii)
changes in risk perceptions and (iii) policy responses in individual countries. The role of
asset markets and financial flows was critical. Downward revision in expected growth led
to falling asset prices, which reduced current income and wealth. Combined with
increased risk premia, calibrated to generate an exchange rate depreciation of the size
being observed in real time in these economies, meant investment and growth collapsed.
The extent to which financial markets responded through intertemporal arbitrage was
crucial to the risk shocks. Finally, the ability to model the anticipated policy responses,
both through price-setting and asset-market adjustments, was crucial to understanding
the subsequent outcomes.
McKibbin (1999) focuses on the second of these factors: the impact on Asian
countries of a jump in the perceived risk of investing in these economies. This paper
argued that a financial shock can quickly become a real shock because of the interde-
pendence of the real and financial economies. Too often policy makers and modelers
A Global Approach to Energy and the Environment: The G-Cubed Model 1021

ignore this interdependence. The reaction of policy makers directly, and in the impli-
cations for risk of their responses are crucial to the evolution of the crisis.
Both McKibbin (1999) and McKibbin and Martin (1998) conclude that the risk
shock was crucial to understanding the Asian crisis. The results for a risk shock are similar
to the results for a fall in expected productivity. The shock leads to capital outflow from
crisis economies and a sharp real and nominal exchange-rate depreciation. This reduces
the value of capital, which, together with a significant revaluation of the US dollar
denominated foreign debt, causes a sharp fall in wealth and a large collapse of private
consumption expenditure. The fall in the return to capital, and the large rise in real long-
term interest rates, lead to a fall in private investment.
Early in the debate over the Asian crisis, the results from G-Cubed were interesting
and controversial because they were counter to popular commentary, both in Australia
and in the US. The model showed that although the international trade effects were
negative for countries that export to Asia, the capital outflow from crisis economies
would push down world interest rates and stimulate non-traded sectors of economies that
were not affected by changes in risk assessment. The model suggested that a country like
Australia would slow only slightly in the short run and the US would experience stronger
growth as a result of the capital reallocation. This is now conventional wisdom.
Furthermore, for Australia, in particular, the existence of markets outside Asia, and
changes in relative competitiveness, meant that substitution was possible for Australian
exports. Models with an aggregate world growth variable or a single exchange rate
variable would not capture this international substitution effect. Models with exogenous
balance of payments could replicate the shock, but it required an exogenous change in
the trade balance and other factors that are exogenous to the model.
15.3.4.2 Global financial crisis
In a number of papers McKibbin and Stoeckel (2010a, 2010b) used the approach of
McKibbin and Martin (1998) together with shock to US housing markets and policy
responses of central banks and fiscal authorities around the world to model the global
financial crisis of 2008. Specifically they modeled the key aspects of the crisis as: (i) the
bursting of the housing bubble and loss in asset prices and household wealth with
consumers cutting back on spending and lifting savings; (ii) a sharp reappraisal of risk
with a spike in bond spreads on corporate loans and interbank lending rates with the cost
of credit, including trade credit, rising with a commensurate collapse of stock markets
around the world; and (iii) a massive policy response including a monetary policy easing,
bailouts of financial institutions and fiscal stimulus.
Simulating the loss in confidence through higher risk premia on the US alone (the
‘epicenter’ of the crisis) showed several things. Had there not been the contagion across
other countries in terms of risk reappraisal, the effects would not have been as dramatic as
subsequently occurred. The adverse trade effects from the US downturn would have
1022 Warwick J. McKibbin and Peter J. Wilcoxen

been offset to some degree by positive effects from a global reallocation of capital. Were
the US alone affected by the crisis, Chinese investment could have actually risen. The
world could have escaped recession. When there is a reappraisal of risk everywhere
including China, investment falls sharply ein a sense there is nowhere for the capital to
go in a global crisis of confidence. The implication is that if markets, forecasters and
policy makers misunderstand the effects of the crisis and mechanisms at work, they can
inadvertently fuel fears of a ‘meltdown’ and make matters far worse.
The bursting of the housing bubble had a bigger effect on falling consumption and
imports in the US than does the reappraisal of risk, but the reappraisal of risk has the
biggest effect on investment. Rising risk causes several effects. The cost of capital rises
that leads to a contraction in the desired capital stock. Hence, there is disinvestment by
business and this can go on for several years ea deleveraging in the popular business
media. The higher perception of risk by households causes them to discount future labor
incomes at a higher risk adjusted interest rate that leads to higher savings and less
consumption, fuelling the disinvestment process by business.
When there is a global reappraisal of risk there is a large contraction in output and
trade ethe scale of which depends on whether the crisis is believed to be permanent or
temporary. The long-run implications for growth and the outlook for the world
economy are dramatically different depending on the degree of persistence of the shock.
These papers found that, as expected, the effects of the crisis are deeper and last longer
when the reappraisal of risk by business and households is expected to be permanent
versus where it is expected to be temporary. A third combination was explored in
McKibbin and Stoeckel (2010b) where agents unexpectedly switch from one scenario of
believing the shock to be permanent to one the temporary scenario several years later.
The dynamics for 2010 are quite different between the temporary scenario and the
expectation revision scenario even though the shocks are identical from 2010 onwards.
One of the key results of both these studies was that there was a substantially larger
contraction in exports relative to the contraction in GDP in all economies. This was
observed in the actual data. This massive shift in the relationship between trade and GDP
is not the result of an assumption about the income elasticity of imports. It reflects some
key characteristics of the model. First, imports are modeled on a bilateral basis between
countries where imports are partly for final demand by households and government and
partly for intermediate inputs across the six sectors. In addition, investment is undertaken
by a capital sector that uses domestic and imported goods from domestic production and
imported sources. As consumption and investment collapse more than GDP, imports will
contract more than GDP. One country’s imports are another country’s exports; thus
exports will contract more than GDP unless there is a change in the trade position of
a particular country. The assumption that all risk premia rise and the results that all real
interest rates fall everywhere implies small changes in trade balances but big changes in
the extent of trade in durable goods. As durable goods have a much bigger share in trade
A Global Approach to Energy and the Environment: The G-Cubed Model 1023

than in GDP the compositional shift of demand away from durable goods due to higher
risk premia causes a structural change in the relationship between trade and GDP.
15.3.5 Pandemics
As part of research for the World Health Organization (WHO) G-Cubed was adapted to
explore two major pandemics: (i) the SARS (severe acute respiratory syndrome)
outbreak in 2003, which was explored in Lee and McKibbin (2004) (ii) the potential of
a pandemic resulting from the outbreak of bird flu, which was examined in McKibbin
and Sidorenko (2006).
In Lee and McKibbin (2004) the authors used emerging data on changes in risk
premiums observed in financial pricing and changes in spending behavior in the affected
countries on Hong Kong, China to develop shocks to country risk, based on observed
exchange rate changes, sector specific shifts in demand away from sectors with high
human-to-human contact (mostly services) and an increase in the input costs of the
service sector of roughly 5%. This study was the first of a new approach to analyzing the
macroeconomic costs of diseases through general equilibrium modeling. The key insight
for policy design and investment in public health was that the short-run cost of major
disease outbreaks is significant. Traditional estimates based on loss of life and income
foregone estimates underestimate the costs of large-scale change in economic behavior
and the spillovers between economies of disease outbreaks. The authors estimated that
the cost in 2003 of SARS for the world economy as a whole was close to $40 billion,
which is the official WHO estimate of the SARS outbreak.
The approach in Lee and McKibbin (2004) on SARS was significantly extended in
McKibbin and Sidorenko (2006) to explore the possible implications of more widespread
influenza pandemics. Based on historical experience of influenza pandemics, McKibbin
and Sidorenko (2006) considered four mortality scenarios under current economic
linkages in the global economy. The scenarios were: (i) a ‘mild’ pandemic, modeled on
1968e1969 Hong Kong Flu; (ii) a ‘moderate’ pandemic, modeled on the Asian Flu of
1957; (iii) a ‘severe’ pandemic similar to the lower estimates of mortality and morbidity
in the Spanish flu of 1918e1919; and (v) an ‘ultra’ pandemic, modeled on high-end
estimates of the Spanish Flu. These scenarios were used to generate a range of shocks to
individual countries and sectors due to the pandemic (including mortality and morbidity
shocks to labor force, increase in cost of doing business, an exogenous shift in consumer
preferences away from exposed sectors, and a re-evaluation of country risk premiums).
These shocks generate a complex response of incomes and prices driving global
economic outcomes. The results illustrated that even a mild pandemic can have signif-
icant consequences for global economic output, with the developing countries expe-
riencing the largest economic loss due to the compounding effect of a weaker public
health response, capital reallocation and monetary policy responses within different
exchange rate regimes. The use of a general equilibrium model that included changes in
1024 Warwick J. McKibbin and Peter J. Wilcoxen

behavior of a large scale in response not only to market signals, but also changes in risk
perceptions can cause a much larger economic loss that conventional estimates of
pandemics imply.
15.3.6 Demographic change
In a series of papers, McKibbin and Nguyen (2004), Bryant and McKibbin (2004),
McKibbin (2006b), and Nguyen (2011) have incorporated overlapping cohorts of
generations in G-Cubed, in order to explore demographic change in various countries.
The approach followed is based on the work of Blanchard (1985), Yaari (1965), and Weil
(1989) as extended by Faruqee (2003). This work was also adapted in Batini et al.(2005)
for the International Monetary Fund World Economic Outlook (International Monetary
Fund, 2004).
The basic approach was to introduce individual cohorts of agents into G-Cubed. By
following the Blanchard approach and assuming a constant probability of death across
cohorts we are able to aggregate agents outside the model and feed in the change in
productivity by agent cohort using estimated age-earnings profiles to generate shocks to
effective labor supply in the model. This short cut approach of assuming a constant
probability of death across cohorts is a strong assumption. To abandon that simplifying
assumption requires an explicit multicohort OLG model which is recently undertaken in
Nguyen (2011).
An analysis of the impact of the global and regional differences in demographic
change needs to take into account the effects of changing growth rates as well as the
numbers of adults and children. McKibbin (2006b) incorporated these projections into
a general equilibrium model that allows for the changing composition of the population,
and captures its effect on labor supply, investment, growth potential, saving, asset
markets, international trade and financial flows.
There are at least two most important policy implications from this research. The first
is that the projected demographic transition in the global economy will likely have
important macroeconomic impacts on growth, trade flows, asset prices (real interest rate
and real exchange rates) and investment rates. The second result is that policy makers
should not ignore the global demographic transition when focusing on domestic issues
related to demographics. The fact that the demographic transition is at different stages
across countries, particularly in industrialized countries relative to developing countries,
implies that the global nature of demographic change cannot be ignored. McKibbin
(2006b) showed that the developing world has important impacts on the industrial
economies.
As well as creating a framework for exploring a range of possible policy responses
directly related to demographics, the model could be used to explore how other policies,
apparently unrelated to demographics, might impact on the macro economy to offset any
negative consequences or reinforce any positive consequences of global demographic
A Global Approach to Energy and the Environment: The G-Cubed Model 1025

change. A first attempt at this is contained in Batini et al.(2005), which explored the
impact of productivity improvements induced by economic reform and lowering
barriers to international capital flows in developing countries. By using a general
equilibrium model other policies in other parts of the economy might have a more
substantial positive contribution to dealing with demographic change than the more
direct policies that are usually proposed, such as increased migration, subsidies to child
birth or changes in retirement ages.
15.4 SAMPLE APPLICATIONS
In this section we present results illustrating the use of G-Cubed for analysis of financial
shocks and international trade agreements. The first analysis draws on McKibbin and
Stoeckel (2010b) and examines the effects of a financial crisis on the global economy.
The second analysis draws on McKibbin and Wilcoxen (2003) and examines the effects
of the proposed FTAA on trade patterns and carbon emissions.
15.4.1 Financial crisis
15.4.1.1 Modeling approach
To analyze a financial crisis, we used an extended version of the model (Aggregation N)
which has greater regional detail. There are seven additional regions including five new
countries (Canada, the UK, Germany, India and New Zealand) and two new aggregates
(Other Asia and Latin America). In addition, the aggregate region representing Europe
has been replaced with a narrower aggregate representing the euro zone countries other
than Germany. The full list of regions is shown in Table 15.4.
We model financial crises as changes in the risks perceived by investors, which are
reflected in the risk premia they demand for holding assets. Risk premia enter the model
in a number of places. They play an important role in the model’s calibration as well as
having large impacts on economic outcomes through intertemporal relationships. For
example, risk premia mkand mjappear in equation (15.30), the arbitrage equation
between returns on domestic and US bonds, which is repeated below:
ikmk¼ijmjþ
_
Ej
k
Ej
k
:(15.39)
In addition, as shown in equation (4) there are risk premia, mei, between bonds and
equity in each sector within each economy, which represent the sector’s equity risk
premium. There is also a risk premium, mh, on the rate at which households discount
future after tax labor income, as shown in equation (15.16).
In calibrating the model, these risk premia are calculated so that the model’s solution
values for forward looking variables in the base year (2006) are equal to the historical
1026 Warwick J. McKibbin and Peter J. Wilcoxen

values of those variables. For example, in the case of country risk (m), a constant is chosen
so that the current exchange rate, which is the expected future path of interest differentials
plus the period Texchange rate, is equal to the actual exchange rate in the base period
2006. The equity risk premium in each sector in each country is chosen so that the stock
market value for sector iin country nis equal to its actual stock market value in 2006.
Once the risk premia are calculated they held constant for most simulations.
However, they can be shocked to explore the impact of changes in perceived risks.
15.4.1.2 Overview of simulations
To illustrate the importance of these risk premia, two experiments are presented in this
section. The first is a rise in country risk in Europe (consisting of the country models for
Germany, the rest of the euro zone and the UK). This shock could represent a sovereign
debt crisis in this region or some other change in perceived risk that causes investors to
demand a higher rate of return on government bonds from that region (relative to the US
government bond rate adjusted by expected exchange rate changes). The second
experiment examines a broader risk shock that extends to the US as well. In this case the
relative risk between Europe and the US is unchanged from the baseline but the risk of
both regions relative to all other countries rises.
15.4.1.3 Reference case
In the reference case the world economy is assume to grow along the model’s baseline
projections. All risk premia are held constant at their calibrated values discussed above.
Table 15.4 Regions in G-Cubed aggregation N
Region Short name Model code
1 US USA U
2 Japan JPN J
3 UK GBR K
4 Germany DEU G
5 Rest of the Euro Zone EUZ E
6 Canada CAN N
7 Australia AUS A
8 New Zealand NZL Q
9 Rest of the OECD OEC O
10 China CHI C
11 India IND D
12 Other Asia OAS X
13 Latin America LAM V
14 Other Developing Countries LDC L
15 Eastern Europe and the Former USSR EEB B
16 Oil Exporting Developing Countries OPC P
A Global Approach to Energy and the Environment: The G-Cubed Model 1027

15.4.1.4 Increased risk in Europe
In the first simulation, country risk in Europe (including Germany, the rest of the euro
zone and the UK) is assumed to rise unexpectedly in 2011 by 300 basis points. This
increase is assumed to be permanent. As a result, investors demand that all financial assets
within Europe pay an additional 300 basis points (relative to competing assets) to
compensate for the additional risk.
The immediate effect of the shock is a reduction in the financial value of Euro-
pean assets as investors reallocate their portfolios away from those assets. Financial
capital flows out of Europe causing a sharp fall in nominal and real European
exchange rates. For example, Figure 15.2 shows the percentage change in the real
effective exchange rate, measured in units of foreign currency per unit of domestic
currency, for four key regions: the US, Germany, the rest of the euro zone (‘Rest of
Europe’ in the figure) and China. Results for the Europe-only financial crisis are
shownbythesolidlinelabeled‘eur’andthoseforthebroaderEuropeeUS crisis are
shown by the dashed red line labeled ‘both’. Germany’s trade-weighted real effective
exchange rate falls by about 20% and that of the rest of the euro zone falls by about
40%. In contrast, the real exchange rates of the US and China increase as capital flows
into both regions.
−60 −40 −20 0 20
Percent Change
0 5 10 15
Year
eur both
Panel 1: USA
−60 −40 −20 0 20
Percent Change
0 5 10 15
Year
eur both
Panel 2: Germany
−60 −40 −20 0 20
Percent Change
0 5 10 15
Year
eur both
Panel 3: Rest of Europe
−60 −40 −20 0 20
Percent Change
0 5 10 15
Year
eur both
Panel 4: China
Figure 15.2 Real effective exchange rate.
1028 Warwick J. McKibbin and Peter J. Wilcoxen

These changes in exchange rates and financial flows are reflected in the trade
balances of each region. As shown in Figure 15.3 European regions experiencing
exchange rate declines and capital outflows see their trade balances move sharply
toward surplus: their exports become more competitive and investors are less willing to
finance trade deficits. For similar reasons, the trade balances of the US and China
(where exchange rates have strengthened and capital inflows have increased) move
toward deficit.
In the longer term, the change in the required financial return causes the marginal
physical product of capital in Europe to rise to re-equilibrate the arbitrage condition
between bonds and equity. This comes about via a decline in European capital stocks:
the stocks initially in place when the shock occurs are too high to generate the
physical return required. The expectation that increased risk is permanent leads to
a long period of falling European capital and higher European interest rates relative to
the reference case. As shown in Figure 15.4 the shock raises European real interest
rates by more than 100 basis points. Interest rates in the US and China, on the other
hand, fall slightly. Real investment, shown in Figure 15.5 also changes as expected:
a sharp immediate drop in Europe, followed by a gradual recovery as European capital
−10 −5 0 5 10
Percent of GDP
0 5 10 15
Year
eur both
Panel 1: USA
−10 −5 0 5 10
Percent of GDP
0 5 10 15
Year
eur both
Panel 2: Germany
−10 −5 0 5 10
Percent of GDP
0 5 10 15
Year
eur both
Panel 3: Rest of Europe
−10 −5 0 5 10
Percent of GDP
0 5 10 15
Year
eur both
Panel 4: China
Figure 15.3 Trade balance.
A Global Approach to Energy and the Environment: The G-Cubed Model 1029

−4 −2 0 2
Percentage Point Change
0 5 10 15
Year
eur both
Panel 1: USA
−4 −2 0 2
Percentage Point Change
0 5 10 15
Year
eur both
Panel 2: Germany
−4 −2 0 2
Percentage Point Change
0 5 10 15
Year
eur both
Panel 3: Rest of Europe
−4 −2 0 2
Percentage Point Change
0 5 10 15
Year
eur both
Panel 4: China
Figure 15.4 Real short-term interest rate.
−40 −20 0 20 40
Percent Change
0 5 10 15
Year
eur both
Panel 1: USA
−40 −20 0 20 40
Percent Change
0 5 10 15
Year
eur both
Panel 2: Germany
−40 −20 020 40
Percent Change
0 5 10 15
Year
eur both
Panel 3: Rest of Europe
−40 −20 020 40
Percent Change
0 5 10 15
Year
eur both
Panel 4: China
Figure 15.5 Real investment.
1030 Warwick J. McKibbin and Peter J. Wilcoxen

stocks converge to their new, lower, long-term levels; and the reverse in the US and
China.
The drop in asset values in Europe lowers the wealth of European households and
causes private consumption to fall, as shown in Figure 15.6 Relative to the reference
case, consumption increases slightly in the US and somewhat more in China due to
China’s slightly large fall in interest rates.
Overall, European regions experience lower consumption and investment, and
higher net exports. On balance, the effect on European GDP is negative, as shown in
Figure 15.7. The results are consistent with supply-side effects: European capital stocks
gradually fall over time and the loss of GDP is exacerbated in the short run by temporary
unemployment. The change in risk is sufficient to cause a recession in Europe with GDP
in the first year down by nearly 5% relative to its reference level. In contrast,
consumption, investment and GDP all rise slightly in the US and China under the
Europe-only shock.
Interestingly, the transmission of the shock to countries outside Europe is positive
(i.e. other countries gain) despite the fall in European GDP. The large outflow of
financial capital from Europe to the rest of the world pushes down long-term real
interest rates in other regions, stimulating non-European investment and expanding
−10 0 10 20
Percent Change
0 5 10 15
Year
eur both
Panel 1: USA
−10 0 10 20
Percent Change
0 5 10 15
Year
eur both
Panel 2: Germany
−10 0 10 20
Percent Change
0 5 10 15
Year
eur both
Panel 3: Rest of Europe
−10 0 10 20
Percent Change
0 5 10 15
Year
eur both
Panel 4: China
Figure 15.6 Real consumption.
A Global Approach to Energy and the Environment: The G-Cubed Model 1031

the supply side of other economies. The capital reallocation effect is sufficiently large
that for most other regions it more than offsets the negative effect of lower import
demands from a weaker Europe. Roughly speaking, the shock tends to reallocate
financial capital rather than destroy it. Although the value of capital drops sharply in
Europe (as reflected in the real exchange rate shown in Figure 15.2), it rises in the US
and China.
15.4.1.5 Increased risk in Europe and the US
In the second simulation, the US also experiences an increase in perceived risk (perhaps
through contagion or its own fiscal crisis). As noted above, results for this simulation are
shown in the earlier figures along with those for the European financial crisis. Broadly
speaking, the effects of the broader crisis on the US are much like those of the narrower
crisis on Europe: the real exchange rate falls, the trade balance moves toward surplus, the
real interest rate increases, and investment, consumption and GDP decline.
Interestingly, however, the spread of the crisis to the US also attenuates the effects on
Germany and the rest of Europe: European losses in consumption, investment and GDP
are reduced relative to the case when the shock is confined to Europe. This result reflects
the role of adjustment costs in capital accumulation in each sector. The US is a large
economy that can absorb a lot of the capital that flows out of Europe without much being
−5 0 5 10
Percent Change
0 5 10 15
Year
eur both
Panel 1: USA
−5 0 5 10
Percent Change
0 5 10 15
Year
eur both
Panel 2: Germany
−5 0 5 10
Percent Change
0 5 10 15
Year
eur both
Panel 3: Rest of Europe
−5 0 5 10
Percent Change
0 5 10 15
Year
eur both
Panel 4: China
Figure 15.7 Real gross domestic product.
1032 Warwick J. McKibbin and Peter J. Wilcoxen

lost to adjustment costs (see equation 15.3). In contrast, when the inflow from Europe to
the US is reduced by the rise in US risk, adjustment costs cause less capital to flow out of
Europe. The remaining countries (other than the US and Europe) have much less
capacity to absorb large inflows of additional capital without incurring rising adjustment
costs from expanding their physical capital stocks. Thus, more financial capital stays
within Europe under the second simulation, reducing the loss of European GDP. This
result illustrates one of the benefits of intertemporal general equilibrium models that
explicitly model the supply side of economies: in more traditional Keynesian macro-
economic models, this effect does not exist and demands driven by trade dominate the
results for the international transmission of economic shocks.
Finally, the results for China in the second simulation show the importance of
assumptions about monetary policy. In G-Cubed, China is assumed to peg its nominal
exchange rate to the US dollar.
37
As a result, China effectively loosens its monetary
policy at the onset of the US shock in order to have its nominal exchange rate move with
the US dollar. Thus, there is a substantial monetary expansion in China. Short-term
interest rates fall, real consumption, investment and GDP rise sharply, and inflation spikes
markedly, as shown in Figure 15.8 (panel 4).
−5 0 5 10 15
Percentage Point Change
0 5 10 15
Year
eur both
Panel 1: USA
−5 0 5 10 15
Percentage Point Change
0 5 10 15
Year
eur both
Panel 2: Germany
−5 0 5 10 15
Percentage Point Change
0 5 10 15
Year
eur both
Panel 3: Rest of Europe
−5 0 5 10 15
Percentage Point Change
0 5 10 15
Year
eur both
Panel 4: China
Figure 15.8 Inflation rate.
37 Strictly speaking, China is assumed to follow the US dollar via a crawling peg that adjusts gradually.
A Global Approach to Energy and the Environment: The G-Cubed Model 1033

15.4.1.6 Summary
These results illustrate the power of G-Cubed to contribute to understanding of
important macroeconomic transmission channels in the global economy. Integrating the
flows of capital and goods, together with explicit modeling of the demand and supply
sides of economies, fundamentally changes the nature of the international transmission of
macroeconomic shocks. The model’s results offer an explanation of the puzzle of
‘delinking’ of trade and financial flows often discussed in the popular press. When both
flows are incorporated in a model, as they are in G-Cubed, it is clear that the details of
a shock’s effects on trade and financial flows are pivotal in determining the outcome of
the transmission process. Real versus financial shocks can affect trade and finance
differently and hence there need not be anything mysterious about changes in the
comovements of the GDP of countries over time when the nature of the macroeco-
nomic shocks they face changes. For example, our results in this section show that
nominal rigidities, such as pegging a nominal exchange rate, can have significant short-
term real consequences during the adjustment period.
15.4.2 FTAA
Our second illustration of analysis using G-Cubed focuses on the proposed FTAA.
We evaluated the FTAA by comparing the evolution of the world economy with
and without the agreement.
38
In McKibbin and Wilcoxen (2003) we considered
a range of competing assumptions about the manner in which the FTAA would be
implemented and the effects it would have on individual economies. In particular,
we evaluated the FTAA under two different assumptions about its effect on
productivity growth and under alternative assumptions about how governments
respond to a decline in tariff revenue. In this section we discuss a subset of those
results.
15.4.2.1 Trade liberalization and growth
The direct effect of reducing tariffs is to improve the efficiency of an economy’s
resource allocation by reducing the wedge between a buyer’s willingness to pay for
an imported product and the product’s marginal cost. Traditionally, general equi-
librium studies of trade reform have focused on measuring these efficiency gains,
and measuring them at a given point in time, usually either the immediate short run
after the reform has been implemented, or far in the future at the model’s long run
equilibrium. By this standard, trade liberalization is usually found to improve welfare
38 Other papers in the literature on trade and the environment include Strutt and Anderson (1999), who find that trade
can improve environmental quality in some circumstances and does little harm otherwise, and Tsigas et al. (2002),
who find that the effect of trade on the environment is ambiguous. Other general equilibrium studies of the FTAA
include Diao and Somwaru (2000) and Adkins and Garbaccio (2002).
1034 Warwick J. McKibbin and Peter J. Wilcoxen

but the magnitude of the improvement tends to be small. For example, Hertel et al.
(1999) find that a worldwide cut in tariffs of 40% would raise world GDP by
0.24%.
39
However, liberalization leads to a host of indirect dynamic effects as well. These can
cause profound changes in an economy by altering its rate of growth. Unfortunately,
they are often very difficult to measure, particularly because competing effects can work
in opposite directions. For example, reductions in tariffs cause imports to rise, pushing
a country’s trade balance toward deficit, leading to a depreciation of its exchange rate and
a consequent increase in its exports. Deterioration of the trade balance is accompanied by
inflows of capital from abroad which augment domestic saving and tend to raise the rate
of investment. At the same time, the drop in tariff revenues will push the country toward
fiscal deficit, raising government borrowing and tending to crowd out private invest-
ment. To further complicate matters, capital accumulation is also affected by reductions
in the prices of imported durable goods, which tend to reduce the cost of new capital and
thereby increase the rate of capital formation and growth. Disentangling these effects
requires a multisector general intertemporal equilibrium model with considerable
financial detail.
In addition to changing capital accumulation, the empirical literature suggests that
trade improves industry productivity by placing additional competitive pressure on
previously protected industries, and by increasing the flow of investment and embodied
technical change across borders (Frankel and Romer, 1999; Chand, 1999). Moreover,
these studies find that trade liberalization has much larger effects than traditional static
analysis suggests: Frankel and Romer, for example, find that a one percentage point
increase in the ratio of trade to GDP raises per capita income by 2e3%.
Although there is clear evidence of a link between trade and productivity at the
aggregate level, the literature is not yet sufficient to permit precise predictions about the
magnitude of improvement in productivity of individual industries. As a result, we
approach the issue by running two sets of simulations. The first employs the traditional
assumption that firms in liberalizing economies do nothing when faced by increased
competition from imports (apart from substituting toward cheaper inputs): they do not
cut costs or adopt better management practices or newer technology. Although this
assumption is conventional, it is quite strong. It says, in effect, that a firm’s technology
choice is not affected by its industry’s level of protection. It is hard to find any empirical
support for that position and there is much evidence for the reverse: there are countless
39 Martin et al. (2003) point out that traditional general equilibrium measures of the gains from liberalization are biased
downward very significantly by aggregation across goods. Tariff rates differ shar ply between individual products and
efficiency costs are proportional to the square of the price changes caused by the tariffs. When products are aggregated,
however, their individual tariffs are replaced by a weighted average. The efficiency cost of an average tariff can be
shown to be smaller than the average of the efficiency costs of the individual tariffs it replaced.
A Global Approach to Energy and the Environment: The G-Cubed Model 1035

examples of industries clinging to obsolete, high-cost technology because protection
allowed them to do so.
Our second set of simulations introduces a link between trade and productivity by
assuming that previously protected industries are able to take modest steps to reduce their
costs.
40
It captures, at least to first order, the empirical features seen in the econometric
literature on trade and growth. Since both sets of simulations depend on assumptions
about the link between trade and productivity, neither one can be interpreted as a precise
forecast of the FTAA’s effects. However, they characterize the set of possible outcomes.
Other general equilibrium studies that have introduced a link between trade and
productivity include Stoeckel et al.(1999), Diao and Somwaru (2000), and Monteagudo
and Watanuki (2001).
Finally, a third indirect effect of trade agreements is that increased openness lowers the
risk premium attached to a country’s sovereign debt by rating agencies such as Standard
and Poor’s and Moody’s (Stoeckel et al., 1999). This can lead to pronounced increases in
capital inflows, particularly for developing countries. However, that mechanism will not
be discussed here.
15.4.2.2 Modeling approach
G-Cubed’s basic design is well-suited to the task because it has a full, integrated treatment
of international trade and financial flows: each country’s current account position must
be offset by its capital account, which in turn leads to accumulation or erosion of its stock
of foreign assets and thus to changes in its future flow of interest payments. In addition, it
accounts for the relative immobility of physical capital and the high mobility of financial
capital.
To analyze the FTAA, we developed an extended version of the model with greater
regional detail in the Western Hemisphere. It includes five regions particularly relevant
for the FTAA: the US, Canada, Mexico, Brazil and an aggregate region representing the
rest of Latin America. To keep the size of the model manageable, Australia was merged
into the Rest of the OECD. The full list of regions is shown in Table 15.5. Within
each region, the disaggregation of production remained the same: the 12 sectors shown
in Table 15.2.
The structure of the model was also modified to facilitate simulations involving
preferential trade agreements. The updated version allowed each region to have two sets
of tariffs: one set for imports from countries within a preferred trade area and one for
imports from everywhere else. For free trade agreements such as NAFTA or the FTAA,
the tariffs on trade within the preferential area are set to zero. It should be noted that the
model does not require participants in a free trade agreement have harmonized external
40 The magnitude of the productivity improvements will be discussed below.
1036 Warwick J. McKibbin and Peter J. Wilcoxen

tariffs: each country retains its original tariffs on trade outside the free trade area unless
otherwise specified in a simulation.
15.4.2.3 Overview of simulations
To determine the effects of the FTAA we carried out a suite of simulations: a reference
case having no multiregion free trade areas; a pair of simulations examining the effects of
NAFTA and the FTAA under the assumption that tariff reductions and trade flows have
no effect on industry productivity; a pair of simulations examining the effect of NAFTA
and the FTAA under an alternative assumption in which tariff reductions and increased
trade lead to modest improvements in the productivity of previously protected industries;
and a pair of simulations investigating the effect of announcing NAFTA or the FTAA 5
years before it is actually implemented. In this chapter we focus only on the simulations
that include productivity effects; full results including the other simulations can be found
in McKibbin and Wilcoxen (2003). The simulations discussed in here are listed in
Table 15.6.
15.4.2.4 Reference case
The first was a reference case having no multiregion free trade agreements, not even
NAFTA.
41
In this simulation, each region’s tariff rates do not distinguish between
imports from different trading partners. For example, the US imposes a single tariff on
imports of durable goods, regardless of whether any particular imported good originates
in Canada, Europe, Brazil or somewhere else. As it does not include NAFTA, the
reference case does not represent the current world economy. However, it allows us to
Table 15.5 Regions in the FTAA-extended G-Cubed model
Region Short name Model code
1 US USA U
2 Japan JPN J
3 Canada CAN N
4 Europe EUW E
5 Rest of the OECD OEC O
6 China CHI C
7 Brazil BRA I
8 Mexico MEX M
9 Rest of Latin America RLA V
10 Other Developing Countries LDC L
11 Eastern Europe and the Former USSR EEB B
12 Oil Exporting Developing Countries OPC P
41 That is, no free trade area not wholly contained within one of the model’s regions (such as Europe).
A Global Approach to Energy and the Environment: The G-Cubed Model 1037

simulate the adoption of NAFTA itself, which is very useful for putting the FTAA results
in context. The reference case tariff rates were derived by aggregating historical data and
are listed in Table 15.7.
42
The pattern of trade under the reference case is exemplified by the figures shown in
Table 15.8 for year 12 of the simulation; refer to Table 15.5 for a list of region codes.
43
Each panel of the table gives the bilateral trade matrix for a particular good; panel nine,
for example, shows trade in durable goods. The columns of Table 15.8 indicate the
origin of each trade flow and the rows indicate the destination. Each entry is the US
dollar value of the corresponding flow of goods.
44
For example, in panel 5 the value in
the ‘U’ row and ‘P’ column is 35.3, which indicates that shipments of crude oil from
OPEC to the US were worth $35.3 billion. Where the value of trade was less than $0.1
billion, the entry is left blank.
As exports from different regions are imperfect substitutes, most goods flow in both
directions between each pair of regions. For example, the US exports $45.5 billion
dollars’ worth of durables to Japan while simultaneously importing $145.3 billion dollars
of Japanese durables. At G-Cubed’s level of aggregation, traded goods are generally far
from homogeneous due to differences in the product mix of exports from different
countries. Aircraft, for example, are an important component of durables exported by
the US but are not a significant portion of US imports of durables from Japan.
Table 15.6 Descriptions of simulations
Name Trade Area Description
1 Reference None
a
No free trade agreements: each region imposes identical tariffs on
imports from all of its trading partners
2 NAFTA-p NAFTA The existing NAFTA: no tariffs on trade between the US, Canada
and Mexico plus productivity improvements in the industries
whose tariffs have been reduced
3 FTAA-p FTAA The FTAA implemented immediately: no tariffs on trade
between the US, Canada, Mexico, Brazil and the Rest of Latin
America plus productivity improvements
4 NAFTA5-p NAFTA Counterfactual in which NAFTA is announced immediately, but
implemented 5 years later; used as a point of reference for
comparison with the FTAA5 simulation
5 FTAA5-p FTAA The FTAA announced immediately but not taking effect until
5 years later; examines the effects of anticipation of the
agreement. Includes productivity improvements
a
Other than Europe, which is treated as a single region in the model.
42 See Hertel (1997) for the original data.
43 Results are presented for year 12 because it is a representative medium-run year for both the immediate and anticipated
versions of the trade agreement.
44 Throughout the paper all values are in constant dollars.
1038 Warwick J. McKibbin and Peter J. Wilcoxen

Table 15.7 Initial tariffs, by country and product (%)
Product
Region
U
N,F
JN
N,F
EOCI
F
M
N,F
V
F
LBP
Electric utilities 0.02 0.11 2.06 0.34
Gas utilities 1.44 0.28
Petroleum refining 2.24 3.31 6.20 3.81 0.44 8.33 5.69 7.07 6.96 10.77 4.93 6.29
Coal mining 4.72 5.46 1.51 1.82 1.81 5.90
Crude oil and gas extraction 0.39 5.25 3.90 4.78 0.77 2.31
Other mining 0.38 0.01 0.02 0.12 0.44 1.95 8.33 3.35 3.08 2.17 6.25
Agriculture 4.72 110.21 12.88 25.44 0.96 26.00 30.41 18.08 9.51 20.30 24.56 37.05
Forestry and wood products 2.08 2.29 5.02 2.57 3.90 8.72 13.07 12.52 12.77 6.06 12.22 12.64
Durable goods 2.60 0.58 4.14 4.17 4.82 14.87 18.00 11.30 12.93 7.81 9.92 11.09
Non-durables 6.93 31.36 21.61 25.74 6.81 28.77 13.91 21.78 15.22 14.04 15.26 34.80
Transportation 0.27 1.19 0.55 1.00 0.12
Services 0.03 2.83 0.25 0.55 0.11
N
Member of NAFTA
F
Member of the FTAA.
A Global Approach to Energy and the Environment: The G-Cubed Model 1039

Table 15.8 Bilateral trade flows in the reference case (billion US$)
Industry 1: Electric Utilities
U J N E O C I M V L B P Sum
U NA 2.0 2.1
JNA
N 0.3 NA 0.3
E NA 0.1 0.7 0.1 0.8
ONA
C NA 0.1 0.1
I NA 0.2
MNA
VNA
L 1.1 0.7 NA 0.1 2.0
B 0.1 NA 0.1
PNA
Sum 0.3 2.0 1.0 0.7 0.2 0.8 0.1 5.1
Industry 2: Gas Utilities
U J N E O C I M V L B P Sum
U NA 3.0 0.1 0.1 3.2
J NA 0.9 1.4 2.4
N 0.1 NA 0.1
E NA 1.6 1.3 3.0
ONA
C NA 0.2 0.1 0.3
I NA 0.1
M 0.2 NA 0.1 0.2
VNA
L 0.8 0.3 0.1 NA 0.2 1.1 2.6
BNA
PNA
Sum 0.3 3.0 0.9 0.4 0.1 0.1 1.1 1.9 4.1 11.9
Industry 3: Refining
U J N E O C I M V L B P Sum
U NA 0.3 0.2 3.3 0.1 0.1 0.3 0.8 2.2 1.6 0.2 2.3 11.3
J 0.3 NA 0.1 0.1 0.1 0.1 4.4 2.4 7.6
N 1.3 NA 0.2 0.1 1.7
E 1.0 0.1 3.4 NA 0.1 0.2 0.4 2.4 7.0 3.9 18.5
O 0.2 NA 0.7 0.2 1.3
C 0.1 0.4 0.1 NA 2.6 0.2 0.1 3.6
I 0.2 0.1 0.3 NA 0.1 0.4 1.1
M 5.0 0.2 0.1 NA 0.2 0.2 5.7
V 1.4 0.5 0.1 0.2 0.1 NA 0.8 0.9 0.6 4.6
(Continued )
1040 Warwick J. McKibbin and Peter J. Wilcoxen

Table 15.8 Bilateral trade flows in the reference case (billion US$)dcont'd
L 1.3 1.0 12.7 1.1 1.3 0.1 NA 1.3 7.4 26.3
B 2.3 0.1 0.1 NA 2.6
P 0.2 0.1 0.5 0.1 0.2 0.5 NA 1.7
Sum 11.1 1.9 3.7 20.3 1.3 2.1 0.6 1.1 3.3 13.4 9.7 17.4 85.9
Industry 4: Coal
U J N E O C I M V L B P Sum
U NA 0.2 0.3 0.1 0.2 0.1 1.0
J 0.2 NA 0.7 2.9 0.6 0.2 0.1 0.5 5.1
N 1.0 NA 0.1 1.1
E 1.1 0.3 NA 1.2 0.3 0.7 1.2 1.5 0.3 6.7
ONA
C 0.1 NA 0.1
I 0.4 0.1 0.1 0.3 0.1 NA 0.1 0.1 1.1
M 0.3 NA 0.4
VNA
L 0.3 0.1 0.4 0.2 2.2 1.1 0.1 NA 0.3 0.9 5.7
B 0.1 0.1 NA 0.3
P NA 0.1
Sum 3.3 0.3 1.9 0.2 7.0 2.2 1.2 1.5 2.1 1.8 21.6
Industry 5: Crude Oil and Gas
U J N E O C I M V L B P Sum
U NA 18.6 6.2 0.4 0.1 10.8 6.3 18.4 0.1 35.3 96.2
J 0.4 NA 0.2 0.3 0.5 0.4 0.1 14.5 20.2 36.6
N 0.5 NA 2.9 0.3 0.7 1.9 6.4
E NA 1.2 0.1 12.4 16.2 36.3 66.2
O NA 3.0 1.6 4.7
C 0.1 0.8 0.3 NA 3.6 0.1 2.0 6.9
I 0.1 0.1 NA 0.1 1.3 0.1 3.5 5.3
M 0.5 NA 0.1 0.2 0.8
V 0.1 1.1 NA 0.4 4.4 5.8 11.8
L 0.4 4.0 2.1 0.4 0.5 NA 2.0 47.7 57.1
B 2.3 0.3 NA 1.8 4.4
P 0.1 0.1 0.1 0.1 0.7 NA 1.1
Sum 2.0 18.7 16.7 3.3 1.2 13.9 7.2 55.1 23.0 156.4 297.4
Industry 6: Mining
U J N E O C I M V L B P Sum
U NA 1.9 0.7 0.1 0.2 0.3 0.8 0.3 0.6 0.1 5.2
J 0.4 NA 0.1 0.3 1.5 0.3 0.5 0.1 1.8 0.9 5.9
N 1.9 NA 0.2 0.1 0.3 0.1 2.7
E 1.3 0.1 1.6 NA 0.7 0.3 1.5 0.1 0.2 3.2 2.3 0.7 12.0
O 0.1 0.1 NA 0.2 0.4
(Continued )
A Global Approach to Energy and the Environment: The G-Cubed Model 1041

Table 15.8 Bilateral trade flows in the reference case (billion US$)dcont'd
C 0.4 0.3 0.1 0.4 0.8 NA 0.3 0.1 0.9 0.2 3.7
I 0.1 0.1 NA 0.2 0.1 0.1 0.5
M 1.1 0.1 0.1 0.1 NA 0.1 0.2 1.8
V 0.1 0.1 0.1 0.1 NA 0.4
L 1.4 0.7 0.5 3.9 2.6 0.5 0.9 0.4 NA 1.1 0.8 12.9
B 0.6 0.1 0.1 0.1 0.4 NA 1.2
P 0.1 0.3 0.2 0.2 0.1 NA 1.1
Sum 6.8 1.2 4.5 6.5 5.8 1.4 4.2 1.1 1.9 7.7 3.9 2.7 47.8
Industry 7: Agriculture
U J N E O C I M V L B P Sum
U NA 0.1 5.7 2.7 0.3 0.6 1.8 4.3 5.9 4.0 0.2 0.4 25.8
J 11.8 NA 1.4 1.4 1.1 3.0 0.5 0.8 3.2 0.1 0.2 23.5
N 4.7 NA 0.3 0.1 0.1 0.1 0.4 0.4 6.2
E 11.3 0.1 1.4 NA 0.5 1.5 4.7 0.4 8.5 15.3 4.0 1.2 48.9
O 0.3 0.2 NA 0.1 0.1 0.5 1.2
C 1.5 1.3 0.2 0.1 NA 0.3 0.9 4.4
I 0.3 0.2 0.1 NA 0.4 1.1
M 8.3 0.6 0.2 0.1 NA 0.1 0.1 9.4
V 4.8 0.5 0.5 0.1 0.3 0.2 NA 0.3 0.2 6.8
L 16.3 0.2 0.7 4.1 9.5 2.7 1.2 0.4 NA 1.1 0.7 36.8
B 0.8 0.1 5.7 0.3 0.4 1.3 1.7 NA 0.2 10.5
P 1.7 1.3 1.4 0.5 0.2 0.2 0.2 3.1 0.1 NA 8.6
Sum 61.8 0.5 13.1 16.7 12.1 8.5 9.4 5.0 18.3 29.4 5.6 2.9 183.3
Industry 8: Forestry
U J N E O C I M V L B P Sum
U NA 17.2 0.9 0.1 0.2 0.5 0.8 0.3 2.1 0.1 2.3 24.6
J 4.1 NA 2.8 0.7 1.1 0.8 0.1 4.6 0.6 2.8 17.7
N 2.3 NA 0.1 0.2 0.2 2.8
E 2.0 0.9 NA 0.3 0.6 0.2 5.5 6.5 1.5 17.7
O 0.1 0.1 0.2 NA 0.3 0.2 0.9
C 0.1 0.1 NA 2.2 0.1 0.4 3.0
I NA 0.1 0.2 0.3
M 0.8 NA 0.1 0.1 1.2
V 0.9 0.1 0.1 NA 0.1 0.1 1.3
L 1.0 0.1 0.3 1.7 0.6 0.7 0.2 0.1 NA 0.7 2.5 7.9
B 0.1 1.0 0.2 NA 0.1 1.4
P 0.3 0.1 0.7 0.4 0.1 NA 1.7
Sum 11.9 0.2 21.6 5.5 1.9 2.1 1.5 0.9 0.8 15.8 8.0 10.3 80.5
Industry 9: Durables
U J N E O C I M V L B P Sum
U NA 145.3 161.3 168.3 2.4 25.9 7.2 92.1 4.4 164.7 7.5 4.3 783.5
J 45.5 NA 1.2 34.9 2.1 13.1 1.5 0.8 0.4 59.0 2.3 2.0 162.6
(Continued )
1042 Warwick J. McKibbin and Peter J. Wilcoxen

Table 15.8 Bilateral trade flows in the reference case (billion US$)dcont'd
N 135.0 6.6 NA 12.8 0.2 1.4 0.3 3.2 0.1 5.6 0.3 0.2 165.7
E 132.1 87.6 8.5 NA 2.5 17.3 4.3 2.6 2.6 139.6 52.5 3.5 453.1
O 15.1 11.3 0.7 18.2 NA 1.2 0.2 12.6 0.1 0.3 59.7
C 9.9 25.2 0.7 20.8 0.4 NA 0.4 18.4 4.9 0.2 80.8
I 17.9 4.3 0.6 19.6 0.1 0.9 NA 1.4 0.6 6.3 0.2 0.2 52.2
M 114.2 11.4 1.5 15.2 0.4 1.3 NA 0.4 6.4 0.2 0.4 151.5
V 15.2 11.8 0.8 10.7 0.8 3.0 2.1 NA 8.7 0.7 1.3 55.1
L 130.6 223.6 4.7 302.6 12.2 45.8 3.4 1.5 0.9 NA 15.2 9.4 749.9
B 5.0 2.7 0.6 82.1 0.8 0.1 0.3 11.8 NA 0.3 103.6
P 21.8 20.9 1.3 40.7 1.6 2.4 0.8 0.5 0.8 16.2 1.6 NA 108.7
Sum 642.2 550.6 181.9 726.0 21.6 109.9 22.4 104.4 10.2 449.4 85.6 22.2 2,926.4
Industry 10: Non-durables
U J N E O C I M V L B P Sum
U NA 18.2 67.8 63.3 2.7 15.8 4.0 15.5 11.9 54.0 2.7 6.2 262.2
J 27.3 NA 3.5 30.4 5.5 23.6 1.2 0.2 0.6 29.6 0.6 3.3 125.8
N 44.2 0.6 NA 7.5 0.6 0.9 0.2 0.2 0.6 3.5 0.2 0.2 58.6
E 54.5 15.6 6.4 NA 6.9 13.9 7.1 1.4 5.1 87.3 36.8 8.5 243.4
O 5.8 1.6 0.5 8.3 NA 1.6 0.2 0.1 5.3 0.8 24.2
C 6.4 9.6 1.2 7.3 2.2 NA 1.0 0.8 20.2 3.0 1.2 52.8
I 4.5 0.5 0.5 5.0 0.2 0.4 NA 0.3 1.4 1.4 0.2 0.2 14.7
M 37.4 0.6 0.4 6.1 0.4 0.2 0.2 NA 0.7 1.6 0.1 0.1 47.8
V 27.4 0.6 1.0 10.6 0.5 1.4 3.1 2.5 NA 4.4 0.3 1.8 53.6
L 43.4 42.6 4.3 174.9 16.5 39.2 3.5 0.4 1.2 NA 24.8 12.6 363.4
B 4.8 0.3 0.3 57.0 0.3 3.4 0.9 0.2 8.7 NA 0.7 76.7
P 5.8 3.4 0.6 15.8 2.0 1.5 0.9 0.2 0.9 10.9 0.4 NA 42.4
Sum 261.4 93.6 86.4 386.2 37.9 101.9 22.2 20.8 23.4 226.8 69.2 35.7 1,365.5
Industry 11: Transportation
U J N E O C I M V L B P Sum
U NA 7.3 2.4 41.9 3.7 2.7 0.5 1.7 2.3 25.5 5.6 6.6 100.1
J 12.6 NA 1.8 18.7 1.7 1.5 0.3 1.4 1.5 11.0 3.0 3.6 57.0
N 3.3 1.1 NA 4.3 0.4 0.3 0.1 0.3 0.3 2.0 0.6 0.7 13.3
E 59.3 22.0 6.8 NA 6.4 12.2 1.8 4.5 6.3 46.5 12.9 11.6 190.3
O 2.8 1.0 0.3 4.0 NA 0.3 0.1 0.2 0.2 2.2 0.5 0.5 12.1
C 2.4 0.7 0.3 7.3 0.3 NA 0.1 0.2 0.3 3.8 0.6 0.8 16.8
I 1.9 0.7 0.2 3.6 0.2 0.4 NA 0.1 0.2 1.8 0.4 0.5 10.2
M 1.9 0.8 0.3 4.1 0.2 0.3 NA 0.3 1.4 0.5 0.4 10.1
V 2.8 1.1 0.3 4.6 0.3 0.3 0.1 0.2 NA 2.2 0.5 0.5 13.0
L 13.4 6.2 1.7 23.6 1.3 3.1 0.8 1.1 1.4 NA 2.9 2.8 58.3
B 5.2 1.7 0.6 7.8 0.5 0.8 0.1 0.4 0.5 4.2 NA 1.1 22.9
P 5.4 2.5 0.7 8.7 0.5 1.2 0.3 0.4 0.5 4.7 1.1 NA 25.9
Sum 111.0 45.0 15.3 128.5 15.6 23.0 4.3 10.5 13.8 105.3 28.5 29.1 530.0
(Continued )
A Global Approach to Energy and the Environment: The G-Cubed Model 1043

The row sums in Table 15.8 show the total value of imports of the good by each
region; the sum of the ‘U’ row in panel 5, for example, shows that total US imports of
crude oil from all of its trading partners were worth $96.2 billion. The column sums
show the total value of exports of the good from each region; the sum of the ‘P’ column
of panel 5 shows that total crude oil exports from OPEC were worth $156.4 billion. The
value in the lower right corner of each panel shows the total US dollar value of trade in
the good; in panel 5, the value is $297.4 billion.
The model’s 12 goods fall into three distinct categories in terms of the total dollar
value of trade. Six sectors each account for less than $100 billion: electricity ($5 billion),
natural gas delivered by utilities ($12 billion), refined petroleum products ($86 billion),
coal ($22 billion), non-fuel mining ($48 billion) and forestry ($80 billion). At the
opposite extreme, two sectors each account for more than a trillion dollars: durables
($2926 billion) and non-durables ($1365 billion). The remaining four sectors fall in
between: crude oil and natural gas ($297 billion), agriculture ($183 billion), trans-
portation ($530 billion) and services ($607 billion). The overwhelming importance of
durables is emphasized by the fact that shipments from Europe (column ‘E’) to devel-
oping countries (row ‘L’) ea single entry in the trade matrix for durables eare worth
$303 billion, or more than the total value of world trade in the six least-traded goods.
In order to evaluate subsequent simulations, it is useful to group the model’s regions
into three aggregates: (i) the NAFTA countries: the US, Canada, Mexico;
45
(ii) the other
Table 15.8 Bilateral trade flows in the reference case (billion US$)dcont'd
Industry 12: Services
U J N E O C I M V L B P Sum
U NA 4.9 3.6 49.2 1.9 0.5 1.0 0.5 1.8 21.7 3.2 6.0 94.2
J 12.4 NA 0.3 23.0 1.0 0.5 0.2 0.2 0.6 6.3 1.8 1.8 48.0
N 6.4 0.4 NA 11.6 0.3 0.1 0.3 0.1 0.2 6.3 0.8 1.3 27.8
E 79.8 20.0 10.5 NA 4.7 2.1 2.1 1.1 2.9 52.8 10.3 11.1 197.4
O 2.2 0.5 0.2 3.8 NA 0.1 0.1 1.5 0.3 0.3 9.0
C 2.2 0.9 0.1 4.3 0.2 NA 0.1 1.4 0.4 0.4 10.1
I 2.5 0.3 0.4 3.7 0.1 NA 0.1 2.1 0.3 0.5 10.1
M 3.7 0.9 0.2 7.9 0.3 0.1 0.1 NA 0.3 1.7 0.5 0.4 16.2
V 3.1 0.7 0.3 4.5 0.2 0.1 0.1 0.1 NA 1.6 0.3 0.4 11.4
L 25.8 5.7 4.1 39.5 1.4 1.0 1.2 0.5 1.0 NA 3.0 3.9 87.1
B 7.4 2.1 1.1 14.2 0.4 0.2 0.2 0.1 0.3 5.2 NA 1.1 32.4
P 23.3 2.5 2.4 20.5 0.7 0.4 0.7 0.3 0.6 9.9 1.4 NA 62.8
Sum 168.9 39.0 23.0 182.3 11.2 5.1 6.0 3.0 8.0 110.4 22.4 27.2 606.6
45 The NAFTA aggregate region is defined to be the US, Canada and Mexico whether or not NAFTA is actually in
force.
1044 Warwick J. McKibbin and Peter J. Wilcoxen

potential participants in the FTAA: Brazil and the Rest of Latin America; and (iii) the
Rest of the World: Japan, Europe, the Rest of the OECD, China, Eastern Europe and
the Former Soviet Union, OPEC, and other developing countries. Table 15.9 shows
the dollar value of trade flows within and between each of these aggregates.
46
As in the
detailed bilateral trade matrices, the column indicates the source of each trade flow and
the row indicates its destination. For example, the entry in the ‘ROW’ column
and ‘NAFTA’ row for crude oil and natural gas (good 5) shows that the US, Canada and
Mexico together import $66 billion worth of crude oil and natural gas from the
countries comprising ROW. The entry in the ‘NAFTA’ column and ‘NAFTA’ row, in
contrast, shows the value of trade in crude oil and gas among the NAFTA countries.
These aggregate regions will be used to assess the overall effects of NAFTA or the FTAA
on the value of trade within the free trade area and between the trade area and the rest
of the world.
15.4.2.5 Immediate tariff reductions under NAFTA
As discussed earlier, there is an empirical literature suggesting that tariff reform and
increased international trade stimulate productivity growth. There are many mecha-
nisms by which this could occur. Industries previously protected by high tariffs and
now faced with increased competition would almost certainly undertake cost-cutting
measures and shift toward global best practices in production. Lower trade barriers on
durables would also allow a freer flow of new technology and embodied technical
change.
To incorporate this effect, we assume that when tariffs are reduced, previously
protected industries are able to make modest improvements in productivity in response.
In particular, we assume they are able to reduce their costs by a percentage equal to half of
the change in their tariff, or by 5%, whichever is smaller. For example, the Canadian
tariff on durables is initially 4.14% so under this assumption trade reform would lead to
a 2.07% improvement in the productivity of the Canadian durables sector. In contrast,
the Mexican tariff on durables is initially 11.3% so the corresponding industry’s
productivity improvement would be limited to 5%.
The full set of productivity shocks used in this section is shown in Table 15.10. Under
the NAFTA-p simulation, tariffs are reduced to zero on trade between the US, Canada
and Mexico, and industries in those three countries receive the productivity improve-
ments listed in Table 15.10. The FTAA-p simulation is similar to NAFTA-p, but also
includes Brazil and the Rest of Latin America. In both cases, it is important to note that
the productivity shocks are one-time changes in productivity levels, not changes in the
rate of productivity growth. They are very modest effects in the sense that they corre-
spond to at most 2e3 years of ordinary productivity improvements. On the other hand,
46 Table 15.9 was computed by summing the corresponding flows in the detailed bilateral trade matrix.
A Global Approach to Energy and the Environment: The G-Cubed Model 1045

Table 15.9 Value of trade flows between regions in the reference case: column shows source and row
shows destination (billions US$; values <1 omitted)
NAFTA B&R ROW
1 Electric Utilities
NAFTA 2
B&R
ROW 3
2 Gas Utilities
NAFTA 3
B&R
ROW 8
3 Refining
NAFTA 7 3 9
B&R 2 4
ROW 7 1 54
4 Coal
NAFTA 2
B&R 1
ROW 3 1 14
5 Crude Oil and Gas
NAFTA 31 6 66
B&R 1 16
ROW 3 1 174
6 Mining
NAFTA 6 1 3
B&R
ROW 6 5 26
7 Agriculture
NAFTA 24 8 9
B&R 6 1 1
ROW 50 19 6 5
8 Forestry
NAFTA 21 1 6
B&R 1 1
ROW 12 1 3 7
9 Durables
NAFTA 507 14 580
B&R 38 4 66
ROW 383 15 1320
A Non-durables
NAFTA 166 18 186
B&R 36 5 27
ROW 167 24 738
(Continued )
1046 Warwick J. McKibbin and Peter J. Wilcoxen

they do not include the effect of any adjustment costs or investment that might arise as
the protected industries adapt. Thus, they allow us to gauge the importance of
productivity changes over the medium to long run but are not a precise prediction.
Table 15.11 shows the effect of NAFTA-p on output, exports and capital stocks by
sector and region in year 12. The results are percentage changes relative to the
reference case. Entries with changes greater than or equal to 1% in magnitude are
given in italics and those with changes less than 0.1% in magnitude are left blank. As
would be expected, the reduction in tariffs leads to an increase in trade among
NAFTA countries. Exports from the US, Canada and Mexico increase significantly.
Table 15.9 Value of trade flows between regions in the reference case: column shows source and row
shows destination (billions US$; values <1 omitted)dcont'd
NAFTA B&R ROW
B Transportation
NAFTA 10 3 110
B&R 6 17
ROW 121 14 248
C Services
NAFTA 15 4 120
B&R 6 15
ROW 174 10 263
Table 15.10 Potential productivity gains, by country and industry (%)
a
Region
U
N,F
JN
N,F
EOCI
F
M
N,F
V
F
LBP
Electric utilities 0.01
Gas utilities
Petroleum refining 1.98 1.73 2.42 2.36
Coal mining 2.73 0.76
Crude oil and gas extraction 2.43 1.76
Other mining 0.19 0.79 3.98 1.49
Agriculture 4.08 5.00 5.00 2.40
Forestry and wood products 1.47 5.00 5.00 5.00
Durable goods 0.77 5.00 4.35 5.00
Non-durables 5.00 3.49 5.00 4.15
Transportation 0.60
Services 1.42
Productivity gains that were capped at 5% are shown in italic.
N
Member of NAFTA
F
Member of the FTAA.
A Global Approach to Energy and the Environment: The G-Cubed Model 1047

Table 15.11 Changes in output, exports and capital stocks: NAFTA-p versus reference case (% change)
US Japan Canada Europe ROECD China Brazil Mexico RLA LDC EEFSU OPEC
Panel 1: Industry output in 2015
Electric utilities 0.3 e0.2 0.1 1.0 0.1 0.1
Gas utilities 0.3 e0.4 0.1 0.3 0.1
Petroleum refining 0.8 0.1 2.1 0.2 e0.1 0.1 0.1 2.2 0.1 0.2
Coal mining 0.3 e1.1 2.4 1.9 0.2 0.1 e0.3
Crude oil and gas
extraction
0.2 e0.1 0.6 4.6
Other mining 0.6 e0.1 2.3 e0.1 0.1 2.4 e0.1
Agriculture 1.0 9.1 0.2 0.2 0.2 0.1 4.3 0.2 0.2 0.1
Forestry and wood
products
0.6 0.1 5.0 0.2 0.1 0.1 0.1 2.4 0.2 0.3
Durable goods 0.6 0.1 2.1 0.1 0.1 5.0 e0.1 0.1
Non-durables 1.0 0.2 11.4 0.2 0.2 0.2 0.1 5.5 0.1 0.2 0.2
Transportation 0.2 0.1 1.2 0.1 0.1 0.1 1.2 0.1 0.1 0.1
Services 0.1 e3.1 e0.1 e3.6 e0.1 e0.1
Panel 2: Exports in 2015
Electric utilities e7.1 3.5 e1.2 1.4 40.0
Gas utilities 0.4
Petroleum refining 1.3 7.9 e0.1 e1.3 4.6 0.1 e0.1
Coal mining e0.3 4.7 0.1 e0.4 0.0 1.1
Crude oil and gas
extraction
0.5 2.1 e0.4 0.3 7.4 0.8 e0.1
Other mining 0.5 5.0 0.2 7.8 e0.3
1048 Warwick J. McKibbin and Peter J. Wilcoxen

Agriculture 2.7 2.3 12.5 0.7 0.4 0.6 0.4 8.9 2.2 0.3 0.2 0.4
Forestry and wood
products
2.5 11.5 1.6 0.9 0.8 1.3 10.9 1.6 0.6 0.5 1.0
Durable goods 2.4 0.2 6.9 0.1 0.4 0.2 0.2 8.6 1.1 0.2 0.2 0.3
Non-durables 5.3 0.8 18.5 0.5 0.5 0.5 0.6 11.9 2.6 0.5 0.3 0.6
Transportation e0.2 e0.3 4.6 e0.4 e0.1 e0.2 0.1 5.5 1.1 e0.2 e0.1 e0.1
Services e0.1 e0.4 1.0 e0.9 e0.2 e0.4 e0.1 1.1 0.5 e0.5 e0.3 e0.4
Panel 3: Capital stocks in 2015
Electric utilities 0.3 e1.0 e0.1 e8.8 e0.1 0.1 e0.1 0.1
Gas utilities 0.3 e2.1 e0.1 e4.1 e0.1 0.1
Petroleum refining 0.2 e3.2 e0.1 e5.6 e0.1 0.1
Coal mining 0.4 e0.2 2.3 1.5 0.2
Crude oil and gas
extraction
0.3 e0.1 0.1 7.1
Other mining 0.4 e0.1 2.3 1.5 e0.1 e0.1 0.1
Agriculture 0.7 e0.1 1.4 0.1 0.2 0.2 0.1 4.8 0.1 0.3 0.2
Forestry and wood
products
0.5 e0.1 3.5 0.2 0.3 0.1 0.1 e0.4 0.2 0.3
Durable goods 0.4 e4.4 1.3 e0.2 0.1 e0.1
Non-durables 0.8 0.1 2.4 0.2 0.1 0.1 0.1 3.7 0.3 0.2
Transportation 0.3 1.4 0.1 0.1 0.1 2.4 0.1 0.1 0.1
Services 0.3 e0.1 e5.8 e0.1 e0.2 e10.8 e0.2 e0.1
A Global Approach to Energy and the Environment: The G-Cubed Model 1049

Exportsofdurables(panel2)riseby2.4%intheUS,6.9%inCanadaand8.6%in
Mexico. Exports of non-durables increase even more because tariffs on non-durables
are initially higher and hence fall more dramatically under a free trade agreement (the
high tariffs on non-durables largely reflects the high levels of protection on textiles
and apparel). Exports of non-durables rise by 5.3% in the US, 18.5% in Canada and
11.9% in Mexico.
The increase in trade raises the level of output of all industries in the US (panel 1),
albeit by very small percentages in most cases. The corresponding capital stocks rise as
well (panel 3). The output of almost all Canadian industries increases, as does the output
of most Mexican industries. A notable exception is services (sector C) for both Canada
and Mexico: output of services falls under NAFTA-p even though exports of services
rise slightly in percentage terms due to the change in exchange rates (as well as increased
demand for services in other countries that expand as a result of the policy). The reason is
straightforward: the price of services rises, in part because the traded sectors grow and
consume more labor, and in part because depreciation of the exchange rate raises the
price of imported intermediate goods other than those whose tariffs have been cut. This
effect is a recurring theme in the results and can be seen clearly in Figure 15.9, which
−5 0 5 10
Percent change
0 5 10 15 20 25
Year
USA
CAN
MEX
Panel 1: Price of Durables
−5 0 5 10
Percent change
0 5 10 15 20 25
Year
USA
CAN
MEX
Panel 2: Output of Durables
−5 0 5 10
Percent change
0 5 10 15 20 25
Year
USA
CAN
MEX
Panel 3: Price of Services
−5 0 5 10
Percent change
0 5 10 15 20 25
Year
USA
CAN
MEX
Panel 4: Output of Services
Figure 15.9 Selected industry prices and output: NAFTA-p versus reference case.
1050 Warwick J. McKibbin and Peter J. Wilcoxen

shows percentage changes from the reference case in selected industry prices and
quantities over time for the US, Canada and Mexico.
The total value of the trade flows among NAFTA regions increases as shown in
Table 15.12 (which also includes results for the FTAA, which will be discussed below).
The value of trade in durables among NAFTA regions, for example, increases by
$16 billion, while the value of trade in non-durables increases by $14 billion. Comparing
the magnitudes of the within-NAFTA flows with those between NAFTA and the rest of
the world shows that NAFTA clearly increases trade rather than just redirecting it. In
fact, the increase in economic activity stimulated within NAFTA actually causes
aggregate NAFTA imports of both durables and non-durables from the rest of the world
to rise. Summing across goods and regions, NAFTA raises the total dollar value of trade
flows in year 12 by $50 billion.
The effects of NAFTA-p on trade and selected macroeconomic variables are shown
in Figure 15.10. The reduction in tariffs in Canada and Mexico causes the demand for
imports in those regions to increase, leading to depreciation of the two exchange rates
relative to the US dollar.
47
The trade balances in the US, Canada and Mexico initially
move toward deficit. The trade balances for Brazil and the Rest of Latin America (as well
as most other regions, although they are omitted from the graph) move toward surplus as
imports by the US, Canada and Mexico rise. Over time, however, the Canadian and
Mexican trade deficits accumulate into increased stocks of foreign debt. The higher debt
levels require larger interest payments, which consume an increasing share of each
country’s current account and eventually force the trade balance back toward surplus.
This mechanism is made even stronger by the effect of exchange rates on each country’s
initial stocks of foreign debt. In particular, the depreciation of the Canadian and Mexican
currencies relative to the US dollar increases the burden of servicing their US dollar
denominated foreign debt.
The reduction in tariffs also has an important fiscal effect: by reducing government
revenue it increases the fiscal deficit in each region. The effect is very small in the US
because tariff revenue is a small part of the government budget but it is significant in
Canada and Mexico. In the short run, the reduction in tariffs functions as a fiscal
stimulus. This effect would be significantly different under alternative monetary and
fiscal assumptions. For example, other taxes could be raised to compensate for the
reduction in tariff revenue, or government spending could be cut. In addition, monetary
policy could be altered ein these simulations the money supply has been held constant at
its base case value. Either policy could reduce or eliminate the macroeconomic effects
caused by the increase in the fiscal deficit.
47 In G-Cubed, exchange rates are defined as the number of US dollars per unit of foreign currency. A decline in the
exchange rate is a reduction in the number of dollars per unit of foreign currency and hence a depreciation of the
currency.
A Global Approach to Energy and the Environment: The G-Cubed Model 1051

Table 15.12 Change in aggregate trade flows under NAFTA-p and FTAA-p: column shows source and
row shows destination (billions US$; values <0.1 billion omitted)
Good Destination
NAFTA-p change from base FTAA-p change from NAFTA-p
NAFTA B&R ROW NAFTA B&R ROW
Electric
utilities
NAFTA
B&R e0.11
ROW
Gas utilities NAFTA
B&R
ROW
Petroleum
refining
NAFTA 0.21
B&R e0.11 e0.46
ROW 0.18
Coal mining NAFTA
B&R
ROW
Crude oil and
gas extraction
NAFTA 0.14
B&R e0.74
ROW 0.42 e0.17
Other mining NAFTA
B&R
ROW
Agriculture NAFTA 1.01 0.17 0.12 0.50
B&R 0.34 0.33
ROW 0.12 0.34 0.10 e0.59 0.20
Forest and
wood
products
NAFTA 1.13 0.24 0.24
B&R
ROW 0.16 0.20
Durable goods NAFTA 16.11 2.98 1.06 1.45
B&R e0.20 e0.18 e0.11 e0.20 e8.61
ROW 1.27 3.15 e0.33 e0.32 e0.83
Non-durables NAFTA 14.04 0.37 3.74 0.64 0.88 1.09
B&R 0.37 1.39 e1.59
ROW 2.40 e1.12 e0.14
Transportation NAFTA e0.28 e0.47 0.12 0.24
B&R e0.10 e0.56 e1.92
ROW 0.50 e0.25 0.57 e0.22
Services NAFTA e0.55 e1.86 0.34
B&R e0.13 e0.65 e2.01
ROW 0.54 0.59 e0.23 0.14
1052 Warwick J. McKibbin and Peter J. Wilcoxen

15.4.2.6 Immediate tariff reductions under FTAA
Table 15.13 shows the effect of FTAA-p relative to NAFTA-p on output, exports
and capital stocks by industry and region for year 12. The results are percentage
changes relative to the NAFTA-p simulation; as before, entries with changes greater
than or equal to 1% in magnitude are given in italics and those below 0.1% are left
blank. The general nature of the results is highly analogous to those for NAFTA-p:
the effect of the FTAA on Brazil (I) and the rest of Latin America (V) is very much
like the effect of NAFTA-p on Canada and Mexico. The main difference is that the
percentage changes tend to be larger in magnitude for Brazil and the Rest of Latin
America. For most industries, output, exports and capital stocks all increase
substantially. This can be seen graphically in Figure 15.11, which shows the effects of
FTAA-p relative to NAFTA-p on selected industry prices and output for all regions.
In general, prices fall significantly in Brazil and the Rest of Latin America, and
output rises accordingly. As with NAFTA-p relative to the reference case, service
sector output falls in Brazil and the Rest of Latin America; however, the magnitude
is relatively small.
The value of trade flows between aggregate regions is shown in Table 15.12 above.
The most notable result is a pronounced decline in imports of durables from non-
−15 −10 −5 0
Percent change
0 5 10 15 20 25
Year
CAN
BRA
MEX
RLA
Panel 1: Real Exchange Rate
−4 −3 −2 −1 0 1
Change in billions of US dollars
0 5 10 15 20 25
Year
USA
CAN
BRA
MEX
RLA
Panel 2: Trade Balance
0 .2 .4 .6
Percent change
0 5 10 15 20 25
Year
USA
CAN
BRA
MEX
RLA
Panel 3: Fiscal Defici t
0 .5 1 1.5 2 2.5
Percent change
0 5 10 15 20 25
Year
USA
CAN
BRA
MEX
RLA
Panel 4: Real GDP
Figure 15.10 Trade accounts and macroeconomic variables: NAFTA-p versus reference case.
A Global Approach to Energy and the Environment: The G-Cubed Model 1053

Table 15.13 Changes in output, exports and capital stocks: FTAA-p versus NAFTA-p (% change)
US Japan Canada Europe ROECD China Brazil Mexico RLA LDC EEFSU OPEC
Panel 1: Industry output in 2015
Electric utilities 0.1 0.1 0.1 0.2 0.1 0.6 0.1 e0.6 0.3 0.1
Gas utilities 0.1 0.2 0.1 0.7 1.1 0.1 e0.1 0.3 0.1
Petroleum refining 0.3 0.1 0.1 0.1 0.2 0.1 4.0 0.1 3.5 0.4 e0.1 e0.2
Coal mining e0.2 e0.2 16.7 3.9 0.2 0.3
Crude oil and gas
extraction
0.1 0.2 0.2 0.1 5.2 e0.1 3.2 0.2 e0.1 e0.1
Other mining e0.2 e0.3 e0.1 e0.1 0.1 5.0 0.1 5.2 0.3 0.1 0.1
Agriculture 0.2 e0.1 0.5 0.1 0.1 0.1 9.5 0.1 5.5 0.2 0.1
Forestry and wood
products
0.3 0.3 0.3 0.3 0.2 0.1 8.3 0.1 8.9 0.4 0.1 0.2
Durable goods 0.1 0.2 0.2 0.1 0.1 6.4 0.2 5.3 0.2 0.1
Non-durables 0.3 0.2 0.3 0.3 0.1 0.1 9.6 0.2 8.2 0.2 0.1 0.1
Transportation 0.1 0.1 3.5 0.1 3.8 0.2 0.1
Services 0.1 0.1 0.1 0.2 0.1 0.1 0.1 e1.2 0.4 0.2
Panel 2: Exports in 2015
Electric utilities e3.8 e0.5 e2.4 e1.4 e28.6 e10.0 e2.6 e9.1
Gas utilities e0.6
Petroleum refining e0.6 0.5 e1.0 e0.2 0.7 e0.5 5.3 14.6 e0.1 e1.6 e1.1
Coal mining e1.0 e0.5 16.1
Crude oil and gas
extraction
e0.2 0.1 100.0 e0.2 11.3 e1.0 e0.1
Other mining e0.9 e0.6 0.3 e0.3 7.0 22.3 e0.2 e0.3
1054 Warwick J. McKibbin and Peter J. Wilcoxen

Agriculture 1.0 1.7 1.4 0.3 0.6 10.4 0.4 22.4 0.2 0.6
Forestry and wood
products
0.9 5.3 0.5 0.6 0.4 0.4 11.0 0.4 22.2 0.1 0.6 0.5
Durable goods 0.1 e0.2 0.2 e0.3 0.1 0.1 11.1 0.3 12.1 e0.3 e1.0
Non-durables 0.8 0.3 0.5 0.2 0.1 0.2 10.1 0.6 24.6 0.1 0.3 0.1
Transportation e0.7 e0.4 e0.8 e0.6 e0.2 e0.2 5.8 e0.1 19.0 e0.4 e0.1 e0.1
Services e0.4 e0.2 e0.5 e0.3 e0.2 e0.2 4.4 e0.2 11.3 e0.2
Panel 3: Capital stocks in 2015
Electric utilities 0.2 0.1 0.2 0.2 0.1 0.1 0.1 0.3 e2.2 0.5 0.1 0.3
Gas utilities 0.2 0.1 0.2 0.2 0.1 e0.5 0.2 e1.8 0.3 0.1 0.2
Petroleum refining 0.1 0.1 0.2 0.1 0.2 e0.1 0.2 e1.3 0.3
Coal mining 0.1 0.1 e0.1 0.1 0.1 6.0 0.2 4.9 0.3 0.1 0.3
Crude oil and gas
extraction
0.2 0.1 0.2 0.2 0.1 0.4 e0.1 1.2 0.3 e0.2 e0.1
Other mining 0.1 e0.2 0.1 e0.2 0.1 9.0 0.3 3.7 0.3 0.1 0.1
Agriculture 0.1 e0.1 0.4 0.1 0.2 0.1 7.3 0.4 3.8 0.5 0.3
Forestry and wood
products
0.4 0.2 0.5 0.4 0.3 0.2 6.2 0.2 3.0 0.5 0.1 0.3
Durable goods 0.2 0.1 0.3 0.3 0.2 0.1 1.6 0.4 e3.5 0.3 0.1 0.4
Non-durables 0.2 0.1 0.3 0.2 0.1 0.1 8.1 0.3 3.2 0.3 0.1 0.2
Transportation 0.1 0.1 0.1 0.2 0.1 0.1 6.3 0.2 3.8 0.3 0.1 0.1
Services 0.3 0.2 0.3 0.3 0.3 0.1 e0.7 0.4 e4.6 0.8 0.1 0.5
A Global Approach to Energy and the Environment: The G-Cubed Model 1055

FTAA countries by Brazil and the Rest of Latin America. The effects on trade and
macroeconomic variables are shown in Figure 15.12. The productivity improvements
in Brazil and the Rest of Latin Amer ica cause several dramatic changes. Production
costs fall by enough in the Rest of Latin America that exports boom and the trade
balance and current account move sharply toward surplus. The economies of Brazil
and the Rest of Latin America both expand enough to raise tax revenue above its
value in the NAFTA-p simulation edespite the decrease in tariff revenue eand shift
the corresponding fiscal deficits toward surplus (i.e. the magnitudes of the fiscal
deficits fall). Consumption rises substantially and the price of new investment goods
drops. Real investment rises and the GDP of both economies is about 6% larger in the
long run.
15.4.2.7 Anticipated tariff reductions under FTAA
The final simulation was an anticipated implementation of the FTAA: the agreement was
announced in the first year of the simulation but took effect in the fifth year. To
distinguish it from the previous FTAA simulation, this version will be denoted FTAA5-p
where the ‘5’ indicates that implementation of the agreement is anticipated 5 years in
advance. The effects of the anticipated FTAA will be equal to the differences between the
−10 −5 0 5 10 15
Percent change
0 5 10 15 20 25
Year
USA
CAN
MEX
BRA
RLA
Panel 1: Price of Durables
−10 −5 0 5 10 15
Percent change
0 5 10 15 20 25
Year
USA
CAN
MEX
BRA
RLA
Panel 2: Output of Durables
−10 −5 0 5 10 15
Percent change
0 5 10 15 20 25
Year
USA
CAN
MEX
BRA
RLA
Panel 3: Price of Services
−10 −5 0 5 10 15
Percent change
0 5 10 15 20 25
Year
USA
CAN
MEX
BRA
RLA
Panel 4: Output of Services
Figure 15.11 Changes in industry prices and output: FTAA-p versus NAFTA-p.
1056 Warwick J. McKibbin and Peter J. Wilcoxen

FTAA5-p simulation and a counterfactual NAFTA simulation, NAFTA5-p, in which
NAFTA was announced 5 years before its implementation.
48
When the FTAA is announced in advance, Figure 15.13 shows that very large
anticipatory changes occur in exchange rates, trade accounts and macroeconomic
variables. The key mechanism by which this occurs is the real exchange rate, which can
be shown to be the price to foreigners of a given country’s domestic assets. When the
FTAA is implemented, real exchange rates for Brazil and the Rest of Latin America will
drop sharply for the reasons discussed in the previous section. Investors anticipating the
fall will be less willing to hold Brazilian and Latin American assets in advance, even at the
beginning of the simulation. As a result, exchange rates for Brazil and Latin America
deteriorate immediately. Financial capital flows out of those regions and into the US and
other large economies, whose trade balances deteriorate as a result. Investment, labor
demand, GDP and consumption all decline in Brazil and the Rest of Latin America
−20 −15 −10 −5 0
Percent change
0 5 10 15 20 25
Year
CAN
BRA
MEX
RLA
Panel 1: Real Exchange Rate
−10 0 10 20 30
Change in billions of US dollars
0 5 10 15 20 25
Year
USA
CAN
BRA
MEX
RLA
Panel 2: Trade Balance
−1.5 −1 −.5 0
Percent change
0 5 10 15 20 25
Year
USA
CAN
BRA
MEX
RLA
Panel 3: Fiscal Defici t
0 2 4 6 8 10
Percent change
0 5 10 15 20 25
Year
USA
CAN
BRA
MEX
RLA
Panel 4: Real GDP
Figure 15.12 Trade accounts and macroeconomic variables: FTAA-p versus NAFTA-p.
48 This approach is used for the same reason that other FTAA simulations are compared to analogous NAFTA runs: the
model’s internal structure does not currently allow the size of a free trade area to change in the middle of a simulation.
In the two simulations discussed in this section, either NAFTA or the FTAA is adopted immediately but remains
dormant for 5 years. During that time, tariffs on imports from trade area partners remain the same as tariffs on other
imports. This approximates an anticipated introduction of the FTAA.
A Global Approach to Energy and the Environment: The G-Cubed Model 1057

during the period of anticipation and then rebound after implementation. By year 12,
output levels, exports and capital stocks become similar to those from the unanticipated
FTAA, as can be seen in Table 15.14.
15.4.2.8 Effect of trade agreements on carbon emissions
Finally, Figure 15.14 shows the effect of each of the simulations on total carbon dioxide
emissions from key regions. In keeping with the modest effects of NAFTA and the
FTAA on industry output and GDP, the effect on carbon emissions is relatively small; in
most cases, the change is 1e2% of emissions in the reference case. The only exception is
the FTAA5-p experiment, which causes emissions to change by more than 3% in some
years.
15.4.2.9 Summary
The overall effects of the FTAA are highly analogous to NAFTA, but smaller in
magnitude. Countries reducing their tariffs see imports rise, exchange rates fall, trade
balances move toward deficit, capital inflows increase and foreign debt levels rise. Trade
increases significantly between member countries and the largest changes arise in
−15 −10 −5 0
Percent change
0 5 10 15 20 25
Year
CAN
BRA
MEX
RLA
Panel 1: Real Exchange Rate
−20 0 20 40
Change in billions of US dollars
0 5 10 15 20 25
Year
USA
CAN
BRA
MEX
RLA
Panel 2: Trade Balance
−1.5 −1 −.5 0.5
Percent change
0 5 10 15 20 25
Year
USA
CAN
BRA
MEX
RLA
Panel 3: Fiscal Deficit
−2 0 2 4 6 8
Percent change
0 5 10 15 20 25
Year
USA
CAN
BRA
MEX
RLA
Panel 4: Real GDP
Figure 15.13 Trade accounts and macroeconomic variables: FTAA 5-p versus NAFTA5-p.
1058 Warwick J. McKibbin and Peter J. Wilcoxen

Table 15.14 Change in output, exports and capital stocks: FTAA 5-p versus NAFTA-p (% change)
US Japan Canada Europe ROECD China Brazil Mexico RLA LDC EEFSU OPEC
Panel 1: Industry output in 2015 under FTAA5-p
Electric utilities 0.1 0.1 0.2 0.2 0.1 0.9 0.2 e0.8 0.4 0.2
Gas utilities 0.1 0.2 0.1 1.1 0.3 0.3 0.2
Petroleum refining 0.3 0.1 0.1 0.2 0.1 0.1 3.9 0.1 3.2 0.4 e0.1 e0.1
Coal mining e0.2 0.1 16.7 3.6 0.3
Crude oil and gas
extraction
0.1 0.1 0.3 0.1 0.1 4.0 2.7 0.3 e0.1
Other mining e0.2 e0.2 e0.1 0.1 5.5 0.1 7.6 0.3 0.1
Agriculture 0.2 e0.1 0.5 0.1 0.1 0.1 9.8 0.1 5.9 0.2 0.1
Forestry and wood
products
0.2 0.1 0.3 0.3 0.2 0.1 9.6 0.1 11.0 0.3 0.1 0.2
Durable goods 0.2 0.1 0.2 0.2 0.1 0.1 7.6 0.3 6.5 0.3 0.1
Non-durables 0.3 0.2 0.2 0.3 0.1 0.1 9.9 0.2 8.6 0.2 0.1 0.1
Transportation 0.1 0.1 4.2 0.1 4.1 0.2 0.1
Services 0.1 0.2 0.2 0.3 0.1 0.1 e0.7 0.2 e2.3 0.6 0.1 0.3
Panel 2: Exports in 2015 under FTAA5-p
Electric utilities e3.7 e0.5 e1.2 e1.4 e16.7 e5.0 e1.3 e9.1
Gas utilities e0.6
Petroleum refining 0.5 e0.8 e0.1 0.7 5.3 13.6 0.1 e1.3 e0.8
Coal mining e0.6 e0.5 0.1 0.0 15.1
Crude oil and gas
extraction
0.5 e0.1 0.1 0.3 100.0 e0.1 9.9 0.1 e0.9
Other mining e0.6 e0.2 0.3 e0.3 6.3 19.5 e0.1
Agriculture 1.1 2.3 1.9 1.4 0.2 0.6 10.0 0.4 22.3 0.2 0.6 0.2
Forestry and wood
products
1.3 5.3 0.4 0.8 0.4 0.4 10.6 0.4 23.8 0.2 0.6 0.5
(Continued )
A Global Approach to Energy and the Environment: The G-Cubed Model 1059

Table 15.14 Change in output, exports and capital stocks: FTAA 5-p versus NAFTA-p (% change)dcont'd
US Japan Canada Europe ROECD China Brazil Mexico RLA LDC EEFSU OPEC
Durable goods 0.5 0.1 0.3 0.2 0.2 10.9 0.4 11.3 0.2 e0.2
Non-durables 1.0 0.4 0.5 0.2 0.1 0.3 9.8 0.7 24.4 0.1 0.3 0.2
Transportation e0.6 e0.4 e0.8 e0.5 e0.2 e0.1 5.3 e0.1 18.4 e0.3 e0.1
Services e0.3 e0.1 e0.4 e0.2 e0.1 2.7 8.0 0.2
Panel 3: Capital stocks in 2015 under FTAA5-p
Electric utilities 0.3 0.1 0.3 0.3 0.2 0.1 e1.8 0.5 e3.5 0.6 0.1 0.5
Gas utilities 0.2 0.1 0.3 0.2 0.2 0.1 e2.1 0.4 e2.9 0.4 0.1 0.3
Petroleum refining 0.2 0.1 0.3 0.2 0.2 0.1 e1.4 0.4 e2.2 0.4 0.1 0.1
Coal mining 0.2 0.2 0.2 0.2 0.1 4.5 0.2 4.2 0.4 0.1 0.5
Crude oil and gas
extraction
0.3 0.2 0.1 0.3 0.2 0.1 e1.5 0.4 0.4 e0.1
Other mining 0.2 0.1 e0.1 0.2 0.1 8.7 0.3 3.2 0.4 0.1 0.2
Agriculture 0.2 0.4 0.1 0.2 0.1 7.0 0.5 3.4 0.5 0.3
Forestry and wood
products
0.4 0.3 0.5 0.5 0.3 0.2 7.6 0.2 4.4 0.5 0.2 0.4
Durable goods 0.3 0.2 0.4 0.4 0.3 0.1 e0.1 0.5 e5.1 0.5 0.1 0.7
Non-durables 0.3 0.2 0.3 0.3 0.2 0.1 7.6 0.3 3.1 0.4 0.1 0.2
Transportation 0.2 0.1 0.1 0.2 0.1 0.1 7.0 0.2 3.9 0.3 0.1 0.2
Services 0.3 0.3 0.4 0.4 0.3 0.2 e3.1 0.6 e6.5 1.1 0.2 0.8
1060 Warwick J. McKibbin and Peter J. Wilcoxen

the most heavily traded sectors: durables and non-durables. The effects are most
pronounced for non-durables because the initial tariffs are highest. Liberalization is good
for importers and exporters, as would be expected. Non-traded sectors, particularly
services, are hurt by the expansion of exporting industries which draw in labor and raise
wages (even though, in percentage terms, exports of services rise slightly).
For several of the FTAA countries the agreement would have a significant fiscal
impact by reducing an importance source of government revenue. Unless another tax is
increased to compensate for the reduction in tariff revenue, the FTAA raises the
country’s fiscal deficit. providing a short-term stimulus but crowding out some private
investment in the long run.
Allowing for industries to respond to liberalization by adopting modest productivity
improvements sharply increases the overall gain from liberalization and reduces the fiscal
drag causes by the drop in tariff revenue (tax revenue from other sources rises). Both
GDP and consumption rise significantly in liberalizing economies. In addition,
productivity effects tend to reduce the changes in trade flows caused by liberalization
because the difference in relative prices between foreign and domestic goods is smaller.
This effect is particularly noticeable with durables imported by Brazil and the rest of
Latin America.
−4 −2 0 2 4
Percent change from reference
0 5 10 15 20 25
Year
USA
CAN
BRA
MEX
RLA
Panel 1: Carbon Emissions, NAFTA−p
−4 −2 0 2 4
Percent change from NAFTA−p
0 5 10 15 20 25
Year
USA
CAN
BRA
MEX
RLA
Panel 2: Carbon Emissions, FTAA−p
−4 −2 0 2 4
Percent change from NAFTA5−p
0 5 10 15 20 25
Year
USA
CAN
BRA
MEX
RLA
Panel 3: Carbon Emissions, FTAA5−p
Figure 15.14 Carbon emissions.
A Global Approach to Energy and the Environment: The G-Cubed Model 1061

The FTAA and NAFTA are not analogous in one respect. NAFTA increases overall
trade rather than just redirecting it away from non-NAFTA countries. The effect of
the FTAA, however, is closer to trade redirection than to trade creation. The
difference stems from the relative effects of the agreements on the US. NAFTA does
more to stimulate the US economy and thus has a larger effect on the US demand for
imports.
Finally, the effect of both NAFTA and the FTAA on carbon emissions are very small.
Neither agreement has much effect on energy consumption. The effect on criteria air
pollutants would be small as well.
15.5 CONCLUSION
G-Cubed bridges three areas of research eeconometric general equilibrium modeling,
international trade theory and modern macroeconomics eto provide a versatile multi-
country, multisector, intertemporal general equilibrium model that can be used for a wide
variety of policy analyses. It distinguishes between financial and physical capital, tracking
financial capital by currency and physical capital by region and sector where it is installed.
Investment, saving and international asset markets are driven by agents solving inter-
temporal optimization problems and having expectations driven by foresight (although
not always perfect foresight). All budget constraints are imposed, including those applying
to regions as a whole: all trade deficits must eventually be repaid by future trade surpluses.
This combination of features allows the model to be used for a wide range of
applications. Its industry detail allows it to be used to examine environmental and tax
policies, which tend to have their largest direct effects on small segments of the economy.
Intertemporal modeling of investment and saving allows it to trace out the transition of
the economy between the short run and the long run. Slow wage adjustment and
liquidity-constrained agents improves the empirical accuracy with which the model
captures the transition.
To date, G-Cubed has been used in nearly 80 studies covering topics ranging from
climate and energy policy to pandemic influenza. Its core strengths are: (i) scenario
analysis, where scenarios are made up of different shocks that might confront the world
economy or an individual country, and (ii) policy evaluation, especially where dynamic
adjustment towards a long-run equilibrium is important. It has also occasionally been
used as a forecasting model although it was not designed for that purpose.
G-Cubed continues to evolve and there a number of areas where research is
underway to improve it. One project currently underway is an analysis of the effects of
alternative fiscal closures on the consequences of imposing a carbon tax in the US. A
second project, also underway, is further disaggregation of the energy sectors, which
will allow analysis of a wider range of primary energy inputs will include explicit
treatment of alternative energy generation technologies. A third project focuses on the
1062 Warwick J. McKibbin and Peter J. Wilcoxen

role of infrastructure. This is particularly important for better understanding the
determinants of economic growth especially in developing countries. It will also be
critical when using the model to evaluate large fiscal consolidation programs in heavily
indebted industrial economies over future years. A fourth area where more work is
underway is improved estimation of G-Cubed’s dynamic adjustment parameters.
Although many of the intratemporal parameters of the model are estimated, the key
dynamic parameters are largely calibrated. A number of alternative approaches are
possible to improve this. Perhaps the most attractive, particularly in adapting the core
model to be used as a forecasting tool is to further develop the approach in Pagan et al.
(1998). In that approach, the impulse response functions from G-Cubed are combined
with vector autoregression techniques on a time series data set. The result is a dynamic
system with the medium- and long-term properties of G-Cubed as well as the
dynamics found in high frequency macro data.
ACKNOWLEDGMENTS
The views expressed are those of the authors and should not be interpreted as reflecting the views of
the trustees, officers or other staff of the Brookings Institution, Australian National University or
Syracuse University. It has benefitted from collaboration with many coauthors including Kym Anderson,
Philip Bagnoli, Tomas Bok, Ralph Bryant, Yiyong Cai, Tim Callen, Hsiao-Chuan Chang, Pim
Chanthapun, Richard Goettle, Gottfried Haber, Dale Henderson, Mun Sing Ho, Yiping Huang,
Tingsong Jiang, Wei Jin, Dale Jorgenson, Giang Le, Jong Wha Lee, Yingying Lu, Will Martin, Adele
Morris, Reinhard Neck, Jeremy Nguyen, Adrian Pagan, Hyejin Park, David Pearce, Ashish Rana,
Jeffrey Sachs, Robert Shackleton, Kanhaiya Singh, Alison Stegman, Andrew Stoeckel, Kang Tan, Hsiao
Chink Tang, K. K. Tang, Stephan Thurman, David Vines, Wing Thye Woo, Yan Yang and Zavkidjon
Zavkiev. W. McKibbin gratefully acknowledges support from ARC Discovery Grant DP0988281.
REFERENCES
Abel, A.B., 1979. Investment and the Value of Capital. Garland, New York.
Abel, A.B., Blanchard, O.J., 1983. An intertemporal model of saving and investment. Econometrica 51,
675e692.
Adkins, L.G., Garbaccio, R.F., 2002. The effects of the proposed FTAA on global carbon emissions:
a general equilibrium analysis. Presented at the Second World Congress of Environmental and
Resource Economists, Monterey.
Allsop, C., Davies, G., McKibbin, W., Vines, D., 1996. Monetary and fiscal stabilisation of demand shocks
within Europe. Rev. Int. Econ. 5 (Suppl), 55e76.
Allsop, C., McKibbin, W., Vines, D., 1999. Fiscal consolidation in Europe: Some empirical issues. In:
Hughes-Hallett, A., Hutchison, M., Hougaard Jensen, S. (Eds.), Fiscal Aspects of European Monetary
Integration. Cambridge University Press, Cambridge, pp. 288e319.
Anderson, K., McKibbin, W., 2000. Reducing coal subsidies and trade barr iers: Their contribution to
greenhouse gas abatement. Environ. Dev. Econ. 5, 457e481.
Armington, P.S., 1969. The geographic pattern of trade and the effects of price changes. IMF Staff Papers
16, 179e199.
Asian Development Bank, 1997. Emerging Asia: Changes and Challenges. Asian Development Bank, Manila.
Auerbach, A.J., Kotlikoff, L.J., 1987. Dynamic Fiscal Policy. Cambr idge University Press, Cambridge.
A Global Approach to Energy and the Environment: The G-Cubed Model 1063

Australian Government, 2008. Carbon Pollution Reduction Scheme: Australia’s Low Pollution Future.
White Paper. Government Printing Office, Canberra.
Bagnoli, P., McKibbin, W., Wilcoxen, P., 1996. Future projections and structural change. In:
Nakicenovic, N., Nordhaus, W., Richels, R., Toth, F. (Eds.), Climate Change: Integrating
Economics and Policy: CP 96-1. International Institute for Applied Systems Analysis, Laxenburg,
pp. 181e206.
Ballard, C.L., Fullerton, D., Shoven, J.B., Whalley, J., 1985. A General Equilibrium Model for Tax Policy
Evaluation. National Bureau of Economic Research, Chicago, IL.
Batini, N., Callen, T., McKibbin, W., 2005. The global impact of demographic change. In: IMF Working
Paper WP/06/9. International Monetary Fund, Washington, DC.
Berklemens, L., Davis, L., McKibbin, W., Stoeckel, A., 2001. Economic Impacts of an Australia eUnited
States Free Trade Area. Centre for International Economics, Canberra.
Blanchard, O., 1985. Debt, deficits, and finite horizons. J. Polit. Econ. 93, 223e247.
Bovenberg, A.L., Goulder, L.H., 1996. Optimal environmental taxation in the presence of other taxes:
general-equilibrium analyses. Am. Econ. Rev. 86, 985e1000.
Bryant, R., McKibbin, W., 2004. Incorporating demographic change in multi-country macroeconomic
models. In: Onofri, P. (Ed.), The Economics of an Ageing Population: Macroeconomic Issues. Edward
Elgar, Cheltenham, pp. 349e408.
Bryant, R., McKibbin, W., 1998. Issues in modeling the global dimensions of demographic change. In:
Brookings Discussion Paper in International Economics 141. Brookings Institution, Washington,
DC.
Burniaux, J.-M., Truong, T.P., 2002. GTAP-E: an energy-environmental version of the GTAP model. In:
GTAP Technical Paper 16. Center for Global Trade Analysis, Purdue University, West Lafayette, IN.
Burniaux, J.-M., Martin, J.P., Nicholetti, G., Oliveira Martins, J., 1992. GREEN: A multi-sector, multi-
region general equilibrium model for quantifying the costs of curbing CO
2
emissions: A technical
manual. In: Working Paper 116. OECD, Paris.
Cagliarini, A., McKibbin, W., 2009. Relative price shocks and macroeconomic adjustment. In: Fry, R.,
Jones, C., Kent, C. (Eds.), Inflation in a Era of Relative Price Shocks. Reserve Bank of Australia,
Sydney, pp. 305e333.
Callen, T., McKibbin, W., 2003. The impact of Japanese economic policies on the Asia region. In:
Callen, T., Ostry, J. (Eds.), Japan’s Lost Decade: Policies for Economics Revival. International
Monetary Fund, Washington DC, pp. 251e271.
Campbell, J., Mankiw, N.G., 1990. Permanent income, current income and consumption. J. Bus. Econ.
Stat. 8, 265e279.
Catinat, M., Italianer, A., 1988. Completing the Internal Market: Primary Microeconomic Effects and
their Impact in Macroeconometrics Models. Commission of the European Communities, Brussels.
Chand, S., 1999. Trade liberalization and productivity growth: time series evidence from Australian
manufacturing. Econ. Rec. 75 (228), 28e36.
Congressional Budget Office, 1993a. Budgetary and Economic Analysis of the North American Free Trade
Agreement. US Government Printing Office, Washington, DC.
Congressional Budget Office, 1993b. The Economic Effects of Reduced Defense Spending. 52 pages. US
Government Printing Office, Washington, DC.
Deardorff, A.V., Stern, R.M., 1985. The Michigan Model of World Production and Trade: Theory and
Applications. MIT Press, Cambridge, MA.
Diao, X., Somwaru, A., 2000. A Dynamic Evaluation of the Effects of a Free Trade Area of the Americas e
An Intertemporal, Global General Equilibrium Model. Journal of Economic Integration. Volume 16,
Number 1, March 2001. Pages 21e47.
Dixon, P.B., Parmenter, B.R., Sutton, J., Vincent, D.P., 1982. ORANI: A Multisectoral Model of the
Australian Economy. North-Holland, Amsterdam.
Doornik, J.A., 2007. Object-Oriented Matrix Programming Using Ox, third ed. Timberlake Consultants
Press, London.
Faruqee, H., 2003. Debt, deficits, and age-specific mortality. Rev. Econ. Dynam. 6, 300e312.
Feldstein, M., Horioka, C., 1980. Domestic savings and international capital flows. Econ. J. 90, 314e329.
1064 Warwick J. McKibbin and Peter J. Wilcoxen

Flavin, M.A., 1981. The adjustment of consumption to changing expectations about future income.
J. Polit. Econ. 89, 974e1009.
Frankel, J.A., Romer, D., 1999. Does trade cause growth? Am. Econ. Rev. 89, 379e399.
Gagnon, J., Masson, P., McKibbin, W., 1996. German unification: what have we learned from multi-
country models. Econ. Model. 13, 467e497.
Gilbert, J., 2001. Free Trade in the Americas from a NortheSouth Perspective. CEPII, Paris.
Gordon, R.H., Bovenberg, A.L., 1994. Why is Capital so Immobile Internationally? Possible Explanations
and Implications for Capital Taxation. American Economic Review, 1996, volume 86, number 5,
December, pages 1057e1075.
Goulder, L.H., 1992. An Intertemporal General Equilibrium Model for Analyzing US Energy and
Environmental Policies: Model Structure. Stanford University, Stanford, CA.
Goulder, L.H., Summers, L.H., 1989. Tax policy, asset prices and growth: A general equilibrium analysis.
J. Publ. Econ. 38, 265e296.
Haber, G., Neck, R., McKibbin, W.J., 2001. Monetary and fiscal policy rules in the European Economic
and Monetary Union: A simulation analysis. In: Choi, J.J., Wrase, J. (Eds.), European Monetary Union
and Capital Markets. Elsevier, Amsterdam, pp. 195e217.
Hall, R.E., 1978. Stochastic implications of the life-cycle hypothesis: Theory and evidence. J. Polit. Econ.
86, 971e987.
Hayashi, F., 1979. Tobin’s marginal qand average q: A neoclassical interpretation. Econometrica 50,
213e224.
Hayashi, F., 1982. The permanent income hypothesis: Estimation and testing by instrumental variables.
J. Polit. Econ. 90, 895e916.
Henderson, D.W., McKibbin, W., 1993. A comparison of some basic monetary policy regimes for open
economies: implications of different degrees of instrument adjustment and wage persistence. Carnegie-
Rochester Conf. Ser. Publ. Pol. 39, 221e318.
Hertel, T.W. (Ed.), 1997. Global Trade Analysis: Modeling and Applications. Cambridge University Press,
Cambridge.
Ho, M.S., 1989. The Effects of External Linkages on US Economic Growth: A Dynamic General
Equilibrium Analysis. PhD Dissertation. Harvard University, Cambridge, MA.
International Monetary Fund, 2004. World Economic Outlook. International Monetary Fund,
Washington, DC.
Johansen, Leif, 1960. A Multi-Sectoral Study of Economic Growth. North-Holland, Amsterdam.
Jorgenson, D.W., Wilcoxen, P.J., 1990. Intertemporal general equilibrium modeling of us environmental
regulation. J. Pol. Model. 12, 314e340.
Kim, J., Lau, L., 1994. The role of human capital in the economic growth of the East Asian newly
industrialized countries, paper presented at the Asia-Pacific Economic Modeling Conference, Sydney.
Lee, J.W., McKibbin, W., Park, Y., 2006. Transpacific trade imbalances: Causes and cures. World Econ. 29,
281e304.
Lee, J.W., McKibbin, W.J., 2004. Globalization and disease: The case of SARS. Asian Econ. Paper. 3,
113e131.
Lucas, R.E., 1967. Optimal investment policy and the flexible accelerator. Int. Econ. Rev. 8, 78e85.
Manchester, J., McKibbin, W., 1994. The macroeconomic consequences of the savings and loan debacle.
Rev. Econ. Stat. 76, 579e584.
Manchester, J., McKibbin, W., 1995. The global macroeconomics of NAFTA. Open Econ. Rev., 203e223.
Martin, W., Winters, L.A., 1995. The Uruguay round and the developing economies. In: World Bank
Discussion Papers 307. World Bank, Washington, DC.
McKibbin, W., 1986. The International Coordination of Macroeconomic Policies. PhD Dissertation.
Harvard University, Cambr idge, MA.
McKibbin, W., 1994. Dynamic adjustment to regional integration: Europe 1992 and NAFTA. J. Jpn. Int.
Econ. 8, 422e453.
McKibbin, W., 1996. Military spending cuts and the global economy. In: Gleditsch, N., Bjerkholt, O.,
Cappelen, A., Smith, R., Dunne, J. (Eds.), The Peace Dividend, Contribution to Economic Analysis.
North-Holland, Amsterdam, pp. 465e489.
A Global Approach to Energy and the Environment: The G-Cubed Model 1065

McKibbin, W., 1997. Which monetary policy regime for Australia? In: Reserve Bank of Australia (Ed.),
Monetary Policy and Inflation Targeting. Reserve Bank of Australia, Canberra, pp. 166e173.
McKibbin, W., 1998a. Regional and multilateral trade liberalization: The effects on trade, investment and
welfare. In: Drysdale, P., Vines, D. (Eds.), Europe, East Asia and APEC: A Shared Global Agenda?
Cambridge University Press, Cambridge, pp. 195e220.
McKibbin, W. 1998b. Unilateral versus multilateral trade liberalization: The importance of inter-
national financial flows. In: US ITC (Ed.), The Economic Implications of Liberalizing APEC
Tariff and Non-Tariff Barriers to Trade: US International Trade Commission Publication 3101,
pp. 10e41e10e66.
McKibbin, W., 1999. Modelling the crisis in Asia. In: Arndt, H., Hill, H. (Eds.), Southeast Asia’s
Economic Crisis: Origins, Lessons, and the Way Forward. Institute of Southeast Asian Studies,
Singapore, pp. 119e127.
McKibbin, W., 2000. Forecasting the world economy with dynamic intertemporal general equilibrium
multi-country models. In: Abelson, P., Joyeux, R. (Eds.), Economic Forecasting. Allen & Unwin,
London, pp. 50e73.
McKibbin, W., 2002. Macroeconomic policy in Japan. Asian Econ. Paper. 1, 133e165.
McKibbin, W., 2006a. Global energy and environmental impacts of an expanding China. China World
Econ. 14, 38e56.
McKibbin, W. 2006b. The global macroeconomic consequences of a demographic transition. Asian Econ.
Paper. 5, 92e141. Also published in G-20 Workshop on Demographic Challenges and Migration.
Australian Government Printing Service, Canberra, pp. 11e44.
McKibbin, W., 2008. China and the global environment. In: Eichengreen, B., Park, Y.C., Wyplosz, C.
(Eds.), China, Asia and the New World Economy. Oxford University Press, Oxford, pp. 18e50.
McKibbin, W., Bok, T., 1995. The impact on the AsiaePacific region of fiscal policy in the United States
and Japan. Asia Pac. Econ. Rev. 1, 25e40.
McKibbin, W., Bok, T., 2001. The European Monetary Union: Were there alternatives to the ECB?
A quantitative evaluation. J. Pol. Model. 23, 775e806.
McKibbin, W., Chanthapun, W., 2009. Exchange rate regimes in the AsiaePacific region and the global
financial crisis. In: Asia Development Bank Working Paper Series on Regional Cooperation 36. Asian
Development Bank, Manila.
McKibbin, W., Huang, Y., 2000. Rapid economic growth in China: implications for the world economy.
In: Drysdale, P., Song, L. (Eds.), China’s Entry to the WTO: Strategic Issues and Quantitative
Assessments. Routledge, London, pp. 194e216.
McKibbin, W., Le, H.-G., 2004. Which exchange rate regime for Asia? In: DeBrouwer, G.,
Kawai, M. (Eds.), Exchange Rate Regimes in East Asia. Routledge Curzon, London, pp.
385e416.
McKibbin, W., Martin, W., 1998. The East Asia crisis: Investigating causes and policy responses
[background paper for the World Bank Report East Asia: The Road to Recovery]. In: Brookings
Discussion Paper in International Economics 142. Brookings Institution, Washington, DC.
McKibbin, W., Nguyen, J., 2004. Modelling global demographic change: results for Japan. In: Brookings
Discussion Papers in International Economics 162. Brookings Institution, Washington DC.
McKibbin, W., Sachs, J., 1991. Global Linkages: Macroeconomic Interdependence and Cooperation in the
World Economy. Brookings Institution, Washington DC.
McKibbin, W., Salvatore, D., 1995. The global economic consequences of the Uruguay Round. Open
Econ. Rev. 6, 111e129.
McKibbin, W., Sidorenko, A., 2006. Global macroeconomic consequences of pandemic influenza. Lowy
Institute Analysis February.
McKibbin, W., Singh, K., 2003. Issues in the choice of a monetary regimes in India. In: Jha, R. (Ed.),
Indian Economic Reforms. Palgrave-Macmillan, London, pp. 11e50.
McKibbin, W., Stoeckel, A., 2010a. The global financial crisis: Causes and consequences. Asian Econ.
Paper. 9, No. 1, pp. 54e86.
McKibbin, W., Stoeckel, A., 2010b. Modeling the Global Financial Crisis. Oxf. Rev. Econ. Pol. 25, No. 4,
pp. 581e607.
1066 Warwick J. McKibbin and Peter J. Wilcoxen

McKibbin, W., Stoeckel, A., 2012. Global Fiscal Consolidation. Asian Economic Papers, 11, No. 1,
pp. 124e146.
McKibbin, W., Tan, K., 2009. Learning and the International Transmission of Shocks. Econ. Model. 31
(3), 463e477.
McKibbin, W., Tang, K.K., 2000. Trade and financial reform in China: Impacts on the world economy.
World Econ. 23, 979e1003.
McKibbin, W., Thurman, S., 1995. The impact on the world economy of reductions in military
expenditures and military arms exports. In: Klein, L., Lo, F., McKibbin, W. (Eds.), Arms Trade:
Economic Implications in the Post Cold War Era. United Nations University Press, New York,
pp. 129e171.
McKibbin, W., Vines, D., 2000. Modelling reality: The need for both intertemporal optimization and
stickiness in models for policymaking. Oxf. Rev. Econ. Pol. 16, 106e137.
McKibbin, W., Wilcoxen, P.J., 1993. The global consequences of regional environmental policies: an
integrated macroeconomic multisectoral approach. In: Kaya, Y., Nakicenovic, N., Nordhaus, W.D.,
Toth, F.L. (Eds.), Costs, Impacts and Benefits of CO
2
Mitigation. International Institute for Applied
Systems Analysis, Laxenburg, pp. 247e272.
McKibbin, W., Wilcoxen, P., 1994. The global costs of policies to reduce greenhouse gas emissions. In:
Final Report on US Environmental Protection Agency Cooperative Agreement CR818579-01-0.
Brookings Institution, Washington, DC.
McKibbin, W., Wilcoxen, P., 1997. The economic implications of greenhouse gas policy. In: English, H.,
Runnalls, D. (Eds.), Environment and Development in the Pacific: Problems and Policy Options.
Addison Wesley, London, pp. 8e34.
McKibbin, W., Wilcoxen, P., 1999a. The theoretical and empirical structure of the G-Cubed model. Econ.
Model. 16, 123e148.
McKibbin, W., Wilcoxen, P., 1999b. Environmental policy and international trade. In: Mahendrarajah, S.,
Jakeman, A., McAleer, M. (Eds.), Modelling Change in Integrated Economics and Environmental
Systems. Wiley, New York, pp. 339e368.
McKibbin, W., Wilcoxen, P., 2000. The role of international permit trading in climate change policy. Oxf.
Energ. Forum 41, 4e6 (May).
McKibbin, W., Wilcoxen, P., 2002a. The role of economics in climate change policy. J. Econ. Perspect. 16,
107e129.
McKibbin, W.J., Wilcoxen, P.J., 2002b. Climate Change Policy after Kyoto: A Blueprint for a Realistic
Approach. Brookings Institution, Washington, DC.
McKibbin, W., Wilcoxen, P., 2004. Estimates of the costs of KyotoeMarrakesh versus the McKib-
bineWilcoxen blueprint. Energ. Pol. 32, 467e479.
McKibbin, W., Wilcoxen, P., 2007. A credible foundation for long term international cooperation on
climate change. In: Aldy, J., Stavins, R. (Eds.), Architectures for Agreement: Addressing Global
Climate Change in the Post-Kyoto World. Cambridge University Press, Cambridge, pp. 185e208.
McKibbin, W., Wilcoxen, P., 2009a. The economic and environmental effects of border adjustments forclimate
policy. In: Brainard, L., Sorkin, I. (Eds.), Climate Change Trade and Competitiveness: Is a Collision
Inevitable? Brookings Trade Forum 2008/09. Brookings Institution, Washington, DC, pp. 1e35.
McKibbin, W., Wilcoxen, P., 2009b. Uncertainty and climate change policy design. J. Pol. Model 31,
463e477.
McKibbin, W., Woo, W., 2004. Quantifying the international economic impact of China’s WTO
membership. China World Econ. 12, 3e19.
McKibbin, W., Pagan, A., Robertson, J., 1998. Some experiments in constructing a hybrid model for
macroeconomic analysis. Carnegie Rochester Ser. Publ. Pol. 49, 113e142.
McKibbin, W., Ross, M., Shackleton, R., Wilcoxen, P.,1999a. Emissions trading, capital flows and the
Kyoto protocol. Energ. J. 287e333. (Special Issue: The Costs of the Kyoto Protocol: A Multi-model
Evaluation). Reprinted from: Economic Impact of Mitigation Measures. Intergovernmental Panel on
Climate Change, Geneva.
McKibbin, W., Shackleton, R., Wilcoxen, P., 1999b. What to expect from an international system of
tradeable permits for carbon emissions. Resource Energ. Econ. 21, 319e346.
A Global Approach to Energy and the Environment: The G-Cubed Model 1067

McKibbin, W., Wang, Z., Coyle, W., 2001. The Asian financial crisis and global adjustments: Impacts on
US agriculture. Jpn. Econ. Rev. 52, 471e490.
McKibbin, W., Lee, J.-W., Cheong, I., 2004. A dynamic analysis of the KoreaeJapan free trade area:
simulations with the G-Cubed Asia-Pacific model. Int. Econ. J. 18 (no 1), 3e32.
McKibbin, W., Pearce, D., Stegman, A., 2007. Long term projections of carbon emissions. Int. J. Forecast.
23, 637e653.
McKibbin, W., Wilcoxen, P., Woo, W., 2008. China can grow and still help prevent the tragedy of the CO
2
commons. In: Garnaut, R., Song, L., Woo, W.T. (Eds.), China’s Dilemma: Economic Growth, the
Environment and Climate Change. Asia Pacific Press, Brookings Institution Press and Social Sciences
Academic Press, Washington, DC, pp. 190e225.
McKibbin, W., Pearce, D., Stegman, A., 2009a. Climate change scenarios and long term projections.
Climatic Change 97, 23e47.
McKibbin, W., Morris, A., Wilcoxen, P., Cai, Y., 2009b. Consequences of Alternative US Cap and Trade
Policies: Controlling Both Emissions and Costs. Brookings Institution, Washington, DC.
McKibbin, W., Morris, A., Wilcoxen, P., 2009c. A Copenhagen collar: Achieving comparable effort
through carbon price agreements. In: Climate Change Policy: Recommendations to Reach
Consensus. Brookings Institution, Washington, DC, pp. 26e34.
McKibbin, W., Morris, A., Wilcoxen, P., 2009d. Expecting the unexpected: Macroeconomic volatility and
climate policy. In: Aldy, J., Stavins, R. (Eds.), Architectures for Agreement: Addressing Global Climate
Change in the Post-Kyoto World. Cambridge University Press, Cambridge, pp. 185e208.
McKibbin, W., Morris, A., Wilcoxen, P., 2010. Compar ing Climate Commitments: A Model Based
Analysis of the Copenhagen Accord. Brookings Institution, Washington, DC.
Monteagudo, J., Watanuki, M., 2001. Regional Trade Agreements for MERCOSUR: The FTAA and the
FTA with the European Union. CEPII, Paris.
Neck, R., Haber, G., McKibbin, W., 2005. Global macroeconomic policy implications of and enlarged
EMU. In: Breuss, F., Hochreiter, E. (Eds.), Challenges for Central Banks in and Enlarged EMU.
Springer, New York, pp. 235e257.
Neck, R., Haber, G., McKibbin, W., 2000. Macroeconomic impacts of European Union membership of
Central and Eastern European economies. Atl. Econ. J. 28, 71e82.
Nguyen, J., 2011. Modelling the Macroeconomic Effects of Population Ageing in Japan and the Inter-
national Economy. PhD Dissertation. The Australian National University, Canberra.
Papageorgiou, D., Choski, A.M., Michaely, M., 1990. Liberalizing Foreign Trade in Developing Coun-
tries: The Lessons of Experience. World Bank, Washington, DC.
Shoven, J.B., Whalley, J., 1984. Applied general equilibrium models of taxation and international trade: An
introduction and survey. J. Econ. Lit. 22, 1007e1051.
Stoeckel, A., McKibbin, W., Tang, K.K., 2000. Productivity, risk and the gains from trade liberalisation. In:
Pelham Papers 9. Melbourne Business School, Melbourne.
Strutt, A., Anderson, K., 1999. Estimating Environmental Effects of Trade Agreements with Global CGE
Models: A GTAP Application to Indonesia. OECD, Paris.
Treadway, A., 1969. On rational entrepreneurial behavior and the demand for investment. Rev. Econ.
Stud. 3, 227e239.
Tsigas, M., Gray, D., Hertel, T., 2002. How to Assess the Environmental Impacts of Trade Liberalization.
Center for Global Trade Analysis, Purdue University, West Lafayette, Indiana.
Uzawa, H., 1969. Time preference and the Penrose effect in a two class model of economic growth.
J. Polit. Econ. 77, 628e652.
Weil, P., 1989. Overlapping families of infinitely-lived agents. J. Publ. Econ. 38, 183e198.
Wilcoxen, P.J., 1988. The Effects of Environmental Regulation and Energy Prices on US Economic
Growth. PhD Dissertation. Harvard University, Cambridge, MA.
Yaari, M.E., 1965. Uncertain lifetime, life insurance, and the theory of the consumer. Rev. Econ. Stud. 32,
137e150.
1068 Warwick J. McKibbin and Peter J. Wilcoxen