THE UNIVERSITY OF
NEW SOUTH WALES
School of Economics
______________________________________
THE
WELFARE
IMPACT
AN Liberalis
Assessing
the Impacts
of OF
Further
AUSTRALIA-CHINA
Trade in Thailand:
FREEATRADE
AGREEMENT
Computable
General Equilibrium Analy
______________________________________
KRISTINE PATRICIO
Bachelor of Commerce (Business Economics / Finance)
Jakree Koosakul
Honours in Business Economics
Supervisor:
Professor
Supervisor: Dr.
Sang-Wook (Stanley)
ChoAlan
Woodland
24 October 2011
School of Economics Honours Thesis
Declaration
I hereby declare that this thesis is my own work and that, to the best of my knowledge
and belief, any contributions or materials from other authors have been appropriately
acknowledged. This thesis has not been submitted to any other university or institution
as part of the requirements for a degree or other award.
Kristine Patricio
24 October 2011
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2
Acknowledgements
Firstly I would like to express my deepest gratitude to my supervisor Dr. Stanley Cho
for his guidance, encouragement, patience and generosity with his time and knowledge
throughout the year. Without his help, this thesis would never have been possible.
I would also like to thank the other staff members of the School of Economics: Alan
Woodland, April Cai, Nigel Stapledon, Peter Kriesler and Scott French for their
questions and feedback during my thesis presentation, and Arghya Ghosh and Valentyn
Panchenko for their assistance throughout Honours.
I never thank my family enough for their unconditional love, support and prayers, so
thank you. Also to my friends, who always know how to make me smile.
I must thank Hong Il Yoo for always being willing to help me with Stata, even on his
birthday.
I am grateful to the Kosmos Asset Management Group for their financial support during
my Honours year.
I would like to thank the 2011 Economics Honours cohort for being an amazing group
to share the year with.
Above all, words will never be able to express my thanks to Jesus, my Lord and
Saviour, who led me all the way.
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3
Table of Contents
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List of Tables
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List of Figures
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Abstract
This thesis investigates the potential welfare effects of an Australia-China free trade
agreement (FTA) on heterogeneous Australian households classified according to age,
income, and education. The analytical framework is a static applied general equilibrium
model calibrated with a Social Accounting Matrix (SAM). The SAM is constructed
using the Input-Output tables for Australia and incorporating the Household
Expenditure Survey data. The Australia-China FTA is then simulated through a
numerical experiment eliminating import tariffs across all sectors, and three sensitivity
experiments are performed involving (1) the partial liberalisation scenario, (2) import
elasticities of substitution differentiated by sector, and (3) varied export elasticities of
substitution, for comparison with the benchmark case. The results following the
implementation of an Australia-China FTA show an increase in domestic production
and decrease in consumption good prices for the main Australian export sectors, with
the opposite effect on the main import sectors. Trade volume with China increases
significantly, and while labour wage decreases, the rental rate increases. The results
show that the distributional welfare effects of an Australia-China FTA on differentiated
Australian households delivers the highest gains to the old, low-income, and unskilled
household groups.
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1 Introduction
Since the commencement in 2005 of Australia’s negotiations for a bilateral free trade
agreement (FTA) with its largest trading partner, China, there has been increasing
interest in who will gain and who will lose as a result of the FTA. Economists are
generally in agreement that a free trade agreement will deliver overall benefits to the
countries involved through the reduction of prices and wider variety of goods available
to consumers, as well as the improved efficiency of resource allocation that leads to
higher productivity and output. However, while trade liberalisation delivers aggregate
gains to the participating economies, the degree in which certain agents benefit may be
more than others, while some may be negatively impacted by it. This matter is
particularly significant when at least one of the participating nations constitutes a
significant portion of the bilateral trade. (Cho and Diaz, 2011).
China is both the biggest export market and import source of Australia, and trade
between the two countries has significantly increased over the last few years due to
China’s rapid economic growth and development, and the complementarity of Australia
and China in the goods traded between them. However, while bilateral trade has
experienced strong growth over the years, significant barriers that obstruct trade remain.
China’s average tariff rate is 9.6%, which is relatively high compared to Australia’s
which is 3.5%.
Given China’s importance to Australia as a trade partner, and the potential opportunities
and challenges arising from the elimination of trade barriers following the FTA, there
have been several studies that focused on analysing the potential effects of the FTA on
the Australian economy, such as Mai et al. (2005), Syquia (2007) and Siriwardana and
Yang (2008). However, while the literature has examined different macroeconomic
effects of the FTA including changes in domestic production across industries, trade
volume, and consumer welfare on the aggregate level, no research as of yet has
analysed the distributional impact of the Australia-China FTA on the welfare of
heterogeneous Australian households.
Thus the primary objective of my thesis is to fill in this gap in existing literature on the
Australia-China FTA by investigating how the welfare of differentiated Australian
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8
household groups will be affected. An FTA delivers varying effects across
heterogeneous households through the resulting changes in: (1) factor prices, since the
proportion of household income sourced from each factor differs across households, and
(2) consumption good prices, given that households have different compositions of their
consumption baskets (Nicita, 2004).
There is an abundance of literature wherein the distributional effect of trade
liberalisation was analysed such as Bennett et al. (2008), who analysed the effect of a
Bolivia-U.S. free trade agreement on Bolivian households categorised according to
income and geographical location, and Seshan (2005) who studied the varying welfare
effects of trade liberalisation on Vietnamese households using a similar classification as
Bennett et al. (2008). However, majority of the research in this area studies households
in developing countries and normally categorises them as “urban” or “rural” based on
their residential location, which is not a particularly relevant classification for
Australian households. One study in this area that focuses on a developed country is
Cho and Diaz (2011), which analysed the impact of Slovenia’s accession to the
European Union on the welfare of Slovenian households differentiated according to age,
income, and education1.
My research aims to employ a similar analytical framework as that used in Cho and
Diaz (2011) and place it in the context of the Australia-China FTA to analyse the degree
to which the welfare effects of the FTA on heterogeneous Australian households will
vary. The model is a static applied general equilibrium model, a trade policy evaluation
tool that is widely used in analysing the effects of trade liberalisation due to its
emphasis on the interaction among the different sectors in the economy (Kehoe and
Kehoe, 1994b; Sobarzo, 1992) and its ability to estimate the economic impact of
resource allocation across sectors. These features make it suitable for identifying the
winners and losers following a change in trade policy (Kehoe and Kehoe, 1994a).
To calibrate the parameters of the model, a Social Accounting Matrix for Australia is
constructed using the Input-Output tables and Households Expenditure Survey data.
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Slovenia has been classified as a developed economy by the International Monetary Fund.
9
The Australia-China FTA is then simulated by setting the tariff rates on imports across
all sectors to zero in the full liberalisation scenario.
The results revealed that the domestic production and consumption good prices of the
main export sectors such as food and beverages increase, while those of the main import
sectors decrease. The sector that experienced the largest change in domestic production
and consumption goods price was textile, clothing and footwear, a main import sector
which was subject to the highest Australian tariff rate, 9.85%, prior to the FTA.
Bilateral trade expanded following the elimination of tariffs as importers and exporters
gained improved market access. Large increases in trade volume were experienced by
sectors which previously possessed high tariff rates, the most notable of which was food
and beverages, the sector which had the highest Chinese tariff (15.3%), and whose
export volume increased by 208% after the removal of tariffs.
Trade liberalisation had a positive impact on aggregate welfare. Social welfare
increased following trade liberalisation, as the gain in consumer welfare outweighed the
decrease in the welfare of the government largely due to the decline in its tariff revenue
by 22%.
Examining the impact of the FTA on disaggregated households, I find that while all
households gained in the full liberalisation benchmark case, there were large relative
differences in the degree to which household groups were affected. The increase in
welfare is inversely proportional to income levels, and the welfare of unskilled
households improved more than that of skilled households. Old households in general
experienced welfare gains that were nearly twice as large as that experienced by young
households, and the old poor households, who had the lowest average income,
experienced welfare gains of over six times that of young rich households, who had the
highest average income and lowest welfare gains. The differences in the welfare impact
across household groups could largely be attributed to their main sources of income as
the FTA produced opposite effects on the factor prices as the rental rate increased while
the labour wage decreased.
I also analysed the case of partial liberalisation to account for how in reality, tariffs are
not immediately eliminated following an FTA. Instead, they are gradually reduced over
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10
a transition period (Mai et al., 2005). In this experiment tariffs across all sectors were
reduced in half. Partial liberalisation delivered similar results to the full liberalisation
case, although smaller in magnitude. This implies that a faster implementation of trade
liberalisation would deliver greater gains to the Australian economy.
The second sensitivity experiment involved taking trade elasticities from the literature
(Hummels, 2001; Rolleigh, 2003; and Anderson et al., 2005) to differentiate the import
elasticities of substitution for each sector, which were set uniformly across different
sectors to a constant value in the benchmark and partial liberalisation simulations.
While the results were generally similar to the benchmark scenario though greater in
magnitude, a significant deviation from the benchmark results was that when the import
elasticities from Rolleigh (2003) and Anderson et al. (2005) were used, the welfare of
young rich households was negatively impacted by an Australia-China FTA. This could
be attributed to the much larger magnitude in the decrease in the labour wage, which
accounts for over half of the income for these households, under this experiment.
For the third sensitivity analysis, I compared the change in results using different values
for the export elasticity. The results show that higher export elasticities of substitution
resulted in greater increases in welfare across all household groups.
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The remainder of this paper is structured as follows. Section 2 highlights the importance
of an Australia-China FTA given China’s significance as Australia’s largest trading
partner and the current bilateral trade barriers that exist. Section 3 reviews existing
research on the impact of an Australia-China FTA, as well as literature that analyse the
distributional welfare effects of trade liberalisation and the applied general equilibrium
model commonly employed in these studies. Section 4 describes the data sources
including the household expenditure survey and the Input-Output tables used to
construct the Social Accounting Matrix. Section 5 presents the components of the
model and Section 6 details its calibration. Section 7 discusses the benchmark results
from running the model and the sensitivity experiments conducted. Section 8 concludes
and lists the limitations of this study with suggestions for future research.
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11
2 Background
2.1 The Australia-China Free Trade Agreement
Australia and China share a strong and rapidly growing trade and economic
relationship. The commitment of both countries to further strengthen and advance this
relationship was re-affirmed in the signing of the Australia-China Trade and Economic
Framework on 24 October 2003.
As part of the Framework, which aims to improve commercial and policy linkages and
strengthen two-way trade, both countries agreed to undertake a joint feasibility study
into a possible bilateral free trade agreement (FTA). The Department of Foreign Affairs
and Trade of Australia together with China’s Ministry of Commerce conducted the
feasibility study that included a detailed analysis of the potential opportunities and
challenges presented by an Australia-China FTA. The study was completed in March
2005 and concluded that the removal of trade barriers through an FTA would deliver
substantial economic gains to both countries.
Following the positive recommendations of the feasibility study, negotiations for the
Australia-China FTA commenced on 18 April 2005. Fifteen rounds of negotiations have
taken place since, with the most recent one held last 7 July 2011.
2.2 The Trade Relationship Between Australia and China
The groundwork of the trade and economic relationship between Australia and China
was the 1973 Trade Agreement between the Government of Australia and the
Government of the People’s Republic of China. This relationship has been enhanced by
the development of further bilateral agreements2 as well as both countries’ commitment
to promoting regional economic development through their involvement in the Asia
Pacific Economic Cooperation (APEC) grouping 3 . Both Australia and China are
members of the World Trade Organization (WTO), following China’s more recent
accession in 2001, and their commitment to fulfilling their obligations as WTO
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3
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A summary of the existing bilateral trade and economic agreements is found in Appendix A.
Australia-China FTA Joint Feasibility Study, 2005!
12
members has furthered the institutional basis for the commercial relationship between
the two countries4.
Against this institutional background, trade between Australia and China has flourished
over the years. China is presently Australia’s largest trading partner in goods and
services, with the value of trade in 2010 increasing by 8.8% from the previous year to
total A$90.3 billion, or 17.6% of Australia’s total trade5.
The two-way trade volume in goods and services of Australia with its ten largest trading
partners is shown in 3/5),'!$. Relative to the trade volume with the other countries, the
significance of China as a trade partner is highlighted. The trade volume of Australia
with China is over A$30 billion more than that with Japan, the second largest trade
partner, nearly twice as large as that with the US, the third largest, and almost triple the
trade volume of the fourth largest partner, the Republic of Korea.
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$Am
100,000
90,000
80,000
70,000
60,000
50,000
40,000
30,000
20,000
10,000
0
Source: Composition of Trade Australia 2009-2010
China’s importance as a trade partner also lies in it being both the largest export market
and import source for Australia. 3/5),'!" and 3/5),'!< display the percentage shares of
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4
5
Australia-China FTA Joint Feasibility Study, 2005
Composition of Trade Australia 2009-2010
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Australia’s top export destinations and import sources, with China comprising 21% of
total exports and 15% of total imports.
L160&9!3!"0$%&'E1'R$!B.I!SJI.&%!D'&59%$!O3HH=P3H+HQ
China
Japan
21%
28%
India
Republic of Korea
United States
15%
3%
3%
United Kingdom
New Zealand
Singapore
4%
8%
4%
6%
Taiwan
7%
Rest of the World
J8),@'Y!18698*/+/82!8=!%,-4'!()*+,-./-!"UUA0"U$U!
!
L160&9!7!"0$%&'E1'M$!B.I!,TI.&%!U.0&(9$!O3HH=P3H+HQ
China
15%
United States
Japan
37%
Thailand
13%
Singapore
Germany
8%
6%
4%
4%
4% 5%
5%
United Kingdom
New Zealand
Malaysia
Rest of the World
!
J8),@'Y!18698*/+/82!8=!%,-4'!()*+,-./-!"UUA0"U$U!
In addition to China’s dominance among Australia’s trading partners, trade between the
two countries has been growing over the years. Total trade with China from the years
!
14
Recent economic indicators:
2006
GDP (US$bn) (current prices):
2,712.9
GDP PPP (US$bn) (c):
6,242.0
per capita
2,064
2005-2010 GDP
had a 5-year
trend in (US$):
growth of 20.2% 6 . 3/5),'! I shows Australia’s
perChina
capita
(US$)
(c): China was Australia’s third
4,749
merchandiseGDP
trade with
over PPP
the years
2004-2010.
Real GDP
growth
(%2004,
change
yoy):
12.7
largest merchandise
trading
partner in
but both
exports and imports have
Current
balance
(US$m):
253,268
increased over
the yearsaccount
as depicted in
3/5),'! I, such
that in 2010 merchandise
trade
account
(% GDP):
was valued Current
at A$82.9 billion,
making balance
China Australia’s
largest partner in merchandise9.3
Goods & services exports (% GDP):
39.1
trade.
Inflation (% change yoy):
1.5
3
7
3
L160&9!>!"0$%&'E1'R$!D9&(C'-/1$9!B&'/9!<1%C!FC1-'!
Australia's merchandise trade with China
Real GD
A$m
%
15
Exports
50,000
12
40,000
30,000
9
Imports
20,000
6
10,000
3
0
2004-05
2005-06
2006-07
2007-08
2008-09
!!!!!
2009-10
2006
2007
2008
!
Australia's trade and investment relationship with C
!!!!!!!!!J8),@'Y!B3(%!
Australian merchandise trade with China, 2009-10:
Exports to China (A$m):
have experienced an increasing trend over time. The growth in trade between China and
Imports from China (A$m):
Australia comes from the complementarity of the two economies which sources mainly
Total trade (exports + imports) (A$m):
3/5),'!I decomposes merchandise trade into exports and imports, and shows that both
from their respective economic endowments, as well as China’s evolving development
Major Australian exports, 2009-10 (A$m):
Iron ore & concentrates
25,112
Coal in agricultural and mineral resources that are increasingly
5,067
Australia is abundant
Copper
ores & concentrates
demanded by China
as it experiences
rapid economic growth and industrialisation. 1,719
The
Wool &asother
(incl
tops)
1,522
increase in urbanisation
a result animal
has spikedhair
China’s
demand
for agricultural products
path.
as a larger percentage of the population relies on commercial food. China has also risen
to become the largest producer of iron and steel globally, producing a subsequent
Australia's trade in services with China, 2009-10:
Exports of services to China (A$m):
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Imports of services from China (A$m):
K
increase in its demand for iron ore from Australia. Trade in resources and agricultural
!Trend growth is derived from log-linear regression using the least squares method. This is a more robust
measure than the “average” annual growth since it takes all observations into account and is thus less
Major
service
exports,
(A$m):
likely to be affected
by theAustralian
end points of a given
period (Composition
of Trade2009-10
Australia 2009-2010).!
!
Education-related travel
Personal travel excl education
4,399
15610
Australia's investment relationship with China, 2009 (e):
products is expected to continue to grow in response to demand resulting from China’s
industrialisation. These trades are underpinned by sizeable long-term contracts such as
those for iron ore, and will remain the basis of Australia’s export trade for coming years
(Mai et al., 2005). Exports of iron ore, which is Australia’s largest export item,
increased by 13.8% between 2009 and 2010, and exports of the second largest export,
coal, increased by 60.9% in 2010 from the previous year, and by over 1000% the year
before that in 2009. The large growth in the resources commodities provides
explanation for exports experiencing higher growth than imports in recent years
(Siriwardana and Yang, 2008) as shown in 3/5),'!I.
With a large supply of labour that is increasingly skilled, China is an efficient and largescale producer of consumer and business products. China uses Australia’s exports of
minerals and primary goods, such as iron ore and wool, as inputs in processing
consumer products for the domestic and export markets. Abundant in labour, China in
turn predominantly exports labour-intensive or processing-derived manufactured goods
to Australia (Mai et al., 2005).
The natural trade complementarities between Australia and China can be seen in Table
1 and Table 2 below which show the top export and import items, respectively. From
Table 1 we see that Australia’s exports are primarily primary commodities such as
minerals and resources, and agricultural goods, whereas Table 2 shows that the main
imports from China are mostly labour intensive or manufactured goods, including
clothing, toys, games, and sporting goods, and furniture.
B'#E9!+!"0$%&'E1'R$!B.I!SJI.&%$!%.!FC1-'!O3H+HQ!
SITC
Code
281
321
287
268
283
333
0
682
686
28
2
Trade Value
(A$ millions)
Principal Exports
Iron ore & concentrates
Coal
Other ores & concentrates
Wool & other animal hair
Copper ores & concentrates
Crude petroleum
Food & live animals
Copper
Zinc
Metalliferous ores & scrap
Crude metals (excluding fuels)
25,185
5,067
1,719
1,522
1,200
1,165
995
909
695
482
454
!
!
16
B'#E9!3!"0$%&'E1'R$!B.I!,TI.&%$!G&.T!FC1-'!O3H+HQ!
SITC
Code
84
752
764
894
821
761
5
74
751
851
78
Trade Value
(A$ millions)
Principal Imports
Clothing
Computers
Telecom equipment & parts
Prams, toys, games & sporting goods
Furniture, mattresses & cushions
Monitors, projectors & TVs
Chemicals & related products
General industrial machinery & parts
Office machines
Footwear
Road vehicles
3,792
3,523
3,358
1,909
1,536
1,409
1,042
1,004
890
882
659
!
J8),@'Y!18698*/+/82!8=!%,-4'!()*+,-./-!"UUA0"U$U!
!
2.3 Australia and China Tariffs
Though China and Australia have experienced strong growth in bilateral trade,
significant barriers to these flows remain, such as tariffs on merchandise trade.
The most recent available average applied MFN tariff rates for Australia and China are
shown in Table 3 and Appendix B presents more detail on the tariffs across different
industries. Australia has bound 96.5% of its tariffs, and the bound rates for Australia
range from zero to 55% (clothing). China has bound 100% of its tariff lines which vary
from zero to 65%.7
B'#E9!7!"0$%&'E1'!'-/!FC1-'!"IIE19/!DLV!B'&1GG!:'%9$!O3HH=Q!!
China (%)
Australia (%)
Agricultural (HS 01-24)
14.5
1.4
Industrial (HS 35 - 97)
8.6
3.4
Average
9.6
3.5
J8),@'Y!D%M!%,-4'!R8./@H!7'P/';!
From Table 3 we see that across agricultural and industrial goods, and on average, the
Chinese tariff levels are significantly higher compared with Australia. Appendix C
shows that the Chinese tariff rate is higher than that of Australia across all sectors, and
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
7
!
WTO Trade Policy Review Australia and China
17
the highest average MFN applied tariffs on Chinese imports from Australia are sugars
and confectionery (27.4%), cereals (24%) and beverages and tobacco (22.9%), which
fall under “Food and Live Animals” in Table 1, a top export of Australia. While
Australia has relatively low tariff rates across sectors, one sector stands out which is
clothing (15.4%)8, the biggest import from China listed in Table 2.
Given that China’s border protection is notably higher than Australia’s, China will be
required to reduce its tariffs by more in order to eliminate its trade barriers as part of the
FTA. Australia’s terms of trade will thus improve as a result, and also provide Australia
with greater access to the Chinese markets (Mai et al., 2005).
Trade and economic bilateral agreements, reforms such as China’s accession to the
WTO, and development in both Australia and China have allowed exporters to benefit
from the opportunities offered by the differences in comparative advantage between the
two countries. This section has highlighted the increasing growth in merchandise trade
over the years, the complementarity of Australia and China in the goods traded between
the two countries, as well the tariff barriers that presently obstruct trade.9 From this we
see that further liberalisation through an Australia-China FTA would improve market
access conditions and enhance commercial opportunities for both countries.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
N!See Appendix B!
A!Australia-China FTA Joint Feasibility Study, 2005!
!
18
3 Literature Review
3.1 The Impact of an Australia-China Free Trade Agreement
Several studies have analysed the potential effects of an Australia-China FTA on the
Australian economy. The first of which was a multi-sector dynamic applied general
equilibrium (AGE) analysis of the economic impacts of an Australia-China FTA by Mai
et al. (2005), which was commissioned by the Australian Department of Foreign Affairs
for the purposes of the Joint FTA Feasibility Study. Mai et al. (2005) found that an FTA
would lead to a significant increase in Australia’s real GDP (0.37%), aggregate industry
output, volume of bilateral trade and aggregate welfare. The study measured welfare
using real GNP and consumption (private and public) and revealed an increase of 0.2%
(or US $1.7 billion) and 0.21%, respectively. Mai et al. (2005) also found an increase in
the exports and domestic production of Australia’s major export sectors, agriculture and
mining products, and an increase in the volume of traditional imports of Australia,
wearing apparel and motor vehicles, as well as a decrease in their local production. In
an extension to this paper, Mai (2005) looked at the impact of an Australia-China FTA
on the different Australian States and Territories and found that the FTA would have a
positive effect on the output across all the States and Territories with Western Australia
gaining the most since it produces a large share of the mining and agriculture output
which expand following the removal of barriers to trade.
Syquia (2007), Siriwardana and Yang (2008), and the Centre for International
Economics (2009) also performed quantitative analyses of the economic effects of an
Australia-China FTA and found similar results to Mai et al. (2005).
In his paper, Syquia (2007) used a five-sector, static AGE model in analysing the
impact of the Australia-China FTA on the production sectors. He found that the
agriculture sector would gain the most from the FTA, with the mining sector also
benefitting though on a smaller scale, whereas the textile, clothing and footwear sector
would suffer the greatest losses. In calibrating his model, Syquia (2007) constructed a
Social Accounting Matrix (SAM) using the 2001-2002 Input-Output (IO) tables for
Australia. A contribution of my paper is that it provides an update of the data by using
the 2006-2007 version of the IO tables, the most recent available. In his research,
!
19
Syquia (2007) considered households on the aggregate level and found that aggregate
consumer welfare would increase. He measured welfare through constructing a real
income index, the same measure of welfare that this paper will also employ. My paper
differs from Syquia (2007) in that in addition to analysing aggregate welfare, it also
determines the welfare effect on households at the disaggregate level.
Siriwardana and Yang (2008) evaluated the economic effects of an Australia-China
FTA using a static AGE model with 20 sectors. Their results show that the FTA would
lead to overall economic benefits for both China and Australia, with Australia gaining
more reflecting China’s higher trade barriers, which is similar to the conclusion of
Cheng (2008) and Hoa (2008). Welfare was measured in terms of real consumption and
equivalent variation (EV), the amount of income that would have to be given or taken
from an economy for it to be as well off before the trade liberalisation as after it. Both
countries experienced positive EVs and increases in consumption expenditure.
The study by the Centre for International Economics (2009) sought to update the work
of Mai et al. (2005) by incorporating more recent data on the economies while using a
dynamic AGE model with the same 57-sectoral disaggregation as the 2005 paper. Such
changes that occurred between years 2005 and 2008 was that the Australian and
Chinese economies grew by 25% and 56% respectively, and that China in 2008
accounted for 12.8% of Australia’s total trade as compared to 9.7% in 2005. The study
found that Australia GDP would increase by 0.7%, which is nearly twice of that found
in Mai et al. (2005). Welfare gains in this paper were measured through real
consumption, presented in net present value (NPV) terms to account for gains that may
not be received until a later time in the future. The study estimated the NPV of real
consumption gains over the years 2008 to 2030 and found that Australia would have a
A$94 billion gain in real consumption from the FTA, which is equivalent to 8.7% of
GDP in 2008.
A report by The Allen Consulting Group (2009) entitled “The Benefits to Australian
Households of Trade with China” revealed that Australian households have experienced
substantial gains from trade with China. The increase in exports to China have led to a
rise in employment, wages and GDP, which has in turn resulted in an increase in the
standard of living of Australian households and their ability to purchase more goods, of
!
20
which a significant proportion comes from China. The report concludes by saying that
the prospects of the welfare of Australian residents are dependent on the development
of the Australia-China FTA.
The studies that have analysed the impact of an Australia-China FTA are all in
agreement that the FTA would deliver economic benefits and overall welfare gains for
Australia arising from the differences in their comparative advantage. However, no
research has been done on the varying degrees to which different types of Australian
households will be impacted by the FTA, which is the main contribution of my paper.
An abundance of literature has explored the distributional impact of trade liberalisation
on the welfare of differentiated households, which is discussed in the next section,
highlights the importance of this result of a free trade agreement.
3.2 The Distributional Welfare Effects of Trade Liberalisation
Two widely accepted propositions in economics are that trade liberalisation leads to
aggregate economic gains, but that it can also harm some agents (Davidson and Matusz,
2006). From the literature in the previous section, we see evidence of this with certain
production sectors enjoying gains from an Australia-China FTA while other sectors lose
as a result of it. Another aspect of the varying effects of trade liberalisation on an
economy is in its impact on differentiated households, which has not yet been
investigated in the existing studies analysing the Australia-China FTA. The importance
of studying this is evident in the abundance of literature examining the distributional
effects of trade liberalisation on households groups, which is discussed in this section.
Cho and Diaz (2011) analysed the distributional welfare effects of trade liberalisation
on Slovenian households following the accession of Slovenia to the European Union
using a static AGE model. They disaggregated households according to sociodemographic characteristics – age, income and education – and found that while all
households benefitted from trade liberalisation, the welfare impact across the household
groups varied in their degrees with the “young rich skilled” households benefitting the
most and the “old poor” households experiencing the least gains.
!
21
Gurgel et al. (2003) used an AGE model in evaluating the effects of the Free Trade
Agreement of the Americas (FTAA) and EU-MERCOSUR on Brazil, a member of the
MERCOSUR customs union, with a focus on the distributional effects on Brazilian
households. In their paper they categorised households as rural or urban according to
geographical location, then further classified them according to income. They found
that both the FTAA and EU-MERCOSUR arrangements would result in economic gains
to Brazil and deliver a progressive distribution of the gains across the Brazilian
households such that the poorest households experience the largest percentage increase
in their incomes for both rural and urban households. Cockburn (2002) used a similar
classification as Gurgel et al. (2003) in his AGE analysis of the impact of trade
liberalisation on heterogeneous Nepalese households. He grouped them according to
income and geographic region and found that trade liberalisation favours urban
households and that the impact of trade liberalisation, which is positive in urban areas
and negative in rural areas, increases with level of income. Seshan (2005) also
employed a similar classification of households for Vietnamese households and found
rural households experienced an increase in welfare with the poor gaining more than the
rich, whereas the welfare or urban households decreased with the poorest hurt the most.
In analysing the effects of trade liberalisation on heterogeneous Mexican households,
Nicita (2004) also classified households according to income and whether they live in
rural or urban areas, but in addition he also classified workers as skilled or unskilled to
trace the effects of trade liberalisation on households through the differences in their
wages. Nicita (2004) found that trade liberalisation affected labour income and
domestic good prices which translated to varying effects across households. The skilled
benefitted relative to the unskilled households as a result of the wages of the unskilled
decreasing following trade liberalisation. He also found that while all income groups
gained, the rich households and those living in states closest to the United States
benefitted more.
Another study looking at the impact of FTAs on households was a paper by Bennett et
al. (2008) which performed an AGE analysis on the how Bolivian households would be
affected by a free trade agreement with the United States, Bolivia’s largest trading
partner. They categorised households according to income and geographical area. Their
main finding was that under a full FTA, the poor Bolivian households would lose while
the rich Bolivian households would gain and this would thus worsen the income
!
22
distribution. Households residing in urban areas would also benefit more than those in
rural areas. According to their study, a restricted FTA would deliver a more even
distribution of trade gains across households and is thus more preferable.
Most of the papers analysing the distributional effect of trade liberalisation studied lowincome, developing countries where local markets are usually poorly integrated into the
international economy in addition to being subjected to high transaction costs. Thus the
regional aspect of price transmission is incorporated through classifying households
according to whether they reside in urban or rural regions (Nicita, 2004). However,
given that Australia is considered to be a developed country, geographical region will
not be taken into consideration in household classification and I will adopt the
categorisation of Slovenian households used by Cho and Diaz (2011), which is more
relevant to Australia.
The literature in this section has examined studies that have shown that trade
liberalisation does result in varying distributional effects on the welfare of
heterogeneous households through the changes in income and prices, and thus my paper
aims to analyse this in the context of the Australia-China FTA.
3.3 Methodology
In the two preceding subsections, we see the prevalence of applied general equilibrium
models in analysing the impact of trade liberalisation, including its effects on the
Australian economy (section 3.1), and the welfare of heterogeneous households across
different countries (section 3.2).
Applied general equilibrium models have been widely used in policy analysis over the
past 30 years for both developed and developing countries (Kehoe, 1994a). The central
idea of the applied general equilibrium analysis lies in converting the Walrasian general
equilibrium structure, formalised by Arrow and Debreu in the 1950s, from an abstract
representation of an economy into realistic models of actual economies. Through
specifying demand and production parameters and incorporating real world data of the
economies, the models can be used in evaluating policy (Shoven and Whalley, 1992).
The numerical applications of general equilibrium were pioneered by Johansen (1960),
!
23
who produced the first empirically-based, multi-sector model calibrated to Norwegian
data for analysing sources of economic growth in Norway, and Harberger (1962), the
first to analyse tax policy numerically using a two-sector AGE model calibrated to U.S.
data. An important stimulus in the work on AGE models came from Scarf, who
developed an algorithm for the numerical calculation of the equilibrium of a Walrasian
system. This contribution from Scarf forged the link between applied general
equilibrium analysis and Arrow and Debreu’s theory of general economic equilibrium,
which influenced many mathematically trained economists to approach general
equilibrium from a computational and practical perspective (Kehoe, 1996; Kehoe, 2003;
Shoven and Whalley, 1984).
An AGE model is a computer simulation of an economy consisting of agents such as
consumers, producers, a local government, and foreign sectors, that perform the same
transactions as their real world counterparts. The parameters of the model economy are
calibrated such that the equilibrium replicates the observed data (Kehoe, 1994a).
Statistical estimation techniques can be used to determine the parameters pertaining to
the agents in the simulated economy if a large quantity of data, such as a time series of
Social Accounting Matrices, is accessible (Kehoe 1996). However, due to data
limitations I will be using the more common method of calibrating the AGE model
parameters with a Social Accounting Matrix (SAM) as demonstrated in Kehoe (1996),
Syquia (2007), and Cho and Diaz (2008, 2011). For the purpose of my research which
focuses on the varying impact of trade liberalisation on differentiated households, I
disaggregate households in the SAM using the Household Expenditure Survey data
following other AGE analyses on this topic which have used the expenditure survey for
the same purpose (Kehoe, 1996; Gurgel et al., 2003; Cockburn, 2002; Cho and Diaz,
2011).
Applied general equilibrium models have become widely used in analysing trade
liberalisation, among many other areas. Kehoe and Kehoe (1994) reported that 11 out of
the 12 studies presented in the U.S. International Trade Commission conference in
1992, on the economic impacts of the North American Free Trade Agreement, used
multi-sectoral AGE models. An AGE analysis has been used in evaluating various other
trade agreements such as the Tokyo Round Trade Agreement (Whalley, 1982), a
Philippines-Japan FTA (Yasutake, 2004), free trade areas for MERCOSUR countries,
!
24
(Domingues et al., 2008), and the Ecuador-U.S. FTA (Cho and Diaz, 2008). As seen in
the previous subsections, the use of AGE models has been widespread in analysing the
welfare effects of trade liberalisation (Bennett et al., 2007; Cho and Diaz, 2011;
Cockburn, 2002; Gurgel et al., 2003; Nicita, 2004; Seshan, 2005), and it was the tool of
choice in studying the impact of an Australia-China FTA (Mai et al., 2005; Siriwardana
and Yang, 2008; Syquia, 2007).
A reason for the popularity of AGE models, and the motivation for using it in my
analysis, is that they emphasize the interaction among different sectors in an economy
(Kehoe and Kehoe, 1994b; Sobarzo, 1992). Through this the AGE models are able to
estimate the economic impact of resource allocation across sectors, which makes them
good tools for identifying those who gain, and those who lose, following a change in
policy (Kehoe and Kehoe, 1994a).
!
25
4 Data
4.1 Sectoral Disaggregation
The focus of this research is to determine the effects of an Australia-China FTA on the
production sectors and differentiated household groups in the Australian economy. In
order to do this, an important step in the analysis is determining the level of sectoral
disaggregation.
I considered several factors in selecting the sectors that would be significantly affected
by the FTA: its importance as a major import or export sector in Australia-China trade,
the level of tariff rates imposed on the sector by the two countries, and given the focus
of this paper on the welfare impact on households, the relative importance of the sector
in total household expenditure based on the Household Expenditure Survey. Hence
principal mining exports such as coal and iron ore, and main import items like
chemicals, were not selected since households do not directly purchase these goods.
The chosen sectoral disaggregation is listed in Table 4 and their respective tariff rates
can be found in Appendix D. Food and beverages is a main export sector to China (see
Table 1), and also has the highest tariff rate imposed on by China, among the other
chosen sectors, at 15.3%. The sectors textile, clothing and footwear (TCF), computers
and electronics, transport, toys, games and sporting goods, and furniture are all major
import sectors as shown in Table 2, and the TCF sector has a relatively high Australian
tariff rate (7.4%). Each of these sectors also constitutes relatively significant
proportions of household expenditure. The other manufactures and primaries sector is
composed of all the industries not categorised into the chosen sectoral decomposition
including minerals, the main export of Australia. !
!
26
B'#E9!>!U9(%.&'E!?1$'66&96'%1.-!
Sectors
Food and Beverages
Textile, Clothing and Footwear
Computers and Electronics
Transport
Toys, Games, Sporting Goods
Furniture
Other Manufactures and Primaries
Services
!
4.2 Social Accounting Matrix
Following the selection of the level of sectoral disaggregation, I construct a Social
Accounting Matrix (SAM) for Australia. A SAM is a record of all the transactions that
take place in an economy over a specified period of time, in this study it is one year, by
the different agents in the economy. The column in the SAM itemizes expenditure by
the agents and the row represents their receipts. The structure of the SAM is such that
the row total must equal the column total, that is total revenue must equal total
expenditure, which reflects the market clearing conditions of the economy (Wing,
2004). The SAM is used to calibrate the parameters of the static AGE model by using
the optimality and market clearing conditions such that the agents in the model
economy replicate the same transactions as their real world counterparts according to
the SAM (Kehoe 1994a). The parameters that cannot be directly calibrated from the
SAM are discussed in Section 6.
A SAM for Australia that contains the level of sectoral disaggregation chosen for this
analysis was not available, thus I constructed a SAM for the Australian economy using
the Input-Output tables as my primary data source. The resulting SAM incorporated the
disaggregation of the Australian economy into eight production sectors as shown in
Appendix E. Because this paper aims to quantify the welfare effects on heterogeneous
households, the household sector in the SAM is also decomposed into groups classified
according to age, income and skill level using the Household Expenditure Survey for
Australia. Correspondingly, the factors of productions are also disaggregated into
capital, unskilled labour, and skilled labour. Further details on the construction of the
SAM are listed in Appendix F.
!
27
4.3 Input-Output Tables
The Input-Output (IO) tables for Australia obtained from the Australian Bureau of
Statistics for the most recent years available, 2006-2007, provide detailed information
on the inter-industry transactions that occur in the Australian economy including the
supply and use of domestic production and imports, as well as taxes and margins on
goods.
The Input-Output tables were used in the construction of a Social Accounting Matrix
for Australia. The IO tables consist of 111 industries and 111 product groups which I
aggregate into the eight sectors represented in the SAM. Given that the sectoral
disaggregation focuses on consumption expenditure sectors whereas the IO tables deal
with production sectors, several of the individual industries from the IO tables needed to
be matched with more than one of the eight sectors. To give an example of the
imputation, the “Tanned Leather, Dressed Fur and Leather Product Manufacturing”
industry listed in the IO tables does not have a sole corresponding category in the SAM
and had to be imputed between the “Textile, Clothing and Footwear” and “Other
Manufactures and Primaries” sectors. The IO industry aggregation is detailed in
Appendix H.
4.4 Household Expenditure Survey
The Household Expenditure Survey (HES) of Australia for 2003-2004 was acquired
from the Australian Bureau of Statistics. The HES provides detailed information on
household characteristics, income, and expenditure of 6,957 Australian households.
Using the HES data, households are classified into nine groups according to the
following socio-demographic characteristics: age, skill, and income level. Using a
similar classification criteria as Cho and Diaz (2011), for age I categorise households
aged 65 and above as “old” and those aged 64 and below as “young”. For skill level,
“skilled” households are those that have obtained qualifications above postsecondary
education, while “unskilled” households have attained only postsecondary education or
below that. For income, households that fall within the first quartile are categorised as
“rich”, within the fourth quartile are “poor”, and “middle-income” are households that
lie in the interquartile range.
!
28
Given that trade liberalisation has have varying effects on factors of production and that
households have differing sources of income, household income is classified into that
earned from labour and capital. The labour share of income consists of that from
employment and half of the income from self-employment10. Income from all other
sources such as investments, superannuation and annuities, is classified as income from
capital. Table 5 displays descriptive statistics of the HES data, including the number of
households under each category, their average weekly income, and labour wage share as
a percentage of total income. Here we see that young households have a larger portion
of their income coming from labour than the old, which consist mostly of retired
households. The labour shares calculated from the HES are used in distributing the
factor income (from labour and capital) across households used in the different
production sectors as shown in Appendix I. The average labour share is 76%.
B'#E9!A!?9$(&1I%1;9!U%'%1$%1($W!X.0$9C.E/!SJI9-/1%0&9!U0&;9K!
No. of Households
Average Weekly
Income (AUD)
Labour
Share (%)
Old poor
332
217
0.5
Old middle-income
660
423
2.4
Old rich
330
1120
24.6
Young poor unskilled
792
384
30.4
Young poor skilled
625
389
40.7
Young middle-income unskilled
1135
1086
81.3
Young middle-income skilled
1677
1129
84.3
Young rich unskilled
420
2354
88.1
Young rich skilled
986
2532
89.1
4.5 Combining the Household Expenditure Survey and Social Accounting Matrix
The Household Expenditure Survey details the average weekly expenditure of
Australian household on over 600 items. Each of these items is categorised into
consumption groups consistent with the sectoral disaggregation in the SAM. Appendix
G lists the sectoral matching of the expenditure items.
Following this, the percentage share of consumption expenditure for each sector is
calculated on the aggregate level using the HES data. The share of each sector in total
consumption is also derived from the SAM. Comparing the consumption shares from
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
$U!(!*/6/.-,[email protected]**/=/@-+/82!=8,!+&'!*&-,'!8=!.-C8,!'-,2/25!;-*!)*'4!CH!1&8!-24!B/-b!W"U$$X!
!
29
the HES and the SAM in Table 6 below, we see that the HES and SAM produce similar
results for aggregate consumption shares across the different sectors.
B'#E9!@!"66&96'%9!F.-$0TI%1.-W!X.0$9C.E/!SJI9-/1%0&9!U0&;9K!;9&$0$!U"D!
HES (%)
SAM (%)
Food and Beverages
15.6
15.0
Textile, Clothing and Footwear
3.4
4.3
Computers and Electronics
3.0
3.7
Transport
4.8
4.4
Toys, Games, Sporting Goods
0.5
1.6
Furniture
1.4
1.4
Other Manufactures and Primaries
9.3
10.6
Services
61.6
58.6
Having classified households into different groups, I calculate the consumption
expenditure shares of each of these groups for the eight sectors using the HES data. This
is shown in Table 7 and from the table we see that share of consumption in the sectors
varies across the household groups. For example, old households generally spend more
on food and beverages than the young, whereas young households spend more on toys,
games and sporting goods. The young and rich households also spend more on transport
than the old and poor. Within income groups, we see that skilled households spend
more on computers and electronics relative to the unskilled who spend more on food
and beverages than their skilled counterparts.
B'#E9!*!SJI9-/1%0&9!UC'&9$!.G!%C9!X.0$9C.E/$!
TCF
(%)
Food
(%)
Elec.
(%)
Furn.
(%)
TGS
(%)
Trans.
(%)
Other
(%)
Services
(%)
Old poor
3.8
23.8
4.5
2.8
0.2
4.0
14.6
46.3
Old middle income
3.5
23.6
3.4
1.3
0.3
4.3
14.2
49.4
Old rich
4.1
17.9
2.0
1.3
0.5
4.8
11.6
57.9
Young poor unskilled
3.5
21.1
3.6
2.2
0.7
4.8
11.8
52.4
Young poor skilled
3.3
19.7
4.4
1.6
0.5
4.2
11.6
54.6
Young middle unskilled
3.4
17.4
3.1
1.5
0.6
5.1
9.9
59.1
Young middle skilled
3.4
14.9
3.3
1.7
0.7
5.5
9.0
61.5
Young rich unskilled
3.1
13.9
2.5
1.2
0.5
5.2
7.4
66.2
Young rich skilled
3.2
11.7
3.0
1.2
0.6
5.4
7.0
68.0
TCF = Textiles, Clothing and Footwear, Food = Food and Beverages, Elec. = Computers and Electronics, Furn. =
Furniture, TGS = Toys, Games and Sporting Goods, Trans. = Transport, Other = Other Manufactures and
Primaries
!
30
!
From this we see that the household groups have different compositions of their
consumption bundles of goods, and thus a free trade agreement which has asymmetric
effects on the prices of consumption goods in different sectors would result in varying
impacts on the heterogeneous households. Appendix K shows the consumption
expenditure across sectors of the different households as listed in the SAM, constructed
by allocating the consumption in each sector to the households groups using the
proportion of expenditure shares found in Table 7.
!
!
!
!
31
5 The Model
The model I use is a static applied general equilibrium model11. In the model economy
of Australia there are several agents: producers, the nine differentiated representative
household consumers, the Australian government, and the two foreign trade partners –
China and the rest of the world. I describe the main features of the agents in detail
below.
5.1 Domestic Production Firms
!
An assumption in the model is that the final good is produced using an imported
component and a locally produced input. The local component of the final good is
produced by the domestic production firms. In production they use intermediate inputs
from all eight sectors specified in the sectoral disaggregation, in fixed proportion. The
domestic production firms combine these with capital and both skilled and unskilled
labour using a Cobb-Douglas technology for output. The production function of the
domestic firm producing good i is shown below:
!!!!
!
!
!
!
!!!!
!!!!
!!!!
!
!
!
! !"#! ! ! ! ! ! ! ! ! ! ! !!! !! !!! !!!!!!! !!!!!!! !!!!!!!!!!!!!!!!! !!"#$
!!!!
!!!!
!!!!
with !!!! ! !!!! ! !!!! ! !! !!! ! !! ! ! ! ! !! , the set of production goods; !!!! is the
!
output of the domestic firm i, !!!!
is the amount of intermediate inputs of good m used
!
in the production of good i, !!!!
is the unit-input requirement of intermediate good m in
the production of good i, and !! ! !!!! and !!!! are, respectively, the capital, skilled labour
and unskilled labour inputs used to produce good i.
!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
$$!%&'!684'.!=8..8;*!+&'!=,-6';8,a!8=!1&8!-24!B/-b!W"U$$Xc!which
was in the tradition of Shoven and
Whalley (1984).!
!
32
5.2 Final Production Goods Firms
The final production good firms produce the final good i by combining the imported
component and the domestic input using an Armington aggregator of the form:
!
!
!!!!! ! ! !! ! !!!! !!!!!!! ! !
!
!!!!
!!!! !!!!!!!
$!%#$
!
!!!
where !!!! ! !!!! ! !!!! !! is the elasticity of substitution between domestic and
imported goods (I allow for possibly different elasticities of substitution for different
production goods), !! is the output of the final good i, !!!! is the domestic component in
final good i, and !!!! is the imported component from each of the trade partners. Note
that when !!!! ! 0, the production function takes the usual Cobb-Douglas form, that is,
!
!! ! ! !! ! !!!!!!! !!!
!!!!
!!! !!!!
. The imports of good i from country f are subject to an ad-
!
valorem tariff rate !!!! . In this model, the foreign prices !!!!
!!!! !!!!!!!
are exogenous.
This comes from the assumption that the Australian economy is not large enough to
change world prices.
5.3 Consumption Goods Firms
In the model I assume that the consumption goods that households purchase are
different from the goods bought by the production firms and the government. The
consumption goods which households buy have a high service component embedded in
them. The consumption goods firms may be viewed as similar to a retailer from which
consumers purchase their goods, rather than directly from a wholesaler. The
consumption goods firms combine the final production goods using a fixed proportion
technology:
!!!! ! !"#!
!
!
!
!
!!!!
!!!!
!!!!
!
!
!
!
!
!
!
!
!
!!!!
!!!!
!!!!
(3)
33
where {1, 2, ..., n} are the goods in !! , the set of consumption goods. We make an
!
additional assumption: !!!!
= 0 for I !j, ser. This implies that the consumption good I
firm only uses as inputs final goods of the same sector and services.
5.4 Investment Good Firm
In the model there exists an investment good which is used to account for the savings
observed in the data. Given the static nature of the model, agents do not save in order to
enjoy future consumption as they do in a dynamic model. Instead, agents derive utility
from consuming the investment good, just as they derive utility from the consumption
goods. Hence there exists a firm in the economy to produce the investment good !!"# .
The investment good firm combines the final goods as intermediate inputs in production
using a fixed proportions technology:
!!"# ! !"#!
!!!!"#
!!!!"#
!!!
!!!!"#
!!!!"#
!!!
!!!!"#
$$$!&#$
!!!!"!
5.5 Consumers
The Australian households are disaggregated into nine representative consumers
classified according to age, income and skill level in order to determine the effect of the
free trade agreement on the different types of households. I denote the set of households
by H. Household preferences are represented by Cobb-Douglas utility functions defined
over the consumption goods and savings. The problem of representative household j is:
!
!!!!!"#
!
!
!
!
!! !"#!! ! ! !!"# !"#!!"# ! !
!!!!
!!!
!
!!! !!
!
!
!
!!
!!
!!
!
!!! !!"#!!
!!!"#!!! ! !! ! ! !! !!!!! !! ! ! !! !! ! !! !
!!!!! !! ! ! !!"#! !!"# ! !
!!!!
!
!!"#!! !"# !!"#!!! !!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!!!
!
where !! is the consumption of good i by household j, !!!!! is the price of consumption
!
good i; !! is the direct tax rate imposed on household j, !! and !! are, respectively, the
!!
!!
!!
wage rate for skilled and unskilled labour, and ! is the rental rate of capital; !! ! !! ! ! ,
are respectively, the endowments of skilled and unskilled labour and capital. Note that
!
34
!!
!!
given our disaggregation of households, we must have that either !! > 0 and !! = 0, or
!!
!!
!! = 0 and !! > 0, but no household can have positive endowments of both skilled and
unskilled labour.
Since this is a static setup, I model household savings as purchases of the investment
!
good. Thus, !!"# represents the purchases of the investment good by household j, and
!!"#! is the price of the investment good. Additionally, if Australia is running a trade
surplus with a trade partner, I model this as household purchases of a foreign
!
investment good (i.e., Australian households are saving abroad). Then, !!"#!!! represents
!
the purchases of the investment good from country f by household j, !!"#!!
, its price
(which is assumed to be exogenous) and !!! is the bilateral real exchange rate. However,
based on the data Australia is not running a trade surplus with either trade partner.
5.6 Government
The Australian government, as depicted in the Social Accounting Matrix, purchases
goods and runs a fiscal surplus. To account for this I follow Whalley (1982) and Kehoe
(1996) in assuming that in the model the government is an agent that derives utility
from consuming the production goods and the investment good. Revenues collected
from direct and indirect taxes and tariffs imposed on imports finance the government
purchases of the goods. The problem of the government is:
!
!!!!!!!!"#
!
!
!
!! !"#!! ! ! !!"# !"#!!"# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!!!!
!
!! !!
!
!! !! ! !!"# !!"# !!!!
!!!!
!!
!!
!! !! !! ! ! !! !! ! !!
!!
!!
!!!
!!!!! !!!! !!!! !
!!!!
!!
!
!!!! !! !!!!
!!!!
!!!!! !!!! !!!! !! ! !
!!!!
!!! !!!!
!
The left-hand side of the budget constraint of the government includes the purchases of
goods and the investment good. The right-hand side of the equation includes the tax and
tariff revenues: the first term is the direct taxes collected from the income of the nine
different households; the second term is the tax revenue from the domestic firms, the
!
35
third term is tax collected from consumption goods firms, and the last term represents
the tariff revenues collected from the two trade partners.
5.7 Foreign Trade Partners
In the model economy Australia has two trade partners: China and the rest of the world
(ROW). I represent the set of trade partners by T = {China, ROW}. For each of the
trade partners f ' T there is a representative household consumer that purchases
imported goods !!!! from Australia, and consumes the local good !!!! ! If the trade
partner is running a trade surplus with Australia, I model these savings as foreign
purchases of the Australian investment good !!"#!!! . The problem of the representative
household in the foreign country f is
!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"#
!
!
!!!! !!!!! ! ! !! !!"#!! !!!!! ! !!! !!!! !!!!! ! ! !! !!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!
!!!!
!
!! !!!!
!! ! !! !!!!! !!!! ! !!!"! !!"#!!! !!!!!! !! ! !!!! !!
!!!!
!
where !! is the ad-valorem tariff rate that country f imposes on the imports of good j, !!
is the parameter that determines the exports elasticity of substitution !! (i.e., !! !
!!!! ! !! !), !!!! is the bilateral real exchange between Australia and country f, and !!
is the (exogenous) income of the household in country f.
5.8 Definition of Equilibrium
An equilibrium for this economy is a set of prices for the domestic goods !!!!!
prices for the final goods !!
consumption goods !!!!!
!!
!
!!!
!
!!!!
!
; foreign prices !!!!
!
!! ! !!"#
!!!! ;
;
a price for the investment good !!"# ; prices for the
; factor prices !! ! !! ! ! ; bilateral exchange rates
!!!! !!!!!!!
; a consumption plan for each type of household
!
!!!! !!!!!!!
!!!!
!
; a consumption plan for the government !! ! !!"#
!!!!
; a
36
consumption plan for the household in country f
!!!!! ! !!"#!!! ! !!!!!
!!!! !!!!!!!
; a
!
!
production plan for the domestic good i firm !!!!! ! !!!!
! ! ! !!!!
! !! ! !!!! ! !!!! ; a
production plan for the final good i firm !!! ! !!!! ! ! ! !!!!
!!!
! ; a production plan for
!!"#! ! !!!!"# ! ! ! !!!!"# ! ; a production plan for the
the investment good firm
!
!
consumption good i firm !!!!! ! !!!!
! ! ! !!!!
! ; such that, given the tax rates and the tariff
rates:
!
!
!
– The consumption plan !! ! !!"# ! !!"#!!
!!!! !!!!!!!
solves the problem of household
j.
!
!
– The consumption plan !! ! !!"#
!!!!
– The consumption plan !!!!! ! !!"#!!!
solves the problem of the government.
!!!!!
! !!!!! solves the problem of the
representative household in country f.
!
!
– The production plan !!!!! ! !!!!
! ! ! !!!!
! !! ! !!!! ! !!!! ! satisfies
!!!! ! !"#!
!
!!!!
!
!!!!
!!!
!
!!!!
!
!!!!
!!!
!
!!!!
!
!!!!
!
!
!
! !! !! !!! !!!!!!! !!!!!!! !!and
!
!! !!!!
!! ! ! !! !!!! ! ! !! !!!! ! !!! ! ! !! ! !!!"!!!!! ! ! !
!! ! !!!!! !!!!! !!!! ! !
!!!!
– The production plan !!! ! !!!! ! !!!!
!!!
! satisfies
!
!! ! !!!! !!! !!!!
!!!! ! ! !!! ! !!!!"!!! ! !!
!! !!! ! !! !!!! !!!!! ! !
!!!
where !!!! and !!!!
!!!
! solve
!
!! ! !!!! !!! !!!!
!!!! !
!"#!!! ! !!!!! !!!!! !!!! ! !
!!!
!
37
!
!
!!!!
!
!! !! !! ! !!!! !!!!!!! ! !
!!!! !!!!!!!
! ! !!
!!!
– The production plan !!"#! ! !!!!"# ! ! ! !!!!"# ! satisfies
!!!!"#
!!"# ! !"#
!!!!"#
!!!
!!!!"#
!!!!"#
!!!
!!!!"#
!"#
!!!!"#
!
!! !!!!"#
! ! !! ! !!!"!!!"# ! ! !
!!!"! !!"# ! !
!!!!
!
!
– The production plan !!!!! ! !!!!
! ! ! !!!!
! satisfies
!!!!
!
!
!
!!!!
!!!!
!!!!
! !"#! ! ! ! ! ! ! ! ! ! !!"#
!!!!
!!!!
!!!!
!
!! !!!!
! ! !! ! !!!"!!!!! ! ! !
!! ! !!!!! !!!!! !!!! ! !
!!!!
– The factor markets clear:
!!
!!!! ! !
!!!!
!! !!!!!!!!!!!!!!!!!!!!!
!!!
!!
!!!! ! !
!!!!
!! !!!!!!!!!!!!!!!!!!
!!!
!! !
!! !
!!!!
!!!
– The goods markets clear:
!
!
!!!!
!
!! !
!!!!
!
!
!!!!
! !!!!"#
! ! ! !! !
!!!!
!!!! !
!!!
!
!!!! !
!!
!!!
!
!!"# !
!!!
!
!
!!"# ! !!"# !
!!"#!!
!!!
38
– The balance of payments condition for each trade partner country f is satisfied:
!
!
!! !!!!
!!!! ! !
!!!!
!
!
!! !!"#!!
!!"#!! ! !
!!!
!! !!!! ! ! ! !!!" !!"#!! !
!!!!
39
6 Calibration of the Model
The AGE model in this paper is a computer representation of the Australian economy
consisting of the agents described in the previous section. The AGE model is a system
of non-linear equations, and I solve these using the software Matlab. To analyse the
effects of an Australia-China FTA using a static AGE model, I employ the comparative
statics methodology: I calibrate the model such that in equilibrium the agents in the
model economy replicate the same transactions that their counterparts in the real world
undertake according to the SAM. I then simulate the FTA by setting the tariff
parameters to zero. From this, a new equilibrium is calculated and I can identify the
changes in prices, trade volume, production and welfare (Kehoe and Kehoe 1994a).
The values of the calibrated parameters for the model economy are found in Appendix
L. Using the optimality and market clearing conditions, most of the parameters
including the preference parameters in the utility functions of the agents, and the input
shares and total factor productivity scale parameters in the production functions, can be
directly calibrated from the SAM. However, there were some parameters that could not
be calibrated from the data, and in this section I discuss how I obtained values for them.
6.1. Tariff rates
The tariff rates that Australia imposes on imports are extracted implicitly from the SAM
and are thus effective tariff rates and are similar to the Australian tariff rates calculated
from the WTO in Appendix D. The tariff rates that China and the rest of the world levy
on Australian imports were computed from the WTO Tariff Download Facility12. The
China tariff rates are for the year 2007 for consistency with the 2006-2007 Input-Output
tables used in the construction of the SAM. The tariff rates of the rest of the world is
represented by the simple average of the tariffs of Japan and the U.S., Australia’s
second and third largest trading partners, respectively, after China13. The tariff rates of
the rest of the world can be found in Appendix M.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
12
Tariff rates for the chosen level of disaggregation were unavailable, hence I calculated the tariff rate for
each sector by matching each of the 5051 items (HS07), along with their corresponding tariff rates listed
to one of the seven merchandise sectors and calculated the simple average. The tariff rates are the MFN
Applied Tariff (Average of AV duties).
13
This approach in calculating the tariffs for the rest of the world was also employed in Cho and Diaz
(2008, 2011) and Syquia (2007).!!
!
40
China imposes higher tariff rates across all sectors, with the highest on food and
beverages, which is a main export to China. There is one sector on which Australia has
set a noticeably high tariff level at 9.85% which is textile, clothing, and footwear, a
main import item.
B'#E9!2!FC1-'!'-/!"0$%&'E1'!U9(%.&!B'&1GG!:'%9$!
China (%)
Australia (%)
Food and Beverages
15.3
0.8
Textile, Clothing and Footwear
15.3
9.9
Computers and Electronics
9.1
0.6
Transport
11.2
4.4
Toys, Games, Sporting Goods
10.9
2.8
Furniture
7.3
3.6
Other Manufactures and Primaries
8.1
0.9
Services
0.0
0.0
6.2. Income of Trade Partners
The income for each of the trade partners, China and the rest of the world, was taken
from the World Bank national accounts data on GDP for the year 200714.
6.3 Direct Tax Rates
From the Household Expenditure Survey data I noted that the amount of direct tax paid
by households to the government varies across the household groups. I obtain a direct
tax rate for each household group as the proportion of disposable income that goes to
direct tax payments. Hence the direct tax rates are effective tax rates.
6.4 Elasticities of Substitution
The elasticities of substitution for exports and imports could not be directly calibrated
from the SAM because of the static nature of the model. For these parameters I use
different sets of values. I set !!!! ! !!!!!!!! ! !! , and !! ! !!! for the benchmark
experiment, which implies an elasticity of import substitution of five, and export
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
$I!38,!@82*/*+'2@H!;/+&!+&'!"UUK0"UU>!?29)+0M)+9)+!+-C.'*!)*'4!/2!@82*+,)@+/25!+&'!J(G#!
!
41
substitution of ten15. Sensitivity experiments I perform involves differentiated import
elasticities for each sector and varied export elasticities of substitution. I obtained the
import elasticities from literature, Hummels (2001), Rolleigh (2003), and Anderson et
al. (2005). The values are found in Table 9.
Anderson et al. (2005) elasticities are significantly higher than the others. These
elasticities were obtained by estimating commodity-specific elasticities of substitution
consistent with a well-fitting model to the data, and their paper found that elasticities
higher than those widely accepted are necessary for modelled behaviour to fully explain
observed variation in bilateral trade flows.
Most of the sectors did not have an exact match with the sector disaggregation in the
papers, thus I got the simple average of the elasticities of the related sectors in the
papers to obtain the elasticities for the sectors16. Also, the import elasticities were only
available for merchandise goods in Hummels (2001) and Rolleigh (2003), so for the
services sector I used the same elasticity as in the benchmark case. The export
elasticities of substitution is held constant at the value in the benchmark case, !! ! !!!
in the sensitivity analysis with differentiated import elasticities.
B'#E9!=!,TI.&%!SE'$%1(1%19$!.G!U0#$%1%0%1.-!(!!!! )!
Sector
Hummels
Rolleigh
Anderson
Food and Beverages
0.78
0.77
0.93
Textile, Clothing and Footwear
0.85
0.92
0.95
Computers and Electronics
0.88
0.77
0.94
Transport
0.86
0.91
0.94
Toys, Games, Sporting Goods
0.80
0.93
0.95
Furniture
0.73
0.94
0.95
Other Manufactures and Primaries
0.80
0.88
0.95
Services
0.80
0.80
0.93
!
!
!
!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
15
16
!
These values are commonly used in literature such as in Cho and Diaz (2008, 2011) and Syquia (2007).
A similar measure for calculating elasticities was used in Cho and Diaz (2011).
42
7 Results
In this chapter I present the results of my simulation. The first section presents the
outcome of a full implementation of the Australia-China FTA. To simulate this I set the
tariffs across all sectors to zero. I also consider the case of partial liberalisation where
tariffs are not completely removed, but reduced by half in each sector. The partial
liberalisation experiment aims to take into account that in reality, trade liberalisation is
implemented over a number of years, that is, tariffs are not immediately eliminated, but
instead gradually reduced over a transition period (Mai et al., 2005).
The next sensitivity experiment I perform is a full liberalisation simulation, but with the
import elasticities of substitution differentiated for each sector, whereas in the
benchmark case I set uniform Armington elasticities across all sectors. The elasticities
for each sector are taken from Hummels (2001), Anderson et al. (2005), and Rolleigh
(2003). The third sensitivity analysis involves varying the export elasticities of
substitution.
All the results are presented as percentage deviations from the case before the
implementation of an Australia-China FTA. I analyse the effects of trade liberalisation
on consumption good and factor prices, domestic production, trade volume with China
and the rest of the world, and welfare.
In analysing welfare 17 , I construct a social real income index that uses both the
consumer real income index and the government real income index to look at the
aggregate welfare index. The consumer real income index is given by
!!
! !!
where j
ranges over the consumption goods and the investment good. The government real
income index is defined as
!!!!
! !!!!
where j ranges over the production goods and the
investment goods consumed by the government. The social real income index is defined
as
!!
! !!
where !! ! !! ! ! !!!! and !! ! !
!! !!!!!!
! !! ! ! !!!! !
. In analysing the effect on welfare
for each of the households groups, I consider only the consumer real income index for
that particular group.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
$>!
!
I take the measure of welfare used in the literature (Syquia, 2007; Cho and Diaz, 2008; 2011).!
43
7.1 Full and Partial Liberalisation
This section presents the results from the simulation of (1) the full liberalisation case
wherein tariffs are set to zero, and (2) the partial liberalisation scenario where tariffs are
reduced in half18.
3/5),'!Q lists the percentage change in the consumption good prices following the free
trade agreement. The prices for the two sectors that are main exports of Australia in my
sectoral disaggregation, (i) food and beverages and (ii) other manufactures and
primaries which include the mining sector, have increased whereas the prices for the
remaining merchandise sectors which are main imports of Australia, have decreased. In
particular, the price of textile, clothing and footwear goods, which is the sector with the
highest tariff rate that Australia imposed on China (9.85%), has decreased significantly
by 2.31% after the elimination of tariffs. Similar effects are experienced under partial
liberalisation in lower magnitude.
L160&9!A!SGG9(%!.G!%C9!LB"!.-!F.-$0TI%1.-!Y../!N&1(9$
!
Food and
Beverages
0.17
Textile,
Clothing and
Footwear
Computers
and
Electronics
Transport
Parts and
Vehicles
Toys, Games
and Sporting
Goods
Furniture
Other
Manufactures
and Primaries
0.10
0.04
0.07
-0.08 -0.04
-0.01 -0.01
-0.21
Services
0.10
0.05
-0.27
-0.41
-0.66
-0.97
-2.31
Full liberalization (%)
Partial liberalization (%)
!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
18
!
Syquia (2007) used the halving of tariff rates in his simulation of partial liberalisation.
44
An effect of trade liberalisation is that it results in the reallocation of resources, such as
labour and capital, across industries which is associated with the increasing product
specialisation in line with each country’s comparative advantage. This increased
specialisation in the goods for which Australia has a comparative advantage in leads to
a more efficient allocation of resources and increased output, benefitting the Australian
economy. As noted in Section 2, Australia’s comparative advantage lies in agriculture
(food and beverages) and mining and unprocessed rural goods (other manufacturing and
primaries) and hence we see an increase in production in these sectors following trade
liberalisation, and a decrease in the production of the goods in which China has a
comparative advantage. 3/5),'! K shows the effect of the free trade agreement on
domestic production. There is a decrease in the production of sectors which are main
merchandise imports of Australia, and an increase for the main export sectors. Textile,
clothing and footwear is once more the sector that experienced the largest change in
magnitude, with a decrease in domestic production of 8.98% under full liberalisation
and 3.96% under partial liberalisation.
L160&9!@!SGG9(%!.G!%C9!LB"!.-!?.T9$%1(!N&./0(%1.-!
!
Food and
Beverages
Textile,
Clothing and
Footwear
Computers
and
Electronics
Transport
Parts and
Vehicles
Toys, Games
and Sporting
Goods
Other
Manufactures
and Primaries
Furniture
Services
2.03
0.24
0.95
0.04
-0.79
-1.59
-0.49
-0.13
-0.30
-0.22
-1.09
-1.26
-2.17
-3.15
-3.96
-8.98
Full liberalization (%)
Partial liberalization (%)
!
Table 10 displays the aggregate effect of trade liberalisation on the trade volume
between Australia and its trade partners. From the table we see that the elimination of
tariff rates has a large impact on the expansion of bilateral trade with China, as total
!
45
exports increase by 49.35%, and total imports by 30.45%, in the full liberalisation case,
and by 22.96% and 13.95%, respectively, under partial liberalisation. Trade with the
rest of the world has also increased in both exports and imports though by a much
smaller extent than that with China, which reveals that the FTA will be trade creating
for the rest of the world on the aggregate level.
B'#E9!+H!SGG9(%!.G!%C9!LB"!.-!"66&96'%9!B&'/9!Z.E0T9!
Full
Liberalisation
(%)
Partial
Liberalisation
(%)
Total Imports from China
49.35
30.45
22.96
13.95
Total Exports to RoW
Total Imports from RoW
0.55
0.49
0.26
0.23
Total Exports to China
In Table 11 we see that exports to China increase in both of the main export sectors
where Australia is considered to have a comparative advantage. The significant increase
in exports of food and beverages goods, 208.52% and 75.74% under the full and partial
liberalisation scenarios, respectively, can largely be attributed to the reduction of tariff
rates, as food and beverages was the sector with the highest China tariff rate (15.3%).
Exports to the rest of the world also increased in other manufactures and primaries, but
not in food and beverages. This decrease, though relatively small, implies the shift of
exports in this sector from the rest of the world to China following trade liberalisation,
because of the higher tariff rate of the rest of the world on this sector. Food and
beverages was the sector which held the second highest tariff rate imposed on by the
rest of the world at 7.1%. This suggests that though the FTA is trade creating on the
whole for the rest of the world, there would be some trade diversion across individual
sectors19
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
$A!The
!
change in trade across all sectors can be found in Appendix N.!
46
B'#E9!++!SGG9(%!.G!%C9!LB"!.-!D'1-!SJI.&%$!%.!FC1-'!'-/!:.[!
Full Liberalisation
(%)
Partial Liberalisation
(%)
China
Food and Beverages
Other Manufactures and Primaries
208.52
66.27
75.74
31.33
Rest of World
Food and Beverages
Other Manufactures and Primaries
-0.79
0.08
-0.38
0.11
Sector
The increase in Australia’s primary exports will lead to increased production for the
Australian industries that produce these goods (Mai et al., 2005), which is what we saw
in 3/5),'! K. Table 12 shows us the effect of the FTA on Australia’s main import
sectors. Under both full and partial liberalisation we see an increase in imports from
China across all sectors, particularly in textile, clothing and footwear and transport, the
two sectors which had the highest Australian tariff rates prior to trade liberalisation.
This will lead to an increase in China’s production in these sectors, but lower Australian
production in these sectors which is seen in 3/5),'!K.
Imports from the rest of the world fall in all the main import sectors implying a shift in
Australia sourcing these goods from China. Following the reduction of Australia and
China tariffs, the most significant decrease in imports from the rest of the world was in
the textile, clothing, and footwear sector which had the highest tariff rate imposed on by
the rest of the world (9.7%).
!
47
B'#E9!+3!SGG9(%!.G!%C9!LB"!.-!D'1-!,TI.&%$!G&.T!FC1-'!'-/!:.[
Full Liberalisation
(%)
Partial Liberalisation
(%)
Textile, Clothing and Footwear
66.45
27.16
Computers and Electronics
16.53
8.17
Transport
41.50
18.55
Toys, Games and Sporting Goods
28.51
14.10
Furniture
32.08
12.70
Textile, Clothing and Footwear
-9.55
-4.19
Computers and Electronics
-1.61
-0.78
Transport
-0.87
-0.39
Toys, Games and Sporting Goods
-2.65
-1.32
Furniture
-3.80
-1.55
Sector
China
Rest of World
The change in factor prices is listed in Table 13 below. The increase of the rental rate is
over five times greater than the decrease of wages in both the full and partial
liberalisation case. This is consistent with the Stopler-Samuelson theorem which
predicts that trade liberalisation will shift income toward a country’s abundant factor.
Hence, free trade will benefit an economy’s abundant factor (e.g. labour will gain in the
case of a labour-abundant country) while the scarce factor loses, from the opening of
trade. China would be the labour abundant country with its population of 1.3 billion and
labour force of 815.3 million it would have more labour per capital compared to
Australia which has a population of 21.7 million and labour force of 11.87 million20.
Hence China’s labour wage will increase and Australia’s will decrease, while the rental
rate of Australia will increase whereas China’s will decrease. This opposite impact on
the factor prices will have varying welfare implications on the households depending on
the source of their income.
!
!
!
!
!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
"U!CIA World Factbook!
!
48
!
B'#E9!+7!SGG9(%!.G!%C9!LB"!.-!FC'-69!1-!L'(%.&!N&1(9$!
Full Liberalisation
(%)
Partial Liberalisation
(%)
Rental rate
0.51
0.23
Wage (skilled)
-0.09
-0.04
Wage (unskilled)
-0.09
-0.04
The effect of the free trade agreement on national welfare is shown in 3/5),'! >.
Aggregate consumers experience an increase in welfare whereas there is a negative
impact on government welfare as its total tariff revenue declines by 22.5% under the
full liberalisation, and by less under partial liberalisation (11.14%) when they still have
some tariff revenue from Chinese imports. The decrease in tariff revenue is not a major
concern as Australia has a very low reliance on trade taxes for revenue (Mai et al.,
2005). The consumer welfare gains outweigh the government loss, as the change in
overall social welfare is positive in both the partial and full liberalisation cases.
L160&9!*!SGG9(%!.G!%C9!LB"!.-!"66&96'%9![9EG'&9
!
Aggregate consumer welfare
Government welfare
Social welfare
0.21
0.10
0.08
0.03
-0.21
-0.43
Full liberalization (%)
Partial liberalization (%)
The main focus of this research is to analyse the welfare impact that the Australia-China
free trade agreement will have on different kinds of households since this is an aspect of
the FTA that has not yet been covered by previous studies.
!
49
!
We see in 3/5),'!N that while all household groups experience welfare gains from trade
liberalisation, the degree to which their welfare improved varies in magnitude across
households which could be attributed to their differences in sources of income and
consumption bundles.
L160&9!2!SGG9(%!.G!%C9!LB"!.-!%C9![9EG'&9!.G!?1$'66&96'%9!X.0$9C.E/$!
Full liberalization (%)
Old poor
Partial liberalization (%)
Old middle income
0.19
Young poor unskilled
0.19
Young poor skilled
Young middle skilled
Young rich unskilled
Young rich skilled
0.47
0.22
Old rich
Young middle unskilled
0.49
0.22
0.12
0.07
0.06
0.04
0.03
0.42
0.42
0.27
0.15
0.13
0.08
0.07
In terms of the effect across age groups, the increase in welfare of old households is
over twice that of young households. The increase in welfare is inversely proportional
to income levels, and the welfare of unskilled households improved more than that of
skilled households.
On average, the greatest gains which is experienced by the old poor households is over
five times that of the young rich skilled, the category with the smallest welfare
improvement in both the full and partial liberalisation scenarios.
The greater increase for old households could be attributed to their main source of
income, which is the rental rate of capital. The free trade agreement results in an
!
50
increase in the rental rate, whereas there is a decrease in the labour wage rate which
negatively affects labour income, the primary source of income for young households.
Apart from the differences in income source and factor price changes, another reason
for the lesser magnitude of welfare gains of young rich households is the change in
consumption prices after the implementation of the FTA, such as the increase in the
price of the service sector. Young rich households have the highest expenditure share on
services (over 65%) according to the data and old poor households have the lowest
(46.3%). The other two sectors which also experience increase in prices were food and
beverages and other manufactures and primaries. While old poor households have larger
expenditure shares in both of these sectors than the young rich (23.8% compared to
14.6% in food and beverages, and 14% as compared to 7.5% for other manufactures and
primaries), the share of expenditure that these two sectors make up is smaller than the
share the services sector constitutes, and thus the negative impact on welfare of the rise
in prices of those two sectors is smaller than that of the increased price of services.
We notice that the unskilled households experience greater welfare gains than their
skilled counterparts within age and income categories. Recall that in Section 4 we saw
that all unskilled households had a lower share of their income coming from labour than
their skilled counterparts. Hence unskilled households would experience a lower
negative impact from the decrease in the wage factor price and higher gains resulting
from the increase in rental rate. The difference in share of labour income and factor
price changes could also explain why the poor households have a greater increase in
welfare, as their share of income from labour is smaller than that of middle income and
rich households.
While it was earlier noted that old and poor households spend more than young rich
households on goods from the food and beverages sector which increased in price after
the FTA, the gain from income appears to outweigh the increase in prices resulting in
the old and poor households gaining more than the young and rich. As with the other
macroeconomic effects, partial liberalisation delivers the same effect as full
liberalisation on a smaller scale.
!
!
!
51
Gini Coefficient
B'#E9!+>!SGG9(%!.G!%C9!LB"!.-!FC'-69!1-!Y1-1!F.9GG1(19-%
Pre-FTA
Post-FTA
Full Liberalisation
0.2092
0.2087
Partial Liberalisation
0.2092
0.2090
The Gini coefficient for households calculated using after-tax income21 according to the
household expenditure survey was 0.352 which is roughly similar to the Gini index for
Australia of 0.305 from the CIA World Factbook22.
The model used for this paper does not generate the after-tax income for each individual
household, only the after-tax income for each household group. Therefore, in
calculating the post-FTA Gini coefficient, I divided the total after-tax income of the
household group by the number of households in that category and used this average as
the after-tax income each household has after the implementation of the FTA. From this
the Gini coefficient was 0.2087 for the full liberalisation case, and 0.2090 under partial
liberalisation. Given this method of calculating the post-FTA Gini index, the Gini
coefficient calculated from the household expenditure is not directly comparable to the
one calculated after trade liberalisation since the expenditure survey data gives us the
after-tax income for each individual household. Hence I calculate a comparable preFTA Gini coefficient by getting the total after-tax income of each household group
before the implementation of the FTA, dividing each group’s total by the number of
households in that group, and taking this as the after-tax income of each individual
household in that group. From this I calculate the Gini coefficient which was 0.2092 for
both scenarios.
From this we can see that the Gini coefficient, which is a measure of income inequality,
decreased after the implementation of the FTA, though by a small margin, from 0.2092
to 0.2087 and 0.2090 under full and partial liberalisation respectively, which implies
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
"$!(=+',0+-L!/2@86'!;-*!)*'4!/2!@-.@).-+/25!+&'!S/2/!@8'==/@/'2+!-*!+&/*!;-*!+&'!/2@86'!4-+-!
5'2',-+'4!CH!+&'!684'.#!%&'!S/2/!@8'==/@/'2+!;-*!8C+-/2'4!)*/25!?2'T4'@8c!+&'!J+-+-!684).'!+8!
@-.@).-+'!/2'T)-./+H!/24/@'*!
"" This is the Gini index for the most recent year available (2006) from the CIA World Factbook, and
also is consistent with the 2006-2007 Input-Output tables used in constructing the SAM.
!
52
that the FTA results in income redistribution which is consistent with what was
presented in 3/5),'!N wherein the welfare of the poor households increased more than
that of the rich.
7.2 Comparison of the Results with Existing Literature
In this section I compare the full liberalisation benchmark results with those found in
existing studies on the Australia-China FTA to check the robustness of my results. I
find that my results are generally consistent with the literature23.
Consumption Good Prices
Syquia (2007) found similar results in the change in consumption good prices, in that
prices increased for the main export industries, agriculture (0.26%) and mining (0.08%),
and decreased for the main import sectors, with the highest decrease in the textile,
clothing and footwear sector (-0.66%).
Domestic Production
The Centre for International Economics (2009) found the largest increases in production
of other animal products (9.5%) and minerals (2.4%), and the largest decrease in textiles
and clothing (-4.3%) and electrical products (-1.4%).
Syquia (2007) found the sectors whose production increased were agriculture (3.3%)
and mining (0.38%) and those that contracted were textile, clothing and footwear
(-2.8%), machinery (-2.2%) and other manufactures and primaries (-0.1%). The results
of Siriwardana and Yang (2008) also show that Australia would have a significant
decrease in production in wearing apparels (10%), textiles (6.3%), and motor vehicles
and parts (0.52%) but increase in ferrous metals (5%), and food products (0.14%).
Trade Volume with China
In the study by the Centre for International Economics (2009), exports to China
increased by a similar amount as reported in this paper, 37%, and imports increased by
24%. Though in each sector the changes in magnitude were smaller than those in my
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
23
Not all the studies reported results on each of the macroeconomic effects analysed in this paper thus I
present the results of the papers when relevant.
!
53
results, the change followed the same direction where there was an increase in the
exports of animal products (28.7%), minerals (3.6%), and other food and beverages
(2.9%) and the imports of textile and clothing (9.9%), electrical products (3.4%), and
transport (1.4%).
Mai et al. (2005) found overall increases for Australia in trade with China, as aggregate
exports increased by 7.3% and imports by 14.8%. Their study also found increases in
main export sectors food products (33.9%) and mining (6.6%), as well as increases in
the volume of major import sectors motor vehicles and parts (31.5%), wearing apparel
(24.5%) and textiles (9%).
Syquia (2007) found an increase in the volume of exports of the major export sectors,
agriculture (138.2%) and mining (36.2%) and the main import sectors, textile clothing
and footwear (121.9%), machinery (43.3%), and other manufactures and primaries
(43.8%). Similar results were reported by Siriwardana and Yang (2008), that an FTA
would produce an increase in Australian exports of food products (134.29%) and
ferrous metals (99%) and in increase in imports of wearing apparel (73%) and motor
vehicles and parts (36.92%).
Trade Volume with the Rest of the World
Syquia (2007) found that on the aggregate level an Australia-China FTA was trade
creating for the rest of the world, with overall exports increasing by 8.81% and imports
by 4.49%. The magnitude of increase is relatively higher than in my results, but the
direction of change is similar. On the disaggregate level, trade in main imports from the
rest of the world decreased, and mixed results were found for the main export sectors as
exports for the mining sector increased (0.01%) but decreased in the agriculture sector
(1.5%).
In terms of trade with the rest of the world, Mai et al. (2005) also found that an
Australia-China FTA would be mildly trade creating, with some evidence of minor
trade diversion in certain sectors. Following the removal of border protection on
merchandise trade, Mai et al. (2005) reported that imports from the rest of the world
increases by 0.1% but exports decreased by 1.6% .
!
54
Welfare
As noted earlier, none of the previous studies have quantified the change in welfare of
heterogeneous Australian households following an Australia-China FTA. However,
welfare effects on the aggregate level has been measured. Syquia (2007) used the same
methodology, including the model and real income index in measuring welfare, as this
paper and found similar results for the gain in consumer welfare (0.28%) and social
welfare (0.23%). However, a contrasting result to my paper is that Syquia (2007) found
that the government would experience welfare gain following trade liberalisation
(0.097%) in spite of the loss in tariff revenue. He did not provide any explanation for
this, but it could possibly be attributed to the effect of the FTA on consumption good
and factor prices, as the government is modeled as a utility-maximizing agent in this
model.
Similar results of a gain in overall welfare for Australia was found by Mai et al. (2005)
who measure welfare (both private and public) in terms of real GNP (0.2%) and real
consumption (0.21%), and Siriwardana and Yang (2008), who quantified consumer
welfare in terms of real consumption expenditure, finding that an FTA would lead to an
increase of 0.65%. The Centre for International Economics (2009) measured welfare in
terms of the net present value of real consumption and found that Australia was
estimated to gain A$94 billion in real consumption.
Partial Liberalisation
For partial liberalisation, overall the results found under this sensitivity experiment were
in a similar direction of change as the full liberalisation scenario though on a smaller
scale. This is also the general finding of Syquia (2007), who also simulated partial
liberalisation by halving all tariff rates, and Mai et al. (2005), who conducted a partial
liberalisation experiment to explore how the results would be affected with a slower
implementation. Mai et al. (2005) used a dynamic AGE model, so partial liberalisation
was simulated by removing trade barriers gradually between 2006 to 2010 in a linear
fashion. From this, he also noted the smaller magnitude of the gains from the partial
liberalisation experiment, which implied that a faster pace of implementation of trade
liberalisation would produce greater benefits to the Australian economy.
!
55
From this overview of findings of other studies on the Australia-China FTA, I find that
my results produce generally similar results in both the partial and full liberalisation
experiments.
7.3 Elasticities of Import Substitution Differentiated by Sector
In this section I conduct a sensitivity experiment wherein I differentiate the import
elasticities of substitution for each sector using elasticities from the literature, Hummels
(2001), Rolleigh (2003), and Anderson et al. (2005), as compared to the benchmark
case of full liberalisation. The parameter that governs the export elasticity of
substitution (!! ! was fixed at 0.9 in each case24.
Table 15 shows the change in consumption good prices under this sensitivity
experiment. We observe a similar pattern as in the benchmark case, with an increase in
prices of main exports and decrease in that of main imports, generally by a larger
magnitude, with the exception of computers and electronics whose sign of price change
is sensitive to the import elasticites, where prices decrease with Hummels (2001) as in
the benchmark case, but increase using the Rolleigh (2003) and Anderson et al. (2005)
elasticities.
B'#E9!+A!SGG9(%!.G!%C9!LB"!.-!F.-$0TI%1.-!Y../!N&1(9$!!!!" ! ! !!" !
Sector
Hummels (%)
Rolleigh (%)
Anderson (%)
Food and Beverages
0.18
0.20
0.21
Textile, Clothing and Footwear
-2.40
-2.66
-2.87
Computers and Electronics
-0.07
0.01
0.06
Transport
-0.02
-0.01
-0.01
Toys, Games, Sporting Goods
-0.38
-0.33
-0.22
Furniture
-0.60
-0.64
-0.49
Other Manufactures and Primaries
0.11
0.15
0.18
Services
0.10
0.09
0.08
!
!
Table 16 displays the change in factor prices, and with each set of elasticities the rental
rate increases while the labour wage decreases as in the benchmark. However, the
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
24
A complete set of results for this experiment is found in Appendix O. In this section I show only the
results most closely related to the changes in welfare which is the focus of this paper.
!
56
magnitude is sensitive to trade elasticities. The higher elasticities of Anderson et al.
(2005) resulted in a significant increase in magnitude of the change, with the decrease
in labour wage four times that of the benchmark case and the increase in rental rate over
sixty percent more than in the benchmark case. The Hummels (2001) and Rolleigh
(2003) elasticities also produced greater magnitude in the change than the benchmark
case, and Hummels (2001), which has the lowest average elasticity, produced the lowest
deviation from the benchmark. These changes in factor prices will have an impact on
the welfare of the disaggregated households through each group’s sources of income.
!
B'#E9!+@!SGG9(%!.G!%C9!LB"!.-!FC'-69$!1-!L'(%.&!N&1(9$!!!!" ! ! !!" !!!!
Hummels (%)
Rolleigh (%)
Anderson (%)
Rental rate
0.56
0.72
0.82
Wage (unskilled)
-0.14
-0.31
-0.44
Wage (skilled)
-0.14
-0.31
-0.44
!
!
3/5),'! A shows the effect on aggregate welfare. Using Anderson et al. (2005)
elasticities which result in a lower decrease, and greater increase, in consumption good
prices, as well as a larger decrease in labour wage income, results in consumer welfare
gains being lowest using Anderson et al. (2005) and government also hurt the most.
Table 17 shows Anderson et al. (2005) also delivers the largest decrease in tariff
revenue. Using this set of trade elasticities, social welfare declines as a result of this.
While social welfare still experiences gains using the Hummels (2001) and Rolleigh
(2003) elasticities, it is at a lower magnitude as compared with the benchmark.
!
57
L160&9!=!SGG9(%!.G!%C9!LB"!.-!"66&96'%9![9EG'&9!!!!" ! ! !!" !!!!
Hummels (%)
Aggr consumer welfare
0.21
0.20
Rolleigh (%)
Anderson (%)
Govt welfare
Social welfare
0.19
0.06
0.01
-0.05
-0.50
-0.75
-0.97
!
!
!
!
B'#E9!+*!SGG9(%!.G!%C9!LB"!.-!B'&1GG!:9;9-09!!!!" ! ! !!" !!!!
Tariff revenue
Hummels (%)
Rolleigh (%)
Anderson (%)
-23.18
-25.53
-27.89
!
!
Looking at 3/5),'!$U which shows the change in welfare of heterogeneous Australian
households, we see a similar pattern to the benchmark wherein the old and poor
households gain the most. They are exceptionally better off relative to the young and
rich households using the Anderson et al. (2005) elasticities. This could again be
attributed to the change in factor price where the magnitude of decrease of the labour
wage, the main source of income for the young rich, and the increase in rental rate, the
primary income source for the old and poor households, have the largest magnitude
using the Anderson et al. (2005) elasticities.
A deviation from the benchmark case, where all households gain can be seen in the
results using the trade elasticities of Rolleigh (2003) and Anderson et al. (2005) wherein
young rich households, who have the highest percentage of their income source from
labour wage, experience a decrease in their welfare following the FTA. This could be
attributed to the larger decrease in magnitude of labour wage using these elasticities.
!
58
L160&9!+H!SGG9(%!.G!%C9!LB"!.-!%C9![9EG'&9!.G!?1$'66&96'%9/!X.0$9C.E/$!
!!!" ! ! !!" !!!!
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
-0.10
-0.20
Old
Young Young
Young Young
Young Young
poor
poor
middle middle
rich
rich
Old poor middle Old rich
income
unskilled skilled unskilled skilled unskilled skilled
Hummels (%)
0.54
0.52
0.47
0.46
0.30
0.13
0.12
0.06
0.04
Rolleigh (%)
0.69
0.67
0.58
0.56
0.36
0.09
0.06
-0.02
-0.04
Anderson (%)
0.78
0.76
0.65
0.63
0.40
0.05
0.01
-0.08
-0.10
Gini Coefficient
B'#E9!+2!SGG9(%!.G!%C9!LB"!.-!FC'-69!1-!Y1-1!F.9GG1(19-%!!!!" ! ! !!" !!!
Pre-FTA
Post-FTA
Hummels
0.2092
0.2088
Rolleigh
0.2092
0.2083
Anderson
0.2092
0.2082
The Gini coefficient, which was 0.2092 prior to the FTA under all cases, decreased the
most when the Anderson et al. (2005) elasticities were used, and the least with the
Hummels (2001) elasticities. The greater reduction in income inequality using the
Anderson et al. (2005) elasticities is in line with the results in Figure 10 wherein the
welfare of the poor increases whereas that of the (young) rich decreases. Figure 10 also
shows that the Hummels (2001) elasticities result in welfare gains for all households
unlike with the Rolleigh (2003) and Anderson et al. (2005) elasticities, though the
!
59
increase of the rich is smaller than that of the poor, which explains the smaller reduction
in income inequality.
!
7.4 Differentiated Export Elasticities of Substitution
The previous section performed a sensitivity analysis with different sets of import
elasticities of substitution while the export elasticity was held constant. In this section I
look at the sensitivity of results using different values of export elasticities of
substitution with the import elasticity held at the same value as the benchmark in all
cases. Because of the lack of availability of data on export elasticities of substitution by
sector, in this experiment I set !! equal to 0.8, 0.867 and 0.95 (as compared to the
benchmark where !! ! !!!! which implies an elasticity of 5, 7.5, and 20 such that the
elasticity value is halved, decreased by 25%, and doubled, respectively, from the
benchmark value.
A reason for considering different cases (i.e. lower and higher elasticities), is because of
the difference in export elasticities of Australian exports on the aggregate and
disaggregate level. At the disaggregate level, exports of Australian goods are less elastic
such as the main export to China, iron ore, which is considered to be of superior quality
as compared to that imported from other sources as it contains low impurities and
average grades exceeding 60% iron25. However, the overall market power in specific
Australian sectors is diluted when commodity exports are examined at the aggregate
level.
Using different export elasticities of substitution, we see in Table 19 that consumption
good prices change in the same direction but that the magnitude increases with the
higher elasticity values. We find the same effect on factor prices in Table 20. The
magnitude of change in consumption good and factor prices is larger when !! = 0.95,
and lower when !! = 0.8.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
25
!
Australian Embassy, China. (http://www.china.embassy.gov.au/bjng/29092011speech_en.html)
60
B'#E9!+=!SGG9(%!.G!%C9!LB"!.-!F.-$0TI%1.-!Y../!N&1(9$!OG.&!/1GG9&9-%!!! Q!
!! ! !! !
!! ! !! !"#
!! ! !! !"
Food and Beverages
0.13
0.15
0.22
Textile, Clothing and Footwear
-1.97
-2.17
-2.66
Computers and Electronics
-0.01
-0.05
-0.15
Transport Parts and Vehicles
0.00
-0.01
-0.03
Toys, Games and Sporting Goods
-0.28
-0.36
-0.56
Furniture
-0.48
-0.59
-0.86
Other Manufactures and Primaries
0.09
0.09
0.12
Services
0.08
0.09
0.13
Sector
B'#E9!3H!SGG9(%!.G!%C9!LB"!.-!FC'-69!1-!L'(%.&!N&1(9$!OG.&!/1GG9&9-%!!! Q!
!! ! !! !
!! ! !! !"#
!! ! !! !"
Rental rate
0.35
0.44
0.70
Wage (skilled)
-0.05
-0.07
-0.13
Wage (unskilled)
-0.05
-0.07
-0.13
Table 21 displays the change in aggregate welfare which moves in the same direction as
the benchmark case and thus in all cases social welfare improves.
B'#E9!3+!SGG9(%!.G!%C9!LB"!.-!"66&96'%9![9EG'&9!!OG.&!/1GG9&9-%!!! Q!
!! ! !! !
!! ! !! !"#
!! ! !! !"
Aggregate consumer welfare
0.15
0.18
0.28
Government welfare
-0.44
-0.44
-0.38
Social welfare
0.03
0.06
0.14
3/5),'! $$ shows the results on different households groups using the different
elasticities. All households gain and follow a similar pattern as the benchmark case. The
larger magnitude of change of higher elasticity of export substitution can be attributed
to the greater increase in rental rate and decrease in wage rate as compared to the
benchmark. The lesser amount by which welfare changed using the lower elasticity
could also be explained by the lower magnitude of change in factor prices.
!
61
L160&9!++!SGG9(%!.G!%C9!LB"!.-!%C9![9EG'&9!.G!?1$'66&96'%9/!X.0$9C.E/$!OG.&!
/1GG9&9-%!!! Q!
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
Old poor
Old
middle
income
Old rich
Young
poor
unskilled
Young
poor
skilled
Young
middle
unskilled
Young
middle
skilled
Young
rich
unskilled
Young
rich
skilled
rhox = 0.8 (%)
0.33
0.31
0.28
0.28
0.19
0.11
0.10
0.07
0.06
rhox = 0.867 (%)
0.42
0.40
0.36
0.36
0.24
0.13
0.12
0.08
0.07
rhox = 0.95 (%)
0.68
0.66
0.59
0.58
0.37
0.19
0.17
0.10
0.08
From this sensitivity analysis we see that when the export elasticity of substitution is
changed, while the direction of change does not vary from the benchmark case, the
increase in magnitude of change increases with the value of the elasticity.
Gini Coefficient
B'#E9!33!SGG9(%!.G!%C9!LB"!.-!FC'-69!1-!Y1-1!F.9GG1(19-%!OG.&!/1GG9&9-%!!! Q!
Pre-FTA
Post-FTA
!! ! !!!
0.2092
0.2089
!! ! !!!"#
0.2092
0.2088
!! ! !!!"
0.2092
0.2085
Higher export elasticities of substitution resulted in larger decreases in the Gini
coefficient which is consistent with Figure 11 where the relative increase in welfare of
the poor compared to the rich was greater using higher export elasticities.
!
62
8 Conclusion
A number of studies have analysed the potential economic impact of an Australia-China
free trade agreement on the Australian economy. However, while the effect on welfare
has been quantified on the aggregate level, no studies have explored the varying
distributional effect of the FTA on heterogeneous Australian households.
That is the primary contribution of my study, which finds that while all households gain
from the FTA in the benchmark case, the old and poor households gain more than
young and rich households, and unskilled households experience greater benefits than
their skilled counterparts. The gains to the old poor households, who benefit the most,
are over six times that of the young rich households who gain the least, and are even
negatively affected by the FTA in the sensitivity experiments using the Rolleigh (2003)
and Anderson et al. (2005) elasticities. This implies that the FTA has a distributional
impact across households which was reinforced by the decrease in the Gini coefficient
after the simulation of the FTA. This can largely be attributed to the opposite effects of
trade liberalisation on factor prices where labour wage decreases and the rental rate
increases, and by larger magnitudes under differentiated import elasticities of
substitution by sector, given that old and poor households source a greater portion of
their income from capital than young and rich households.
My research also finds similar results to that of previous studies on the Australia-China
FTA, where the main export sectors of Australia experience an increase in prices,
domestic production, and the volume of exports, and the main import sectors experience
a decrease in prices and domestic production, and an increase in the volume of imports,
with the greatest change resulting in sectors previously holding the highest tariff rates,
notably in the textile, clothing, and footwear sector.
I also found greater gains under the full liberalisation cases as compared to partial
liberalisation which implies that faster implementation of the free trade agreement will
deliver greater economic gains than slower implementation.
For my analysis I used a static applied general equilibrium model. Because of the static
nature of the model, this research does not capture the dynamic aspects of trade
!
63
liberalisation such as capital flows, labour force adjustment and dynamic productivity
gains arising from investment liberalisation. An extension to this paper that incorporates
these dynamic features in the model would capture theses aspects of an Australia-China
FTA as well as the long term effects of the FTA reforms.
Another limitation of this paper is that while I used skill level to classify households,
due to data availability constraints I did not evaluate the effects on labour wage for
skilled and unskilled labour separately. Incorporating the skill premium in wage effects
across these two types of households would provide further insight on the varying
effects of an Australia-China FTA on heterogeneous Australian households.
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
64
Appendix
Appendix A – Summary of Existing Bilateral Trade and Economic Agreements26
2004
2004
2004
2004
2003
2003
2003
2003
2003
2003
2003
2003
2002
1995
2001
2001
2001
2000
1999
Agreement between the Government of Australia and the Government of the People's Republic of
China relating to Air Services
Memorandum of Understanding on Two Way Investment Promotion Cooperation Between
Austrade, Invest Australia, and the Investment Promotion Agency, MOFCOM of China
Memorandum of Understanding on Investment Promotion Cooperation between the National
Development and Reform Commission of the People’s Republic of China and Invest Australia, the
inwards investment agency of the Commonwealth of Australian Government
Memorandum of Understanding on Customs Cooperation and Mutual Assistance between
Australian Customs and the General Administration of Chinese Customs
Trade and Economic Framework between Australia and the People’s Republic of China
Arrangement on Higher Education Qualifications Recognition between Australia and the People’s
Republic of China
Memorandum of Understanding on the Management and Implementation of the Australia-China
Natural Gas Technology Partnership Fund between the Commonwealth, Western Australia and the
ALNG consortium, and China’s National Development Reform Commission
Memorandum of Understanding on Scientific and Technological Cooperation in Food Safety
between Food Standards Australia and the Ministry of Science and Technology of the People’s
Republic of China
Protocol on Australian Wheat and Barley Exports to China between Australia’s Department of
Agriculture, Fisheries and Forestry and China’s Administration of Quality Supervision Inspection
and Quarantine
Memorandum of Understanding on Sanitary and Phytosanitary Cooperation between Australia’s
Department of Agriculture, Fisheries and Forestry and China’s General Administration of Quality
Supervision Inspection and Quarantine
Memorandum of Understanding on Cooperative Activities in Water Resources between Australia’s
Department of Agriculture, Fisheries and Forestry and China’s Ministry of Water Resources
Memorandum of Understanding relating to Air Services between Australia and the People’s
Republic of China
Memorandum of Understanding on Cooperation on Animal and Plant Quarantine and Food Safety
for the 2008 Beijing Olympic and Paralympic Games between Australia’s Department of
Agriculture, Fisheries and Forestry and China’s General Administration of Quality Supervision
Inspection and Quarantine
Memorandum of Understanding on Cooperation in Education and Training between Australia’s
Department of Education, Science and Technology and China’s Ministry of Education 2002. MOU
also signed in 1999 and 1995.
Memorandum of Understanding between the Department of Transport and Regional Services of
Australia and the State Development Planning Commission of the People’s Republic of China on
Cooperation in the Transport Sector
Memorandum of Understanding between the Department of Transport and Regional Services of
Australia and the Ministry of Communications of the People’s Republic of China on Cooperation in
Highway and Waterway Transport
Memorandum of Understanding between the Department of Transport and Regional Services of
Australia and the Ministry of Railways of the People’s Republic of China on Cooperation in Rail
Transport
Memorandum of Understanding between the Australian Department of Industry, Science and
Resources and the State Development Planning Commission of the People’s Republic of China on
the establishment of a Bilateral Dialogue Mechanism on Resources Cooperation
Memorandum of Understanding on Cooperation in the Mining Sector between Australia’s
Department of Industry, Tourism and Resources and China’s Ministry for Land and Resources
!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
"K!“This annex only includes major bilateral trade and economic agreements/arrangements between agencies of the
Australian Government and Chinaʼs Central Government. The annex does not include more technical agreements,
including for projects under technical cooperation programs, or records of discussion, joint announcements or
implementation programs or arrangements that simply amend or implement previous agreements and other
arrangements. The number at the end of each entry represents the year in which the agreement/arrangement entered
into force or was last amended.”(Feasibility Study)
!
!
65
1999
1999
1999
1999
1999
1995
1990
1990
1988
1988
1987
1986
1986
2004
1985
1984
1984
1984
1984
1984
1983
1981
1981
1980
1974
1973
Memorandum of Understanding between the Department of Communications, Information
Technology and the Arts of Australia and the Ministry of Information Industry of the People’s
Republic of China concerning Cooperation in the Information Industries
Exchange of Letters on Approved Destination Status (ADS) Group Tourism Arrangements between
Australia and the People’s Republic of China
Exchange of Letters between the Australian Embassy, Beijing, and the China National Tourism
Administration concerning Outward Bound Travel by Chinese Citizens to Australia
Memorandum of Understanding between the Department of Industry, Science and Resources of
Australia and the State Development Planning Commission of the People’s Republic of China on
Cooperation on Trade and Investment in the Mining and Energy Sectors
Memorandum of Understanding between the Department of Industry, Science and Resources of
Australia and the Ministry of Land and Resources of the People’s Republic of China on
Cooperation in the Mining Sector
Memorandum of Understanding between the Department of the Environment, Sport and Territories
of Australia and the National Environment Protection Agency of the People’s Republic of China on
Environmental Cooperation
Exchange of Notes constituting an agreement to amend Article 3 of the Agreement between the
Government of Australia and the Government of the People's Republic of China on a Program of
Technical Cooperation for Development of 2 October 1981
Agreement between the Government of Australia and the Government of the People's Republic of
China for the Avoidance of Double Taxation and the Prevention of Fiscal Evasion with Respect to
Taxes on Income
Agreement on Fisheries between the Government of Australia and the Government of the People’s
Republic of China
Agreement with the People's Republic of China on the Reciprocal Encouragement and Protection of
Investments
Exchange of Notes constituting an arrangement between the Department of Primary Industry of
Australia and the Ministry of Forestry of the People’s Republic of China on Forestry Cooperation
Exchange of Notes constituting an Agreement between the Government of Australia and the
Government of the People's Republic of China to amend the Trade Agreement of 24 July 1973
Agreement between the Government of Australia and the Government of the People's Republic of
China for the Avoidance of Double Taxation of Income and Revenues Derived by Air Transport
Enterprises and International Air Transport
Joint Announcement on the formation of the Sino-Australia Joint Ministerial Economic
Commission (JMEC) 1986: JMEC meetings were held annually from 1987 to 1993. The 8th
meeting was held in 1995, 9th meeting in 1999 and 10th meeting in 2004.
Memorandum of Understanding between the Government of Australia and the Government of the
People's Republic of China regarding Wool Cooperation
Joint Communiqué of the Australia-China Joint Agricultural Commission - Inaugural Session
Memorandum of Understanding on the establishment of a Legal Exchange Program between
Australia’s Attorney-General’s Department and China’s Ministry of Justice
Protocol between the Government of Australia and the Government of the People's Republic of
China on a Program of Cooperation in Agricultural Research for Development
Agreement between the Government of Australia and the Government of the People's Republic of
China Relating to Civil Air Transport
Agreement between the Government of Australia and the Government of the People's Republic of
China on Agricultural Cooperation
Understanding relating to Quarantine and Health Requirements for Cattle Exported from Australia
to the People’s Republic of China
Agreement between the Government of Australia and the Government of the People's Republic of
China on a Program of Technical Co-operation for Development
Protocol on Economic Cooperation with the Government of the People’s Republic of China
Agreement between the Government of Australia and the Government of the People's Republic of
China on Cooperation in Science and Technology
Exchange of Notes constituting an Agreement between the Government of Australia and the
Government of the People's Republic of China concerning the Registration of Trademarks
Trade Agreement between the Government of Australia and the Government of the People’s
Republic of China
!
J8),@'Y!()*+,-./-01&/2-!3,''!%,-4'!(5,''6'2+!3'-*/C/./+H!J+)4H!
!
66
Appendix B - Australia Tariff Rates (2009)
!
Final bound duties
Product Groups
AVG
Duty-free
in %
Max
MFN applied duties
Binding
in %
AVG
Duty-free
in %
Max
Animal Products
1.5
68.8
16
100
0.4
91.2
5
Dairy Products
4.2
20.0
22
100
3.6
75.0
22
Fruit, vegetables, plants
3.7
23.5
29
100
1.6
68.9
5
Coffee, tea
3.9
50.0
17
100
1.0
79.2
5
Cereals & preparations
2.7
26.6
17
100
1.3
72.4
5
Oilseeds, fats & oils
3.1
31.2
14
100
1.6
67.4
5
Sugars and confectionery
7.5
0.0
22
100
1.9
59.4
5
10.7
3.3
25
100
3.6
28.8
5
Cotton
1.2
40.0
2
100
0.0
100.0
0
Other agricultural products
2.1
28.0
20
100
0.3
94.7
5
Fish & fish products
0.7
80.7
10
100
0.0
99.2
5
Minerals & metals
6.6
22.5
45
97.7
2.8
45.3
10
Petroleum
0.0
100.0
0
100
0.0
100.0
0
Chemicals
9.0
8.8
55
100
1.8
64.3
18
Wood, paper, etc.
7.0
25.4
25
100
3.4
33.2
10
Textiles
18.6
13.5
55
90.3
6.8
16.3
18
Clothing
41.2
6.7
55
94
15.4
8.0
18
Leather, footwear, etc.
15.2
10.0
55
84.8
5.5
16.7
18
Beverages & tobacco
Non-electrical machinery
8.3
18.1
50
96.2
3.1
43.4
10
Electrical machinery
10.4
30.3
45
98.4
3.2
42.2
10
Transport equipment
12.6
8.9
40
99.2
5.1
34.5
249
Manufactures, n.e.s.
6.3
33.5
40
98.6
1.4
73.5
10
J8),@'Y!D%M!D8,.4!%-,/==!R,8=/.'*!"U$U!
!
!
!
!
!
!
!
!
67
Appendix C - China Tariff Rates (2009)
!
Final bound duties
MFN applied duties
Product Groups
AVG
Duty-free
in %
Max
Binding
in %
AVG
Duty-free
in %
Max
Animal Products
14.9
10.4
25
100
14.8
10.1
25
Dairy Products
12.2
0.0
20
100
12.0
0.0
20
Fruit, vegetables, plants
14.9
4.9
30
100
14.8
5.9
30
Coffee, tea
14.9
0.0
32
100
14.7
0.0
32
Cereals & preparations
23.7
3.3
65
100
24.2
3.4
65
Oilseeds, fats & oils
11.0
7.2
30
100
10.9
5.4
30
Sugars and confectionery
27.4
0.0
50
100
27.4
0.0
50
Beverages & tobacco
23.2
2.1
65
100
22.9
2.2
65
Cotton
22.0
0.0
40
100
15.2
0.0
40
Other agricultural products
12.1
9.2
38
100
11.5
9.4
38
Fish & fish products
11.0
6.2
23
100
10.7
6.2
23
Minerals & metals
8.0
5.6
50
100
7.4
8.8
50
Petroleum
5.0
20.0
9
100
4.4
20.0
9
Chemicals
6.9
0.5
47
100
6.6
2.0
47
Wood, paper, etc.
5.0
22.3
20
100
4.4
35.3
20
Textiles
9.8
0.2
38
100
9.6
0.0
38
Clothing
16.1
0.0
25
100
16.0
0.0
25
Leather, footwear, etc.
13.7
0.6
25
100
13.4
0.6
25
Non-electrical machinery
8.5
7.7
35
100
7.8
9.1
35
Electrical machinery
9.0
25.3
35
100
8.0
24.0
35
Transport equipment
11.4
0.8
45
100
11.5
0.8
45
Manufactures, n.e.s.
12.2
15.1
35
100
11.9
9.6
35
!
J8),@'Y!D%M!D8,.4!%-,/==!R,8=/.'*!"U$U!
!
!
!
!
!
!
!
!
68
!
Appendix D - Tariff Rates of the Sectors27
China Tariff
Rates (%)
Australia Tariff
Rates (%)
Food and Beverages
15.3
1.1
Textile, Clothing and Footwear
15.3
7.4
Computers and Electronics
9.1
2.7
Transport
11.2
3.2
Toys, Games, Sporting Goods
10.9
3.7
Furniture
7.3
4.3
Other Manufactures and Primaries
8.1
2.6
!
J8),@'Y!D%M!D8,.4!%-,/==!R,8=/.'*!"U$U!
!
!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
27
Tariff rates for my chosen level of disaggregation were unavailable, hence I calculated the tariff rate for
each sector by matching each of the 5051 items (HS07), along with their corresponding tariff rates, listed
for both countries to one of the seven merchandise sectors and calculated the simple average. The tariff
rates are the MFN Applied Tariff (Average of AV duties) taken from the WTO Tariff Download Facility
for China is for the year consistent with the 2006-2007 Input Output tables.!
!
69
!
Production
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
L
K
Households
Government
Direct Tax
Indirect Tax
Tariffs TOTAL
China
RoW
Capital
Imports TOTAL
China
RoW
TOTAL
Consumption
!
892
979
512
467
9 941
5 199
4 742
30 270
- 5 297
96
4
92
11 596
507
11 089
174 517
37 674
6 773
30 901
78 488
- 1 531
219
39
180
- 1 312
24 176
12 917
3
15
64
1 660
0
139
0
3 153
0
29 380
1 010
28 370
79 754
- 918
1 295
45
1 250
377
23 318
8 201
3 695
1 708
1 987
22 770
170
103
48
55
273
5 571
2 749
Food and Beverages
Textile, Clothing and Footwear
Computers and Electronics
Transport
Toys, Games and Sporting Goods
Furniture
Other Manufactures and Primaries
1 871
- 5 201
1
2
3
4
5
6
7
12 250
4 686
2
843
679
0
0
0
0
0
0
40 847
53 900
`
1
49 529
210
356
160
1 257
53
10 514
11 294
Production
4
5
28
665
107
120
1 404
431
9 900
0
762
2 628
0
0
6 277
2 293
0
4 344
L
K
C
G
I
X
RoW
2 862
1 205
1 657
11 287
313
103
43
60
416
4 461
976
6
0
161
42
40
129
85
2 114
0
- 42 319
0
0
0
- 42 319
377 644
265 176
8
23 702
5 115
30 261
16 668
6 663
1 149
112 069
243 703
91 291
9 475
9 475
1
66 631
0
0
0
0
0
0
15 185
Payment to labour
Payment to capital
Final consumption
Government expenditure
Final investment
Exports
Rest of the world
96 189
22 123
10 738
1 475
85 451
20 648
479 691 1 061 955
- 14 883
881
98
783
- 14 002
69 790
141 140
7
1 028
314
811
0
0
0
158 493
25 927
26 421
1 694
1 694
2
0
21 004
0
0
0
0
0
3 723
22 790
3 709
3 709
3
0
0
11 073
0
0
0
0
8 008
27 172
3 152
3 152
10 072
832
832
Consumption
4
5
0
0
0
0
0
0
17 753
0
0
8 652
0
0
0
0
6 267
588
8 795
544
544
6
0
0
0
0
0
7 187
0
1 065
Social Accounting Matrix for Australia for 2006-2007, Millions (AUSD)
64 952
21 093
21 093
7
0
0
0
0
0
0
21 372
22 487
K
558 058 489 745
L
259 882
179 589
91 291
26 421
22 790
27 172
10 072
8 795
64 952
356 839
C
38 423
I
10 637
TOTAL
23
2 237
29 815
0
0
2 495
0
26 778
5 671
0
30 013
5 220
698
924
917
0
2 631
181
6 010
32 886 124 508
179 319 213 473 34 016
G
356 839 558 058 489 745 1 047 803 224 473 308 942 213 460
64 282
64 282
8
0
0
0
0
0
0
0
292 557
28 615
8 763
X
China
394
17
30
68
2
5
14 918
4 417
TOTAL
RoW
29 421 174 517
2 478
30 270
5 641
78 488
5 152
79 754
914
22 770
176
11 287
109 591 479 691
29 599 1 061 955
91 291
26 421
22 790
27 172
10 072
8 795
64 952
356 839
558 058
489 745
1 047 803
44 884
179 589
41 208
3 676
789
2 887
1 873
308 942
213 460
28 615
184 845
184 845
Appendix E - The Social Accounting Matrix for Australia
70
Appendix F - Constructing the Social Accounting Matrix
These are the main points in constructing the SAM shown in Appendix E.
i.)
Industry Classification: The primary source in constructing the Social
Accounting Matrix is the Input-Output (IO) tables for Australia. The IO
tables I use are for the year 2006-2007 and contain transaction data on 111
industries. Thus, the first step is to aggregate these industries into the eight
sectors identified for this research as listed in Table 4. This sectoral
matching is shown in Appendix H.
ii.)
From the IO tables I construct Use and Supply matrices which contain data
on where products per industry were utilised and sourced from. In the
Supply matrix I also include columns on the indirect taxes, tariffs and
margins per sector, and to the Use matrix I add a row for the value added
and
columns
representing
consumption,
investment,
government
expenditure, and exports for each sector. The IO tables provide us with data
for all of these items.
iii.)
In the Use matrix, I add the commercial margins to the intermediate inputs
of services for all sectors except the services sector. I transpose the import,
tariff, and indirect tax columns in the Supply matrix as rows in the Use
matrix.
iv.)
At this point the row totals are not equal to the column totals which is
required of a SAM. Here I assume that outputs are produced in fixed
proportions and inputs are used in fixed proportions as is given in the model
set-up. This assumption allows me to subtract the off-diagonal elements of
the Supply Matrix from the corresponding element of the Use matrix. I then
add these elements to the corresponding diagonal elements to preserve row
sums. Following this I now have one balanced input-output matrix with row
totals equalling column totals.
!
71
v.)
Value-added is divided between payment to labour and to capital rows
according to the proportions calculated from the IO tables.
vi.)
I add eight additional rows and columns, corresponding to each sector from
my sectoral disaggregation, to incorporate the production of the consumption
good firm in the model economy. The consumption good firm adds a service
component which is assumed to be 25% of the margin (from the Supply
table). The final production component of the consumption good of a sector
which is listed across the diagonals is equivalent to the consumption (C) of
that sector less the services component and indirect tax paid for that sector.
For indirect tax on the consumption goods of each sector I assumed 10% of
the payment to labour and capital for the sector. A row for the government
was added, the values for which is the sum of indirect tax and tariffs.
Another row for direct tax is incorporated which is the total income tax
obtained from the Australian Bureau of Statistics.
vii.)
Column for returns to labour and to capital are added as well as a row for
households who receive these payments. A row for capital is also added and
the corresponding entry under the exports sector is equivalent to total
imports minus total exports, for the government entry is total government
revenue less expenditure, and for the consumption entry is total final
investment less the export and government entry for capital.
viii.)
I correct for negative entries in the production sectors by subtracting them
from themselves to equal zero, and so as to keep the row and column totals
equal, I subtract the same amount from the adjacent off-diagonal entry, and
add the amount to the two adjacent diagonal entries.
ix.)
I disaggregate exports, tariffs, and imports into that corresponding to China
and that for the rest of the world.
x.)
Finally, I distribute consumption, payments to capital and labour, direct tax,
to that for the disaggregated households groups, using proportions calculated
from the Household Expenditure survey data.
!
72
Appendix G - Sectoral Matching of Household Expenditure Items
Item Code
0601000000
0601010000
0601020000
0601030000
0601040000
0601050199
-
0601019998
0601029998
0601039998
0601049998
Item Code
0300000000
0301010101 - 0301049999
0302000000 - 0302999999
0303000000 - 0303019999
0304019999 - 0304019999
0305010201 - 0305019999
0306010101 - 030619999
0307000000 - 0307030201
0308000000 - 0308999999
0309010101 - 03090399999
Textile, Clothing and Footwear
Item Code
0601990101
0602010000
0602010200
0602010300
0701010601
1101051103
Item Description
Clothing nfd
Men's clothing
Women's clothing
Boy's clothing
Girl's clothing
Infant's clothing
-
0601999999
0602010199
0602010299
0602010399
0702019999
Food and Beverages
Item Description
Item Code
Food and non-alcoholic beverages nfd
0309040101 - 0309040401
Wheat- and rice -based food products
0309050101 - 03010069999
Meat products
0310000000 - 0310050201
Fish & seafood products
0311010101 - 0399010101
Eggs and egg products
0399010201
Dairy products
0401000000 - 0401040201
Edible fats & oils products
0501010101 - 0501010201
Fruit & nut products
1104010200 - 1104010299
Vegetable products
1301990401
Sugar products & confectionery
0705019901 - 0705019904
1101010101 - 1101019999
Computers and Electronics
Item Description
Item Code
Whitegoods & other electrical tools &
1101020101 - 1101030199
appliances
Communication equipment
1101030201 - 1101039999
Audiovisual equipment & parts
1101050101
Item Code
0701010201
0701010301
0701010401
Item Description
Bedroom furniture
Lounge/dining room furniture
Outdoor/garden furniture
Item Code
1101050901
Item Description
Toys
1101051001
Camping equipment
1101051105
1101051100
1101051101
1101051102
Sports equipment nfd
Fishing equipment
Golf equipment (excluding specialist
sports shoes)
1101051198
1101059901
1101059999
Item Code
0703010101 - 0705010301
Item Description
Other articles of clothing
Men's footwear
Women's footwear
Children & infants footwear
Linen, rugs, other textiles
Specialist sports shoes
Item Description
Food additives
Canned & prepared meals
Non-alcoholic liquids
Meals & other food
Non-alcoholic beverages nec
Alcoholic beverages
Cigarettes & tobacco
Animal food
Ice
Item Description
Computer equipment &
software
Recording media
Photographic equipment
(excluding film and
chemicals)
Furniture
Item Code
1001010101
1001010201
1001020101
Item Code
0701010501
0702020101
0702029999
Toys, Games and Sporting Goods
Item Code
1101051104
Transport
Item Description
Item Code
Purchase of motor vehicle (other than
1101050801
motor cycle)
Purchase of motor cycle
1101050899
1001050101
1001020201
1001020301
Purchase of caravan (other than
selected dwelling)
Purchase of trailer
Purchase of bicycle
1101050701
Purchase of boat
1001059901
1101050799
Boat purchase, parts and operation nec
1001059902
!
1001050201
1001050301
Item Description
Other furniture
Paintings, carvings, sculptures
Ornamental furnishings nec
Item Description
Water sport, snow sport and
skating equipment
Bats, sticks, racquets and balls
for field and court
Sports equipment nec
Above ground pool
Recreational and educational
equipment nec
Item Description
Purchase of aircraft
Aircraft purchase, parts and
operation nec
Motor vehicle batteries
Tyres and tubes
Motor vehicle electrical
accessories (purchased
separately)
Vehicle parts purchased
separately nec
Vehicle accessories purchased
separately nec
73
Other Manufactures and Primaries
Item Code
1101050201
Item Code
0201010201 - 0299999999
Item Description
Fuel and power
0601050101
0701010801 - 0702010801
Nappies
Floor & window coverings
1101050301 - 1101050401
1101050601 - 1101059902
0703030101
0704010101 - 0704019999
1103010501 - 1103010502
1104010101 - 1104019903
0705010101 - 0801010101
0801010201 - 0801010501
0801010601 - 0801010801
0801010901
Non-electrical household appliances
Glassware, tableware, cutlery and
household utensils
Household tools
Household cleaning products
Gardening & swimming pool products
Foodwraps (excluding paper)
0801019999
Household non-durables nec
1301999902
0903000000 - 0903029999
1001030000 - 1001030401
1101040101 - 1101049999
Medical supplies
Oils, lubricants, fuels
Printed matter
1301999903
1301999999
Hygiene products
Stationery equipment
Watches, clocks, jewellery
Travel goods, handbags,
umbrellas, wallets, etc
Baby goods (excluding
clothing)
Christmas decorations
Miscellaneous goods nec
Item Code
1103010101 - 1103020602
1104010000 - 1104019999
1201020000 - 1201029999
1302010101 - 1302010401
1302020000 - 1302030301
Item Description
Holiday travel services
Animal expenses
Hair & personal care services
Loan repayments
Education services
1302040001 - 1302050000
Other property expenses
1302050101 - 1302059902
1302990101 - 1302990299
Professional & legal fees
Cash gifts, donations, etc
1302990301 - 1302999998
Miscellaneous payments &
services
Income tax
Item Code
0101010101 - 0101040103
0101050101 - 0101070101
0101070201 - 0201020101
0603010101 - 0603010401
0801020101 - 0801039999
0801040101 - 0801049999
0801050000 - 0801050201
0801060101 - 0801080199
0901010101 - 0999990201
1001040101 - 1101050802
1102010000 - 1102019999
1102020101 - 1102029999
1102030101 - 1102999998
Services
Item Description
Housing payments
Repairs & maintenance services
Utilities
Clothing & footwear cleaning, repairs
Communication services (post,
telephone, etc)
Housekeeping & other household
services
Child care services
Repair & maintenance of household
furnishings & appliances
Health services
1201010101 - 1201019998
1301010000 - 1301019999
1301990101 - 1301990201
1301990301
Transport services (insurance,
servicing, fares, etc)
Gambling & lottery services
1401010101
Hire services (electronics, sporting
equipment)
Recreational & leisure services
1701010101
1501010101 - 1601019999
Item Description
Photographic film and
chemicals (including
developing)
Optical goods
Music, arts & crafts related
materials
Holiday petrol
Animal purchases
Mortgage repayments &
property improvement
Superannuation and annuities
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
74
Appendix H - Sectoral Matching of the Input-Output Industries
!
Sector
Input-Output Table Industry
1) Textile, Clothing and Footwear
1301 Textile Manufacturing
1302 Tanned Leather, Dressed Fur and Leather Product Manufacturing (*half imputed)
1303 Textile Product Manufacturing
1304 Knitted Product Manufacturing
1305 Clothing Manufacturing
1306 Footwear Manufacturing
2) Food and Beverages
101 Sheep, Grains, Beef and Dairy Cattle
102 Poultry and Other Livestock
103 Other Agriculture
201 Aquaculture
401 Fishing, hunting and trapping
1101 Meat and Meat product Manufacturing
1102 Processed Seafood Manufacturing
1103 Dairy Product Manufacturing
1104 Fruit and Vegetable Product Manufacturing
1105 Oils and Fats Manufacturing
1106 Grain Mill and Cereal Product Manufacturing
1107 Bakery Product Manufacturing
1108 Sugar and Confectionery Manufacturing
1109 Other Food Product Manufacturing
1201 Soft Drinks, Cordials and Syrup Manufacturing
1202 Beer Manufacturing
1205 Wine, Spirits and Tobacco
3) Computers and Electronics
2401 Professional, Scientific, Computer and Electronic Equipment Manufacturing
2403 Electrical Equipment Manufacturing
2404 Domestic Appliance Manufacturing
4) Furniture
2501 Furniture Manufacturing
5) Toys, Games and Sporting Goods
1901 Polymer Product Manufacturing (*half imputed)
1902 Natural Rubber Product Manufacturing (*half imputed)
9101 Sports and Recreation
6) Transport
2301 Motor Vehicles and Parts; Other Transport Equipment manufacturing
2302 Ships and Boat Manufacturing
2304 Aircraft Manufacturing
2303 Railway Rolling Stock Manufacturing
7) Other Manufactures and Primaries
301 Forestry and Logging
601 Coal mining
701 Oil and gas extraction
801 Iron Ore Mining
802 Non Ferrous Metal Ore Mining
901 Non Metallic Mineral Mining
1302 Tanned Leather, Dressed Fur and Leather Product Manufacturing (*half imputed)
1401 Sawmill Product Manufacturing
1401 Sawmill Product Manufacturing
1402 Other Wood Product Manufacturing
1501 Pulp, Paper and Paperboard Manufacturing
1502 Paper Stationery and Other Converted Paper Product Manufacturing
1601 Printing (including the reproduction of recorded media)
1701 Petroleum and Coal Product Manufacturing
1801 Human Pharmaceutical and Medicinal Product Manufacturing
1802 Veterinary Pharmaceutical and Medicinal Product Manufacturing
1803 Basic Chemical Manufacturing
1804 Cleaning Compounds and Toiletry Preparation Manufacturing
1901 Polymer Product Manufacturing (*half imputed)
1902 Natural Rubber Product Manufacturing (*half imputed)
2001 Glass and Glass Product Manufacturing
!
75
7) Other Manufactures and Primaries (cont.)
2002 Ceramic Product Manufacturing
2003 Cement, Lime and Ready-Mixed Concrete Manufacturing
2004 Plaster and Concrete Product Manufacturing
2005 Other Non-Metallic Mineral Product Manufacturing
2101 Iron and Steel Manufacturing
2102 Basic Non-Ferrous Metal Manufacturing
2201 Forged Iron and Steel Product Manufacturing
2202 Structural Metal Product Manufacturing
2203 Metal Containers and Other Sheet Metal Product manufacturing
2204 Other Fabricated Metal Product manufacturing
2405 Specialised and other Machinery and Equipment Manufacturing
2502 Other Manufactured Products
5401 Publishing (except Internet and Music Publishing)
8) Services
501 Agriculture, Forestry and Fishing Support Services
1001 Exploration and Mining Support Services
2601 Electricity Generation
2605 Electricity Transmission, Distribution, On Selling and Electricity Market Operation
2701 Gas Supply
2801 Water Supply, Sewerage and Drainage Services
2901 Waste Collection, Treatment and Disposal Services
3001 Residential Building Construction
3002 Non-Residential Building Construction
3101 Heavy and Civil Engineering Construction
3201 Construction Services
3301 Wholesale Trade
3901 Retail Trade
4401 Accommodation
4501 Food and Beverage Services
4601 Road Transport
4701 Rail Transport
4801 Water, Pipeline and Other Transport
4901 Air and Space Transport
5101 Postal and Courier Pick-up and Delivery Service
5201 Transport Support services and storage
5501 Motion Picture and Sound Recording
5601 Broadcasting (except Internet)
5701 Internet Publishing and Broadcasting and Services Providers, Websearch Portals and Data
Processing Services
5801 Telecommunication Services
6001 Library and Other Information Services
6201 Finance
6301 Insurance and Superannuation Funds
6401 Auxiliary Finance and Insurance Services
6601 Rental and Hiring Services (except Real Estate)
6701 Ownership of Dwellings
6702 Non-Residential Property Operators and Real Estate Services
6901 Professional, Scientific and Technical Services
7001 Computer Systems Design and Related Services
7201 Building Cleaning, Pest Control, Administrative and Other Support Services
7501 Public Administration and Regulatory Services
7601 Defence
7701 Public Order and Safety
8001 Education and Training
8401 Health Care Services
8601 Residential Care and Social Assistance Services
8901 Heritage, Creative and Performing Arts
9201 Gambling
9401 Automotive Repair and Maintenance
9402 Other Repair and Maintenance
9501 Personal Services
9502 Other Services
!
76
Appendix I – SAM: Factor Income (A$ million)
Labour Input
Poor
Middle-income
Old
Rich
Poor unskilled
Poor skilled
Middle-income unskilled
Young
Middle-income skilled
Rich unskilled
Rich skilled
Capital Input
Poor
Middle-income
Old
Rich
Poor unskilled
Poor skilled
Middle-income unskilled
Young
Middle-income skilled
Rich unskilled
Rich skilled
Food
40847.4
0.2
32.7
513.6
590.0
662.9
6772.3
10284.9
6927.5
15063.4
53900.4
1642.7
6258.5
6598.3
5138.0
3528.8
7142.4
9301.4
4484.7
9805.5
TCF
12249.8
0.1
9.8
154.0
176.9
198.8
2030.9
3084.3
2077.5
4517.4
4685.6
142.8
544.1
573.6
446.7
306.8
620.9
808.6
389.9
852.4
Elec.
24176.5
0.1
19.4
304.0
349.2
392.3
4008.3
6087.3
4100.2
8915.6
12917.2
393.7
1499.9
1581.3
1231.3
845.7
1711.7
2229.1
1074.8
2349.9
Production
Transport TGS Furniture Other Services
23318.3 5571.4
4461.2 69790.2 377643.6
0.1
0.0
0.0
0.4
1.9
18.7
4.5
3.6
55.9
302.5
293.2
70.1
56.1
877.5
4748.3
336.8
80.5
64.4
1008.0
5454.3
378.4
90.4
72.4
1132.5
6128.3
3866.1
923.7
739.6
11570.9 62611.4
5871.2
1402.8
1123.3 17572.3 95085.8
3954.6
944.9
756.6
11836.0 64046.2
8599.1
2054.6
1645.2 25736.7 139264.7
8201.1
2748.6
975.6 141139.9 265176.5
249.9
83.8
29.7
4301.4
8081.6
952.2
319.2
113.3
16388.2 30790.4
1004.0
336.5
119.4
17278.0 32462.2
781.8
262.0
93.0
13453.9 25277.6
536.9
180.0
63.9
9240.3 17360.9
1086.7
364.2
129.3
18702.7 35139.0
1415.2
474.3
168.3
24356.0 45760.5
682.4
228.7
81.2
11743.4 22063.8
1491.9
500.0
177.5
25675.9 48240.5
!
!
77
Appendix J - Top Household Expenditure Items28
!
1401010101.
1001010101.
0101020101.
0101010101.
1501010101.
1001030101.
0311010201.
1701010101.
0311010101.
0201010101.
0801030101.
1601010401.
0901010101.
1601010301.
1001060201.
0501010101.
1001040201.
0101030201.
0701010301.
1001040103.
0801030102.
1302010101.
0601000000.
0101030101.
1103020502.
0301010101.
1601019999.
0401020101.
0902010301.
0300000000.
0101060199.
1101020101.
Expenditure Item
Aggregate
Weekly
Expenditure
Income tax
Purchase of motor vehicle (other than motor cycle)
Mortgage repayments - interest component (selected dwelling)
Rent payments
Mortgage repayments - principal component (selected dwelling)
Petrol
Fast food and takeaway (not frozen)
Superannuation and annuities
Meals in restaurants, hotels, clubs and related
Electricity (selected dwelling)
Fixed telephone account
Internal renovations
Hospital, medical and dental insurance
Additions and extensions
Vehicle servicing (including parts and labour)
Cigarettes
Other insurance of motor vehicle (other than motor cycle)
Local government rates (selected dwelling)
Lounge/dining room furniture
Combined compulsory registration and insurance of motor vehicle (other than motor cycle)
Mobile telephone account
Mortgage repayments - interest component (other property)
Clothing nfd
Water and sewerage rates and charges (selected dwelling)
Airfare inclusive package tours - overseas (4 nights or more)
Bread
Capital housing costs nec
Wine for consumption off licensed premises
Dental fees
Food and non-alcoholic beverages nfd
Repairs and maintenance (materials only) nec
Home computer equipment (including pre-packaged software)
1,507,613
335,419
326,017
305,408
250,843
208,251
157,417
149,164
132,294
127,152
113,652
113,003
110,845
99,182
81,743
73,890
71,302
66,551
64,530
62,819
60,212
45,968
45,324
43,349
42,084
41,606
39,847
39,431
39,178
39,146
39,074
38,967
0401010101. Beer for consumption off licensed premises
0101040103. House and contents insurance - inseparable (selected dwelling)
0305010101. Fresh milk
0701010201. Bedroom furniture
1103020102. Holiday airfares - overseas (4 nights or more)
1601010901. Other outside improvements
1301999999. Miscellaneous goods nec
1103010102. Holiday air fares - Australia (4 nights or more)
0201010201. Mains gas (selected dwelling)
1302010301. Interest payments on credit card purchases
1302050301. Accountant and tax agent fees
1302010201. Loans for vehicle - interest component
0310010101. Soft drinks
0902010201. Specialist doctor's fees
1101040101. Books
0302020199. Beef and veal nec
0401010201. Beer for consumption on licensed premises
1601010201. Purchase of selected dwelling or other property (excluding mortgage repayments but including outright
purchase, deposit, net of sales)
38,592
38,154
38,100
37,132
36,895
36,026
34,207
33,561
32,547
31,385
31,365
30,266
29,529
28,364
27,984
26,999
26,898
26,854
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
28
!
Due to space limitation only the top 100 (out of 608) household expenditure items are listed here.
78
Expenditure Item
1302020403.
1302030199.
0903010101.
1601010101.
1201019998.
1103010602.
0801050199.
1101010101.
1102010201.
0903019999.
0302050199.
0101050301.
1302050502.
0101050101.
1601010701.
0309030201.
0305010301.
0301030201.
0703029999.
1201020201.
0301030101.
0401030101.
1104010201.
1701010201.
0309039999.
1103010601.
1103011002.
1102999902.
0101030001.
1001050201.
0703020101.
1601010601.
1101040201.
1301990201.
1102999901.
0401000201.
1302990202.
0801019999.
1302030101.
0801010501.
1001059901.
1101050901.
0310020101.
1104010301.
1302010299.
0701010601.
0101050201.
1601010801.
0705019999.
0101059999.
!
Secondary school fees (excluding school sports fees) (independent) - excluding Catholic
Higher education institution fees nec
Prescriptions
Mortgage repayments - principal component (other property)
Toiletries and cosmetics nec
Holiday hotel/motel charges - Australia (4 nights or more)
Formal child care services nec
Televisions
Lotto type games and instant lottery (scratch cards)
Medicines and pharmaceutical products nec
Poultry nec
Repairs and maintenance (contractors) - plumbing
Financial institution charges and fees on financial institution accounts
Repairs and maintenance (contractors) - repainting
Outside building
Chocolate confectionery
Cheese
Biscuits
Whitegoods and other electrical appliances nec
Hair services (female)
Cakes, tarts and puddings (fresh or frozen)
Spirits for consumption off licensed premises
Prepared dog and cat food
Life insurance
Confectionery nec
Holiday hotel/motel charges - Australia (less than 4 nights)
Airfare inclusive package tours - Australia (4 nights or more)
Internet charges (account)
Rate payments (selected dwelling) nfd
Tyres and tubes
Refrigerators and freezers
In-ground swimming pool
Newspapers
Jewellery
Pay TV fees
Alcoholic beverages nfd for consumption on licensed premises
Cash gifts, donations to churches, synagogues and related
Household non-durables nec
HECS
Household paper products (excluding stationery)
Vehicle parts purchased separately nec
Toys
Fruit juice
Veterinary charges
Loans - interest component (excluding housing loans) nec
Carpets
Repairs and maintenance (contractors) - electrical work
Landscape contractor
Tools and other household durables nec
Repairs and maintenance (contractors) - nec
Aggregate
Weekly
Expenditure
26,838
26,605
26,457
25,859
25,832
25,714
25,436
24,687
23,984
22,869
22,728
22,513
22,368
22,346
22,273
21,923
21,529
21,259
21,256
21,239
21,116
20,997
20,833
20,610
20,184
19,838
19,669
19,590
19,514
19,279
19,208
19,049
18,896
18,816
18,649
18,557
17,854
17,769
17,352
17,345
17,264
17,104
16,926
16,236
16,136
16,063
15,830
15,712
15,520
15,406
79
Appendix K – SAM: Household Consumption (A$ million)
!
Consumption
C
Consumption
Old
Middle
Poor income
1995.9 6073.2
427.6 1195.2
457.9 1065.9
293.2
979.4
58.3
249.7
234.1
330.5
1483.7 4417.2
3752.1 12276.6
Rich
5043.9
1548.7
681.3
1172.3
419.9
362.0
3939.4
15722.8
Food
TCF
Elec.
Transport
TGS
Furniture
Other
Services
91291
26421
22790
27172
10072
8795
64952
356839
Food
TCF
Elec.
Transport
TGS
Furniture
Other
Services
Food and Beverages
Textile, Clothing and Footwear
Computers and Electronics
Transport
Toys and Sporting Goods
Furniture
Other Manufactures and Primaries
Services
Young
Poor
Middle-income
Rich
Unskilled Skilled Unskilled Skilled Unskilled Skilled
6183.7 5092.0 14074.5 23416.1 7608.2 21803.4
1351.7 1127.4 3673.3 7056.1 2225.6 7815.4
1272.9 1367.5 3069.7 6327.9 1691.3 6855.5
1222.4
953.4
3607.6 7574.7 2499.6 8869.3
634.0
381.3
1373.9 2977.6
783.5
3193.9
655.7
426.4
1204.2 2661.8
670.8
2249.4
4181.1 3620.9 9674.3 17098.7 4864.1 15672.5
14850.7 13584.3 46145.9 93025.8 34904.9 122576.2
!
!
80
Appendix L - Calibrated Parameters
Table L1: Preference Parameters (!! – Aggregate Consumer and Government
Consumer
Government
Food and Beverages
0.1051
0.0001
Textile, Clothing and Footwear
0.0304
0.0000
Computers and Electronics
0.0262
0.0000
Transport
0.0313
0.0000
Toys, Games and Sporting Goods
0.0116
0.0031
Furniture
0.0101
0.0000
Other Manufactures and Primaries
0.0748
0.0268
Services
0.4110
0.7988
Investment good
0.2993
0.1712
Table L2: Preference Parameters (!! - Old Households
!
Old Poor
Old middle-income
Old rich
Food and Beverages
0.1332
0.1062
0.0822
Textile, Clothing and Footwear
0.0285
0.0209
0.0252
Computers and Electronics
0.0306
0.0186
0.0111
Transport
0.0196
0.0171
0.0191
Toys, Games and Sporting Goods
0.0039
0.0044
0.0068
Furniture
0.0156
0.0058
0.0059
Other Manufactures and Primaries
0.0990
0.0772
0.0642
Services
0.2504
0.2146
0.2563
Investment good
0.4192
0.5352
0.5291
81
Table L3: Preference Parameters (!! - Young Households
Poor
Middle-income
Rich
Unskilled
Skilled
Unskilled
Skilled
Unskilled
Skilled
0.1164
0.1290
0.1051
0.1256
0.0732
0.1001
Footwear
0.0255
0.0286
0.0274
0.0379
0.0214
0.0359
Computers and Electronics
0.0240
0.0346
0.0229
0.0339
0.0163
0.0315
Transport
0.0230
0.0242
0.0269
0.0406
0.0241
0.0407
Goods
0.0119
0.0097
0.0103
0.0160
0.0075
0.0147
Furniture
0.0123
0.0108
0.0090
0.0143
0.0065
0.0103
Primaries
0.0787
0.0917
0.0722
0.0917
0.0468
0.0719
Services
0.2796
0.3441
0.3445
0.4990
0.3360
0.5627
Investment good
0.4285
0.3273
0.3816
0.1410
0.4682
0.1322
Food and Beverages
Textile, Clothing and
Toys, Games and Sporting
Other Manufactures and
Table L4: Domestic Good Firm Parameters (!! !! ! !! ! !)
!
!
!!
!!
!
Food and Beverages
0.5689
0.2745
0.1566
4.5158
Textile, Clothing and Footwear
0.2767
0.4606
0.2627
3.3102
Computers and Electronics
0.3482
0.4150
0.2367
3.2014
Transport
0.2602
0.4711
0.2687
4.4855
Toys, Games and Sporting Goods
0.3304
0.4264
0.2432
6.6691
Furniture
0.1794
0.5225
0.2980
4.1950
Other Manufactures and Primaries
0.6691
0.2107
0.1202
4.2508
Services
0.4125
0.3741
0.2134
4.6817
82
Table L5: Armington Aggregators (!! !)
!
!!"#
!!"
!!"#
Food and Beverages
2.5482
0.4279
0.2423
0.3298
Textile, Clothing and Footwear
3.1268
0.3427
0.3301
0.3271
Computers and Electronics
2.9404
0.3547
0.2982
0.3471
Transport
2.8525
0.3709
0.2626
0.3666
Toys, Games and Sporting Goods
2.8673
0.3805
0.3074
0.3121
Furniture
2.9573
0.3656
0.3122
0.3223
Other Manufactures and Primaries
2.7889
0.3884
0.2742
0.3374
Services
2.3895
0.4556
0.2365
0.3079
!
!
83
Appendix M - Tariff Rates: Rest of the World29
!
Sector
ROW (%)
Food and Beverages
7.45
Textile, Clothing and Footwear
9.00
Computers and Electronics
0.14
Transport
0.06
Toys, Games, Sporting Goods
1.08
Furniture
0.67
Other Manufactures and Primaries
1.91
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
"A!Tariff rates for the rest of the world are the simple average of tariffs of Japan and U.S.. Tariffs for
Japan and the U.S. for my chosen level of disaggregation were unavailable, hence I calculated the tariff
rate for each sector by matching each of the 5051 items (HS07), along with their corresponding tariff
rates, listed for both countries to one of the seven merchandise sectors and calculated the simple average.
The tariff rates are the MFN Applied Tariff (Average of AV duties) taken from the WTO Tariff
Download Facility for the years in consistency with the 2006-2007 Input Output tables.!
!
84
Appendix N - Trade Volume by Sector: Full and Partial Liberalisation
Table N1: Effect of the FTA on Main Exports to China
Full
liberalisation
(%)
Partial
liberalisation
(%)
Food and Beverages
208.52
75.74
Textile, Clothing and Footwear
318.14
99.73
Computers and Electronics
86.26
38.62
Transport
Toys, Games and Sporting
Goods
121.84
50.51
124.71
51.66
Furniture
Other Manufactures and
Primaries
66.93
30.59
66.27
31.33
Services
-24.46
-10.88
Sector
Table N2: Effect of the FTA on Main Exports to RoW
Full
liberalisation
(%)
Partial
liberalisation
(%)
Food and Beverages
-0.79
-0.38
Textile, Clothing and Footwear
33.30
12.76
Computers and Electronics
3.20
1.55
Transport
1.57
0.70
Toys, Games and Sporting Goods
5.71
2.77
Furniture
9.23
3.63
Sector
Other Manufactures and Primaries
0.08
0.11
Services
-0.00
-0.03
Table N3: Effect of the FTA on Main Imports from
China
Full
liberalisation
(%)
Partial
liberalisation
(%)
Food and Beverages
20.77
10.67
Textile, Clothing and Footwear
66.45
27.16
Computers and Electronics
16.53
8.17
Transport
41.50
18.55
Toys, Games and Sporting Goods
28.51
14.10
Furniture
32.08
12.70
Other Manufactures and Primaries
23.51
12.17
Services
14.74
5.79
Sector
!
85
!
Table N4: Effect of the FTA on Main Imports from
Row
Full
liberalisation
(%)
Partial
liberalisation
(%)
Food and Beverages
0.73
0.28
Textile, Clothing and Footwear
-9.55
-4.19
Computers and Electronics
-1.61
-0.78
Transport
Toys, Games and Sporting
Goods
-0.87
-0.39
-2.65
-1.32
Furniture
Other Manufactures and
Primaries
-3.80
-1.55
2.56
1.19
Services
-0.27
-0.11
!
!
!
!
!
!
!
Sector
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
86
Appendix O - Results: Differentiated Import Elasticities of Substitution
!
Table O1: Effect of the FTA on Domestic Production
Hummels
(%)
Rolleigh
(%)
Anderson
(%)
0.27
0.41
0.46
Textile, Clothing and Footwear
-13.45
-28.50
-45.16
Computers and Electronics
-2.28
-0.37
0.72
Transport
-0.56
-0.08
0.69
Toys, Games, Sporting Goods
-1.96
-5.19
-4.84
Furniture
Other Manufactures and
Primaries
Services
-1.90
-9.60
-7.70
2.34
3.05
3.52
-0.25
-0.14
0.02
Sector
Food and Beverages
Table O2: Effect of the FTA on Change in Aggregate Trade Volume
Hummels (%)
Rolleigh (%)
Anderson (%)
Total Imports from China
55.52
35.33
74.77
50.46
95.38
66.45
Total Exports to RoW
Total Imports from RoW
0.42
0.37
0.46
0.41
0.40
0.36
Total Exports to China
Table O3: Effect of the FTA on Exports to China
Sector
!
Hummels (%)
Rolleigh (%)
Anderson (%)
Food and Beverages
221.69
263.12
307.48
Textile, Clothing and Footwear
341.34
415.31
494.08
Computers and Electronics
93.94
116.62
141.05
Transport
131.71
161.97
194.12
Toys, Games and Sporting Goods
133.59
163.03
192.14
Furniture
73.02
96.62
117.24
Other Manufactures and Primaries
73.05
94.18
116.83
Services
-21.14
-10.66
0.49
87
Table O4: Effect of the FTA on Exports to RoW
Sector
Hummels (%)
Rolleigh (%)
Anderson (%)
Food and Beverages
-0.85
-0.55
-0.38
Textile, Clothing and Footwear
34.85
39.91
43.99
Computers and Electronics
2.99
2.21
1.54
Transport
1.69
2.16
2.39
Toys, Games and Sporting Goods
5.32
5.38
4.49
Furniture
8.51
9.57
8.07
Other Manufactures and Primaries
-0.17
-0.46
-0.77
Services
0.06
0.71
1.14
Hummels (%)
Rolleigh (%)
Anderson (%)
Food and Beverages
16.53
9.96
14.76
Textile, Clothing and Footwear
87.78
158.95
235.82
Computers and Electronics
24.84
7.09
8.76
Transport
59.31
80.50
100.30
Toys, Games and Sporting Goods
26.06
62.67
60.45
Furniture
21.42
90.13
76.85
Other Manufactures and Primaries
21.38
24.13
29.09
Services
12.34
5.65
-0.72
Rolleigh (%)
Anderson (%)
Table O5: Effect of the FTA on Imports from China
Sector
Table O6: Effect of the FTA on Imports from RoW
Sector
Food and Beverages
!
Hummels (%)
0.74
0.70
1.08
Textile, Clothing and Footwear
-14.34
-31.10
-49.34
Computers and Electronics
-2.45
-0.87
-2.30
Transport
-1.23
-2.17
-3.43
Toys, Games and Sporting Goods
-2.46
-7.46
-8.60
Furniture
-2.47
-13.63
-13.91
Other Manufactures and Primaries
2.96
4.11
6.21
Services
-0.26
-0.49
-1.62
88
Appendix P – Results: Differentiated Export Elasticities of Substitution
Table P1: Effect of the FTA on Changes in Domestic Production
!! = 0.8(%)
!! !!0.867(%)
!! ! 0.95(%)
Food and Beverages
0.11
0.17
0.56
Textile, Clothing and Footwear
-8.89
-9.13
2.16
Computers and Electronics
-0.78
-1.25
-1.36
Transport Parts and Vehicles
-0.26
-0.38
-0.41
Toys, Games and Sporting Goods
-1.57
-1.93
-0.97
Furniture
-2.29
-2.79
-1.54
Other Manufactures and Primaries
1.27
1.67
1.25
Services
-0.10
-0.19
-0.43
Sector
Table P2: Effect of the FTA on Change in Aggregate Trade Volume
!! = 0.8(%)
!! !!0.867(%)
!! ! 0.95(%)
Total Exports to China
30.32
40.51
77.88
Total Imports from China
20.71
26.27
42.38
Total Exports to RoW
0.24
0.40
1.12
Total Imports from RoW
0.19
0.34
1.03
!! = 0.8(%)
!! !!0.867(%)
!! ! 0.95(%)
Food and Beverages
90.07
144.99
141.60
Textile, Clothing and Footwear
116.30
203.51
273.69
Computers and Electronics
46.36
66.67
53.89
Transport Parts and Vehicles
60.59
90.72
77.62
Toys, Games and Sporting Goods
60.66
91.86
86.13
Furniture
38.00
53.09
41.11
Other Manufactures and Primaries
39.23
53.71
31.96
Services
-6.06
-15.02
-46.09
Table P3: Effect of the FTA on Main Exports to China
Sector
!
89
Table P4: Effect of the FTA on Main Exports to RoW
!! = 0.8(%)
!! !!0.867(%)
!! ! 0.95(%)
Food and Beverages
-0.18
-0.45
-2.28
Textile, Clothing and Footwear
13.10
22.53
49.84
Computers and Electronics
0.89
1.95
7.25
Transport Parts and Vehicles
0.63
1.08
2.30
Toys, Games and Sporting Goods
2.05
3.77
10.13
Furniture
3.37
6.13
16.14
Other Manufactures and Primaries
0.03
0.06
-0.09
Services
0.09
0.06
-0.84
Sector
Table P5: Effect of the FTA on Main Imports from China
!! = 0.8(%)
!! !!0.867(%)
!! ! 0.95(%)
Food and Beverages
11.41
16.79
15.90
Textile, Clothing and Footwear
54.32
61.03
17.23
Computers and Electronics
8.72
13.26
13.37
Transport Parts and Vehicles
31.39
37.27
14.20
Toys, Games and Sporting Goods
19.81
24.85
13.51
Furniture
23.51
28.49
12.80
Other Manufactures and Primaries
13.25
19.11
16.64
Services
6.36
11.23
8.91
!! = 0.8(%)
!! !!0.867(%)
!! ! 0.95(%)
Food and Beverages
0.34
0.54
1.09
Textile, Clothing and Footwear
-9.45
-9.68
2.25
Computers and Electronics
-0.88
-1.30
-1.12
Transport Parts and Vehicles
-0.61
-0.75
-0.40
Toys, Games and Sporting Goods
-1.99
-2.39
-1.00
Furniture
-2.87
-3.41
-1.62
Other Manufactures and Primaries
1.55
2.09
1.73
Services
-0.18
-0.22
-0.17
Sector
Table P6: Effect of the FTA on Main Imports from Row
Sector
!
90
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