foreign-domestic substitution, import ......keywords: foreign-domestic substitution, armington...
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FOREIGN-DOMESTIC SUBSTITUTION, IMPORT PENETRATION
AND CGE MODELLING
Kenneth Clements (University of Western Australia), Marc Jim Mariano (KPMG Economics), and
George Verikios (KPMG Economics and Griffith University)
2020-05
1
FOREIGN-DOMESTIC SUBSTITUTION, IMPORT PENETRATION
AND CGE MODELLING
Kenneth Clements (University of Western Australia), Marc Jim Mariano (KPMG Economics), and
George Verikios (KPMG Economics and Griffith University)
Abstract
Foreign-domestic substitution elasticities (the so-called βArmington elasticitiesβ) determine the
degree of competitiveness in demand between similar products produced in different countries
and are key parameters in a variety of numerical models of international trade. Armington
elasticities are part of the explanation of the large increases in market shares of foreign products
relative to locally produced ones in Australia, for example. The existing literature provides only
limited evidence on these elasticities for Australia with the most disaggregated produced some
time ago by Alaouze et al. (1977).
This paper provides up-to-date parametric estimates of Armington elasticities for Australia with
a reasonable degree of sectoral disaggregation. We use 22-years of data for 20 types of
merchandise commodities, using OLS, panel and restricted-panel approaches. Our estimates
range from 0.30 to 2.26, with higher elasticities for Transport and Equipment products and
lower ones for Energy and. We illustrate the use of our elasticities with a trade-policy
simulation using a computable generable equilibrium model of the Australian economy. We
analyse the sensitivity of the results to the Armington elasticities by also using those estimated
by Alaouze et al. (1977). We find an overestimation of economic effects when using the old
Armington values.
Keywords: Foreign-domestic substitution, Armington elasticities, CGE analysis, International
trade, Tariff policy
1. Introduction
The substitutability between foreign and domestic goods is a key driver (in part at least) of
the effects of policy change in open economies -- the impacts and welfare gains of trade
liberalisation, import tariffs adjustments, exchange-rate adjustments, lowering restrictions on
capital inflows, among others.1 The sensitivity of the allocation of expenditure on these source-
1 The elasticity of substitution between capital and labour plays a similarly important role in empirical analysis of policy changes. For a
comprehensive survey on estimates of the elasticity of capital-labour substitution and its importance in empirical analysis see Chirinko (2008).
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specific goods to their relative prices is measured by the substitution elasticity. To illustrate the
potential role for substitution between foreign and domestic goods, consider Figure 1, which
shows for Australia the share of domestic goods in the sales of beverage and tobacco products
and the corresponding price ratio. Over the last two decades the relative price of the domestic
variety has approximately doubled, while the sales share has fallen by 6+ percentage points.
Prima facie, this indicates a substitution elasticity greater than unity. One of the objectives of
this paper is to estimate the substitution elasticity for this and other products.
A prominent application of foreign-domestic substitution is in computable general
equilibrium (CGE) models where the demand for domestically produced and imported goods
is typically based on an Armington (1969) structure. Here, the domestic variety of a particular
good is distinguished from its imported counterpart and the two are treated as imperfect
substitutes within a constant-elasticity-of-substitution (CES) framework. Within this literature,
the elasticity of substitution has come to be known as the βArmington elasticityβ. The
Armington approach of product differentiation captures the frequently observed phenomenon
of cross-hauling where a country simultaneously imports and exports the same commodities.
In CGE models, the demands for domestic and imported varieties are typically aggregated to
form composite demands for goods, with the composites then governed by a conventional
demand system such as LES or AIDS. The choice of the value of the Armington elasticities is
important as they can greatly influence the modelling results (Cassoni and Flores, 2008; Zhang,
2006; McDaniel and Balistreri, 2003). For leading examples of applications of the Armington
approach, see Dixon et al. (2016), Zhai (2008), Lloyd and Zhang (2006), Zhang and Verikios
(2003), Shiells and Reinert (1993), and Bandara (1991).
The existing literature provides estimates of Armington elasticities that vary widely across
countries (Bajzik et al., 2019; Olekseyuk and Schurenberg-Frosch, 2016). In Australia, the
availability of estimates is limited, with the most disaggregated produced some time ago by
Alaouze et al. (1977). More recent work by Zhang and Verikios (2006) applied a non-
parametric approach which derived the elasticities from successive GTAP databases, a multi-
country CGE model. The disparate values of previous estimates imply that one cannot simply
take the numbers from the literature and apply them to other countries. This paper provides up-
to-date parametric estimates of these elasticities with a reasonable degree of sectoral
disaggregation. We also use an Australian CGE model to demonstrate the sensitivity of key
results to the values of the Armington elasticity.
The remainder of the paper is laid out as follows. The next section presents the theoretical
underpinnings of the CES estimation framework. We describe the data sources and provide
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summary statistics of the data in Section 3 with some preliminary analysis of the data in Section
4. The estimation results are presented in Section 5 and the CGE simulated are in Section 6.
The final section contains some concluding remarks.
2. The CES Framework Suppose the consumption of good π in the domestic economy is a composite of locally
produced and a number of imported varieties of that good. Armington (1969) introduced a
manageable way to deal with this type of environment with the demand for goods distinguished
by place of origin. In this framework, goods of the same type from different sources are
imperfect substitutes. When foreign and domestic goods are perfect substitutes, the
corresponding import demand is the excess demand, but when the two varieties are less-than-
perfect substitutes, the conventional demand function for the foreign good is equivalent to the
import demand function. This section sets out the Armington approach of CES aggregation
over imported and domestic varieties of a particular good. The Armington approach is an
attractively simple way of reducing a large-scale problem to something more manageable,
which accounts for its popularity.
Suppose that each of C countries supply a different variety of each of the n goods to
consumers in the country under consideration. One of the C countries is the domestic one. There
are now n goods from each of C countries, so the total number consumed is π Γ πΆ. For example,
if π = 12 goods and πΆ = 150 countries, there is a total of ππΆ = 1,800 goods from all sources
of supply. As it is not feasible to analyse a problem of this dimension in an unrestricted manner,
a substantial structuring of the analysis is needed in the form of restrictions on behaviour. It is
natural to group goods of the same variety, that is, to place within the same group the varieties
of same basic good supplied by the various countries. It could then be reasonably assumed that
goods belonging to the same group are closer substitutes than goods from different groups. For
example, French and Australian wines (members of the wine group) are likely to be a closer
substitutes than smart phones from China (the electronic group) and socks from Bangladesh
(clothing).
The above discussion is consistent with the case of block-independent preferences whereby
utility is additive in the n groups of goods:
π’(π1, β― , ππ) = β π’π(ππ)ππ=1 , (1)
where π’π(β) is the sub-utility function of group i and ππ is the vector of quantities belonging to
group i. Within each group, goods can interact fully in a marginal utility sense, while there are
4
no between-group effects. In other words, there is preference independence with respect to
groups of goods. Let π’π(β) in (1) be CES:
π’π(ππ) = (β πΌπππβπ
πβπΊπ)
β1
π, πΌπ > 0, π β₯ β1, π β 0.
(2)
Here, πΊπ is the set of varieties of good i. The elasticity of substitution is π =1
1+π> 0 and the
corresponding conditional demands are:
ππ =πππΌπ
πππβπ
β πΌππππ
1βππβπΊπ
, π β πΊπ , (3)
where ππ is the quantity demanded of π β πΊπ , ππ = β ππ πβπΊπππ is total expenditure on group i
and ππ is the price of π β πΊπ . When prices are constant, consumption is proportional to group
expenditure, which means conditional income elasticities are all unity. The CES can
accommodate a wide range of behaviour, from approaching no substitution ( π β 0, the
Leontief case), to Cobb-Douglas (π β 1), to perfect substitution (π β β, linear utility).
Equation (3) can be expressed more compactly by defining the group price index as
ππ = (β πΌππππ
1βππβπΊπ
)1
1βπ. The denominator in (3), β πΌππππ
1βππβπΊπ
, can then be replaced with
ππ1βπ to give
ππ = πΌππ ππ
ππ(
ππ
ππ)
βπ, π β πΊπ .
(4)
This makes clear the dependence of consumption on real expenditure on the group, ππ
ππ, and the
own-
relative price, ππ
ππ.
A frequent practice in applications of the Armington approach is to specify each group as
having just two goods, the foreign and domestically produced varieties of the good. If goods 1
and 2 are the domestic and foreign varieties of a certain good i with quantities demanded ππ1
and ππ2, then we can write equation (4) as
ππ1 = (πΌπ
1)ππππ
ππ(
ππ1
ππ)
βππ
, ππ2 = (πΌπ
2)ππππ
ππ(
ππ2
ππ)
βππ
,
where ππ1 and ππ
2 are the corresponding prices. Taking logs then gives the relative demand for
the goods:
πππ (ππ
1
ππ2) = π½π β ππ πππ (
ππ1
ππ2), (5)
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where π½π = ππ log (πΌπ
1
πΌπ2) is an intercept. This equation implies that for good i, a one-percent
increase in the price of the domestic variety, relative to the foreign one, leads to a fall in the
volume of domestic sales relative to foreign sales of ππ percent. Equation (5) can be estimated
with time-series data by single-equation OLS to give an estimate of the elasticity of substitution.
3. The Data The data used in this study refer to Australia and were assembled from the supply-use
tables (SUT), international trade statistics (ITS) and national accounts produced by the
Australian Bureau of Statistics (ABS). The supply-use tables provide annual data on the value
of imported and domestic goods by product and industry disaggregation. The producer price
indices of domestically produced goods are published quarterly in the national accounts. The
international trade statistics provides quarterly data on the value and price indexes of exported
and imported goods. A number of transformations were applied to the ABS data to produce a
data set of π = 20 goods in π = 23 years.
1. Commodity aggregation. The SUT data has 114 commodities and these include
both merchandise and non-merchandise goods. We only consider merchandise
goods since non-merchandise goods mostly do not have an import component, and
domestic services are mostly complimentary rather than substitutable with
imported services. The values of the 60 merchandise goods in the SUT were
aggregated into 20 broad commodity groups (see Appendix 1 for details).
Although the data on price indices have a slightly different classification to that of
the values data, the majority of the sectors are similar and were matched
accordingly.
2. Time aggregation. The price indices are quarterly, while the value data are
published on an annual basis. We converted the quarterly prices into annual data
using value-share-weighted average prices. The data span from 1995 to 2017.
3. Domestic sales. The differences between domestic production and export sales
were used to define domestic sales of a given product.
4. Scale deflation. All values were placed on a per capita basis by deflating by
population.
5. Quantities. Domestic and import quantities were derived by dividing nominal
values by the corresponding prices.
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Table 1 gives the means and standard deviations of the domestic shares, and log-changes
in the relative price and quantity. This table shows that the domestic shares of agricultural-
related products is higher compared to other commodities, with about 90% of total consumption
domestically produced. In particular, Australia imports only 4% of the total demand for Meat
products (item 1 of Table 1), while less than 10% of Dairy and eggs (item 2) is imported. The
import shares of other agri-forest products [e.g., Fish (3), Wood (11), Paper (12) and Furniture
(13)] and mining products [Non-metals (14), Iron and steel (15) and Non-ferrous metals (16)]
are higher, roughly ranging between 10 and 20%. Australia does not manufacture its own cars
but produces vehicle parts and transport equipment, so that there are high import shares for the
relevant products (items 17-19). The import share of Clothing and footwear (10) is also higher
than most others at about 30%.
Figure 2 contains histograms for the relative price and quantity log-changes. In most cases,
the quantity changes fall within the range [-5% to 0%], while price changes fall within [0% to
5%]. However, quantities are more volatile than prices as the standard deviations are 14.2%
and 10.6%, respectively. This larger dispersion of quantities is also evident from Figure 3,
which is a plot of the changes for each good in each year. Additionally, there are substantial
βspikesβ in the quantities for (a) Non-ferrous metals and (b) Transport equipment; the higher
dispersion for these sectors is also clear from the last column of Table 1. Appendices 2 and 3
contain time-series plots of the domestic share of each good and its relative price, and price-
quantity plots. For most of the items these reveal evidence of a negative association between
the two variables.
A scatter plot of relative price against relative quantity log-changes for all 20 goods
combined is given in Figure 4. It can be seen that while there is considerable variability, there
is a distinct negative relation between quantity and price changes: the regression line is
negatively sloped with a coefficient of -0.75. Thus, if we wished to combine all goods together,
an initial rough-and-ready estimate of the common elasticity of substitution π is 0.8. As in
Figure 3, the quantities of commodity 16 (Non-ferrous metal) and commodity 19 (Transport
equipment) are more dispersed relative to the others. All in all, there is preliminary evidence of
substitution between domestic and foreign sources of supply.
4. Changes in Market Shares over Two Decades
International trade has become a more prominent feature of the Australian economy over
the last two decades or more, with substantial increases in exports and imports relative to GDP.
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Thus, we should expect to see falls in many domestic market shares and corresponding rises in
the foreign components. In this section, we use our data in a descriptive analysis of this process,
identify sectors with large falls in competitiveness and point to approximate determinants.
For a certain commodity, let ππ be the price of the domestically produced variety and ππ
the corresponding quantity, so that ππππ is expenditure. Similarly, let ππ , ππ and ππππ be the
foreign counterparts. Then, total expenditure on the good is ππππ + ππππ = π and the budget
shares of the two varieties are π€ =ππππ
π, 1 β π€ =
ππππ
π. Define Divisia price and volume
indexes as
π(πππ π) = π€π(πππ ππ) + (1 β π€)π(πππ ππ),
π(πππ π) = π€π(πππ ππ) + (1 β π€)π(πππ ππ).
The change in total expenditure is then:
π(log π) = π(log π) + π(log π).
(6)
Using (6), the change in the domestic share can be expressed as
π(πππ π€) = π(πππ ππ) + π(πππ ππ) β π(πππ π) = π (πππππ
π) + π (πππ
ππ
π),
where π (logππ
π) = π(log ππ) β π(log π); π (log
ππ
π) = π(log ππ) β π(log π). Thus, the
change in the share is made up of the change in the relative price, π (logππ
π), and the change in
the relative quantity, π (logππ
π). The changes in the relative price of domestic goods and the
corresponding quantity are
π (πππππ
π) = (1 β π€){π(πππ ππ) β π(πππ ππ)},
π (πππππ
π) = (1 β π€){π(πππ ππ) β π(πππ ππ)}.
Thus, the change in the domestic share becomes
π(log π€) = (1 β π€) {π (logππ
ππ) + π (log
ππ
ππ)}. (7)
For discrete changes from year t-s to t, define the log-change operator as π·π₯π‘ =
log π₯π‘ β log π₯π‘βπ = logπ₯π‘
π₯π‘βπ , π₯ > 0, 0 < π < π‘. A discrete approximation to equation (7) for
the transition from year t-s to t is
π·π€π‘ β (1 β οΏ½ΜοΏ½) {π· (ππ‘
π
ππ‘π) + π· (
ππ‘π
ππ‘π)},
(8)
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where οΏ½ΜοΏ½ is some value of the share lying between t-s and t.
Table 2 summarises the evolution of domestic market shares for the 20 commodities over
the 23-year period (for convenience, column 2 is reproduced from Table 1). Over the last two
decades, all 20 domestic shares have fallen (column 5). The most substantial fall is the 27
percentage points for motor vehicles (item 18). From column 6, the log-change (Γ 100) of this
share is -51.7, which implies a percentage change of 100(πβ51.7 100β β 1) = β40%.
To apply the decomposition in (8), we interpret οΏ½ΜοΏ½ as the sample mean share and use the
logarithmic changes from 1995 to 2017. Table 3 gives the results. As can be seen from columns
4 and 5, in the vast majority of cases, quantity changes exceed price changes in terms of absolute
values, consistent with the higher volatility of quantities mentioned above. Additionally, in
about one-half of cases, price and quantity changes are of the opposite sign. From equation (8),
multiplying the price and quantity changes by 1 β οΏ½ΜοΏ½ gives the contribution of each to the
change in the share, which are given in columns 6 and 7. Taking motor vehicles (commodity
18) as an example, the log-change (Γ 100) of its domestic share is -51.7; this is made up of an
increase in the domestic relative price of 4.75 less the quantity fall of 53.21. As β51.7 β 4.8 +
53.2 = β3.3, there is the approximation error of somewhat more than 3 percent in this case.
Columns 6 and 7 show that in the majority of cases, the quantity component substantially
exceeds the price component, while the approximation errors of column 8 are modest.
5. Estimates of the Elasticity of Substitution
This section estimates the elasticity of substitution for each sector by regressing log (ππ
1
ππ2)
on log (ππ
1
ππ2) , where ππ
1 is the volume of domestically sourced consumption of good i
(π = 1, β― ,20), ππ2 is the volume of the imported variety and the
ππ1
ππ2 is the corresponding relative
price.
There are three sets of estimates. The first set involves single-equation OLS estimates for
each of the 20 goods. Table 4 presents the estimated elasticities with levels and changes. Most
of the substitution elasticities are significant and positive, particularly for the change
formulation, where they range from 0.14 for Motor Vehicles to 2.06 for Transport Equipment,
and the majority of the significant estimates are either less than unity or very close to it. There
appears to be significant autocorrelation when levels are used, with low Durbin-Watson
statistics, but this is much less of a problem for changes. From the last row of Table 4, on the
basis of a Wald test, we fail to reject a common π for changes (but not for levels).
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Our second pass uses a panel approach, which has two advantages. First, it allows for the
easy incorporation of a time effect for each year that affects all goods simultaneously such as a
transport disruption that delays the delivery of all imported goods. A second advantage is that
the panel approach facilitates the testing of hypotheses covering multiple sectors, as will be
demonstrated. Table 5 contains the results. Now, the elasticities tend to be more significant than
before; they are also larger, ranging from 0.355 to 2.246. The Wald test rejects a common π for
both levels and changes.
Next, we test if groups of commodities share the same elasticity, rather than it being the
same every commodity individually. If true, this could represent a considerable simplification.
Table 6 presents the results for when the 20 commodities form 7 broad groups. Results show
that only within the βTransport, machinery and equipmentβ group are the elasticities statistically
different. Table 7 presents the estimates with the group-pattern restrictions imposed. All the
substitution elasticities are positive and are mostly significant. For changes, the average
elasticity is 1.046.
A summary of the three sets of elasticities (for changes) is presented in Table 8 and Figure
5, where the name βPanel Iβ (βPanel IIβ) refers to the case in which 20 (9) sectors are
distinguished. As can be seen, on average the substitution elasticities in Panel II are the highest,
closely followed by Panel I. At the same time, however, the panel estimates are not too different
to the unrestricted OLS counterparts.
Finally, we return to the case of Tobacco and Beverages for a brief examination of how the
models track the data. Figure 6 plots the actual values of the relative quantity and price, as well
as two sets of fitted values, the OLS and Panel I values. This reveals that the fitted values are
fairly close to actual values. The Panel I estimates are between actual and the OLS fitted values
in the first half of the period, and thereafter the values seem to converge.
6. Application to CGE modelling
Here we simulate a discrete policy change using a CGE model of Australia to test the
sensitivity of model results using two sets of Armington elasticities. The policy change is the
complete removal of tariffs on all imported goods. We conduct this simulation twice with
differences only in the Armington elasticities β one simulation uses our new elasticity estimates
and the other uses older estimates from the literature. With this simulation design any
differences in the results between the two simulations are purely due to the differences in the
elasticities.
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The CGE model
CGE models provide a comprehensive tool for analysing major changes in the economy or
changes that affect many parts of the economy simultaneously. They represent the economy as
a complex system of interrelated activities by different agents. Figure 7 is a stylised
representation of the interrelationships represented in the model we apply here (Verikios et al.,
2020).
The model is represented by equations that specify behavioural and market interactions of
agents such as households, firms, the government and foreigners. These equations are based
on economic theory. Key theoretical features of the model include:
β’ optimising behaviour by households and businesses in the context of competitive
markets with explicit resource constraints and budget constraints;
β’ the price mechanism operates to clear markets for goods and capital;
β’ the labour market operates with a degree of friction so that some labour is always
unemployed but the rate of unemployment is held constant in the long-run; and
β’ marginal costs are equal to marginal revenues in all activities.
The model combines data from input-output tables, labour force surveys and other sources
with the model theory to quantify sophisticated behavioural responses such as:
β’ price and wage adjustments driven by resource constraints;
β’ household spending and government spending and taxing adjustments driven by
budget constraints; and
β’ allowance for input substitution possibilities in production (e.g., allowing the
combination of labour, capital, and other inputs required to produce a particular output
to vary in response to relative price changes).
The strength of behavioural responses are governed by expenditure and price elasticities β
one of which is the Armington elasticity assigned to each commodity. These elasticities affect
model results by influencing the magnitude of demand responses by agents to changes in
domestic and imported commodity prices. A high (low) Armington elasticity value means that
commodity users are more (less) sensitive to changes in domestic and imported prices.
Armington specification in the CGE model
The model assumes that domestically-produced and imported goods are imperfectly
substitutable due to their heterogeneous nature. This assumption applies for all commodity
users - households, firms and the government. The degree of heterogeneity will vary by
commodity and source and is reflected by the elasticity of substitution: higher elasticity values
11
imply less heterogeneity, lower values imply greater heterogeneity. In this application, we
employ new estimates of the Armington elasticities for Australia.
Each commodity available to users is a constant-elasticity-of-substitution aggregate of the
domestically-produced and imported varieties. The utility-maximising problem of the
representative consumer for a given level of total expenditure for good i ( )iY is
( )1 1 1
1
i
i i i
i i
i i ii iMax U D M
β β β = + β
(9)
subject to M
i i i i i
DY p D p M= + i,
(10)
where i
U is the utility from consuming good i of the representative consumer; i
D and i
M is
demand for domestic and imported goods; i
is the elasticity of substitution between domestic
and imported good i; i
is a distribution parameter, and D
ip M
ip are the prices of domestic and
imported good i.
Using the objective function (9) and budget constraint (10), the first-order conditions for
the least-cost combination of domestic and imported good i are
1i iD
i
M
i
i i
i i
M P
D P
β=
.
(11)
In log form, equation (11) coincides with equation (5) above, i.e., there is a consistency of
the CES demand specification between the econometric model in which the Armington
parameter is being estimated, and the CGE model in which the elasticity is being used. As
emphasized by Shoven and Whalley (1992) and McKitrick (1998) this consistency of functional
form provides a significant bearing on the empirical validity of the CGE model in which the
parameter is used as this can greatly influence the modelling results. According to (11), a one
per cent increase in the price ratio of domestic and imported good i will cause a i
per cent
increase in the ratio of imported and domestic good i.
Simulation design
The simulation is the complete removal of all tariffs levied on imported products entering
Australia. In the modelβs initial database the tariff rate of each commodity is implicitly captured
by the ratio of tariff revenue to the c.i.f. value. The initial tariff rates are summarised in Table
12
9. Note that there are 117 sectors in the model but only 48 commodities are subject to tariffs in
the initial database.
The model is dynamic so we run the model twice over a 50-year horizon to implement the
removal of tariffs. This comprises a baseline simulation and a counterfactual simulation.
1. A baseline simulation that includes the tariffs. Here the economy begins in a steady-
state and moves along a balanced growth path over the forecast period. The economy
reaches a new-steady state at the end of the simulation where the capital-labour ratio
stops changing. The balanced growth path is represented by 3.2% annual growth in
most quantities and 2% annual growth in consumer prices. The unemployment rate is
fixed as is the current account to GDP ratio. The second assumption has the effect of
stabilising the ratio of net foreign liabilities to GDP over the forecast period. We also
apply a slow reduction in the government budget deficit to GDP ratio, which is
accommodated by an endogenous average personal income tax rate.
2. A counterfactual simulation that generates a counterfactual economy without the
tariffs. Here the model closure is the same as the baseline with the only difference
being the tariff removal. The tariff rates are reduced annually by 20% of their initial
value over the first 5 years of the 50-year simulation period. The tariff cut starts in
2020-21 (year 2) such that all imported commodities will be tariff-free at the
beginning of 2025-26 (year 7).
The differences in values between the baseline and counterfactual simulations quantify the
economic impact of the tariff shock.
For our sensitivity analysis, we first simulate the effect of the policy shock using an older
estimates of the Armington elasticities taken from Alaouze et al. (1977) (hereafter referred to
as Simulation 1). Then, we run a similar simulation but using our new estimates of the
Armington elasticities (hereafter referred to as Simulation 2). Table 10 summarises the value
of the Armington elasticities applied in each simulation. There are two things to note about
these parameters. First, services are not assigned with any Armington elasticity since these non-
merchandise goods are mostly complimentary rather than substitutable with imported services.
Second, we adopt the Armington values from Alaouze et al. (1977) for commodities where we
lack new estimates due to lack of price and quantity data (e.g., primary agriculture like livestock
and raw minerals).
Simulation Results
13
In presenting the results, we first explain the effects of the tariff removal using the results
of Simulation 1 and then later compare this with the results of Simulation 2. Figures 8 to 11
show the macro and sectoral effects: the results of Simulation 1 are presented as solid lines in
these figures. Key results are as follows.
1. The direct impact of the tariff cut is a reduction in the purchaserβs price of imported
products relative to their domestic counterparts. This price effect induces substitution
in favour of the relatively cheaper imported products. Figure 8 shows a large positive
deviation in real imports relative to baseline.
2. The fall in the cost of imports reduced domestic production costs relative to the
foreign producers. This means exports are now cheaper and demand is therefore
higher. This is indicated in Figure 8 by the large positive deviation in real exports. As
exporters are assumed to face slightly downward-sloping demand curves, higher
export demand implies lower export prices. Thus, the terms of trade fall slightly (see
Figure 10) as f.o.b. export prices decrease relative to c.i.f. import prices.
3. The substitution of imports for domestic commodities creates pressure for the trade
balance and the current account to move towards deficit. However, the ratio of the
current account to GDP is fixed. To prevent the movement towards deficit there is a
rise in the household saving rate. A higher saving rate will generally mean lower
consumption and higher exports. Figure 8 shows that consumption falls in the short
run but recovers in the long run.
4. As investment is import intensive in Australia, the fall in the cost of imports decreases
the price of investment, which causes a short run increase in rates of return (Figure
10). Higher rates of return encourages more investment (Figure 8) leading to a larger
capital stock (Figure 9). In the long run, investment growth stabilises as rates of
return fall.
5. The removal of tariffs causes a general reduction in consumer prices and this increases
pre-tax real wage rates for workers (Figure 10) leading to a fall in labour demand for
some industries. Nevertheless, in the short run there is also a temporary reduction in
post-tax real wage rates as the personal income tax rate must rise to replace the lost
tariff revenue (approximately $3.7 billion). The short run fall in post-tax real wage
rates reduces labour supply and with fixed unemployment rates labour demand and
employment fall in the short run. The post-tax real wage gradually recovers in the
long run as capital per worker rises thus causing employment to also recover in the
long run.
14
6. Export-oriented industries such as Agriculture and Mining expand as more of their
output is sold overseas (Figure 11). Nonetheless, as the mining sector is a capital-
intensive sector its expansion in output is dampened by the fall in the rates of return.
The Wholesale and Retail trade industry benefits strongly from the tariff removal as
more of these services are required to facilitate the sales of non-services commodities.
Import-competing industries such as those in the Manufacturing sector contract due to
the substitution effects of the tariff removal on merchandise goods.
The results of Simulation 2 (which uses the new Armington elasticities) are shown as
dashed lines in Figures 8 - 11. We see that the CGE results are sensitive to the new estimates
of Armington elasticities. Although the pattern of effects are similar in both simulations, the
magnitude of results are different. At the macro level, the absolute deviations in real variables
are higher in Simulation 1 than in Simulation 2. As discussed in Section 4, there has been a
remarkable growth in the market shares of imported goods in the past two decades. This change
in market structure alters the way consumers respond to the relative price of domestic and
imported products. Our new estimates of Armington elasticities reveal that consumers are less
responsive to recent changes in the relative price of domestic and imported goods as indicated
by a lower average of the new elasticities (1.16) relative to the old elasticities (1.54).
Consequently, this generates smaller responses in quantity variables in Simulation 2 when the
new Armington elasticities are implemented. At the sectoral level, a noticeable result is in the
output deviation for Manufacturing. Simulation 1 has a bigger initial response to the tariff
removal relative to Simulation 2. That is, the old Armington elasticities generate a larger and
longer reduction in output while the new Armington elasticities generate a smaller and
temporary fall in output. This reflects less substitution of imported goods for domestically-
produced goods when the relative price of imported goods falls. There is also a substantial
difference in the output results for Mining. In the short run, Simulation 1 shows a larger increase
in Mining output relative to Simulation 2. In the long run, the output gain in Simulation 1 is
enough to outweigh the dampening effect on output of lower rates of return whereas this is not
the case for Simulation 2 where the Mining sector contracts due to the larger long run fall in
the rate of return.
6. Concluding comments The Armington elasticity (or the elasticity of substitution) measures the responsiveness of
commodity demand with respect to changes in the relative price of domestic and imported
15
varieties of a particular good. The current literature provides a number of estimates of these
elasticities that varies across countries indicating the spatial variability of foreign-domestic
substitution. In Australia, previous estimates were produced long ago with the most
comprehensive work done in the 1970s. A lot has changed in the sourcing of commodities sold
in the domestic market with greater influx of imported products for many commodities. These
changes in market shares change consumer behaviour over time and thereby previous estimates
of Armington elasticities may no longer be relevant. In this context, this paper contributes to
the existing literature in three aspects. First, we analyse the changes in the domestic market
shares of 20 types of merchandise commodities in Australia using a 22-year panel of data on
prices and quantities. We found that there are large falls in the domestic market shares of motor
vehicles, clothing and footwear, and furnishing products. This fall in competitiveness is
dominated by a larger fall in relative quantity changes than relative price changes between
domestic and imported products. Second, this paper provides up-to-date estimates of foreign-
domestic substitution elasticities for the 20 commodities. The demands for foreign-domestic
commodities are modelled as a system of CES demand equations with one equation for each
commodity type. Three sets of substitution elasticities were derived from three types of
estimation methods: an OLS approach, a panel approach, and a restricted panel approach; the
results are in broad agreement with each other. According to our estimates, the elasticity of
substitution ranges from 0.298 to 2.26. Transport and Equipment goods have a higher elasticity
that averages 1.55, while those for Energy and Mining products are lower (0.78). Third, we
demonstrate the role of Armington elasticities in generating robust results from a general-
equilibrium trade model. We run two simulations in a dynamic CGE model imposing the same
shock (i.e., complete removal of tariffs on all Australian commodities) but using two different
sets of Armington elasticity β one simulation uses our new estimates of Armington elasticities
and the other simulation uses older estimates from the literature. We find that the CGE results
are sensitive to the foreign-domestic substitution elasticities with an overestimation of
economic effects when using the old Armington estimates.
16
Table 1. Summary statistics.
Commodity
Domestic
sales shares
Relative prices (Pd/Pf)
(Log-changes)
Relative quantities (Qd/Qf)
(Log-changes)
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
1 Meat Products 95.61 1.290 -0.126 13.541 -4.037 16.641
2 Dairy Products and Egg 92.70 1.523 -0.526 12.182 -2.370 15.079
3 Fish Products 78.96 3.421 -1.361 10.938 -0.582 13.469
4 Other Food Products 85.72 4.013 -0.363 7.547 -3.988 6.901
5 Beverage and Tobacco 93.06 2.527 3.002 5.365 -7.975 8.858
6 Coal, Petroleum and Gas 74.16 8.420 0.257 26.578 -4.964 20.668
7 Basic Chemicals 66.53 3.680 0.040 10.005 -1.988 8.852
8 Pharmaceutical Products 60.76 5.762 1.739 4.091 -4.728 8.878
9 Textile and Leather 69.12 3.129 0.917 8.889 -2.050 10.411
10 Clothing and Footwear 71.00 7.076 -0.229 8.541 -5.185 9.143
11 Wood and Products 88.82 1.143 -1.183 6.052 0.696 10.290
12 Paper Products 83.98 2.561 0.761 8.218 -2.527 9.100
13 Furniture 80.72 7.413 -0.865 9.018 -6.343 10.111
14 Non Metals 79.34 2.325 0.608 9.212 -2.787 6.742
15 Iron Steel 80.47 2.122 -2.188 10.566 0.672 10.049
16 Non Ferrous Metals 80.84 5.732 -1.551 14.142 -4.217 30.868
17 Machinery Equipment 49.08 3.902 0.275 7.620 -2.978 7.907
18 Motor Vehicles and Parts 56.94 8.719 0.501 3.422 -5.617 8.046
19 Transport Equipment 55.00 6.146 -1.984 8.226 0.799 29.586
20 Other Manufactures 66.94 5.920 2.259 9.502 -6.991 10.402
Average 75.49 13.310 -0.001 1.356 -3.358 2.257
Note: All entries are x 100
17
Table 2. Domestic market shares.
Commodity Mean
domestic share
Share in Change
in share 1995-2017
Log-Change
in share 1995-2017 1995 2017
(1) (2) (3) (4) (5) (6)
1 Meat Products 95.61 97.57 94.15 -3.43 -3.57
2 Dairy Products and Egg 92.70 93.63 88.59 -5.03 -5.52
3 Fish Products 78.96 81.66 74.38 -7.28 -9.34
4 Other Food Products 85.72 90.59 78.70 -11.89 -14.06
5 Beverage and Tobacco 93.06 96.08 89.13 -6.95 -7.50
6 Coal, Petroleum and Gas 74.16 86.08 68.72 -17.37 -22.54
7 Basic Chemicals 66.53 68.05 58.11 -9.94 -15.79
8 Pharmaceutical Products 60.76 73.71 59.23 -14.48 -21.87
9 Textile and Leather 69.12 68.02 62.38 -5.64 -8.66
10 Clothing and Footwear 71.00 82.50 58.89 -23.61 -33.71
11 Wood and Products 88.82 88.20 87.04 -1.16 -1.32
12 Paper Products 83.98 85.15 79.55 -5.61 -6.81
13 Furniture 80.72 92.02 70.25 -21.77 -27.00
14 Non Metals 79.34 82.69 74.74 -7.96 -10.12
15 Iron Steel 80.47 82.59 77.26 -5.33 -6.67
16 Non Ferrous Metals 80.84 90.77 73.44 -17.33 -21.19
17 Machinery Equipment 49.08 58.20 43.45 -14.75 -29.23
18 Motor Vehicles and Parts 56.94 67.48 40.24 -27.24 -51.70
19 Transport Equipment 55.00 63.18 56.94 -6.24 -10.40
20 Other Manufactures 66.94 78.54 56.37 -22.16 -33.16
Mean 75.49 81.34 69.58 -11.76 -17.01
Std. dev. 13.31 11.44 14.94 7.62 12.88
Note: All entries are x 100
18
Table 3. Decomposition of domestic market shares.
Commodity Mean domestic
share
Log-change 1995-2017
Domestic share
Relative price
Relative quantity
Contribution to change in share
Relative price
Relative quantity Error
(1) (2) (3) (4) (5) (6) (7) (8)
1 Meat Products 95.61 -3.57 -2.78 -88.82 -0.12 -3.90 0.448
2 Dairy Products and Egg 92.70 -5.52 -11.58 -52.15 -0.85 -3.81 -0.870
3 Fish Products 78.96 -9.34 -29.95 -12.81 -6.30 -2.69 -0.341
4 Other Food Products 85.72 -14.06 -7.98 -87.73 -1.14 -12.53 -0.394
5 Beverage and Tobacco 93.06 -7.50 66.05 -175.44 4.58 -12.17 0.086
6 Coal, Petroleum and Gas 74.16 -22.54 5.66 -109.21 1.46 -28.22 4.218
7 Basic Chemicals 66.53 -15.79 0.87 -43.74 0.29 -14.64 -1.441
8 Pharmaceutical Products 60.76 -21.87 38.26 -104.01 15.01 -40.81 3.922
9 Textile and Leather 69.12 -8.66 20.18 -45.09 6.23 -13.93 -0.967
10 Clothing and Footwear 71.00 -33.71 -5.04 -114.08 -1.46 -33.09 0.837
11 Wood and Products 88.82 -1.32 -26.02 15.32 -2.91 1.71 -0.128
12 Paper Products 83.98 -6.81 16.75 -55.60 2.68 -8.91 -0.587
13 Furniture 80.72 -27.00 -19.03 -139.55 -3.67 -26.91 3.583
14 Non Metals 79.34 -10.12 13.38 -61.32 2.76 -12.67 -0.214
15 Iron Steel 80.47 -6.67 -48.13 14.77 -9.40 2.89 -0.152
16 Non Ferrous Metals 80.84 -21.19 -34.13 -92.77 -6.54 -17.78 3.127
17 Machinery Equipment 49.08 -29.23 6.06 -65.51 3.08 -33.36 1.045
18 Motor Vehicles and Parts 56.94 -51.70 11.02 -123.57 4.75 -53.21 -3.237
19 Transport Equipment 55.00 -10.40 -43.65 17.58 -19.64 7.91 1.327
20 Other Manufactures 66.94 -33.16 49.71 -153.80 16.43 -50.85 1.254
Mean 75.49 -17.01 -0.02 -73.88 0.26 -17.85 0.58
Std. dev. 13.31 12.88 29.83 55.59 7.90 17.50 1.91
Note: All entries are x 100
19
Table 4. Single-equation OLS estimates of substitution elasticities.
Commodity
Levels of logs Log-changes
Elasticity of
substitution π
Std. error
Durbin-
Watson
Elasticity of
substitution π
Std. error
Durbin-
Watson
1 Meat Products 2.181 *** 0.419 0.426 1.000 *** 0.160 2.675
2 Dairy Products and Egg 0.050 0.234 0.860 0.906 *** 0.189 2.404
3 Fish Products 0.073 0.276 0.574 0.608 ** 0.239 2.407
4 Other Food Products 0.493 0.967 0.037 0.710 *** 0.129 2.337
5 Beverage and Tobacco 2.406 *** 0.100 0.678 1.009 *** 0.292 2.457
6 Coal, Petroleum and Gas -0.121 0.415 0.342 0.642 *** 0.098 1.816
7 Basic Chemicals 1.371 *** 0.358 0.363 0.637 *** 0.137 2.158
8 Pharmaceutical Products 2.191 *** 0.432 0.253 0.523 0.471 1.514
9 Textile and Leather 0.771 *** 0.298 0.355 0.682 *** 0.213 2.320
10 Clothing and Footwear 2.055 *** 0.531 0.176 0.775 *** 0.165 2.017
11 Wood and Products 0.252 0.217 1.101 0.823 ** 0.333 2.328
12 Paper Products 1.765 *** 0.171 0.515 0.858 *** 0.157 1.926
13 Furniture 1.634 * 0.886 0.060 0.826 *** 0.170 1.782
14 Non Metals 1.353 *** 0.191 0.445 0.552 *** 0.107 2.439
15 Iron Steel 0.518 *** 0.088 1.257 0.301 0.202 2.907
16 Non Ferrous Metals -0.144
0.275 1.095 1.002 ** 0.434 2.528
17 Machinery Equipment 1.256 *** 0.316 0.212 0.742 *** 0.162 1.736
18 Motor Vehicles and Parts 4.864 *** 0.986 0.425 0.143 0.525 2.258
19 Transport Equipment 0.522 0.425 1.194 2.056 *** 0.660 2.608
20 Other Manufactures 1.767 *** 0.135 0.518 0.742 *** 0.180 2.214
Test for common sigma Value df Prob Value df Prob
Chi-square 360.652 19 0.000 20.305 19 0.376
***, **, *significant at 1%, 5% and 10% levels
20
Table 5. Panel estimates of substitution elasticities.
Commodity
Levels of logs Log-change
Elasticity of
substitution π
Std.
error
Elasticity of
substitution π
Std.
error
1 Meat Products 1.632 0.224 1.133 *** 0.185
2 Dairy Products and Egg 1.403 *** 0.230 1.051 *** 0.203
3 Fish Products 1.215 *** 0.249 0.839 *** 0.230
4 Other Food Products 0.745 0.461 1.013 *** 0.331
5 Beverage and Tobacco 1.506 *** 0.127 1.268 *** 0.462
6 Coal, Petroleum and Gas 0.352 ** 0.158 0.747 *** 0.093
7 Basic Chemicals 0.260 0.349 0.849 *** 0.251
8 Pharmaceutical Products -0.046 0.312 0.730 0.603
9 Textile and Leather -0.820 ** 0.325 1.004 *** 0.282
10 Clothing and Footwear 1.217 *** 0.254 1.033 *** 0.295
11 Wood and Products 2.363 *** 0.369 1.210 *** 0.413
12 Paper Products 0.510 ** 0.205 1.009 *** 0.303
13 Furniture 1.487 *** 0.268 0.991 *** 0.279
14 Non Metals 0.211 0.229 0.777 *** 0.273
15 Iron Steel 1.649 *** 0.160 0.494 ** 0.235
16 Non Ferrous Metals 0.648 *** 0.141 1.048 *** 0.174
17 Machinery Equipment -0.076 0.317 0.978 *** 0.330
18 Motor Vehicles and Parts 1.580 *** 0.556 0.355 0.728
19 Transport Equipment 1.862 *** 0.265 2.246 *** 0.306
20 Other Manufactures 0.983 *** 0.121 0.905 *** 0.264
Fixed effects Cross-section, Time Cross-section, Time
R-squared 0.98 0.48 Durbin-Watson 0.74 2.50
Test for common sigma Value df Value df
F-statistic 9.571 (18, 398) 1.745 (18, 379)
Chi-square 172.279 18 31.405 18
***, **, *significant at 1%, 5% and 10% levels
21
Table 6. Test for common sigma for seven commodity groups.
Group name Commodities Null hypothesis Test statistic Value df Probability
1 Agricultural
commodities
1. Meat Products
2. Dairy Products and Egg
3. Fish Products
4. Other Food Products
5. Beverage and Tobacco
ππ = ππΌ,
π = 1, β― ,5
F-statistic 0.341 (4, 379) 0.851
Chi-square 1.362 4 0.851
2 Energy and
minerals
6. Coal, Petroleum and Gas
14. Non-metals
15. Iron Steel
16. Non-Ferrous Metal
ππ = ππΌπΌ,
π = 6,14,15,16
F-statistic 1.344 (3, 379) 0.260
Chi-square 4.031 3 0.258
3 Chemical 7. Basic Chemicals
8. Pharmaceutical Products
ππ = ππΌπΌπΌ,
π = 7,8
F-statistic 0.147 (1, 379) 0.702
Chi-square 0.147 1 0.702
4 Textile, leather,
clothing and
footwear
9. Textile and Leather
10. Clothing and Footwear
ππ = ππΌπ,
π = 9,10
F-statistic 0.005 (1, 379) 0.942
Chi-square 0.005 1 0.942
5 Wood and
paper products
11. Wood and Products
12. Paper Products
ππ = ππ,
π = 11,12
F-statistic 0.161 (1, 379) 0.688
Chi-square 0.161 1 0.688
6 Miscellaneous
manufactures
13. Furniture
20. Other Manufactures
ππ = πππΌ,
π = 13,20
F-statistic 0.054 (1, 379) 0.816
Chi-square 0.054 1 0.816
7 Transport,
machinery and
equipment
17. Machinery Equipment
18. Motor Vehicles and Parts
19. Transport Equipment
ππ = πππΌπΌ,
π = 17,18,19
F-statistic 5.867 (2, 379) 0.003
Chi-square 11.734 2 0.003
22
Table 7. Panel estimates for 9-sector model.
Commodity
Levels of logs Log-change
Elasticity of
substitution π Standard Error
Elasticity of
substitution π Standard Error
1 Agricultural Commodities 1.472 *** 0.098 1.051 *** 0.114
2 Energy and Minerals 0.741 *** 0.090 0.782 *** 0.075
3 Chemicals and Pharmaceuticals 0.257
0.258 0.848 *** 0.232
4 Textile, Leather and Wearing Apparel 0.582 ** 0.226 1.043 *** 0.214
5 Wood, Wood Products, and Paper 0.992 *** 0.193 1.095 *** 0.247
6 Miscellaneous Manufactures 1.134 *** 0.121 0.961 *** 0.196
7 Machinery Equipment 0.065 0.345 0.997 *** 0.327
8 Motor Vehicles and Parts 1.881 *** 0.604 0.374 0.722
9 Transport Equipment 1.765 *** 0.290 2.264 *** 0.302
Fixed effects Cross-section, Time Cross-section, Time
R-squared 0.97 0.47
Durbin-Watson stat 0.49 2.52
***, **, *significant at 1%, 5% and 10% levels
Table 8. Summary of substitution elasticities.
Source Mean Standard Deviation
1. OLS model 0.777 0.373
2. Panel I model 0.984 0.370
3. Panel II model 1.046 0.507
23
Table 9. Tariff rates in the initial database.
Commodity Tariff rate Commodity Tariff rate
Other Agriculture* 0.38% Cleaning Compounds and Toiletry 1.50%
Oil and gas extraction 0.04% Polymer 1.20%
Dairy Product Manufacturing 0.73% Natural Rubber 1.72%
Fruit and Vegetable Product 1.07% Glass and Glass 1.09%
Oils and Fats 0.43% Ceramic 2.08%
Grain Mill and Cereal Product 0.60% Plaster and Concrete 0.26%
Bakery Product 1.04% Other Non-Metallic Mineral 1.75%
Sugar and Confectionery 1.69% Iron and Steel 1.29%
Other Food Product 0.37% Basic Non-Ferrous Metal 0.35%
Soft Drinks, Cordials and Syrup 0.84% Forged Iron and Steel 1.91%
Wine, Spirits and Tobacco 0.88% Structural Metal 1.94%
Textile Manufacturing 1.38% Metal Containers and Other Sheet Metal 0.93%
Leather Product 1.93% Other Fabricated Metal 1.38%
Textile Product 2.12% Motor Vehicles and Parts 1.59%
Knitted Product 3.19% Ships and Boat 0.65%
Clothing 2.29% Railway Rolling Stock 0.93%
Footwear 2.40% Prof., Scientific, & Electronic Equipment 0.09%
Sawmill Product 0.77% Electrical Equipment 0.75%
Other Wood Product 1.61% Domestic Appliance 0.64%
Pulp, Paper and Paperboard 0.59% Specialised Machinery & Equipment 0.63%
Paper Product 1.71% Furniture 2.08%
Printing 1.32% Other Manufactured 0.96%
Petroleum and Coal 0.22% Heavy and Civil Engineering Construction 0.63%
Basic Chemical 0.59% Publishing 0.12%
Source: Australian Bureau of Statistics (2018). *This commodity mainly consists of sugar cane, cotton and crops and plants not elsewhere classified.
24
Table 10. Armington elasticities for Simulation 1 and 2.
Commodity Armington Elasticity
Commodity Armington Elasticity
Sim 1 Sim 2 Sim 1 Sim 2
Sheep, Grains, Beef and Dairy 1.10 1.10 Paper Product 1.10 1.01
Poultry and Other Livestock 1.70 1.70 Printing 2.00 1.01
Other Agriculture 2.00 2.00 Petroleum and Coal 0.40 0.75
Aquaculture 0.50 0.50 Human Pharmaceutical 2.00 0.73
Forestry and Logging 2.00 2.00 Veterinary Pharmaceutical 2.00 0.73
Fishing, hunt & trap 0.50 0.50 Basic Chemical 2.01 0.85
Coal mining 0.50 0.50 Cleaning Compounds 1.65 0.85
Oil and gas extraction 2.00 2.00 Polymer 1.75 0.78
Iron Ore Mining 0.50 0.50 Natural Rubber 1.50 0.78
Non Ferrous Metal Ore Mining 0.50 0.50 Glass and Glass 1.20 0.78
Non Metallic Mineral Mining 2.00 2.00 Ceramic 1.20 0.78
Exploration & Mining Services 2.00 2.00 Cement, Lime & Concrete 0.38 0.78
Meat and Meat product 0.50 1.13 Plaster and Concrete 1.10 0.78
Processed Seafood 0.50 0.84 Other Non-Metallic Mineral 1.20 0.78
Dairy Product Manufacturing 1.60 1.05 Iron and Steel 0.82 0.49
Fruit and Vegetable Product 0.80 0.80 Basic Non-Ferrous Metal 1.10 1.05
Oils and Fats 1.70 1.70 Forged Iron and Steel 0.82 0.49
Grain Mill and Cereal Product 2.10 2.10 Structural Metal 1.50 1.05
Bakery Product 1.10 1.10 Other Sheet Metal 1.50 1.05
Sugar and Confectionery 1.25 1.25 Other Fabricated Metal 1.75 1.05
Other Food Product 0.50 1.01 Motor Vehicles and Parts 5.20 1.58
Soft Drinks, Cordials and Syrup 1.10 1.27 Ships and Boat 0.50 2.25
Beer Manufacturing 1.10 1.27 Railway Rolling Stock 0.50 2.25
Wine, Spirits and Tobacco 3.40 1.27 Aircraft 0.50 2.25
Textile Manufacturing 2.76 1.00 Prof. & Scientific Equipment 0.50 0.98
Leather Product 2.00 1.00 Electrical Equipment 1.37 0.98
Textile Product 1.93 1.03 Domestic Appliance 1.60 0.98
Knitted Product 1.90 1.03 Specialised Machinery 0.50 0.98
Clothing 2.80 1.03 Furniture 2.30 0.99
Footwear 6.80 1.03 Other Manufactured 1.83 0.91
Sawmill Product 2.30 1.21 Water, Pipeline, Other Transport 2.00 2.00
Other Wood Product 1.45 1.21 Air Passenger 2.00 2.00
Pulp, Paper and Paperboard 1.10 1.01 Air Freight 2.00 2.00
25
Figure 1. Time series plot of sales share and relative price, Beverage and Tobacco, Australia.
Figure 2. Distribution of log-changes in relative prices and quantities.
0.30
0.50
0.70
0.90
1.10
1.30
0.87
0.89
0.91
0.93
0.95
0.97
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
20
11
20
13
20
15
20
17
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Domestic share Relative Price
0
20
40
60
80
100
-70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50
Series: LQC
Sample 1995 2017
Observations 440
Mean -3.199325
Median -3.353887
Maximum 48.55089
Minimum -70.72059
Std. Dev. 14.17842
Skewness -0.460762
Kurtosis 6.903450
Jarque-Bera 294.9124
Probability 0.000000 0
20
40
60
80
100
120
-50 -40 -30 -20 -10 0 10 20 30 40 50 60
Series: LPC
Sample 1995 2017
Observations 440
Mean -0.043300
Median 0.601080
Maximum 55.50480
Minimum -50.45736
Std. Dev. 10.52337
Skewness -0.055697
Kurtosis 6.634039
Jarque-Bera 242.3418
Probability 0.000000
PriceSample 1995 2017Observations 440
Mean -0.001Std. Dev. 10.622Skewness -0.055Kurtosis 6.570
QuantitySample 1995 2017Observations 440
Mean -3.358Std. Dev.14.202Skewness -0.488Kurtosis 6.983
26
Figure 3. Time-series plot of price and quantity log-changes for all goods.
27
Figure 4. Scatter plot of price-quantity log changes for all 20 goods combined.
-80
-60
-40
-20
0
20
40
60
-60 -40 -20 0 20 40 60
(LPC1,LQC1) (LPC2,LQC2)
(LPC3,LQC3) (LPC4,LQC4)
(LPC5,LQC5) (LPC6,LQC6)
(LPC7,LQC7) (LPC8,LQC8)
(LPC9,LQC9) (LPC10,LQC10)
(LPC11,LQC11) (LPC12,LQC12)
(LPC13,LQC13) (LPC14,LQC14)
(LPC15,LQC15) (LPC16,LQC16)
(LPC17,LQC17) (LPC18,LQC18)
(LPC19,LQC19) (LPC20,LQC20)
Qty = -0.745PRC -3.0912R2 = 0.3071
Quantity
Price
60
40
20
0
-20
-40
-60
-80
-60 -40 -20 0 20 40 60
-80
-60
-40
-20
0
20
40
60
-60 -40 -20 0 20 40 60
(LPC1,LQC1) (LPC2,LQC2)
(LPC3,LQC3) (LPC4,LQC4)
(LPC5,LQC5) (LPC6,LQC6)
(LPC7,LQC7) (LPC8,LQC8)
(LPC9,LQC9) (LPC10,LQC10)
(LPC11,LQC11) (LPC12,LQC12)
(LPC13,LQC13) (LPC14,LQC14)
(LPC15,LQC15) (LPC16,LQC16)
(LPC17,LQC17) (LPC18,LQC18)
(LPC19,LQC19) (LPC20,LQC20)
28
Figure 5. Three sets of substitutions elasticities.
Figure 6. Actual and fitted, Beverage and Tobacco
0.00
0.50
1.00
1.50
2.00
2.50
OLS Panel I Panel II
0.30
0.45
0.60
0.75
0.90
1.05
1.20
1.35
0
5
10
15
20
25
30
35
40
199
5
199
7
199
9
200
1
200
3
200
5
200
7
200
9
201
1
201
3
201
5
201
7
Rel
ativ
e p
rice
Do
mes
tic-
fore
ign
sal
es r
atio
Actual Qd/Qf Fitted Qd/Qf (OLS)
Fitted Qd/Qf (Panel I) Relative price (Pd/Pf)
29
Figure 7. System of interrelationships between economic agents in CGE model
Figure 8. GDP expenditure components, percentage deviation from baseline.
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
% c
han
ge
Year
Consumption Investment Exports Imports SIM2 Results
30
Figure 9. GDP and income components, percentage deviation from baseline.
Figure 10. Price indexes, percentage deviation from baseline.
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
% c
han
ge
Year
Employment Capital GDP SIM2 Results
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
% c
han
ge
Year
CPI Real wage Real wage (post tax)
Rates of return Terms of trade SIM2 Results
31
Figure 11. Industry output, percentage deviation from baseline.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon
reasonable request.
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
% c
han
ge
Year
Agriculture Mining Manufacturing
Retail and Wholesale trade Other services SIM2 Results
32
Appendix
Appendix 1. Data Transformations
Table A1 summarises the sources of data used in our estimation. As indicated in Section 3, data from
different sources has to be reconciled to create a common commodity classification and time aggregation. This
involved the following data transformations:
1. The values of total production, exports and imports for 60 merchandise goods are extracted from the
114-sector Supply-Use tables. Then, we derive the domestic sales by subtracting the value of export
sales from total supply. This annual data span from 1995 to 2017.
2. The quarterly import and producer prices were converted into an annual data to be consistent with the
values data. We do this by first summing up the monthly values of merchandise import values from the
Trade tables into quarterly data. Then we transform the quarterly prices into annual data by taking
weighted sums of the quarterly data, the weights being the quarterly trade shares. For the annual
producer price index was formed as a simple average of the quarterly data.
3. The values data and producer price data are then aggregated into 20 sectoral classification using the
mapping in Table A2, while the import price index are aggregated using the mapping in Table A3.
4. The annual quantities of domestic sales and imports are derived by dividing nominal values by
corresponding prices.
Table A1. Data sources and characteristics
Data
Type Start period
End period
Frequency Industry detail Source
1 Import price
Index
Sep-1981 Mar-2019 Quarterly Standard International Trade Classification (SITC) - 49 commodities (2-
digit) - 10 broad
commodities (1-digit)
ABS-6457 Trade tables
2 Export price
Index
Sep-1981 Mar-2019 Quarterly STIC classification - 42 commodities (2-
digit) - 10 broad
commodities (1-digit)
ABS-6457 Trade tables
3 Merchandise imports
Value Jan-1988 Apr-2019 Monthly STIC classification - 66 commodities (2-
digit) - 10 broad
commodities (1-digit)
ABS-5368 Trade tables
4 Merchandise exports
Value Jan-1988 Apr-2019 Monthly STIC classification - 66 commodities (2-
digit) - 10 broad
commodities (1-digit)
ABS-5368 Trade tables
5 Domestic production
Value 1995 2017 Annual ANZSIC classification - 114 commodities (2-
digit)
ABS Supply-Use table
6 Export sales
Value 1995 2017 Annual ANZSIC classification - 114 commodities (2-
digit)
ABS Supply-Use table
7 Producer price
Index Mar-1990 Jun-2019 Quarterly ANZSIC classification - 114 commodities (2-
digit)
ABS-64270 National accounts
33
Table A2. Mapping of SUT merchandise commodities to 20 broad commodities
SUT product group Mapping to 20 broad commodities
Aquaculture Fish
Coal mining CoalPetGas
Oil and gas extraction CoalPetGas
Iron Ore Mining CoalPetGas
Non Ferrous Metal Ore Mining CoalPetGas
Non Metallic Mineral Mining CoalPetGas
Meat and Meat product Manufacturing MeatPrep
Processed Seafood Manufacturing Fish
Dairy Product Manufacturing DairyEgg
Fruit and Vegetable Product Manufacturing FruVeg
Oils and Fats Manufacturing OilsFat
Grain Mill and Cereal Product Manufacturing OtherFood
Bakery Product Manufacturing OtherFood
Sugar and Confectionery Manufacturing OtherFood
Other Food Product Manufacturing OtherFood
Soft Drinks, Cordials and Syrup Manufacturing BevTob
Beer Manufacturing BevTob
Wine, Spirits and Tobacco BevTob
Textile Manufacturing TextLeat
Tanned Leather, Dressed Fur and Leather Product Manufacturing TextLeat
Textile Product Manufacturing TextLeat
Knitted Product Manufacturing ClothFoot
Clothing Manufacturing ClothFoot
Footwear Manufacturing ClothFoot
Sawmill Product Manufacturing WoodManuf
Other Wood Product Manufacturing WoodManuf
Pulp, Paper and Paperboard Manufacturing PulpPaper
Paper Stationery and Other Converted Paper Product Manufacturing PulpPaper
Printing (including the reproduction of recorded media) PulpPaper
Petroleum and Coal Product Manufacturing CoalPetGas
Human Pharmaceutical and Medicinal Product Manufacturing PharmChem
Veterinary Pharmaceutical and Medicinal Product Manufacturing PharmChem
Basic Chemical Manufacturing BasicChem
Cleaning Compounds and Toiletry Preparation Manufacturing BasicChem
Polymer Product Manufacturing NonMet
Natural Rubber Product Manufacturing NonMet
Glass and Glass Product Manufacturing NonMet
Ceramic Product Manufacturing NonMet
Cement, Lime and Ready-Mixed Concrete Manufacturing NonMet
Plaster and Concrete Product Manufacturing NonMet
Other Non-Metallic Mineral Product Manufacturing NonMet
Iron and Steel Manufacturing IronSteel
Basic Non-Ferrous Metal Manufacturing NonFerMet
Forged Iron and Steel Product Manufacturing OthMet
Structural Metal Product Manufacturing OthMet
Metal Containers and Other Sheet Metal Product manufacturing OthMet
Other Fabricated Metal Product manufacturing OthMet
Motor Vehicles and Parts; Other Transport Equipment manufacturing MotVeh
Ships and Boat Manufacturing TranEqp
Railway Rolling Stock Manufacturing TranEqp
Aircraft Manufacturing TranEqp
Professional, Scientific, Computer and Electronic Equipment Manufacturing SpecMachEq
Electrical Equipment Manufacturing MachEqp
Domestic Appliance Manufacturing MachEqp
Specialised and other Machinery and Equipment Manufacturing SpecMachEq
Furniture Manufacturing FurnFixture
Other Manufactured Products MiscManuf
34
Table A3. Mapping of STIC commodities to 20 broad commodities
Imports and export values Mapping to 20 broad commodities
01 Meat and meat preparations ; MeatPrep
02 Dairy products and birdsβ eggs ; DairyEgg
03 Fish (excl. marine mammals) crustaceans, molluscs and aquatic invertebrates, and preparations ; Fish
04 Cereals and cereal preparations ; OtherFood
05 Vegetables and fruit ; OtherFood
06 Sugars, sugar preparations and honey ; OtherFood
07 Coffee, tea, cocoa, spices, and manufactures thereof ; OtherFood
08 Feeding stuff for animals (excl. unmilled cereals) ; OtherFood
09 Miscellaneous edible products and preparations ; OtherFood
1 Beverages and tobacco ; BevTob
2 Crude materials, inedible, except fuels ; 3 Mineral fuels, lubricants and related materials ; 32 Coal, coke and briquettes ; CoalPetGas
33 Petroleum, petroleum products and related materials ; CoalPetGas
34 Gas, natural and manufactured ; CoalPetGas
4 Animal and vegetable oils, fats and waxes ; 5 Chemicals and related products, nes ; BasicChem
51 Organic chemicals ; BasicChem
52 Inorganic chemicals ; BasicChem
53 Dyeing, tanning and colouring materials ; BasicChem
54 Medicinal and pharmaceutical products ; PharmChem
55 Essential oils and resinoids and perfume materials; toilet, polishing and cleansing preparations ; PharmChem
6 Manufactured goods classified chiefly by material ; 61 Leather, leather manufactures, nes, and dressed furskins ; TextLeather
62 Rubber manufactures, nes ; 63 Cork and wood manufactures (excl. furniture) ; WoodManuf
64 Paper, paperboard and articles of paper pulp, of paper or of paperboard ; PaperManuf
65 Textile yarn, fabrics, made-up articles nes, and related products ; TextLeather
66 Non-metallic mineral manufactures, nes ; NonMet
67 Iron and steel ; IronSteel
68 Non-ferrous metals ; NonFerMet
69 Manufactures of metals, nes ; 7 Machinery and transport equipment ; 71 Power generating machinery and equipment ; MachEqp
72 Machinery specialized for particular industries ; MachEqp
73 Metalworking machinery ; MachEqp
74 General industrial machinery and equipment, nes, and machine parts, nes ; 75 Office machines and automatic data processing machines ; 76 Telecommunications and sound recording and reproducing apparatus and equipment ; 77 Electrical machinery, apparatus and appliances, nes, and electrical parts thereof; 78 Road vehicles (incl. air-cushion vehicles) ; MotVeh
79 Transport equipment (excl. road vehicles) ; TranEqp
8 Miscellaneous manufactured articles ; MiscManuf
82 Furniture and parts thereof; Furniture
83 Travel goods, handbags and similar containers ; 84 Articles of apparel and clothing accessories ; ClothFoot
85 Footwear ; ClothFoot
35
Appendix 2. Domestic Shares and Relative Prices
The figures below are plots of domestic sales share and relative price for each of the 20 commodities.
Appendix 3. Price-Quantity Scatter Plots
Below are scatter plots for each commodity of the relative quantity against the relative price, in log-
change form. In all cases, there is a negative relationship between prices and quantities, as indicated by the
downward sloping regression lines. This provides some visual evidence of substitution between domestic and
foreign sources of supply.
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
0.92
0.93
0.94
0.95
0.96
0.97
0.98
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Meat Products
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
0.85
0.86
0.87
0.88
0.89
0.90
0.91
0.92
0.93
0.94
0.95
0.96
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Dairy Products and Egg
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
1.20
1.30
0.68
0.70
0.72
0.74
0.76
0.78
0.80
0.82
0.84
0.86
0.88
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Fish
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.72
0.74
0.76
0.78
0.80
0.82
0.84
0.86
0.88
0.90
0.92
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Other Food
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
1.20
1.30
1.40
0.84
0.86
0.88
0.90
0.92
0.94
0.96
0.98
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Beverage and Tobacco
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Coal, Petroleum and Gas
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
0.50
0.55
0.60
0.65
0.70
0.75
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Basic Chemicals
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
1.20
1.30
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Pharmaceutical Products
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
1.20
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Textile and Leather
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Clothing and Footwear
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
1.20
0.80
0.82
0.84
0.86
0.88
0.90
0.92
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Wood Products
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
0.74
0.76
0.78
0.80
0.82
0.84
0.86
0.88
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Paper Products
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Furniture
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
0.70
0.72
0.74
0.76
0.78
0.80
0.82
0.84
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Non Metals
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
0.72
0.74
0.76
0.78
0.80
0.82
0.84
0.86
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Iron Steel
0.00
0.50
1.00
1.50
2.00
2.50
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Non Ferrous Metals
0.50
0.60
0.70
0.80
0.90
1.00
1.10
0.30
0.35
0.40
0.45
0.50
0.55
0.60
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Machinery Equipment
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Motor Vehicles and Parts
0.50
0.60
0.70
0.80
0.90
1.00
1.10
1.20
0.20
0.30
0.40
0.50
0.60
0.70
0.80
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Transport Equipment
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
Rel
ativ
e p
rice
Do
mes
tic
shar
e
Other Manufactures
Domestic share (Qd/(Qd+Qf)) Relative price (Pd/Pf)
36
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y = -1.0002x - 4.1635
-60.00
-40.00
-20.00
0.00
20.00
40.00
-40.00 -30.00 -20.00 -10.00 0.00 10.00 20.00 30.00
Meat Products
y = -0.9058x - 2.8469-40.00
-30.00
-20.00
-10.00
0.00
10.00
20.00
30.00
40.00
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Dairy Products and Egg
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-60.00
-40.00
-20.00
0.00
20.00
-30.00 -20.00 -10.00 0.00 10.00 20.00
Fish
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-20.00
-10.00
0.00
10.00
20.00
-25.00 -20.00 -15.00 -10.00 -5.00 0.00 5.00 10.00
Other Food
y = -1.009x - 4.9454
-30.00
-20.00
-10.00
0.00
10.00
20.00
-10.00 -5.00 0.00 5.00 10.00 15.00
Beverage and Tobacco
y = -0.6415x - 4.7988-60.00
-40.00
-20.00
0.00
20.00
40.00
-60.00 -40.00 -20.00 0.00 20.00 40.00 60.00 80.00
Coal, Petroleum and Gas
y = -0.6369x - 1.963
-30.00
-20.00
-10.00
0.00
10.00
20.00
-40.00 -30.00 -20.00 -10.00 0.00 10.00 20.00
Basic Chemicals
y = -0.5226x - 3.8185
-30.00
-20.00
-10.00
0.00
10.00
20.00
-10.00 -5.00 0.00 5.00 10.00 15.00
Pharmaceuticals
y = -0.6817x - 1.4244
-30.00
-20.00
-10.00
0.00
10.00
20.00
30.00
-30.00 -20.00 -10.00 0.00 10.00 20.00 30.00
Textile and Leather
y = -0.7753x - 5.3631-30.00
-20.00
-10.00
0.00
10.00
20.00
-25.00 -20.00 -15.00 -10.00 -5.00 0.00 5.00 10.00 15.00 20.00
Clothing and Footwear
y = -0.8232x - 0.2771
-30.00
-20.00
-10.00
0.00
10.00
20.00
30.00
-15.00 -10.00 -5.00 0.00 5.00 10.00 15.00
Wood Products
y = -0.8579x - 1.8742-20.00
-10.00
0.00
10.00
20.00
30.00
-25.00 -20.00 -15.00 -10.00 -5.00 0.00 5.00 10.00 15.00 20.00
Paper Products
y = -0.8258x - 7.0576-30.00
-20.00
-10.00
0.00
10.00
20.00
-30.00 -20.00 -10.00 0.00 10.00 20.00
Furniture
y = -0.552x - 2.4517-20.00
-15.00
-10.00
-5.00
0.00
5.00
10.00
15.00
20.00
-20.00 -15.00 -10.00 -5.00 0.00 5.00 10.00 15.00 20.00
Non Metals
y = -0.3012x + 0.0125
-30.00
-20.00
-10.00
0.00
10.00
20.00
-30.00 -20.00 -10.00 0.00 10.00 20.00 30.00
Iron Steel
y = -1.0019x - 5.7711
-80.00
-60.00
-40.00
-20.00
0.00
20.00
40.00
60.00
-40.00 -30.00 -20.00 -10.00 0.00 10.00 20.00
Non Ferrous Metals
y = -0.7423x - 2.7731-20.00
-10.00
0.00
10.00
20.00
-20.00 -15.00 -10.00 -5.00 0.00 5.00 10.00 15.00 20.00
Machinery Equipment
y = -0.1425x - 5.5453
-30.00
-20.00
-10.00
0.00
10.00
20.00
-8.00 -6.00 -4.00 -2.00 0.00 2.00 4.00 6.00 8.00 10.00
Motor Vehicles and Parts
y = -2.0559x - 3.2799
-80.00
-60.00
-40.00
-20.00
0.00
20.00
40.00
60.00
-20.00 -15.00 -10.00 -5.00 0.00 5.00 10.00 15.00
Transport Equipment
y = -0.7424x - 5.3135-30.00
-20.00
-10.00
0.00
10.00
20.00
-15.00 -10.00 -5.00 0.00 5.00 10.00 15.00 20.00 25.00
Other Manufactures
37
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