Download Understanding structural change in the Brunei Darussalam

Understanding structural change in the Brunei Darussalam Economy,
2005 - 2011
Tsue Ing Yap and Janine Dixon
Centre of Policy Studies, Victoria University
Abstract
This paper describes the historical simulation of the Brunei Darussalam (hereafter Brunei)
economy using BRUGEM, a recursive dynamic, computable general equilibrium (CGE)
model based on ORANIG-RD. The simulation covers the period from 2005 to 2011. We
estimate typically unobservable variables that describe the structural features of the economy
such as the technology and household taste or preference changes. The estimates for these
structural variables can help in explaining the observable features of the Brunei economy
over the period under study.
Findings from the historical simulation indicate that the Brunei economy is characterised by
declining per-capita real economic growth, low capital growth, low investment by share of
gross domestic product (GDP), but high growth in public and private consumption, high real
wage, high imports and declining exports with real appreciation of Brunei’s currency and an
improvement in the terms of trade attributable to high oil and gas prices. Real currency
appreciation has eroded the competitiveness of the economy and in the face of declining
hydrocarbon exports, this has placed Brunei in a challenging position in its preparation for
the post-hydrocarbon era.
DRAFT – NOT FOR QUOTATION
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1. Introduction
Brunei is a small and open economy that has been dependent on its hydrocarbon sectors for
many decades. Brunei derived most of its revenues (93.1% of total government revenues in
the 2011/2012 fiscal year) from the exports of oil and gas which made up nearly 90% of total
exports in 2011 (DEPD 2011a). Like most mineral resources, oil and gas are finite. The BP
Statistical Review of World Energy June 2013 report, ceteris paribus, stated that based on the
2012 figures for existing proven reserves and reserve-to-production ratios, oil will last 18.2
years while gas will last 22.9 years. This implied that without new oil and gas finds or
improvement in technology to extract more out of maturing fields, at the current reserves
level and extraction rate, Brunei will run out of hydrocarbons by the year 2035.
Diversification efforts in Brunei have shown slow progress despite the various National
Development Plans (NDP) and development initiatives in place (Tisdell 1998, Bhaskaran
2010, Lawrey 2010).
There are numerous articles on the topic of diversification of Brunei’s economy highlighting
the barriers to diversification with some authors (Crosby 2007, Bhaskaran 2007) and past
studies (major past studies are listed in Bhaskaran 2007, p. 9) suggesting the areas Brunei
could diversify into. Most of the studies are of qualitative nature and there are limited
empirical studies on how these strategies can be carried out and what the economy-wide
impacts would be if such policies were implemented.
Before appropriate strategies can be formulated, it is useful to have an overall snapshot of the
underlying economic structure of the economy through a quantitative method. To achieve this
objective, this paper aims to identify and quantify the key features of the Brunei economy for
the period 2005 to 2011 using the Computable General Equilibrium (CGE) Modelling
approach.
A CGE model is an economy-wide model that depicts the inter-linkages between the different
sectors in the real economy within the macroeconomic framework of a small country. It is
able to capture the effects through changes in relative prices of inputs and outputs, brought
about by shocks from either supply or demand sides of the economy and identify the tradeoffs amongst the different economic agents and the sectors. CGE modelling has been at the
forefront of economic analysis in Australia for forty years, providing policymakers with
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insights that can be clearly attributed to policy levers and other underlying structural change.
This can be done at both macroeconomic and sectoral levels.
Based on the ORANIG-RD model built by Horridge (2002) of the Centre of Policy Studies,
we have developed a CGE model of Brunei’s economy called BRUGEM with dynamic
features. Using the official input-output (IO) data published in 2011 for the base year 2005,
and the macro statistics for 2011, the most recent complete set available, an historical
simulation is implemented to uncover some underlying structural changes that can help cast
some light moving forward while examining the factors that can contribute to Brunei’s
successful diversification.
2. Modelling approach
2.1 Overview of the BRUGEM model
BRUGEM is the core model used in this paper. BRUGEM retains not only the key features of
the well-documented comparative static ORANI-G model (Dixon et al. 1982, Horridge 2003),
it also extends to include additional equations to produce a Recursive Dynamic model, which
is similar to a simplified version of the MONASH model which is the successor of the
ORANI-G model as described in Dixon and Rimmer (2002). A recursive dynamic model is a
multi-period CGE model that is solved sequentially on year-to-year basis over a number of
years. For each year, the starting data is utilising the end-of-period results updated by
previous simulation. Therefore each model solution represents the changes between one year
and the next. Under the recursive dynamic setting, it is assumed that the economic agents’
behaviours only depend on the past and current states of the economy; they are not aware of
future values of the variables in the model. In this aspect, a recursive dynamic model is
different to a fully inter-temporal model where all periods are solved simultaneously and
agents take into consideration their expectations of the future.
BRUGEM features detailed sectoral disaggregation and the model used in this paper features
73 industries and 73 commodities. The main departures from the original 74 sectors IO table
are the incorporation of an additional sector to represent the ownership of dwellings, the
aggregation of three tiny industries into one, and the attribution of some value added in the
oil and gas sectors to natural resources which are treated as a fixed factor.
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In terms of the underlying theory which is largely based on Dixon and Rimmer (2002),
BRUGEM is a Johansen-style model where the key behaviour assumptions of the economic
agents identified in the model (producers, investors, household consumers, importers,
exporters and government) are drawn from the neoclassical microeconomic theory.
Producers and households are assumed to make decisions based on their maximising
behaviours. Each representative industry is assumed to operate under the condition of cost
minimisation subject to its constant returns to scale production function and given input
prices. Households maximise their utility subject to their budget constraints and their
demands are modelled via a representative household in the economy using the Klein-Rubin
utility maximisation function or the linear expenditure system (LES). Investors will allocate
new capital to industries on the basis of the expected rates of return where the capital stock
will grow in relation to the equilibrium expected rate of return and is limited by the logistic
capital supply function as described in Dixon and Rimmer (2002, p. 190-192).
Imported and domestic varieties of each commodity demanded are assumed to be imperfect
substitutes and are modelled using the Constant Elasticity of Substitution (CES) assumption
of Armington as the approach adopted (Dixon et. al 1982, p. 69). Therefore at the sourcing
nest level, the units of given inputs are differentiated by source and are combined such that
the total cost is minimised to provide a unit of composite commodity demanded by producers.
The capital creators will demand inputs to investment by choosing the cost-minimising input
mix from domestically produced or imported commodities subject to a constant CES capitalcreation function. There is no primary factor being used directly as inputs to this capital
creation process.
The demand for domestically produced commodity by foreigners is assumed to be inversely
related to the export price denominated in foreign currency. The higher is the export price,
the lesser is the demand for exports of the domestic commodity.
Unlike other agents, the government is not an optimiser in this model. Its consumption and
investment are modelled in such a way that they can be set as exogenous or assumed to
change via a simple relationship with other relevant variable with the use of swaps in the
model closure. Typical assumptions used are such that government consumption is
proportional to private consumption in a welfare-enhancing policy change (Dixon and
Rimmer 2002, p. 151) and industry-specific government investment is proportional to total
investment in each industry.
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Relative prices play an active role in the determination of economic outcomes in this type of
model (Parmenter and Meagher 1985). There are different valuation of prices for the
domestically produced goods and services: basic prices, producers’ prices and purchasers’
prices. Basic prices are the prices received by the sellers rather than the prices paid by the
users. They are the basic values of domestically produced goods received by producers. The
basic price of an import is the landed-duty-paid price and for domestic products it is the
factory-door price. Basic prices are assumed to be uniform across producing industries and
across users, and also importers in the case of imported goods. Producer prices are prices that
include the indirect sales taxes but exclude markups (Dixon et. al 1992, p.31). Markups are
margin flows representing quantities of retail and wholesale services or transport needed to
deliver each basic flow of good to the user. The difference between the basic prices and
purchasers’ prices is due to sales taxes and margin flows such as wholesale, retail and
transport costs.
In general, markets are assumed to be competitive with no pure profits in any economic
activity and markets will clear with demand equals to the supply for all domestically
produced goods and services. All agents will take input and output prices as given for their
decision-making.
Under the dynamic setting of the BRUGEM model, time paths can also be produced under
different scenarios for a large number of economic variables. This feature of the model is
useful in implementing simulations to either track the past history for better understanding or
to forecast the future, using available data and dynamic relationships of the equations within
the model.
The recursive dynamic model has forecasting component utilising 3 mechanisms: (i) the
stock-flow relation between investment and capital stock with one year gestation lag assumed;
(ii) a positive relation between investment and rate of profit; and (iii) a relation between wage
growth and employment. This dynamic component can be used to construct a plausible base
forecast for the next ten years or more. Additional data such as the capital stocks, exogenous
predictions about the future directions of technological change, employment, import prices
and position of export demand curves are required to produce such forecasts (Horridge, 2002).
Predictions can be either based on assumptions, say uniform growth rates, or can be obtained
from detailed forecasts made by government and private agencies or even international
organisations such as International Monetary Fund (IMF). A second forecast can be
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constructed to examine a perturbed scenario whereby some variables are shocked to different
values from the base scenario to simulate the policy impact, say, more investment in
agriculture industry. The difference between these two scenarios can then be interpreted as
the effect of the policy change.
BRUGEM is a large model system consisting of a wide variety of equations with many in
non-linear functional forms. Adopting similar approach to developed model like MONASH,
BRUGEM is solved using the Euler solution method for linear approximation, described in
more details by Dixon and Rimmer (2002). The simulation is run in 50 steps for a high
degree of accuracy.
Briefly, if we can represent the BRUGEM model as a system of equations describing the
economic activities at year t in vector form shown in Equation (E.1),
F[V (t )]  0
(E.1)
where F is a vector of length m of differentiable functions and V (t ) is a vector of length n
of prices, quantities, household tastes and other variables for year t . Since n  m , we need to
define a closure or to specify the combination of values for n  m exogenous variables in
order to solve for the remaining m endogenous variables. Dixon and Rimmer (2002)
described the four basic choices in choosing the sets of n  m exogenous variables: historical,
decomposition, forecasting and policy closures. For the purpose of this paper, the “historical
closure” is used and will be described in the next section.
Under the computation method used by GEMPACK1 (Harrison & Pearson 1996), the nonlinear equations (E.1) are converted into a system of linear equations by taking the total
differentials of each equation and then expressed them in percentage change form:
A(V (t ))v(t )  0
(E.2)
where A(V (t )) is a m x n matrix of coefficients such as cost and sale shares, evaluated at an
initial solution V of (E.1) and v is the vector of deviations in the model’s variables away
from V . From the IO published data, we have the initial solution for year t  0 where
1
GEMPACK is a suite of software application developed by the Centre of Policy Studies to solve general
equilibrium models.
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F[V ]  0 . Using initial solution for year t , the first computation will create a solution for the
year t+1 via movement of the exogenous variables from initial (year t ) values to the required
values in year t+1. This solution for year t+1 will in turn become the initial solution for a
computation that moves exogenous variables from their values at year t+1 to t+2 and so on.
For the period under study, the chosen initial year is 2005 being the base year used for the
sole published IO tables available. Since there are no published IO tables after that, the data
after 2005 is subsequently updated in a sequence of period-to-period simulations until the
year 2011 where there are latest published statistics available. Using a step-by-step approach,
the baseline is developed for the years 2005-2011 with the use of BRUGEM model where
historical data is available for the macroeconomic and some price variables of the Brunei
economy obtained from the Department of Statistics (DEPD 2011a).
2.2 The historical closure in a stylised “back-of-the-envelope” model
To achieve the aim of this paper, simulations need to be run using BRUGEM with the
appropriate closure. We use a stylised back-of-the-envelope (BOTE) model to illustrate the
development of a historical closure, from the starting point of a “natural” long run closure.
The system of equations used in the BOTE model (Dixon and Rimmer 2002, Giesecke 2004)
is shown in Table 1.
BOTE model is useful to support the interpretation of macro results by identifying the main
mechanisms and the data items underlying certain results. This is especially important as a
tool to explain to policymakers who may not be familiar with the large CGE model. As a
CGE model integrates detailed structural and dynamic information, it can reveal useful
theoretical insights supported by calculations. BOTE calculations can be used to conduct
sensitivity analysis under alternative assumptions and parameter values, and can also be used
to check for any errors in coding and data handling.
The 8-equation BOTE illustrated in Table 1 requires 8 endogenous variables. In the “natural”
long run closure, Y, C, I, G, X, M, K and TOT are the endogenous variables while the
structural variables driving economic activity (L, ROR, A, APC, Γ, Ψ, V and T) are
exogenous. It is not simple to trace causality through the BOTE, but if Equations (2) and (8)
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are considered to explain Y and K (ignoring TOT), then Equation (3) explains C, Equation (4)
explains G, Equation (5) explains M and Equation (7) explains I. With Y, C, I, G and M
explained, Equation (1) explains X, and finally Equation (6) explains TOT. The point is that
this distilled version of the full CGE model illustrates how the structural variables such as
employment (linked to population in the long run), productivity, and the conditions in the
world economy are the determinants of economic growth and structural change in the full
model.
The historical closure in the right hand panel has the same 8 equations, but a different set of 8
unknowns.
The exogenous variables are now observed macroeconomic indicators. The
equations can be solved for the endogenous structural variables. By setting Y, C, G, X and
M exogenously, I can be determined in Equation (1). APC can be solved from Equation
(3). T can be determined from Equation (5) and V, can be determined from Equation (6).
With two Equations (2) and (8) with two unknowns K and A, these can be solved
simultaneously. With variable K known, Ψ can be determined.
Table 1: Natural and historical closures of the BOTE model
“Natural” long run closure
Historical Closure
(1) Y=C+I+G+X−M
(1) Y=C+I+G+X−M
(2) Y=(1/A)*F(K,L)
(2) Y=(1/A)*F(K,L)
(3) C=B(APC,Y,TOT)
(3) C= B(APC,Y,TOT)
(4) C/G=Γ
(4) C/G=Γ
(5) M=H(Y,TOT,T)
(5) M=H(Y,TOT,T)
(6) TOT = J(X,V)
(6) TOT = J(X,V)
(7) I/K=Ψ
(7) I/K=Ψ
(8) K/L=N(ROR,A,TOT)
(8) K/L=N(ROR,A,TOT)
Bold = exogenous variables, the rest are endogenous
Key:
K – capital stock
V – demand-shift variable
Y – GDP
TOT – terms of trade
C – private consumption
L – labour
I – investment
ROR – rate of return
Γ- ratio of private to public
spending
Ψ – investment to capital
ratio
T – import preference
variable
G – government expenditure
X – exports
M - imports
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A – primary-factor
augmenting technology
APC – average propensity to
consume
3. Historical simulation
The historical simulation reveals variables that describe the structural features of the
economy such as the technology and taste or preference changes of the Brunei economy.
In the historical simulation, the observed variables are exogenised and shocked with the
calculated shock values to ensure that the output data for the year 2011 is consistent with the
statistical data. Variables related to primary-factor-saving technical change, preference for
imported goods relative to domestic goods, average propensity to consume, shifts in export
demand curve are endogenised in order to hit these targets. We informed the model the
observed real growth rates in GDP, private consumption, government consumption, exports,
imports, oil and gas export volume and corresponding purchasers’ export prices, import
prices, terms of trade, the number of households and employment. Aggregate capital stock is
predetermined by the investment last year and the model will solve for next year’s capital
stock, capital rental, primary factor augmented technical change, preference in favour of
imports and other endogenous variables.
Dixon and Rimmer (2002) and several other authors (Giesecke 2004, Dixon and Rimmer
2003, Tran and Giesecke 2008 just to name a few) have outlined the theories and steps
undertaken to conduct historical simulation. In our simulations, phi, the exchange rate
defined as Brunei dollar to the world currency is used as the numeraire. This reflects the
current situation whereby the Brunei dollar is fixed at par to the Singapore dollar. The
general features of the macroeconomy in BRUGEM are as illustrated in the BOTE model in
Table 1.
With the historical simulation, we found out some characteristics of the Brunei economy
which will give the context for forecast to address question like what would happen if these
features continue into the future period when oil and gas have run out.
Starting with the natural closure, the historical closure is developed in seven steps (Step 1
through Step 7) cumulatively based on the available macroeconomic data where each step
consists of the previous step plus a small number of swaps between endogenous and
exogenous variables, maintaining a valid closure throughout. By Step 7, the full historical
closure is implemented. Results are then compared over subsequent successive historical
simulations showing the effects of the additional data introduced at each step. The BRUGEM
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results from this step-by-step approach for the period 2005 to 2011 are shown in Table 2. The
difference between the columns shows the effects of introducing the new shock in the
subsequent step. Step 7 will show the results of the entire historical simulation when
observed data have been incorporated. The final column shows the average annual percentage
change and the figures in bold indicate the average annual shocked values.
Step 1 : Number of households, aggregate employment and homotopy variable
The number of households, aggregate employment and the homotopy variable which
activates the capital accumulation equation in the model are shocked.
The shock to the number of households only has an impact on the composition of household
expenditure. In the model there is no explicit link between households and labour supply.
For this reason, employment is also shocked in this step. Aggregate nominal household
expenditure in the model is determined by nominal incomes and the average propensity to
consume, and the number of households has no direct impact. However, the allocation of the
household budget by commodity is determined by the linear expenditure system (LES) for
which there is a subsistence component of consumption on each commodity. The subsistence
component is fixed per household, so an increase in the number of households leads to an
increase in subsistence expenditure, and for a given budget, a decrease in supernumerary
expenditure.
In the absence of technical change but with small positive growth in employment, the real
GDP (row 1) grows by 9.42% in six years as the economy expands from the supply side in
Equation (2) of Table 1. Without any change in technology and the rate of return, the capitalto-labour ratio is expected to remain unchanged according to Equation (8) in the right hand
side column of Table 1. Therefore the capital stock (row 14) must increase to maintain this
ratio. The slight increase in the capital stock relative to employment is explained by the
increase in investment (row 2).
The shock to aggregate employment is accommodated in the model by endogenising the
wage. As expected, an increase in employment with no corresponding shift in demand
schedules leads the economy to produce more than it consumes domestically, and the result is
a real devaluation of the currency (row 15) in order to accommodate an increase in net
exports.
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Given the large real devaluation, the result for net exports is rather small. Imports (row 5) are
expected to increase as real GDP grows via Equation (5) which is also brought about by the
increase in private consumption via Equation (3).
Exports from Brunei consist almost
entirely of oil and gas, both heavily capital intensive activities with limited scope to take
advantage of the increased employment. Indeed, although the currency devalues by 9.12%,
the export prices of both oil and gas only fall by around 1%, effectively limiting the fall in the
terms of trade to 1.33%.
Nominal aggregate household expenditure is linked to nominal income, but aggregate real
household expenditure increases by slightly less than GDP. This reflects the slight decrease
in the terms of trade, making imports, an important component of household expenditure,
more expensive. We find an increase in investment expenditure more than double the
increase in GDP. This reflects not only the modest increase in household expenditure and net
exports, but also the exogeneity of government expenditure in this step.
Step 2 : Real gross domestic product (GDP)
The observed real GDP growth is in fact lower than the result shown in Step 1 (row 1). In
order to implement the available real GDP growth data in Step 2, we endogenise primaryfactor technical change uniformly over all industries. The real GDP growth is shocked by
0.92% per annum and with this growth rate being lower than the employment growth,
regressive technical change of 3.32% in aggregate (row 25) is observed over the period 2005
to 2011. The link between the decline in productivity and that the growth rate of the economy
is in Equation (2).
Compared to Step 1, the effect of declining real wage is slightly dampened as the increase in
employment in Step 1 did not produce as much economic output as expected due to reduced
productivity (row 25). Regressive technical change affects the capital-to-labour ratio and
subsequently the real wage which is expected to fall via Equation (8) leading to the decline in
capital stock (row 14) and also investment (row 2). The decrease in real GDP growth rate is
also reflected in the fall in private consumption (row 3) via Equation (3). With less economic
activities, imports will fall (row 5) via Equation (5) and also the exports (row 6) via Equation
(1) leading to an improvement in terms of trade (row 16).
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Step 3 : Real private consumption
The fall in private consumption anticipated in Step 2 is however not observed in the historical
data. In Step 3, private consumption is given a positive shock of 4.28% per annum. In order
to implement this shock, we endogenise the ratio of nominal consumption to nominal GDP
(f3tot) or the average propensity to consume out of GDP.
Since aggregate private consumption grows much faster than GDP and hence incomes, the
average propensity to consume increases by 31.47% (row 29) from 2005-2011. With no
change in GDP at this step, the increase in private consumption crowds out other types of
expenditure, leading to a reduction in investment and net exports and real appreciation of the
currency compared to Step 2.
As the share of imports in household consumption is high relative to the share of imports in
other sectors, we might expect a large increase in imports as a result of the increase in
consumption as a share of GDP. In this context, the increase in imports appears quite modest,
well below the increase in consumption. For a given value of net exports, further expansion
of imports requires further expansion of exports. The major export sectors, oil and gas have
limited capacity for expansion due to their use of natural resources (a fixed factor).
Recalling the national accounting identity, X-M = S-I, the expected negative effect on
investment (I) of a reduction in domestic saving (S) is dampened by the fall in net exports (XM). The large fall in the value of net exports accounts for almost all of the reduction in
savings, and investment continues to increase by more than GDP, albeit at a lesser rate than
after Step 2.
Step 4 : Real public consumption
With the statistical data available for government expenditure, the aggregate public
consumption variable is shocked by 6.58% per annum in Step 4, making it the fastest
growing component of expenditure on GDP. Since there is no change of GDP from supply
side and the private consumption is fixed by previous step, the increase in public
consumption must lead to the decrease in other expenditure items on the demand side to
maintain the GDP identity. There is a marked decline in both the investment (row 2) and net
exports (rows 5 and 6).
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Public consumption is the most labour intensive component of expenditure on GDP,
accounting for 24% of GDP, over 50% of employment and just 5% of capital in the base year.
The large increase in expenditure on Public Administration, Education and Health Services
draws a further 20% of the labour force into these activities, leading to a reduction labour and
output in almost all other activities. This is facilitated by a very large increase in the real
wage (row 17).
The increase in consumption introduced in Step 4 now must be satisfied by a large increase in
imports (row 5). The large increase in domestic expenditure, far in excess of the increase in
GDP, is accompanied by a significant loss in competitiveness, shown in the large real
appreciation of the currency (row 15).
An interesting feature of the result in Step 4 is that although aggregate investment declines
(row 2), there is a slight increase in aggregate capital stock (row 14). This is a compositional
effect. These aggregates are value-weighted sums of investment and capital stock by industry.
In each industry, the increase in investment is commensurate with the increase in capital
stock. However, in the three government industries (Public Administration, Education and
Health Services), the labour to capital ratio is so large and rapidly increasing, that the
increase in the rate of return on capital far exceeds the national average. As a result, the
value-weights of capital stock for these industries increase, while the value-weights of
investment (reflecting the cost of capital creation) do not change. Thus the increase in capital
stock in these industries is afforded more importance in the calculation of aggregate capital
stock, while the increase in investment is outweighed by the decline in investment in all other
industries in the calculation of aggregate investment.
This step has the largest impact on the distribution of expenditure in the economy, taking the
value share of government expenditure in GDP from 24% in the base year 2005 to 52% in
2011. The increase is attributed to both volume and price increases. As a major employer,
the labour constraint (growth of just 1.6% per annum compared with government expenditure
growth of 6.6% per annum) has a significant impact on wages and therefore costs. With
consumption already fixed in Step 3, the expansion in the government sector has a deleterious
effect on the other sectors of the economy, that is, net exports and investment.
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Step 5 : Total imports and foreign currency import prices
In Step 5, the import volume variable is exogenised and a change of 7.62% per annum is
introduced. At this stage, GDP and all its expenditure side components except exports and
investment are exogenous. The variable chosen to accommodate imports is the investment
slack variable (invslack), effectively introducing a shift in the required rate of return schedule
uniformly over all industries.
The exogenous CIF foreign currency import price variable (pf0cif) is also uniformly shocked
by -0.67% per annum. As price data was not available by commodity, the average price
movement was assigned to all commodities.
The decrease in import prices is directly
reflected in further real currency appreciation (row 15), which has a negative impact on
exports (row 6). The increase in imports also releases some domestic capacity, dampening the
decline in investment. However, the main effect of this is to shift even more workers into the
government sector. The compositional effect described in Step 4 is further exacerbated with
another large increase in aggregate capital stocks (row 14) accompanied by only a small
increase in investment (row 2).
The decline in import prices leads to further improvement in terms of trade (row 16) which in
turn increases the capital-to-labour ratio via Equation (8) and hence the real wage (row 17).
With the aggregate employment fixed in Step 1, the aggregate capital stock must increase
(row 14) and also investment (row 2) via Equation (7).
Step 6: Total exports and changes to sectoral oil and gas export volumes and prices
The large decline in total exports volume at Step 5 is not observed from the data published by
the Brunei’s Department of Statistics.
Therefore the overall export volume variable is
increased by shocking it with the observed value of -0.6% per annum, which is
accommodated by endogenising the general shifter on export demand curves (a uniform shift
for all commodities). The large shift in the export demand curve (row 32) facilitates this
increase in exports due to favourable changes in foreign preference for Brunei goods.
Given the dominance of oil and gas in Brunei’s economy, data on the oil and gas export
volumes and purchaser’s prices of exports are introduced together with the aggregate export
shock. For the period under study, the Brunei’s Department of Statistics is able to provide
data on the changes in export volume and export prices for oil and gas commodities. The
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volumes of oil and gas exports are shocked by -3.58% and 0.13% per annum respectively by
endogenising the positions of the export demand schedules for the two commodities, oil and
gas. To reflect both the demand and supply conditions of the hydrocarbons market, the
positive shocks of 7.31% and 14.12% per annum are also included for the oil and gas prices
respectively. Observations of quantity and price of oil and gas are accommodated by
endogenising the positions of both the supply and demand schedules. The shift in the
demand schedule is achieved simply by endogenising the position of the export demand
schedule. The shift in the supply schedule is more complex. We exogenise the investment
paths of oil and gas sectors based on reasonable estimates of capital accumulation over the
period. The remaining movement required in the supply schedule is accommodated via
endogenous technical change.
The export markets for oil and gas are illustrated respectively in the following Figures 1 and
2. For the oil market, although the price of crude oil has increased, the export volume
declined. This is illustrated in Figure 1 where the supply curve has shifted to the left
indicating a fall in the supply of crude oil for exports despite an upward shift of demand
schedule (row 33), resulting in a higher price. The decline in supply is brought about by the
fall in capital stocks (row 35) and the adverse all-factor augmenting technical change (row 26)
for the oil sector.
Figure 1: Changes to demand and supply for exports of oil
Export price
S
Px1
Px0
B
A
D
Export quantity
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In Figure 2, both the price and volume of exports increase for natural gas, but the increase in
volume is not commensurate with the large observed increase in price. Therefore we see the
supply curve shifted leftwards with a very large shift of the demand schedule (row 34). The
level of capital stocks was high previously due to earlier high investment in the gas sector but
it has subsequently declined (row 36).
Figure 2: Changes to demand and supply for exports of gas
Export price
S
B
Px1
Px0
A
D
Export quantity
An implication of this approach is the productivity decline observed in the earlier steps is
reduced and redistributed (row 25). With the relative large productivity decline estimated for
oil (row 26), the remaining sectors must experience an improvement in productivity to
maintain the average estimated in earlier step. However, our estimate of the average
productivity decline has also reduced. This is because we have now estimated a smaller
capital stock in oil and gas (rows 35 and 36), and oil and gas account for around 70% of
Brunei’s total capital stock. Less capital stock for a given set of shocks to GDP and
employment implies a productivity gain, and the productivity loss observed up to Step 5 is
now reduced (row 28). The contribution of technical change to GDP is now -3.15%. The
decline in the capital to labour ratio also has the impact of dampening the increase in real
wages (row 17).
16
An interesting implication of the shocks is that the effective increase in exports (as compared
to the result in the previous step) is accompanied by an increase in investment (row 2). With
GDP and its other expenditure components all tied down, the increase in both of the
remaining components of expenditure is counterintuitive. This is a compositional effect. The
change in real GDP is the value-weighted sum of the changes in its real expenditure
components. The very large increase in prices for oil and gas (rows 9 and 10) means that the
value share of exports in GDP increases from just 20% in the final year of Step 5 to 48% in
the final year of Step 6. Furthermore, the decrease in real wages reduces the price of
activities related to government expenditure, which tend to be labour intensive. These factors
lead to a downward adjustment in the value share of government expenditure from 57% after
Step 5 to just 39%. This has the effect of reducing the share-weighted contribution to GDP of
the very large increase in real government expenditure.
Step 7: Terms of trade
In the final step, to target the terms of trade change based on historical data, this variable is
exogenised by swapping with the variable determining local preferences for imported
varieties. The shock to the terms of trade 9.75% per annum represents a downward
adjustment from the terms of trade result in Step 6. To achieve this requires an increase in
preferences for imported varieties which devalues the currency (row 15) and reduces real
wages (row 17).
The change in investment from Step 6 to Step 7 is again counterintuitive, when we consider
that none of the real macro expenditure aggregates or GDP has changed.
Again, the
explanation is in the value shares. The devaluation makes imports more expensive when
measured in domestic currency, increasing their negative share in GDP as compared to Step 6.
The reduction in real wages decreases the value share of government expenditure.
Reductions in these shares reduce the contribution of the large real increases in imports and
government expenditure to GDP, thereby allocating a larger residual to investment.
With all observed major macro aggregates now in place, we have a complete picture of
economic growth in Brunei from 2005 to 2011. The final result for investment, which has
remained endogenous throughout the simulation, did not accord as closely with official
statistics in the national accounts and was lower than expected. There are several factors
contributing to this. The national accounts contain statistical discrepancy in the GDP
17
expenditure approach as a reconciliation item to match the GDP by production approach
(DEPD 2011a, p. 60). In the computation of the real shocks in percentage change for the
simulation, GDP in constant prices are used and this statistical discrepancy is distributed by
share of the expenditure aggregates.
There are also differences between the expenditure aggregates shares in GDP in the national
accounts and the IO database for the base year 2005 as pointed out in a report (DEPD 2011b,
p. 36). The published input-output table for 2005 with lower investment to GDP ratio than the
national accounts is however accepted, since better sources of information were utilised and
therefore is used as the starting point to build a more detailed and updated database for the
period 2005-2011. Investment or capital formation growth rate is determined as a residual by
putting in the growth rates of other expenditure aggregates and real GDP in this simulation.
Step 7 completes the historical simulation whereby it is observed that the Brunei economy is
characterised by low real economic growth, low capital growth, low investment by share of
GDP, high consumption, high imports and declining exports, high real wage, real currency
appreciation, improvement in terms of trade and a small technological improvement of 5.87%
over the period of 2005 to 2011 or an average rate of 1% per year as shown in the last column
in Table 2.
18
Table 2: Step-wise development of the historical simulation for 2005-2011
Description
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
Step 7
Av.
Annual
Observed economic variables
Percentage changes between 2005 and 2011
1
2
3
4
5
6
7
8
9
10
11
12
13
Real GDP
Real investment
Real private consumption
Real public consumption
Real imports
Real exports
Oil export volume
Gas export volume
Oil export price
Gas export price
Import price c.i.f.
Number of households
Aggregate employment
9.42
22.41
8.86
0.00
8.40
10.26
9.09
9.20
-0.80
-1.23
0.00
12.95
9.86
5.65
17.67
4.93
0.00
5.40
5.81
5.01
5.08
-0.45
-0.69
0.00
12.95
9.86
5.65
10.79
28.59
0.00
15.79
0.62
3.03
2.96
-0.27
-0.41
0.00
12.95
9.86
5.65
-22.38
28.59
46.57
45.85
-18.58
-6.35
-6.14
0.60
0.90
0.00
12.95
9.86
5.65
-19.89
28.59
46.57
55.37
-20.36
-7.88
-7.51
0.76
1.10
-3.95
12.95
9.86
5.65
-16.06
28.59
46.57
55.37
-3.55
-19.65
0.78
52.70
120.89
-3.95
12.95
9.86
5.65
1.19
28.59
46.57
55.37
-3.55
-19.65
0.78
52.70
120.89
-3.95
12.95
9.86
0.92
0.20
4.28
6.58
7.62
-0.60
-3.58
0.13
7.31
14.12
-0.67
2.05
1.58
14
15
16
Other macroeconomic indicators
Aggregate capital stock
Real devaluation
Terms of trade
10.94
9.12
-1.33
10.52
5.75
-0.79
10.25
-7.64
0.19
11.45
-63.65
4.16
20.42
-69.49
8.80
11.46
-58.59
79.83
11.05
-49.61
74.75
1.76
-10.79
9.75
17
18
19
20
21
22
23
Price ratios
Labour to CPI (real wage)
Capital to investment (rate of return)
Cost of capital
Investment price to GNE price
Consumption price to GNE price
Govt consum. price to GNE price
GDP to primary factors
-17.67
2.90
2.34
3.73
5.00
-7.69
0.16
-14.89
-2.34
0.72
2.46
3.53
-5.26
3.49
-7.99
-8.97
-2.28
-1.68
3.68
-3.74
4.80
182.58
-43.85
-42.37
-17.39
-35.91
43.94
-1.25
237.02
-52.48
-50.56
-18.04
-41.99
52.25
0.62
172.84
-10.58
-26.81
-11.64
-31.58
37.10
-3.14
144.70
1.71
-20.63
-8.14
-26.92
31.19
-3.28
16.08
0.28
-3.78
-1.41
-5.09
4.63
-0.55
-17.67
0.00
-14.89
3.32
-7.99
3.30
182.58
2.65
237.02
6.05
172.84
-4.19
144.70
-5.87
16.08
-1.00
0.00
3.32
3.30
2.65
6.05
19.24
20.98
3.22
0.00
3.32
3.30
2.65
6.05
2.24
2.91
0.48
0.00
-2.74
-2.70
-2.24
-5.10
-3.15
-3.17
-0.53
0.00
0.00
31.47
-1.83
-10.91
-20.34
-21.84
-4.02
0.00
0.00
0.00
0.00
0.00
11.13
11.10
25.94
25.60
0.00
0.00
0.00
0.00
0.00
0.00
10.58
10.57
19.29
19.10
0.00
0.00
0.00
0.00
0.00
0.00
8.98
8.91
0.76
0.02
0.00
46.57
0.00
0.00
0.00
0.00
4.76
4.63
-50.50
-51.86
0.00
46.57
38.77
0.00
0.00
0.00
9.31
9.08
-49.98
-51.60
0.00
46.57
64.59
799.40
778.59
2972.76
-0.82
5.90
-69.60
-21.72
0.00
46.57
123.74
299.94
1871.34
6771.72
-0.83
5.90
-69.60
-21.72
93.32
6.58
14.36
25.99
64.36
102.39
-0.14
0.96
-18.00
-4.00
11.61
24
25
26
Structural variables
27
28
29
30
31
32
33
34
35
36
37
38
39
19
Corresponding variables
Overall wage shifter
All factor augmenting tech change
(non-mining sectors)
All factor augmenting tech change in
oil sector
All factor augmenting tech change in
gas sector
Contribution of technical change to
GDP
Average propensity to consume out of
GDP
Overall govt demand shifter
Investment slack variable
General exports shifter
Quantity shifter in oil export demand
Quantity shifter in gas export demand
Capital stock in oil sector
Capital stock in gas sector
Investment in oil sector
Investment in gas sector
Uniform import/domestic twist
4. Conclusions
Using a step-by-step approach to an historical simulation, this study has uncovered several
interesting characteristics of the Brunei economy for the period 2005 to 2011. The step-wise
implementation of the shocks has enabled us to attribute structural shifts in the economy to
specific causes. Some examples of these attributions are: high wage growth is attributed
mainly to growth in the government sector; modest productivity growth in non-mining
sectors is attributed to an increase in non-mining output relative to the capital stock; and the
final result for the terms of trade is in fact lower than it would have been based on the
increases in oil and gas prices, revealing an increase in preferences for imports.
We find that with the large increase in domestic absorption relative to the low growth of GDP,
the Brunei economy experienced high real wage (average 16.1% per annum), increased
domestic price level (averaging 3.4% per annum) and strong real appreciation in currency
(average 10.8% per annum). These factors contributed to the high import growth and
declining exports observed as producers faced increased costs from both the primary factors
as well as intermediate inputs. The real appreciation of Brunei’s currency had impeded
Brunei economy’s competitiveness in exports as a downward export trend was observed over
the period under study. With the dwindling oil and gas reserves and therefore limited future
hydrocarbon exports, it is important to address the challenge of expanding the exports of
other non-oil and gas sectors.
The government is managing the production of its oil and gas in order to extend their
productive life from the existing fields while exploring for new ones. There remains much
uncertainty as to when the production from any new discovery can come online and also the
timing when productions are stopped for maintenance. The government is continuing its
efforts to broaden its economic base and has identified several industries for development.
These include downstream petrochemicals, tourism, Islamic financial services and production
of halal products, information and communication technology services (ADB 2013, p. 217).
With the limitation of its domestic market size, the government is focusing on exportorientated industries.
The findings of this study are in line with the current situation. In aggregate, it appears that
Brunei economy has progressive technological change for the period 2005-2011, however the
20
technical regression in the oil and gas sectors cannot be overlooked due to their significant
impact on the economy. Clarifying the situation with respect to mining capital stocks is an
important area for further investigation. With a small and weak private sector, the
government remains the key driver in the economy. Without strong investment growth,
government spending will continue to be the source of economic stimulus, crowding out the
private sector through high wages and other costs, and the associated loss of competitiveness.
This poses a challenge to the growth of the private sector moving forward into the posthydrocarbon era.
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