THE UNIVERSITY OF NEW SOUTH WALES

School of Economics

THE WELFARE IMPACT OF AN

AUSTRALIA-CHINA

FREE TRADE AGREEMENT

KRISTINE PATRICIO

Bachelor of Commerce (Business Economics / Finance)

Honours in Business Economics

Supervisor: Dr. Sang-Wook (Stanley) Cho

24 October 2011

_________________________________________

Assessing the Impacts of Further Liberalisation of

Trade in Thailand: A Computable General Equilibrium Analysis _________________________________________

Jakree Koosakul

Supervisor: Professor Alan Woodland

School of Economics Honours Thesis

Bachelor of Economics (Economics and Econometrics)

October 24th 2011

! 2

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

!

!

! 3

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.

!

! 4

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! 5

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List of Tables

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! 7

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.

!!!!!!!!!!!!!!!!!!!!

! 8

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

! 9

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.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1 Slovenia has been classified as a developed economy by the International Monetary Fund.

! 10

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

! 11

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.

!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.

! 12

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) grouping3 . 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

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!2 A summary of the existing bilateral trade and economic agreements is found in Appendix A. 3 Australia-China FTA Joint Feasibility Study, 2005!

! 13

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.

!L160&9!+!"0$%&'E1'M$!B&'/9!<1%C!1%$!B.I!B&'/9!N'&%-9&$!O3HH=P3H+HQ!

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

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!4 Australia-China FTA Joint Feasibility Study, 2005 5 Composition of Trade Australia 2009-2010 !

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000

100,000 $Am

! 14

Australia’s top export destinations and import sources, with China comprising 21% of

total exports and 15% of total imports.

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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

21%

15%

8%

7% 6% 4%

4% 3%

3%

28%

China

Japan

India

Republic of Korea

United States

United Kingdom

New Zealand

Singapore

Taiwan

Rest of the World

15%

13%

8%

6%

5% 5% 4% 4%

4%

37%

China

United States

Japan

Thailand

Singapore

Germany

United Kingdom

New Zealand

Malaysia

Rest of the World

! 15

2005-2010 had a 5-year trend in growth of 20.2%6. 3/5),'! I shows Australia’s

merchandise trade with China over the years 2004-2010. China was Australia’s third

largest merchandise trading partner in 2004, but both exports and imports have

increased over the years as depicted in 3/5),'!I, such that in 2010 merchandise trade

was valued at A$82.9 billion, making China Australia’s largest partner in merchandise

trade.

L160&9!>!"0$%&'E1'R$!D9&(C'-/1$9!B&'/9!<1%C!FC1-'!

!!!!! !!!!!!!!!!J8),@'Y!B3(%!

3/5),'!I decomposes merchandise trade into exports and imports, and shows that both

have experienced an increasing trend over time. The growth in trade between China and

Australia comes from the complementarity of the two economies which sources mainly

from their respective economic endowments, as well as China’s evolving development

path.

Australia is abundant in agricultural and mineral resources that are increasingly

demanded by China as it experiences rapid economic growth and industrialisation. The

increase in urbanisation as a result has spiked China’s demand for agricultural products

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

increase in its demand for iron ore from Australia. Trade in resources and agricultural

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!K!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 likely to be affected by the end points of a given period (Composition of Trade Australia 2009-2010).!

Fact Sheet

General information: Fact sheets are updated biannually; June and December

Capital: Beijing Head of State:Surface area: 9,561 thousand sq km President HE Mr Hu JintaoOfficial language: MandarinPopulation: 1,341.4 million (2010) Head of Government:Exchange rate: A$1 = 6.1111 Yuan (Aug 2010) Premier of the State Council HE Mr Wen Jiabao

Recent economic indicators: 2006 2007 2008 2009 2010(a) 2011(b)GDP (US$bn) (current prices): 2,712.9 3,494.2 4,520.0 4,984.7 5,745.1 6,422.3GDP PPP (US$bn) (c): 6,242.0 7,337.6 8,217.4 9,047.0 10,084.4 11,195.4GDP per capita (US$): 2,064 2,645 3,404 3,735 4,283 4,764GDP per capita PPP (US$) (c): 4,749 5,553 6,188 6,778 7,518 8,304Real GDP growth (% change yoy): 12.7 14.2 9.6 9.2 10.3 9.6Current account balance (US$m): 253,268 371,833 436,107 297,100 269,870 324,797Current account balance (% GDP): 9.3 10.6 9.6 6.0 4.7 5.1Goods & services exports (% GDP): 39.1 38.4 35.0 26.7 26.7 25.7Inflation (% change yoy): 1.5 4.8 5.9 -0.7 3.5 2.7

Australia's trade and investment relationship with China (d):Australian merchandise trade with China, 2009-10: Total share: Rank: Growth (yoy):

Exports to China (A$m): 46,448 23.1% 1st 18.1%Imports from China (A$m): 36,368 17.9% 1st -1.8%Total trade (exports + imports) (A$m): 82,817 20.5% 1st 8.4%

Major Australian exports, 2009-10 (A$m): Major Australian imports, 2009-10 (A$m):Iron ore & concentrates 25,112 Clothing 3,792Coal 5,067 Computers 3,524Copper ores & concentrates 1,719 Telecom equipment & parts 3,359Wool & other animal hair (incl tops) 1,522 Prams, toys, games & sporting goods 1,909

Australia's trade in services with China, 2009-10: Total share:Exports of services to China (A$m): 5,802 11.0%Imports of services from China (A$m): 1,614 3.0%

Major Australian service exports, 2009-10 (A$m): Major Australian service imports, 2009-10 (A$m):Education-related travel 4,399 Personal travel excl education 629Personal travel excl education 610 Transport 487

Australia's investment relationship with China, 2009 (e): Total: FDI:Australia's investment in China (A$m): 6,327 2,347China's investment in Australia (A$m): 16,637 9,167

China's global merchandise trade relationships:China's principal export destinations, 2009: China's principal import sources, 2009:

1 United States 18.4% 1 Japan 13.0%2 Hong Kong, SAR of China 13.8% 2 Republic of Korea 10.2%3 Japan 8.1% 3 Taiwan 8.5%

11 Australia 1.7% 7 Australia 3.9%

Compiled by the Market Information and Research Section, DFAT, using the latest data from the ABS, the IMF and various international sources.(a) All recent data subject to revision; (b) IMF forecast; (c) PPP is purchasing power parity; (d) Total may not add due to rounding; (e) Stock, as at 31 December. Released annually

by the ABS. na Data not available. np Data not published. .. Data not meaningful.

Australia's merchandise trade with China Australia's merchandise exports to ChinaReal GDP growth

CHINA

10,000

20,000

30,000

40,000

Primary STM ETM Other

A$m2004-05

2009-10

0

3

6

9

12

15

2006 2007 2008 2009 2010 2011

%

10,000

20,000

30,000

40,000

50,000

2004-05 2005-06 2006-07 2007-08 2008-09 2009-10

A$m

Imports

Exports

! 16

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 large-

scale 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 Principal Exports

Trade Value (A$ millions)

281 Iron ore & concentrates 25,185 321 Coal 5,067 287 Other ores & concentrates 1,719 268 Wool & other animal hair 1,522 283 Copper ores & concentrates 1,200 333 Crude petroleum 1,165

0 Food & live animals 995 682 Copper 909 686 Zinc 695 28 Metalliferous ores & scrap 482 2 Crude metals (excluding fuels) 454

!

! 17

B'#E9!3!"0$%&'E1'R$!B.I!,TI.&%$!G&.T!FC1-'!O3H+HQ!

SITC Code Principal Imports

Trade Value (A$ millions)

84 Clothing 3,792 752 Computers 3,523 764 Telecom equipment & parts 3,358 894 Prams, toys, games & sporting goods 1,909 821 Furniture, mattresses & cushions 1,536 761 Monitors, projectors & TVs 1,409

5 Chemicals & related products 1,042 74 General industrial machinery & parts 1,004

751 Office machines 890 851 Footwear 882 78 Road vehicles 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

! 18

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!

! 19

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,

! 20

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

! 21

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 socio-

demographic 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.

! 22

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

! 23

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 low-

income, 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),

! 24

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,

! 25

(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).

! 26

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. !

! 27

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.

! 28

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.

! 29

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/.-,!@.-**/=/@-+/82!=8,!+&'!*&-,'!8=!.-C8,!'-,2/25!;-*!)*'4!CH!1&8!-24!B/-b!W"U$$X!

! 30

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

! 31

!

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.

!!!

! 32

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).!

! 33

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)

! 34

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

! 35

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

! 36

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 !! !!!!; a price for the investment good !!"#; prices for the

consumption goods !!!!! !!!! ; factor prices !!! !!! ! ; bilateral exchange rates

!! !!!; foreign prices !!!!! !!!!!!!!!!!; a consumption plan for each type of household

!!! ! !!"#! !!!!!!!!!!!; a consumption plan for the government !!!! !!"#! !!!!

; a

! 37

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

the investment good firm !!"#!! !!!!"# !! ! !!!!"#! ; a production plan for the

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 !!!! !!"#! !!!! solves the problem of the government.

– The consumption plan !!!!!! !!"#!!! !!!!! ! !!!!! solves the problem of the

representative household in country f.

– The production plan !!!!!! !!!!! !! ! !!!!! ! !! ! !!!! ! !!!! ! satisfies

!!!! ! !"#! !!!!!

!!!!!!! ! !!!!

!

!!!!!!! ! !!!!

!

!!!!!!!!!!

!!!!!!!!!!!!!!!!

!!!! !!and

!!! !!!!!!!!!!!!!! ! ! !!!!!!!!!!!

!!! !!!!!!! ! !!!!!!! ! !!! !! !!! !!!"!!!!! !! !

– The production plan !!!!!!!! ! !!!! !!!! satisfies

!!!!! ! !!!!!!!!!!! ! ! !!! !!!!!!!!!!!! !!!! !!!!!

!!!! !!!!"!!! ! !!

where !!!! and !!!! !!!! solve

!"#!!!! !!!!!!!!!!!!!! ! ! !!! !!!!!!!!!!!! !!!!!!!!

! 38

!! !! !! ! !!!!!!!!!!!! ! ! !!!!!!!!

!!!!

!!!

!!!!!

! !!!

– The production plan !!"#!! !!!!"#!! ! !!!!"#! satisfies

!!"# ! !"# !!!!"#!!!!"#

!! ! !!!!"#!!!!"#!! ! !!!!"#!!!!"#

!"#

!!!"!!!"# ! ! !!!!!!"#!

!!!!!! !!! !!!"!!!"# !! !

– The production plan !!!!!! !!!!! !! ! !!!!! ! satisfies

!!!! ! !"#! !!!!!

!!!!!!! ! !!!!

!

!!!!!!! ! !!!!

!

!!!!!!!"#

!!! !!!!!!!!!!!!!! ! ! !!!!!!!!!!!

!! !!! !!!"!!!!! !! !

– The factor markets clear:

!!!!!!!!

! ! !!!!!!!

!!!!!!!!!!!!!!!!!!!!! !!!!!!!!

! ! !!!!!!!

!!!!!!!!!!!!!!!!!! !!!!!!

! !!!!!

!

– The goods markets clear:

!! ! !!!!!!!!!

! !!!!!!!!!

! !!!!"#! !! !!!! ! !!!!!!!

!

!!!! ! !!!!!!

!!"# ! !!"#!!!!

! !!"#! ! !!"#!!!!!

! 39

– The balance of payments condition for each trade partner country f is satisfied:

!!!!!!! !!!!!!!!!

! !!!!"#!!! !!"#!!! !! !!!!!!!!!!

!!!!

! !!!!"!!"#!!!

! 40

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).!!

! 41

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#!

! 42

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 These values are commonly used in literature such as in Cho and Diaz (2008, 2011) and Syquia (2007). 16 A similar measure for calculating elasticities was used in Cho and Diaz (2011).

! 43

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 welfare17, 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).!

! 44

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.-$0TI%1.-!Y../!N&1(9$

!

!

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!18 Syquia (2007) used the halving of tariff rates in his simulation of partial liberalisation.

0.17

-2.31

-0.08 -0.01

-0.41 -0.66

0.10 0.10 0.07

-0.97

-0.04 -0.01 -0.21

-0.27

0.04 0.05

Food and Beverages

Textile, Clothing and

Footwear

Computers and

Electronics

Transport Parts and Vehicles

Toys, Games and Sporting

Goods Furniture

Other Manufactures and Primaries Services

Full liberalization (%) Partial liberalization (%)

! 45

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.-!

!

!

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

0.24

-8.98

-1.59

-0.49

-2.17 -3.15

2.03

-0.30

0.04

-3.96

-0.79 -0.22

-1.09 -1.26

0.95

-0.13

Food and Beverages

Textile, Clothing and

Footwear

Computers and

Electronics

Transport Parts and Vehicles

Toys, Games and Sporting

Goods Furniture

Other Manufactures and Primaries Services

Full liberalization (%) Partial liberalization (%)

! 46

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 Exports to China 49.35 22.96 Total Imports from China 30.45 13.95 Total Exports to RoW 0.55 0.26 Total Imports from RoW 0.49 0.23

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.!

! 47

B'#E9!++!SGG9(%!.G!%C9!LB"!.-!D'1-!SJI.&%$!%.!FC1-'!'-/!:.[!

Sector Full Liberalisation (%)

Partial Liberalisation (%)

China Food and Beverages 208.52 75.74 Other Manufactures and Primaries 66.27 31.33

Rest of World Food and Beverages -0.79 -0.38 Other Manufactures and Primaries 0.08 0.11

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%).

! 48

B'#E9!+3!SGG9(%!.G!%C9!LB"!.-!D'1-!,TI.&%$!G&.T!FC1-'!'-/!:.[

Sector Full Liberalisation (%)

Partial Liberalisation (%)

China

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

Rest of World

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

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!

! 49

!

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

!

!

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.

0.21

-0.43

0.08 0.10

-0.21

0.03

Aggregate consumer welfare Government welfare Social welfare

Full liberalization (%) Partial liberalization (%)

! 50

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$9C.E/$!

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

0.49

0.47

0.42

0.42

0.27

0.15

0.13

0.08

0.07

0.22

0.22

0.19

0.19

0.12

0.07

0.06

0.04

0.03

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

Full liberalization (%) Partial liberalization (%)

! 51

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.

!

!

! 52

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 pre-

FTA 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.

! 53

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.

! 54

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% .

! 55

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.

! 56

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.

! 57

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.

! 58

L160&9!=!SGG9(%!.G!%C9!LB"!.-!"66&96'%9![9EG'&9!!!!" !! !!"!!!!

!!!!

B'#E9!+*!SGG9(%!.G!%C9!LB"!.-!B'&1GG!:9;9-09!!!!" !! !!"!!!!

Hummels (%) Rolleigh (%) Anderson (%)

Tariff revenue -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.

0.21

-0.50

0.06 0.20

-0.75

0.01

0.19

-0.97

-0.05

Aggr consumer welfare Govt welfare Social welfare

Hummels (%) Rolleigh (%) Anderson (%)

! 59

L160&9!+H!SGG9(%!.G!%C9!LB"!.-!%C9![9EG'&9!.G!?1$'66&96'%9/!X.0$9C.E/$!

!!!" !! !!"!!!!

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

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 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

-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

! 60

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)

! 61

B'#E9!+=!SGG9(%!.G!%C9!LB"!.-!F.-$0TI%1.-!Y../!N&1(9$!OG.&!/1GG9&9-%!!!Q!

Sector !! ! !!! !! ! !!!"# !! ! !!!"

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

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.

! 62

L160&9!++!SGG9(%!.G!%C9!LB"!.-!%C9![9EG'&9!.G!?1$'66&96'%9/!X.0$9C.E/$!OG.&!/1GG9&9-%!!!Q!

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.

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

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

! 63

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

! 64

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.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

! 65

Appendix

Appendix A – Summary of Existing Bilateral Trade and Economic Agreements26

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"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) !

2004 Agreement between the Government of Australia and the Government of the People's Republic of China relating to Air Services

2004 Memorandum of Understanding on Two Way Investment Promotion Cooperation Between Austrade, Invest Australia, and the Investment Promotion Agency, MOFCOM of China

2004 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

2004 Memorandum of Understanding on Customs Cooperation and Mutual Assistance between Australian Customs and the General Administration of Chinese Customs

2003 Trade and Economic Framework between Australia and the People’s Republic of China

2003 Arrangement on Higher Education Qualifications Recognition between Australia and the People’s Republic of China

2003 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

2003 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

2003 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

2003 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

2003 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

2003 Memorandum of Understanding relating to Air Services between Australia and the People’s Republic of China

2002

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

1995 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.

2001 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

2001 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

2001 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

2000 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

1999 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

!

! 66

J8),@'Y!()*+,-./-01&/2-!3,''!%,-4'!(5,''6'2+!3'-*/C/./+H!J+)4H!

1999 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

1999 Exchange of Letters on Approved Destination Status (ADS) Group Tourism Arrangements between Australia and the People’s Republic of China

1999 Exchange of Letters between the Australian Embassy, Beijing, and the China National Tourism Administration concerning Outward Bound Travel by Chinese Citizens to Australia

1999 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

1999 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

1995 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

1990 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

1990 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

1988 Agreement on Fisheries between the Government of Australia and the Government of the People’s Republic of China

1988 Agreement with the People's Republic of China on the Reciprocal Encouragement and Protection of Investments

1987 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

1986 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

1986 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

2004 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.

1985 Memorandum of Understanding between the Government of Australia and the Government of the People's Republic of China regarding Wool Cooperation

1984 Joint Communiqué of the Australia-China Joint Agricultural Commission - Inaugural Session

1984 Memorandum of Understanding on the establishment of a Legal Exchange Program between Australia’s Attorney-General’s Department and China’s Ministry of Justice

1984 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

1984 Agreement between the Government of Australia and the Government of the People's Republic of China Relating to Civil Air Transport

1984 Agreement between the Government of Australia and the Government of the People's Republic of China on Agricultural Cooperation

1983 Understanding relating to Quarantine and Health Requirements for Cattle Exported from Australia to the People’s Republic of China

1981 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

1981 Protocol on Economic Cooperation with the Government of the People’s Republic of China

1980 Agreement between the Government of Australia and the Government of the People's Republic of China on Cooperation in Science and Technology

1974 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

1973 Trade Agreement between the Government of Australia and the Government of the People’s Republic of China

!

! 67

Appendix B - Australia Tariff Rates (2009) !

Product Groups Final bound duties MFN applied duties

AVG Duty-free

in % Max 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 Beverages & tobacco 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 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!

!

!

!

!

!

!

!

! 68

Appendix C - China Tariff Rates (2009) !

Product Groups Final bound duties MFN applied duties

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!

!

!

!

!

!

!

!

! 69

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.!

! 70

Appendix E - The Social Accounting Matrix for Australia

!

Socia

l Acc

ount

ing

Mat

rix fo

r Aus

tralia

for 2

006-

2007

, Milli

ons

(AUS

D)

1 2

3 4

5 6

7 8

1 2

3 4

5 6

7 8

TOTA

LCh

ina

RoW

1 4

9 52

9 8

43 1

5 2

8 6

65 0

1 0

28 2

3 70

2 6

6 63

1 0

0 0

0 0

0 0

23

2 2

37 2

9 81

5 3

94 2

9 42

1 1

74 5

17 2

210

679

64

107

120

161

314

5 1

15 0

21

004

0 0

0 0

0 0

0 0

2 4

95 1

7 2

478

30

270

3 3

56 0

1 6

60 1

404

431

42

811

30

261

0 0

11

073

0 0

0 0

0 0

26

778

5 6

71 3

0 5

641

78

488

4 1

60 0

0 9

900

0 4

0 0

16

668

0 0

0 1

7 75

3 0

0 0

0 0

30

013

5 2

20 6

8 5

152

79

754

5 1

257

0 1

39 7

62 2

628

129

0 6

663

0 0

0 0

8 6

52 0

0 0

698

924

917

2 9

14 2

2 77

0 6

53

0 0

0 0

85

0 1

149

0 0

0 0

0 7

187

0 0

0 2

631

181

5 1

76 1

1 28

7 7

10

514

0 3

153

6 2

77 2

293

2 1

14 1

58 4

93 1

12 0

69 0

0 0

0 0

0 2

1 37

2 0

6 0

10 3

2 88

6 1

24 5

08 1

4 91

8 1

09 5

91 4

79 6

91 8

11

294

0 0

0 4

344

0 2

5 92

7 2

43 7

03 1

5 18

5 3

723

8 0

08 6

267

588

1 0

65 2

2 48

7 2

92 5

57 1

79 3

19 2

13 4

73 3

4 01

6 4

417

29

599

1 06

1 95

5 1

91

291

91

291

2 2

6 42

1 2

6 42

1 3

22

790

22

790

4 2

7 17

2 2

7 17

2 5

` 1

0 07

2 1

0 07

2 6

8 7

95 8

795

7 6

4 95

2 6

4 95

2 8

356

839

356

839

40

847

12

250

24

176

23

318

5 5

71 4

461

69

790

377

644

558

058

53

900

4 6

86 1

2 91

7 8

201

2 7

49 9

76 1

41 1

40 2

65 1

76 4

89 7

45 5

58 0

58 4

89 7

451

047

803

- 5 2

01 1

871

- 1 3

12 3

77 2

73 4

16- 1

4 00

2- 4

2 31

9 9

475

1 6

94 3

709

3 1

52 8

32 5

44 2

1 09

3 6

4 28

2 4

4 88

4 1

79 5

89 1

79 5

89- 5

297

892

- 1 5

31-

918

170

313

- 14

883

- 42

319

9 4

75 1

694

3 7

09 3

152

832

544

21

093

64

282

41

208

Tarif

fsTO

TAL

96

979

219

1 2

95 1

03 1

03 8

81 0

3 6

76Ch

ina

4 5

12 3

9 4

5 4

8 4

3 9

8 0

789

RoW

92

467

180

1 2

50 5

5 6

0 7

83 0

2 8

87 2

59 8

82 3

8 42

3 1

0 63

7 8

763

1 8

73 3

08 9

42Im

ports

TOTA

L 1

1 59

6 9

941

37

674

29

380

3 6

95 2

862

96

189

22

123

213

460

Chin

a 5

07 5

199

6 7

73 1

010

1 7

08 1

205

10

738

1 4

75 2

8 61

5Ro

W 1

1 08

9 4

742

30

901

28

370

1 9

87 1

657

85

451

20

648

184

845

174

517

30

270

78

488

79

754

22

770

11

287

479

691

1 06

1 95

5 9

1 29

1 2

6 42

1 2

2 79

0 2

7 17

2 1

0 07

2 8

795

64

952

356

839

558

058

489

745

1 04

7 80

3 2

24 4

73 3

08 9

42 2

13 4

60 2

8 61

5 1

84 8

45

1L

Paym

ent t

o la

bour

2K

Paym

ent t

o ca

pita

l3

CFi

nal c

onsu

mpt

ion

4G

Gov

ernm

ent e

xpen

ditu

re5

IFi

nal in

vest

men

t6

XEx

ports

7Ro

WRe

st o

f the

wor

ld

Text

ile, C

loth

ing

and

Foot

wear

Toys

, Gam

es a

nd S

porti

ng G

oods

Oth

er M

anuf

actu

res

and

Prim

arie

sFu

rnitu

re

XTO

TAL

Food

and

Bev

erag

es

Com

pute

rs a

nd E

lect

roni

csTr

ansp

ort

Gov

ernm

ent

Dire

ct T

ax

Prod

uctio

nCo

nsum

ptio

nL

K

Hous

ehol

ds

Capi

tal

Indi

rect

Tax

TOTA

L

CG

I

Production Consumption

L K

! 71

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.

! 72

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.

! 73

Appendix G - Sectoral Matching of Household Expenditure Items

Textile, Clothing and Footwear Item Code Item Description Item Code Item Description 0601000000 Clothing nfd 0601990101 - 0601999999 Other articles of clothing 0601010000 - 0601019998 Men's clothing 0602010000 - 0602010199 Men's footwear 0601020000 - 0601029998 Women's clothing 0602010200 - 0602010299 Women's footwear 0601030000 - 0601039998 Boy's clothing 0602010300 - 0602010399 Children & infants footwear 0601040000 - 0601049998 Girl's clothing 0701010601 - 0702019999 Linen, rugs, other textiles 0601050199 Infant's clothing 1101051103 Specialist sports shoes

Food and Beverages Item Code Item Description Item Code Item Description 0300000000 Food and non-alcoholic beverages nfd 0309040101 - 0309040401 Food additives 0301010101 - 0301049999 Wheat- and rice -based food products 0309050101 - 03010069999 Canned & prepared meals 0302000000 - 0302999999 Meat products 0310000000 - 0310050201 Non-alcoholic liquids 0303000000 - 0303019999 Fish & seafood products 0311010101 - 0399010101 Meals & other food 0304019999 - 0304019999 Eggs and egg products 0399010201 Non-alcoholic beverages nec 0305010201 - 0305019999 Dairy products 0401000000 - 0401040201 Alcoholic beverages 0306010101 - 030619999 Edible fats & oils products 0501010101 - 0501010201 Cigarettes & tobacco 0307000000 - 0307030201 Fruit & nut products 1104010200 - 1104010299 Animal food 0308000000 - 0308999999 Vegetable products 1301990401 Ice 0309010101 - 03090399999 Sugar products & confectionery

Computers and Electronics Item Code Item Description Item Code Item Description 0703010101 - 0705010301 Whitegoods & other electrical tools &

appliances 1101020101 - 1101030199 Computer equipment &

software 0705019901 - 0705019904 Communication equipment 1101030201 - 1101039999 Recording media 1101010101 - 1101019999 Audiovisual equipment & parts 1101050101 Photographic equipment

(excluding film and chemicals)

Furniture

Item Code Item Description Item Code Item Description 0701010201 Bedroom furniture 0701010501 Other furniture 0701010301 Lounge/dining room furniture 0702020101 Paintings, carvings, sculptures 0701010401 Outdoor/garden furniture 0702029999 Ornamental furnishings nec

Toys, Games and Sporting Goods Item Code Item Description Item Code Item Description 1101050901 Toys 1101051104 Water sport, snow sport and

skating equipment 1101051001 Camping equipment 1101051105 Bats, sticks, racquets and balls

for field and court 1101051100 Sports equipment nfd 1101051198 Sports equipment nec 1101051101 Fishing equipment 1101059901 Above ground pool 1101051102 Golf equipment (excluding specialist

sports shoes) 1101059999 Recreational and educational

equipment nec

Transport Item Code Item Description Item Code Item Description 1001010101 Purchase of motor vehicle (other than

motor cycle) 1101050801 Purchase of aircraft

1001010201 Purchase of motor cycle 1101050899 Aircraft purchase, parts and operation nec

1001020101 Purchase of caravan (other than selected dwelling)

1001050101 Motor vehicle batteries

1001020201 Purchase of trailer 1001050201 Tyres and tubes 1001020301 Purchase of bicycle 1001050301 Motor vehicle electrical

accessories (purchased separately)

1101050701 Purchase of boat 1001059901 Vehicle parts purchased separately nec

1101050799 Boat purchase, parts and operation nec 1001059902 Vehicle accessories purchased separately nec

! 74

Other Manufactures and Primaries Item Code Item Description Item Code Item Description 0201010201 - 0299999999 Fuel and power 1101050201 Photographic film and

chemicals (including developing)

0601050101 Nappies 1101050301 - 1101050401 Optical goods 0701010801 - 0702010801 Floor & window coverings 1101050601 - 1101059902 Music, arts & crafts related

materials 0703030101 Non-electrical household appliances 1103010501 - 1103010502 Holiday petrol 0704010101 - 0704019999 Glassware, tableware, cutlery and

household utensils 1104010101 - 1104019903 Animal purchases

0705010101 - 0801010101 Household tools 1201010101 - 1201019998 Hygiene products 0801010201 - 0801010501 Household cleaning products 1301010000 - 1301019999 Stationery equipment 0801010601 - 0801010801 Gardening & swimming pool products 1301990101 - 1301990201 Watches, clocks, jewellery 0801010901 Foodwraps (excluding paper) 1301990301 Travel goods, handbags,

umbrellas, wallets, etc 0801019999 Household non-durables nec 1301999902 Baby goods (excluding

clothing) 0903000000 - 0903029999 Medical supplies 1301999903 Christmas decorations 1001030000 - 1001030401 Oils, lubricants, fuels 1301999999 Miscellaneous goods nec 1101040101 - 1101049999 Printed matter

Services Item Code Item Description Item Code Item Description 0101010101 - 0101040103 Housing payments 1103010101 - 1103020602 Holiday travel services 0101050101 - 0101070101 Repairs & maintenance services 1104010000 - 1104019999 Animal expenses 0101070201 - 0201020101 Utilities 1201020000 - 1201029999 Hair & personal care services 0603010101 - 0603010401 Clothing & footwear cleaning, repairs 1302010101 - 1302010401 Loan repayments 0801020101 - 0801039999 Communication services (post,

telephone, etc) 1302020000 - 1302030301 Education services

0801040101 - 0801049999 Housekeeping & other household services

1302040001 - 1302050000 Other property expenses

0801050000 - 0801050201 Child care services 1302050101 - 1302059902 Professional & legal fees 0801060101 - 0801080199 Repair & maintenance of household

furnishings & appliances 1302990101 - 1302990299 Cash gifts, donations, etc

0901010101 - 0999990201 Health services 1302990301 - 1302999998 Miscellaneous payments & services

1001040101 - 1101050802 Transport services (insurance, servicing, fares, etc)

1401010101 Income tax

1102010000 - 1102019999 Gambling & lottery services 1501010101 - 1601019999 Mortgage repayments & property improvement

1102020101 - 1102029999 Hire services (electronics, sporting equipment)

1701010101 Superannuation and annuities

1102030101 - 1102999998 Recreational & leisure services

!!

!!!!!!!!!!!!

! 75

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

! 76

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

! 77

Appendix I – SAM: Factor Income (A$ million)

!

Food TCF Elec. Transport TGS Furniture Other Services40847.4 12249.8 24176.5 23318.3 5571.4 4461.2 69790.2 377643.6

Poor 0.2 0.1 0.1 0.1 0.0 0.0 0.4 1.9Middle-income 32.7 9.8 19.4 18.7 4.5 3.6 55.9 302.5

Rich 513.6 154.0 304.0 293.2 70.1 56.1 877.5 4748.3Poor unskilled 590.0 176.9 349.2 336.8 80.5 64.4 1008.0 5454.3Poor skilled 662.9 198.8 392.3 378.4 90.4 72.4 1132.5 6128.3

Middle-income unskilled 6772.3 2030.9 4008.3 3866.1 923.7 739.6 11570.9 62611.4Middle-income skilled 10284.9 3084.3 6087.3 5871.2 1402.8 1123.3 17572.3 95085.8

Rich unskilled 6927.5 2077.5 4100.2 3954.6 944.9 756.6 11836.0 64046.2Rich skilled 15063.4 4517.4 8915.6 8599.1 2054.6 1645.2 25736.7 139264.7

53900.4 4685.6 12917.2 8201.1 2748.6 975.6 141139.9 265176.5Poor 1642.7 142.8 393.7 249.9 83.8 29.7 4301.4 8081.6

Middle-income 6258.5 544.1 1499.9 952.2 319.2 113.3 16388.2 30790.4Rich 6598.3 573.6 1581.3 1004.0 336.5 119.4 17278.0 32462.2

Poor unskilled 5138.0 446.7 1231.3 781.8 262.0 93.0 13453.9 25277.6Poor skilled 3528.8 306.8 845.7 536.9 180.0 63.9 9240.3 17360.9

Middle-income unskilled 7142.4 620.9 1711.7 1086.7 364.2 129.3 18702.7 35139.0Middle-income skilled 9301.4 808.6 2229.1 1415.2 474.3 168.3 24356.0 45760.5

Rich unskilled 4484.7 389.9 1074.8 682.4 228.7 81.2 11743.4 22063.8Rich skilled 9805.5 852.4 2349.9 1491.9 500.0 177.5 25675.9 48240.5

Old

Young

Old

Young

Production

Labour Input

Capital Input

! 78

Appendix J - Top Household Expenditure Items28 !

Expenditure Item Aggregate

Weekly Expenditure

1401010101. Income tax 1,507,613 1001010101. Purchase of motor vehicle (other than motor cycle) 335,419 0101020101. Mortgage repayments - interest component (selected dwelling) 326,017 0101010101. Rent payments 305,408 1501010101. Mortgage repayments - principal component (selected dwelling) 250,843 1001030101. Petrol 208,251 0311010201. Fast food and takeaway (not frozen) 157,417 1701010101. Superannuation and annuities 149,164 0311010101. Meals in restaurants, hotels, clubs and related 132,294 0201010101. Electricity (selected dwelling) 127,152 0801030101. Fixed telephone account 113,652 1601010401. Internal renovations 113,003 0901010101. Hospital, medical and dental insurance 110,845 1601010301. Additions and extensions 99,182 1001060201. Vehicle servicing (including parts and labour) 81,743 0501010101. Cigarettes 73,890 1001040201. Other insurance of motor vehicle (other than motor cycle) 71,302 0101030201. Local government rates (selected dwelling) 66,551 0701010301. Lounge/dining room furniture 64,530 1001040103. Combined compulsory registration and insurance of motor vehicle (other than motor cycle) 62,819 0801030102. Mobile telephone account 60,212 1302010101. Mortgage repayments - interest component (other property) 45,968 0601000000. Clothing nfd 45,324 0101030101. Water and sewerage rates and charges (selected dwelling) 43,349 1103020502. Airfare inclusive package tours - overseas (4 nights or more) 42,084 0301010101. Bread 41,606 1601019999. Capital housing costs nec 39,847 0401020101. Wine for consumption off licensed premises 39,431 0902010301. Dental fees 39,178 0300000000. Food and non-alcoholic beverages nfd 39,146 0101060199. Repairs and maintenance (materials only) nec 39,074 1101020101. Home computer equipment (including pre-packaged software) 38,967 0401010101. Beer for consumption off licensed premises 38,592 0101040103. House and contents insurance - inseparable (selected dwelling) 38,154 0305010101. Fresh milk 38,100 0701010201. Bedroom furniture 37,132 1103020102. Holiday airfares - overseas (4 nights or more) 36,895 1601010901. Other outside improvements 36,026 1301999999. Miscellaneous goods nec 34,207 1103010102. Holiday air fares - Australia (4 nights or more) 33,561 0201010201. Mains gas (selected dwelling) 32,547 1302010301. Interest payments on credit card purchases 31,385 1302050301. Accountant and tax agent fees 31,365 1302010201. Loans for vehicle - interest component 30,266 0310010101. Soft drinks 29,529 0902010201. Specialist doctor's fees 28,364 1101040101. Books 27,984 0302020199. Beef and veal nec 26,999 0401010201. Beer for consumption on licensed premises 26,898 1601010201. Purchase of selected dwelling or other property (excluding mortgage repayments but including outright purchase, deposit, net of sales)

26,854

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!28 Due to space limitation only the top 100 (out of 608) household expenditure items are listed here.

! 79

Expenditure Item Aggregate

Weekly Expenditure

1302020403. Secondary school fees (excluding school sports fees) (independent) - excluding Catholic 26,838 1302030199. Higher education institution fees nec 26,605 0903010101. Prescriptions 26,457 1601010101. Mortgage repayments - principal component (other property) 25,859 1201019998. Toiletries and cosmetics nec 25,832 1103010602. Holiday hotel/motel charges - Australia (4 nights or more) 25,714 0801050199. Formal child care services nec 25,436 1101010101. Televisions 24,687 1102010201. Lotto type games and instant lottery (scratch cards) 23,984 0903019999. Medicines and pharmaceutical products nec 22,869 0302050199. Poultry nec 22,728 0101050301. Repairs and maintenance (contractors) - plumbing 22,513 1302050502. Financial institution charges and fees on financial institution accounts 22,368 0101050101. Repairs and maintenance (contractors) - repainting 22,346 1601010701. Outside building 22,273 0309030201. Chocolate confectionery 21,923 0305010301. Cheese 21,529 0301030201. Biscuits 21,259 0703029999. Whitegoods and other electrical appliances nec 21,256 1201020201. Hair services (female) 21,239 0301030101. Cakes, tarts and puddings (fresh or frozen) 21,116 0401030101. Spirits for consumption off licensed premises 20,997 1104010201. Prepared dog and cat food 20,833 1701010201. Life insurance 20,610 0309039999. Confectionery nec 20,184 1103010601. Holiday hotel/motel charges - Australia (less than 4 nights) 19,838 1103011002. Airfare inclusive package tours - Australia (4 nights or more) 19,669 1102999902. Internet charges (account) 19,590 0101030001. Rate payments (selected dwelling) nfd 19,514 1001050201. Tyres and tubes 19,279 0703020101. Refrigerators and freezers 19,208 1601010601. In-ground swimming pool 19,049 1101040201. Newspapers 18,896 1301990201. Jewellery 18,816 1102999901. Pay TV fees 18,649 0401000201. Alcoholic beverages nfd for consumption on licensed premises 18,557 1302990202. Cash gifts, donations to churches, synagogues and related 17,854 0801019999. Household non-durables nec 17,769 1302030101. HECS 17,352 0801010501. Household paper products (excluding stationery) 17,345 1001059901. Vehicle parts purchased separately nec 17,264 1101050901. Toys 17,104 0310020101. Fruit juice 16,926 1104010301. Veterinary charges 16,236 1302010299. Loans - interest component (excluding housing loans) nec 16,136 0701010601. Carpets 16,063 0101050201. Repairs and maintenance (contractors) - electrical work 15,830 1601010801. Landscape contractor 15,712 0705019999. Tools and other household durables nec 15,520 0101059999. Repairs and maintenance (contractors) - nec 15,406

! 80

Appendix K – SAM: Household Consumption (A$ million) !

!

C

Unskilled Skilled Unskilled Skilled Unskilled SkilledFood 91291 1995.9 6073.2 5043.9 6183.7 5092.0 14074.5 23416.1 7608.2 21803.4TCF 26421 427.6 1195.2 1548.7 1351.7 1127.4 3673.3 7056.1 2225.6 7815.4Elec. 22790 457.9 1065.9 681.3 1272.9 1367.5 3069.7 6327.9 1691.3 6855.5

Transport 27172 293.2 979.4 1172.3 1222.4 953.4 3607.6 7574.7 2499.6 8869.3TGS 10072 58.3 249.7 419.9 634.0 381.3 1373.9 2977.6 783.5 3193.9

Furniture 8795 234.1 330.5 362.0 655.7 426.4 1204.2 2661.8 670.8 2249.4Other 64952 1483.7 4417.2 3939.4 4181.1 3620.9 9674.3 17098.7 4864.1 15672.5

Services 356839 3752.1 12276.6 15722.8 14850.7 13584.3 46145.9 93025.8 34904.9 122576.2

Food Food and BeveragesTCF Textile, Clothing and FootwearElec. Computers and Electronics

Transport Transport TGS Toys and Sporting Goods

Furniture FurnitureOther Other Manufactures and Primaries

Services Services

Con

sum

ptio

n

Rich

ConsumptionOld Young

Poor Middle-income RichPoor

Middle income

! 81

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

! 82

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

Table L3: Preference Parameters (!! - Young Households Poor Middle-income Rich

Unskilled Skilled Unskilled Skilled Unskilled Skilled

Food and Beverages 0.1164 0.1290 0.1051 0.1256 0.0732 0.1001

Textile, Clothing and

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

Toys, Games and Sporting

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

Other Manufactures and

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

! 83

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

!

! 84

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.!

! 85

Appendix N - Trade Volume by Sector: Full and Partial Liberalisation

Table N1: Effect of the FTA on Main Exports to China

Sector

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 121.84 50.51 Toys, Games and Sporting Goods 124.71 51.66 Furniture 66.93 30.59 Other Manufactures and Primaries 66.27 31.33 Services -24.46 -10.88

Table N2: Effect of the FTA on Main Exports to RoW

Sector

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 Other Manufactures and Primaries 0.08 0.11 Services -0.00 -0.03

Table N3: Effect of the FTA on Main Imports from China

Sector

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

! 86

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Table N4: Effect of the FTA on Main Imports from Row

Sector

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 -0.87 -0.39 Toys, Games and Sporting Goods -2.65 -1.32 Furniture -3.80 -1.55 Other Manufactures and Primaries 2.56 1.19 Services -0.27 -0.11

! 87

Appendix O - Results: Differentiated Import Elasticities of Substitution !

Table O1: Effect of the FTA on Domestic Production Sector Hummels

(%) Rolleigh

(%) Anderson

(%) Food and Beverages 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 -1.90 -9.60 -7.70 Other Manufactures and Primaries 2.34 3.05 3.52

Services -0.25 -0.14 0.02

Table O2: Effect of the FTA on Change in Aggregate Trade Volume Hummels (%) Rolleigh (%) Anderson (%)

Total Exports to China 55.52 74.77 95.38 Total Imports from China 35.33 50.46 66.45 Total Exports to RoW 0.42 0.46 0.40 Total Imports from RoW 0.37 0.41 0.36

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

! 88

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

Table O5: Effect of the FTA on Imports from China Sector 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

Table O6: Effect of the FTA on Imports from RoW Sector Hummels (%) Rolleigh (%) Anderson (%)

Food and Beverages 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

! 89

Appendix P – Results: Differentiated Export Elasticities of Substitution

Table P1: Effect of the FTA on Changes in Domestic Production

Sector !!= 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

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

Table P3: Effect of the FTA on Main Exports to China Sector !!= 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

! 90

Table P4: Effect of the FTA on Main Exports to RoW Sector !!= 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

Table P5: Effect of the FTA on Main Imports from China Sector !!= 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

Table P6: Effect of the FTA on Main Imports from Row Sector !!= 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

! 91

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