foreign-domestic substitution, import ......keywords: foreign-domestic substitution, armington...

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 “Armington elasticities”) 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 “Armington elasticity”. 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 𝑖 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 𝑛 × 𝐶. For example,

if 𝑛 = 12 goods and 𝐶 = 150 countries, there is a total of 𝑛𝐶 = 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:

𝑢(𝒒1, ⋯ , 𝒒𝑛) = ∑ 𝑢𝑖(𝒒𝑖)𝑛𝑖=1 , (1)

where 𝑢𝑖(∙) is the sub-utility function of group i and 𝒒𝑖 is the vector of quantities belonging to

group i. Within each group, goods can interact fully in a marginal utility sense, while there are

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no between-group effects. In other words, there is preference independence with respect to

groups of goods. Let 𝑢𝑖(∙) in (1) be CES:

𝑢𝑖(𝒒𝑖) = (∑ 𝛼𝑗𝑞𝑗−𝜌

𝑗∈𝑺𝑖)

−1

𝜌, 𝛼𝑗 > 0, 𝜌 ≥ −1, 𝜌 ≠ 0.

(2)

Here, 𝑺𝑖 is the set of varieties of good i. The elasticity of substitution is 𝜎 =1

1+𝜌> 0 and the

corresponding conditional demands are:

𝑞𝑗 =𝑀𝑖𝛼𝑗

𝜎𝑝𝑗−𝜎

∑ 𝛼𝑘𝜎𝑝𝑘

1−𝜎𝑘∈𝑺𝑖

, 𝑘 ∈ 𝑺𝑖 , (3)

where 𝑞𝑗 is the quantity demanded of 𝑗 ∈ 𝑺𝑖 , 𝑀𝑖 = ∑ 𝑝𝑘 𝑘∈𝑺𝑖𝑞𝑘 is total expenditure on group i

and 𝑝𝑘 is the price of 𝑘 ∈ 𝑺𝑖 . 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 ( 𝜎 → 0, the

Leontief case), to Cobb-Douglas (𝜎 → 1), to perfect substitution (𝜎 → ∞, linear utility).

Equation (3) can be expressed more compactly by defining the group price index as

𝑃𝑖 = (∑ 𝛼𝑘𝜎𝑝𝑘


)1

1−𝜎. The denominator in (3), ∑ 𝛼𝑘𝜎𝑝𝑘


, can then be replaced with

𝑃𝑖1−𝜎 to give

𝑞𝑗 = 𝛼𝑗𝜎 𝑀𝑖

𝑃𝑖(

𝑃𝑗

𝑃𝑖)

−𝜎, 𝑗 ∈ 𝑺𝑖 .

(4)

This makes clear the dependence of consumption on real expenditure on the group, 𝑀𝑖

𝑃𝑖, and the

own-

relative price, 𝑃𝑗

𝑃𝑖.

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 𝑞𝑖1

and 𝑞𝑖2, then we can write equation (4) as

𝑞𝑖1 = (𝛼𝑖

1)𝜎𝑖𝑀𝑖

𝑃𝑖(

𝑝𝑖1

𝑃𝑖)

−𝜎𝑖

, 𝑞𝑖2 = (𝛼𝑖

2)𝜎𝑖𝑀𝑖

𝑃𝑖(

𝑝𝑖2

𝑃𝑖)

−𝜎𝑖

,

where 𝑝𝑖1 and 𝑝𝑖

2 are the corresponding prices. Taking logs then gives the relative demand for

the goods:

𝑙𝑜𝑔 (𝑞𝑖

1

𝑞𝑖2) = 𝛽𝑖 − 𝜎𝑖 𝑙𝑜𝑔 (

𝑝𝑖1

𝑝𝑖2), (5)

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where 𝛽𝑖 = 𝜎𝑖 log (𝛼𝑖

1

𝛼𝑖2) 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 𝜎𝑖 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 𝑛 = 20 goods in 𝑇 = 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

“spikes” 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 𝜎 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 𝑝𝑑 be the price of the domestically produced variety and 𝑞𝑑

the corresponding quantity, so that 𝑝𝑑𝑞𝑑 is expenditure. Similarly, let 𝑝𝑓 , 𝑞𝑓 and 𝑝𝑓𝑞𝑓 be the

foreign counterparts. Then, total expenditure on the good is 𝑝𝑑𝑞𝑑 + 𝑝𝑓𝑞𝑓 = 𝑀 and the budget

shares of the two varieties are 𝑤 =𝑝𝑑𝑞𝑑

𝑀, 1 − 𝑤 =

𝑝𝑓𝑞𝑓

𝑀. Define Divisia price and volume

indexes as

𝑑(𝑙𝑜𝑔 𝑃) = 𝑤𝑑(𝑙𝑜𝑔 𝑝𝑑) + (1 − 𝑤)𝑑(𝑙𝑜𝑔 𝑝𝑓),

𝑑(𝑙𝑜𝑔 𝑄) = 𝑤𝑑(𝑙𝑜𝑔 𝑞𝑑) + (1 − 𝑤)𝑑(𝑙𝑜𝑔 𝑞𝑓).

The change in total expenditure is then:

𝑑(log 𝑀) = 𝑑(log 𝑃) + 𝑑(log 𝑄).

(6)

Using (6), the change in the domestic share can be expressed as

𝑑(𝑙𝑜𝑔 𝑤) = 𝑑(𝑙𝑜𝑔 𝑝𝑑) + 𝑑(𝑙𝑜𝑔 𝑞𝑑) − 𝑑(𝑙𝑜𝑔 𝑀) = 𝑑 (𝑙𝑜𝑔𝑝𝑑

𝑃) + 𝑑 (𝑙𝑜𝑔

𝑞𝑑

𝑄),

where 𝑑 (log𝑝𝑑

𝑃) = 𝑑(log 𝑝𝑑) − 𝑑(log 𝑃); 𝑑 (log

𝑞𝑑

𝑄) = 𝑑(log 𝑞𝑑) − 𝑑(log 𝑄). Thus, the

change in the share is made up of the change in the relative price, 𝑑 (log𝑝𝑑

𝑃), and the change in

the relative quantity, 𝑑 (log𝑞𝑑

𝑄). The changes in the relative price of domestic goods and the

corresponding quantity are

𝑑 (𝑙𝑜𝑔𝑝𝑑

𝑃) = (1 − 𝑤){𝑑(𝑙𝑜𝑔 𝑝𝑑) − 𝑑(𝑙𝑜𝑔 𝑝𝑓)},

𝑑 (𝑙𝑜𝑔𝑞𝑑

𝑄) = (1 − 𝑤){𝑑(𝑙𝑜𝑔 𝑞𝑑) − 𝑑(𝑙𝑜𝑔 𝑞𝑓)}.

Thus, the change in the domestic share becomes

𝑑(log 𝑤) = (1 − 𝑤) {𝑑 (log𝑝𝑑

𝑝𝑓) + 𝑑 (log

𝑞𝑑

𝑞𝑓)}. (7)

For discrete changes from year t-s to t, define the log-change operator as 𝐷𝑥𝑡 =

log 𝑥𝑡 − log 𝑥𝑡−𝑠 = log𝑥𝑡

𝑥𝑡−𝑠, 𝑥 > 0, 0 < 𝑠 < 𝑡. A discrete approximation to equation (7) for

the transition from year t-s to t is

𝐷𝑤𝑡 ≈ (1 − �̃�) {𝐷 (𝑝𝑡

𝑑

𝑝𝑡𝑓) + 𝐷 (

𝑞𝑡𝑑

𝑞𝑡𝑓)},

(8)

8

where �̃� 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 (× 100) of this

share is -51.7, which implies a percentage change of 100(𝑒−51.7 100⁄ − 1) = −40%.

To apply the decomposition in (8), we interpret �̃� 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 − �̃� 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 (× 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 −51.7 − 4.8 +

53.2 = −3.3, 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 (𝑞𝑖

1

𝑞𝑖2)

on log (𝑝𝑖

1

𝑝𝑖2) , where 𝑞𝑖

1 is the volume of domestically sourced consumption of good i

(𝑖 = 1, ⋯ ,20), 𝑞𝑖2 is the volume of the imported variety and the

𝑝𝑖1

𝑝𝑖2 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 𝜎 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 𝜎 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 ‘Transport, machinery and equipment’ 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 ‘Panel I’ (‘Panel II’) 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 – 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:

• optimising behaviour by households and businesses in the context of competitive

markets with explicit resource constraints and budget constraints;

• the price mechanism operates to clear markets for goods and capital;

• 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

• 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:

• price and wage adjustments driven by resource constraints;

• household spending and government spending and taxing adjustments driven by

budget constraints; and

• 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 –

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

− − − = + −

(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

−=

.

(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 model’s 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 purchaser’s 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 – 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


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


19

Table 4. Single-equation OLS estimates of substitution elasticities.

Commodity

Levels of logs Log-changes

Elasticity of

substitution 𝜎

Std. error

Durbin-

Watson

Elasticity of

substitution 𝜎

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 𝜎

Std.

error

Elasticity of

substitution 𝜎

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


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

𝜎𝑖 = 𝜎𝐼,

𝑖 = 1, ⋯ ,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

𝜎𝑖 = 𝜎𝐼𝐼,

𝑖 = 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

𝜎𝑖 = 𝜎𝐼𝐼𝐼,

𝑖 = 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

𝜎𝑖 = 𝜎𝐼𝑉,

𝑖 = 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

𝜎𝑖 = 𝜎𝑉,

𝑖 = 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

𝜎𝑖 = 𝜎𝑉𝐼,

𝑖 = 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

𝜎𝑖 = 𝜎𝑉𝐼𝐼,

𝑖 = 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 𝜎 Standard Error

Elasticity of

substitution 𝜎 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


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)










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











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



3 Merchandise imports

Value Jan-1988 Apr-2019 Monthly STIC classification - 66 commodities (2-

digit) - 10 broad



4 Merchandise exports

Value Jan-1988 Apr-2019 Monthly STIC classification - 66 commodities (2-

digit) - 10 broad



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 birds’ 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

References

Alaouze C.M., Marsden J.S. and Zeitsch J. (1977) Estimates of the elasticity of substitution

between imported and domestically produced commodities at the four digit ASIC level.

IMPACT project, Industries Assistance Commission, Working paper no. 0-11, Melbourne,

January.

Armington, P. S. (1969) A Theory of Demand for Products Distinguished by Place of

Production. IMF Staff Papers 16(1): 159-78.

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

-30.00 -20.00 -10.00 0.00 10.00 20.00 30.00

Dairy Products and Egg

y = -0.6081x - 1.4099

-60.00

-40.00

-20.00

0.00

20.00

-30.00 -20.00 -10.00 0.00 10.00 20.00

Fish

y = -0.71x - 4.2455

-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

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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https://ideas.repec.org/s/fau/wpaper.html

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https://ideas.repec.org/s/ris/prodsw.html

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https://ideas.repec.org/p/uwa/wpaper/06-10.html

https://ideas.repec.org/s/uwa/wpaper.html

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